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Artificial Intelligence

What is Artificial Intelligence (AI)? Tutorial, Meaning - Javatpoint

Artificial Intelligence (AI) Tutorial Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Artificial Intelligence (AI)? Why Artificial Intelligence? Goals of Artificial Intelligence History of AI What Comprises to Artificial Intelligence? Types of Artificial Intelligence Advantages of Artificial Intelligence Disadvantages of Artificial Intelligence Challenges of AI AI Tools and Services Prerequisite Audience Problems Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews AI Type 1: Based on Capabilities AI Type 2: Based on Functionality Contact info Follow us Tutorials Interview Questions Online Compiler The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc. Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts. In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc. AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human. Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power." So, we can define AI as: Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans. Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI: Following are the main goals of Artificial Intelligence: Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: Artificial Intelligence can be categorized in several ways, primarily based on two main criteria: capabilities and functionality. Following are some main advantages of Artificial Intelligence: Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI: Artificial Intelligence offers incredible advantages, but it also presents some challenges that need to be addressed: AI tools and services are advancing quickly, and this progress can be linked back to a significant moment in 2012 when the AlexNet neural network came onto the scene. This marked the start of a new era for high-performance AI, thanks to the use of GPUs and massive data sets. The big shift was the ability to train neural networks using huge amounts of data on multiple GPU cores simultaneously, making it a more scalable process. Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily: Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals. We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

Application of AI - Javatpoint

Applications of AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. AI in Astronomy 2. AI in Healthcare 3. AI in Gaming 4. AI in Finance 5. AI in Data Security 6. AI in Social Media 7. AI in Travel & Transport 8. AI in Automotive Industry 9. AI in Robotics: 10. AI in Entertainment 11. AI in Agriculture 12. AI in E-commerce 13. AI in education: Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast. Following are some sectors which have the application of Artificial Intelligence: The applications of AI are vast and diverse, touching nearly every aspect of our lives. From healthcare to finance, astronomy to gaming, and transportation to entertainment, AI is reshaping industries and propelling us into a future where the possibilities seem limitless. As AI continues to advance, its impact on society is poised to grow, promising increased efficiency, better decision-making, and innovative solutions to some of our most pressing challenges. Embracing and responsibly harnessing the power of AI will be key to unlocking its full potential and ensuring a brighter future for all. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

History of Artificial Intelligence - Javatpoint

History of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Maturation of Artificial Intelligence (1943-1952) The birth of Artificial Intelligence (1952-1956) The golden years-Early enthusiasm (1956-1974) The first AI winter (1974-1980) A boom of AI (1980-1987) The second AI winter (1987-1993) The emergence of intelligent agents (1993-2011) Deep learning, big data and artificial general intelligence (2011-present) Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development. Between 1943 and 1952, there was notable progress in the expansion of artificial intelligence (AI). Throughout this period, AI transitioned from a mere concept to tangible experiments and practical applications. Here are some key events that happened during this period: From 1952 to 1956, AI surfaced as a unique domain of investigation. During this period, pioneers and forward-thinkers commenced the groundwork for what would ultimately transform into a revolutionary technological domain. Here are notable occurrences from this era: At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time. The period from 1956 to 1974 is commonly known as the "Golden Age" of artificial intelligence (AI). In this timeframe, AI researchers and innovators were filled with enthusiasm and achieved remarkable advancements in the field. Here are some notable events from this era: The initial AI winter, occurring from 1974 to 1980, is known as a tough period for artificial intelligence (AI). During this time, there was a substantial decrease in research funding, and AI faced a sense of letdown. Between 1980 and 1987, AI underwent a renaissance and newfound vitality after the challenging era of the First AI Winter. Here are notable occurrences from this timeframe: Between 1993 and 2011, there were significant leaps forward in artificial intelligence (AI), particularly in the development of intelligent computer programs. During this era, AI professionals shifted their emphasis from attempting to match human intelligence to crafting pragmatic, ingenious software tailored to specific tasks. Here are some noteworthy occurrences from this timeframe: From 2011 to the present moment, significant advancements have unfolded within the artificial intelligence (AI) domain. These achievements can be attributed to the amalgamation of deep learning, extensive data application, and the ongoing quest for artificial general intelligence (AGI). Here are notable occurrences from this timeframe: Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high intelligence. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

Types of Artificial Intelligence - Javatpoint

Types of Artificial Intelligence: Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI type-1: Based on Capabilities Artificial Intelligence type-2: Based on functionality Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Weak AI or Narrow AI: 2. General AI: 3. Super AI: 1. Reactive Machines 2. Limited Memory 3. Theory of Mind 4. Self-Awareness Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence can be divided in various types, there are mainly two types of main categorization which are based on capabilities and based on functionally of AI. Following is flow diagram which explain the types of AI. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Types of AI Agents - Javatpoint

Types of AI Agents Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Simple Reflex agent: 2. Model-based reflex agent 3. Goal-based agents 4. Utility-based agents 5. Learning Agents Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Agents can be grouped into five classes based on their degree of perceived intelligence and capability. All these agents can improve their performance and generate better action over the time. These are given below: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Intelligent Agent | Agents in AI - Javatpoint

Agents in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is an Agent? Intelligent Agents: Rational Agent: Structure of an AI Agent PEAS Representation Example of Agents with their PEAS representation Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Rationality: PEAS for self-driving cars: Note: Rational agents in AI are very similar to intelligent agents. Note: Rationality differs from Omniscience because an Omniscient agent knows the actual outcome of its action and act accordingly, which is not possible in reality. Contact info Follow us Tutorials Interview Questions Online Compiler An AI system can be defined as the study of the rational agent and its environment. The agents sense the environment through sensors and act on their environment through actuators. An AI agent can have mental properties such as knowledge, belief, intention, etc. An agent can be anything that perceiveits environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting. An agent can be: Hence the world around us is full of agents such as thermostat, cellphone, camera, and even we are also agents. Before moving forward, we should first know about sensors, effectors, and actuators. Sensor: Sensor is a device which detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. Actuators: Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc. Effectors: Effectors are the devices which affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screen. An intelligent agent is an autonomous entity which act upon an environment using sensors and actuators for achieving goals. An intelligent agent may learn from the environment to achieve their goals. A thermostat is an example of an intelligent agent. Following are the main four rules for an AI agent: A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. A rational agent is said to perform the right things. AI is about creating rational agents to use for game theory and decision theory for various real-world scenarios. For an AI agent, the rational action is most important because in AI reinforcement learning algorithm, for each best possible action, agent gets the positive reward and for each wrong action, an agent gets a negative reward. The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of following points: The task of AI is to design an agent program which implements the agent function. The structure of an intelligent agent is a combination of architecture and agent program. It can be viewed as: Following are the main three terms involved in the structure of an AI agent: Architecture: Architecture is machinery that an AI agent executes on. Agent Function: Agent function is used to map a percept to an action. Agent program: Agent program is an implementation of agent function. An agent program executes on the physical architecture to produce function f. PEAS is a type of model on which an AI agent works upon. When we define an AI agent or rational agent, then we can group its properties under PEAS representation model. It is made up of four words: Here performance measure is the objective for the success of an agent's behavior. Let's suppose a self-driving car then PEAS representation will be: Performance: Safety, time, legal drive, comfort Environment: Roads, other vehicles, road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Agent Environment in AI - Javatpoint

Agent Environment in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Features of Environment 1. Fully observable vs Partially Observable: 2. Deterministic vs Stochastic: 3. Episodic vs Sequential: 4. Single-agent vs Multi-agent 5. Static vs Dynamic: 6. Discrete vs Continuous: 7. Known vs Unknown 8. Accessible vs Inaccessible Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler An environment is everything in the world which surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it. An environment is mostly said to be non-feministic. As per Russell and Norvig, an environment can have various features from the point of view of an agent: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Turing Test in AI - Javatpoint

Turing Test in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials History of Turing Test Variations of the Turing Test Chatbots to attempt the Turing test: The Chinese Room Argument: Features required for a machine to pass the Turing test: Limitation of Turing Test Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler In 1950, Alan Turing introduced a test to check whether a machine can think like a human or not, this test is known as the Turing Test. In this test, Turing proposed that the computer can be said to be an intelligent if it can mimic human response under specific conditions. Turing Test was introduced by Turing in his 1950 paper, "Computing Machinery and Intelligence," which considered the question, "Can Machine think?" The Turing test is based on a party game "Imitation game," with some modifications. This game involves three players in which one player is Computer, another player is human responder, and the third player is a human Interrogator, who is isolated from other two players and his job is to find that which player is machine among two of them. Consider, Player A is a computer, Player B is human, and Player C is an interrogator. Interrogator is aware that one of them is machine, but he needs to identify this on the basis of questions and their responses. The conversation between all players is via keyboard and screen so the result would not depend on the machine's ability to convert words as speech. The test result does not depend on each correct answer, but only how closely its responses like a human answer. The computer is permitted to do everything possible to force a wrong identification by the interrogator. The questions and answers can be like: Interrogator: Are you a computer? PlayerA (Computer): No Interrogator: Multiply two large numbers such as (256896489*456725896) Player A: Long pause and give the wrong answer. In this game, if an interrogator would not be able to identify which is a machine and which is human, then the computer passes the test successfully, and the machine is said to be intelligent and can think like a human. "In 1991, the New York businessman Hugh Loebner announces the prize competition, offering a $100,000 prize for the first computer to pass the Turing test. However, no AI program to till date, come close to passing an undiluted Turing test". The Turing Test, introduced by Alan Turing in 1950, is a crucial milestone in the history of artificial intelligence (AI). It came to light in his paper titled 'Computing Machinery and Intelligence.' Turing aimed to address a profound question: Can machines mimic human-like intelligence? This curiosity arose from Turing's fascination with the concept of creating thinking machines that exhibit intelligent behavior. He proposed the Turing Test as a practical method to determine if a machine can engage in natural language conversations convincingly, making a human evaluator believe it's human. Turing's work on this test laid the foundation for AI research and spurred discussions about machine intelligence. It provided a framework for evaluating AI systems. Over time, the Turing Test has evolved and remains a topic of debate and improvement. Its historical importance in shaping AI is undeniable, continuously motivating AI researchers and serving as a benchmark for gauging AI advancements. Over the years, different versions of the Turing Test have appeared to overcome its constraints and deliver a more thorough assessment of AI capabilities: ELIZA: ELIZA was a Natural language processing computer program created by Joseph Weizenbaum. It was created to demonstrate the ability of communication between machine and humans. It was one of the first chatterbots, which has attempted the Turing Test. Parry: Parry was a chatterbot created by Kenneth Colby in 1972. Parry was designed to simulate a person with Paranoid schizophrenia(most common chronic mental disorder). Parry was described as "ELIZA with attitude." Parry was tested using a variation of the Turing Test in the early 1970s. Eugene Goostman: Eugene Goostman was a chatbot developed in Saint Petersburg in 2001. This bot has competed in the various number of Turing Test. In June 2012, at an event, Goostman won the competition promoted as largest-ever Turing test content, in which it has convinced 29% of judges that it was a human.Goostman resembled as a 13-year old virtual boy. There were many philosophers who really disagreed with the complete concept of Artificial Intelligence. The most famous argument in this list was "Chinese Room." In the year 1980, John Searle presented "Chinese Room" thought experiment, in his paper "Mind, Brains, and Program," which was against the validity of Turing's Test. According to his argument, "Programming a computer may make it to understand a language, but it will not produce a real understanding of language or consciousness in a computer." He argued that Machine such as ELIZA and Parry could easily pass the Turing test by manipulating keywords and symbol, but they had no real understanding of language. So it cannot be described as "thinking" capability of a machine such as a human. The Turing Test still serves as a pivotal benchmark for assessing AI's conversational skills in today's context. It continues to be instrumental in the development and evaluation of chatbots and virtual assistants. Many companies and developers employ different versions of the test to gauge how well their AI systems can engage in conversation. However, it's worth noting that while the Turing Test maintains its relevance, the AI field has progressed significantly beyond its scope. Modern AI systems leverage advanced natural language processing, machine learning, and deep learning techniques, empowering them to execute tasks much more intricate than imitating human dialogue. AI's applications now span a wide array of fields, from healthcare and finance to autonomous vehicles and image recognition, showcasing its diverse capabilities that extend well beyond mere conversation. 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Problem-solving

Search Algorithms in AI - Javatpoint

Search Algorithms in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction Search Algorithm Terminologies: Properties of Search Algorithms: Importance of Search Algorithms in Artificial Intelligence Types of search algorithms Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Problem-solving agents: Uninformed/Blind Search: Informed Search Contact info Follow us Tutorials Interview Questions Online Compiler Search algorithms in AI are the algorithms that are created to aid the searchers in getting the right solution. The search issue contains search space, first start and end point. Now by performing simulation of scenarios and alternatives, searching algorithms help AI agents find the optimal state for the task. Logic used in algorithms processes the initial state and tries to get the expected state as the solution. Because of this, AI machines and applications just functioning using search engines and solutions that come from these algorithms can only be as effective as the algorithms. AI agents can make the AI interfaces usable without any software literacy. The agents that carry out such activities do so with the aim of reaching an end goal and develop action plans that in the end will bring the mission to an end. Completion of the action is gained after the steps of these different actions. The AI-agents finds the best way through the process by evaluating all the alternatives which are present. Search systems are a common task in artificial intelligence by which you are going to find the optimum solution for the AI agents. In Artificial Intelligence, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly used these search strategies or algorithms to solve a specific problem and provide the best result. Problem-solving agents are the goal-based agents and use atomic representation. In this topic, we will learn various problem-solving search algorithms. Following are the four essential properties of search algorithms to compare the efficiency of these algorithms: Completeness: A search algorithm is said to be complete if it guarantees to return a solution if at least any solution exists for any random input. Optimality: If a solution found for an algorithm is guaranteed to be the best solution (lowest path cost) among all other solutions, then such a solution for is said to be an optimal solution. Time Complexity: Time complexity is a measure of time for an algorithm to complete its task. Space Complexity: It is the maximum storage space required at any point during the search, as the complexity of the problem. Here, are some important factors of role of search algorithms used AI are as follow. 1. Solving problems: "Workflow" logical search methods like describing the issue, getting the necessary steps together, and specifying an area to search help AI search algorithms getting better in solving problems. Take for instance the development of AI search algorithms which support applications like Google Maps by finding the fastest way or shortest route between given destinations. These programs basically conduct the search through various options to find the best solution possible. 2. Search programming: Many AI functions can be designed as search oscillations, which thus specify what to look for in formulating the solution of the given problem. 3. Goal-based agents: Instead, the goal-directed and high-performance systems use a wide range of search algorithms to improve the efficiency of AI. Though they are not robots, these agents look for the ideal route for action dispersion so as to avoid the most impacting steps that can be used to solve a problem. It is their main aims to come up with an optimal solution which takes into account all possible factors. 4. Support production systems: AI Algorithms in search engines for systems manufacturing help them run faster. These programmable systems assist AI applications with applying rules and methods, thus making an effective implementation possible. Production systems involve learning of artificial intelligence systems and their search for canned rules that lead to the wanted action. 5. Neural network systems: Beyond this, employing neural network algorithms is also of importance of the neural network systems. The systems are composed of these structures: a hidden layer, and an input layer, an output layer, and nodes that are interconnected. One of the most important functions offered by neural networks is to address the challenges of AI within any given scenarios. AI is somehow able to navigate the search space to find the connection weights that will be required in the mapping of inputs to outputs. This is made better by search algorithms in AI. Based on the search problems we can classify the search algorithms into uninformed (Blind search) search and informed search (Heuristic search) algorithms. The uninformed search does not contain any domain knowledge such as closeness, the location of the goal. It operates in a brute-force way as it only includes information about how to traverse the tree and how to identify leaf and goal nodes. Uninformed search applies a way in which search tree is searched without any information about the search space like initial state operators and test for the goal, so it is also called blind search.It examines each node of the tree until it achieves the goal node. It can be divided into six main types: Informed search algorithms use domain knowledge. In an informed search, problem information is available which can guide the search. Informed search strategies can find a solution more efficiently than an uninformed search strategy. Informed search is also called a Heuristic search. A heuristic is a way which might not always be guaranteed for best solutions but guaranteed to find a good solution in reasonable time. Informed search can solve much complex problem which could not be solved in another way. An example of informed search algorithms is a traveling salesman problem. 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Problem-solving

Uninformed Search Algorithms - Javatpoint

Uninformed Search Algorithms Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction: 1. Breadth-first Search: 2. Depth-first Search 3. Depth-Limited Search Algorithm: 4. Uniform-cost Search Algorithm: 5. Iterative deepeningdepth-first Search: 6. Bidirectional Search Algorithm: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Example: Example: Example: Example: Example: Note: Backtracking is an algorithm technique for finding all possible solutions using recursion. Contact info Follow us Tutorials Interview Questions Online Compiler Uninformed search is one in which the search systems do not use any clues about the suitable area but it depend on the random nature of search. Nevertheless, they begins the exploration of search space (all possible solutions) synchronously,. The search operation begins from the initial state and providing all possible next steps arrangement until goal is reached. These are mostly the simplest search strategies, but they may not be suitable for complex paths which involve in irrelevant or even irrelevant components. These algorithms are necessary for solving basic tasks or providing simple processing before passing on the data to more advanced search algorithms that incorporate prioritized information. Following are the various types of uninformed search algorithms: Advantages: Disadvantages: In the below tree structure, we have shown the traversing of the tree using BFS algorithm from the root node S to goal node K. BFS search algorithm traverse in layers, so it will follow the path which is shown by the dotted arrow, and the traversed path will be: Time Complexity: Time Complexity of BFS algorithm can be obtained by the number of nodes traversed in BFS until the shallowest Node. Where the d= depth of shallowest solution and b is a node at every state. T (b) = 1+b2+b3+.......+ bd= O (bd) Space Complexity: Space complexity of BFS algorithm is given by the Memory size of frontier which is O(bd). Completeness: BFS is complete, which means if the shallowest goal node is at some finite depth, then BFS will find a solution. Optimality: BFS is optimal if path cost is a non-decreasing function of the depth of the node. Advantage: Disadvantage: In the below search tree, we have shown the flow of depth-first search, and it will follow the order as: Root node--->Left node ----> right node. It will start searching from root node S, and traverse A, then B, then D and E, after traversing E, it will backtrack the tree as E has no other successor and still goal node is not found. After backtracking it will traverse node C and then G, and here it will terminate as it found goal node. Completeness: DFS search algorithm is complete within finite state space as it will expand every node within a limited search tree. Time Complexity: Time complexity of DFS will be equivalent to the node traversed by the algorithm. It is given by: T(n)= 1+ n2+ n3 +.........+ nm=O(nm) Where, m= maximum depth of any node and this can be much larger than d (Shallowest solution depth) Space Complexity: DFS algorithm needs to store only single path from the root node, hence space complexity of DFS is equivalent to the size of the fringe set, which is O(bm). Optimal: DFS search algorithm is non-optimal, as it may generate a large number of steps or high cost to reach to the goal node. A depth-limited search algorithm is similar to depth-first search with a predetermined limit. Depth-limited search can solve the drawback of the infinite path in the Depth-first search. In this algorithm, the node at the depth limit will treat as it has no successor nodes further. Depth-limited search can be terminated with two Conditions of failure: Advantages: Disadvantages: Completeness: DLS search algorithm is complete if the solution is above the depth-limit. Time Complexity: Time complexity of DLS algorithm is O(bℓ) where b is the branching factor of the search tree, and l is the depth limit. Space Complexity: Space complexity of DLS algorithm is O(b×ℓ) where b is the branching factor of the search tree, and l is the depth limit. Optimal: Depth-limited search can be viewed as a special case of DFS, and it is also not optimal even if ℓ>d. Uniform-cost search is a searching algorithm used for traversing a weighted tree or graph. This algorithm comes into play when a different cost is available for each edge. The primary goal of the uniform-cost search is to find a path to the goal node which has the lowest cumulative cost. Uniform-cost search expands nodes according to their path costs form the root node. It can be used to solve any graph/tree where the optimal cost is in demand. A uniform-cost search algorithm is implemented by the priority queue. It gives maximum priority to the lowest cumulative cost. Uniform cost search is equivalent to BFS algorithm if the path cost of all edges is the same. Advantages: Disadvantages: Completeness: Uniform-cost search is complete, such as if there is a solution, UCS will find it. Time Complexity: Let C* is Cost of the optimal solution, and ε is each step to get closer to the goal node. Then the number of steps is = C*/ε+1. Here we have taken +1, as we start from state 0 and end to C*/ε. Hence, the worst-case time complexity of Uniform-cost search isO(b1 + [C*/ε])/. Space Complexity: The same logic is for space complexity so, the worst-case space complexity of Uniform-cost search is O(b1 + [C*/ε]). Optimal: Uniform-cost search is always optimal as it only selects a path with the lowest path cost. The iterative deepening algorithm is a combination of DFS and BFS algorithms. This search algorithm finds out the best depth limit and does it by gradually increasing the limit until a goal is found. This algorithm performs depth-first search up to a certain "depth limit", and it keeps increasing the depth limit after each iteration until the goal node is found. This Search algorithm combines the benefits of Breadth-first search's fast search and depth-first search's memory efficiency. The iterative search algorithm is useful uninformed search when search space is large, and depth of goal node is unknown. Here are the steps for Iterative deepening depth first search algorithm: Advantages: Disadvantages: Following tree structure is showing the iterative deepening depth-first search. IDDFS algorithm performs various iterations until it does not find the goal node. The iteration performed by the algorithm is given as: 1'st Iteration-----> A2'nd Iteration----> A, B, C3'rd Iteration------>A, B, D, E, C, F, G4'th Iteration------>A, B, D, H, I, E, C, F, K, GIn the fourth iteration, the algorithm will find the goal node. Completeness: This algorithm is complete is ifthe branching factor is finite. Time Complexity: Let's suppose b is the branching factor and depth is d then the worst-case time complexity is O(bd). Space Complexity: The space complexity of IDDFS will be O(bd). Optimal: IDDFS algorithm is optimal if path cost is a non- decreasing function of the depth of the node. Bidirectional search algorithm runs two simultaneous searches, one form initial state called as forward-search and other from goal node called as backward-search, to find the goal node. Bidirectional search replaces one single search graph with two small subgraphs in which one starts the search from an initial vertex and other starts from goal vertex. The search stops when these two graphs intersect each other. Bidirectional search can use search techniques such as BFS, DFS, DLS, etc. Advantages: Disadvantages: In the below search tree, bidirectional search algorithm is applied. This algorithm divides one graph/tree into two sub-graphs. It starts traversing from node 1 in the forward direction and starts from goal node 16 in the backward direction. The algorithm terminates at node 9 where two searches meet. Completeness: Bidirectional Search is complete if we use BFS in both searches. Time Complexity: Time complexity of bidirectional search using BFS is O(bd). Space Complexity: Space complexity of bidirectional search is O(bd). Optimal: Bidirectional search is Optimal. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Informed Search Algorithms in AI - Javatpoint

A* Search Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials An Introduction to A* Search Algorithm in AI History of the A* Search Algorithm in Artificial Intelligence How does the A* search algorithm work in Artificial Intelligence? Advantages of A* Search Algorithm in Artificial Intelligence Disadvantages of A* Search Algorithm in Artificial Intelligence Applications of the A* Search Algorithm in Artificial Intelligence C program for A* Search Algorithm in Artificial Intelligence A* Search Algorithm Complexity in Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews C++ program for A* Search Algorithm in Artificial Intelligence Java program for A* Search Algorithm in Artificial Intelligence Contact info Follow us Tutorials Interview Questions Online Compiler A* (pronounced "A-star") is a powerful graph traversal and pathfinding algorithm widely used in artificial intelligence and computer science. It is mainly used to find the shortest path between two nodes in a graph, given the estimated cost of getting from the current node to the destination node. The main advantage of the algorithm is its ability to provide an optimal path by exploring the graph in a more informed way compared to traditional search algorithms such as Dijkstra's algorithm. Algorithm A* combines the advantages of two other search algorithms: Dijkstra's algorithm and Greedy Best-First Search. Like Dijkstra's algorithm, A* ensures that the path found is as short as possible but does so more efficiently by directing its search through a heuristic similar to Greedy Best-First Search. A heuristic function, denoted h(n), estimates the cost of getting from any given node n to the destination node. The main idea of A* is to evaluate each node based on two parameters: Algorithm A* selects the nodes to be explored based on the lowest value of f(n), preferring the nodes with the lowest estimated total cost to reach the goal. The A* algorithm works: However, choosing a suitable and acceptable heuristic function is essential so that the algorithm performs correctly and provides an optimal solution. It was developed by Peter Hart, Nils Nilsson, and Bertram Raphael at the Stanford Research Institute (now SRI International) as an extension of Dijkstra's algorithm and other search algorithms of the time. A* was first published in 1968 and quickly gained recognition for its importance and effectiveness in the artificial intelligence and computer science communities. Here is a brief overview of the most critical milestones in the history of the search algorithm A*: The A* (pronounced "letter A") search algorithm is a popular and widely used graph traversal algorithm in artificial intelligence and computer science. It is used to find the shortest path from a start node to a destination node in a weighted graph. A* is an informed search algorithm that uses heuristics to guide the search efficiently. The search algorithm A* works as follows: The algorithm starts with a priority queue to store the nodes to be explored. It also instantiates two data structures g(n): The cost of the shortest path so far from the starting node to node n and h(n), the estimated cost (heuristic) from node n to the destination node. It is often a reasonable heuristic, meaning it never overestimates the actual cost of achieving a goal. Put the initial node in the priority queue and set its g(n) to 0. If the priority queue is not empty, Remove the node with the lowest f(n) from the priority queue. f(n) = g(n) h(n). If the deleted node is the destination node, the algorithm ends, and the path is found. Otherwise, expand the node and create its neighbors. For each neighbor node, calculate its initial g(n) value, which is the sum of the g value of the current node and the cost of moving from the current node to a neighboring node. If the neighbor node is not in priority order or the original g(n) value is less than its current g value, update its g value and set its parent node to the current node. Calculate the f(n) value from the neighbor node and add it to the priority queue. If the cycle ends without finding the destination node, the graph has no path from start to finish. The key to the efficiency of A* is its use of a heuristic function h(n) that provides an estimate of the remaining cost of reaching the goal of any node. By combining the actual cost g (n) with the heuristic cost h (n), the algorithm effectively explores promising paths, prioritizing nodes likely to lead to the shortest path. It is important to note that the efficiency of the A* algorithm is highly dependent on the choice of the heuristic function. Acceptable heuristics ensure that the algorithm always finds the shortest path, but more informed and accurate heuristics can lead to faster convergence and reduced search space. The A* search algorithm offers several advantages in artificial intelligence and problem-solving scenarios: Although the A* (letter A) search algorithm is a widely used and powerful technique for solving AI pathfinding and graph traversal problems, it has disadvantages and limitations. Here are some of the main disadvantages of the search algorithm: The search algorithm A* (letter A) is a widely used and robust pathfinding algorithm in artificial intelligence and computer science. Its efficiency and optimality make it suitable for various applications. Here are some typical applications of the A* search algorithm in artificial intelligence: These are just a few examples of how the A* search algorithm finds applications in various areas of artificial intelligence. Its flexibility, efficiency, and optimization make it a valuable tool for many problems. Explanation: Sample Output Explanation: Sample Output Explanation: Sample Output The A* (pronounced "A-star") search algorithm is a popular and widely used graph traversal and path search algorithm in artificial intelligence. Finding the shortest path between two nodes in a graph or grid-based environment is usually common. The algorithm combines Dijkstra's and greedy best-first search elements to explore the search space while ensuring optimality efficiently. Several factors determine the complexity of the A* search algorithm. Graph size (nodes and edges): A graph's number of nodes and edges greatly affects the algorithm's complexity. More nodes and edges mean more possible options to explore, which can increase the execution time of the algorithm. Heuristic function: A* uses a heuristic function (often denoted h(n)) to estimate the cost from the current node to the destination node. The precision of this heuristic greatly affects the efficiency of the A* search. A good heuristic can help guide the search to a goal more quickly, while a bad heuristic can lead to unnecessary searching. In practice, however, A* often performs significantly better due to the influence of a heuristic function that helps guide the algorithm to promising paths. In the case of a well-designed heuristic, the effective branching factor is much smaller, which leads to a faster approach to the optimal solution. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Hill Climbing Algorithm in AI - Javatpoint

Hill Climbing Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Features of Hill Climbing: State-space Diagram for Hill Climbing: Different regions in the state space landscape: Types of Hill Climbing Algorithm: Problems in Hill Climbing Algorithm: Applications of Hill Climbing Algorithm Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Advantages of Hill climb algorithm: Disadvantages of Hill Climbing Algorithm 1. Simple Hill Climbing: Algorithm for Simple Hill Climbing: 2. Steepest-Ascent hill climbing: Algorithm for Steepest-Ascent hill climbing: 3. Stochastic hill climbing: Simulated Annealing: Contact info Follow us Tutorials Interview Questions Online Compiler The merits of Hill Climbing algorithm are given below. Following are some main features of Hill Climbing Algorithm: The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost. On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum. Local Maximum: Local maximum is a state which is better than its neighbor states, but there is also another state which is higher than it. Global Maximum: Global maximum is the best possible state of state space landscape. It has the highest value of objective function. Current state: It is a state in a landscape diagram where an agent is currently present. Flat local maximum: It is a flat space in the landscape where all the neighbor states of current states have the same value. Shoulder: It is a plateau region which has an uphill edge. Simple hill climbing is the simplest way to implement a hill climbing algorithm. It only evaluates the neighbor node state at a time and selects the first one which optimizes current cost and set it as a current state. It only checks it's one successor state, and if it finds better than the current state, then move else be in the same state. This algorithm has the following features: The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This algorithm examines all the neighboring nodes of the current state and selects one neighbor node which is closest to the goal state. This algorithm consumes more time as it searches for multiple neighbors Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state. 1. Local Maximum: A local maximum is a peak state in the landscape which is better than each of its neighboring states, but there is another state also present which is higher than the local maximum. Solution: Backtracking technique can be a solution of the local maximum in state space landscape. Create a list of the promising path so that the algorithm can backtrack the search space and explore other paths as well. 2. Plateau: A plateau is the flat area of the search space in which all the neighbor states of the current state contains the same value, because of this algorithm does not find any best direction to move. A hill-climbing search might be lost in the plateau area. Solution: The solution for the plateau is to take big steps or very little steps while searching, to solve the problem. Randomly select a state which is far away from the current state so it is possible that the algorithm could find non-plateau region. 3. Ridges: A ridge is a special form of the local maximum. It has an area which is higher than its surrounding areas, but itself has a slope, and cannot be reached in a single move. Solution: With the use of bidirectional search, or by moving in different directions, we can improve this problem. A hill-climbing algorithm which never makes a move towards a lower value guaranteed to be incomplete because it can get stuck on a local maximum. And if algorithm applies a random walk, by moving a successor, then it may complete but not efficient. Simulated Annealing is an algorithm which yields both efficiency and completeness. In mechanical term Annealing is a process of hardening a metal or glass to a high temperature then cooling gradually, so this allows the metal to reach a low-energy crystalline state. The same process is used in simulated annealing in which the algorithm picks a random move, instead of picking the best move. If the random move improves the state, then it follows the same path. Otherwise, the algorithm follows the path which has a probability of less than 1 or it moves downhill and chooses another path. The hill climbing technique has seen wide-spread usage in artificial intelligence and optimization respectively. It methodically solves those problems via coupled research activities by systematically testing options and picking out the most appropriate one. Some of the application are as follows: Some of the application are as follows: 1. Machine Learning: Fine tuning of machine learning models frequently is doing the hyper parameter optimization that provides the model with guidance on how it learns and behaves. Another exercise which serves the same purpose is hill training. Gradual adjustment of hyperparameters and their evaluation according to the respectively reached the essence of the hill climbing method. 2. Robotics: In robotics, hill climbing technique turns out to be useful for an artificial agent roaming through a physical environment where its path is adjusted before arriving at the destination. 3. Network Design: The tool may be employed for improvement of network forms, processes, and topologies in the telecommunications industry and computer networks. This approach erases the redundancy thus the efficiency of the networks are increased by studying and adjusting their configurations. It facilitates better cooperation, efficiency, and the reliability of diverse communication system. 4. Game playing: Altough the hill climbing can be optimal in game playing AI by developing the strategies which helps to get the maximum scores. 5. Natural language processing: The software assists in adjusting the algorithms to enable the software to be efficient at dealing with the tasks at hand such as summarizing text, translating languages and recognizing speech. These abilities owing to it as a significant tool for many applications. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Means-Ends Analysis in AI - Javatpoint

Means-Ends Analysis in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials How means-ends analysis Works: Operator Subgoaling Algorithm for Means-Ends Analysis: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example of Mean-Ends Analysis: Solution: Contact info Follow us Tutorials Interview Questions Online Compiler The means-ends analysis process can be applied recursively for a problem. It is a strategy to control search in problem-solving. Following are the main Steps which describes the working of MEA technique for solving a problem. In the MEA process, we detect the differences between the current state and goal state. Once these differences occur, then we can apply an operator to reduce the differences. But sometimes it is possible that an operator cannot be applied to the current state. So we create the subproblem of the current state, in which operator can be applied, such type of backward chaining in which operators are selected, and then sub goals are set up to establish the preconditions of the operator is called Operator Subgoaling. Let's we take Current state as CURRENT and Goal State as GOAL, then following are the steps for the MEA algorithm. The above-discussed algorithm is more suitable for a simple problem and not adequate for solving complex problems. Let's take an example where we know the initial state and goal state as given below. In this problem, we need to get the goal state by finding differences between the initial state and goal state and applying operators. To solve the above problem, we will first find the differences between initial states and goal states, and for each difference, we will generate a new state and will apply the operators. The operators we have for this problem are: 1. Evaluating the initial state: In the first step, we will evaluate the initial state and will compare the initial and Goal state to find the differences between both states. 2. Applying Delete operator: As we can check the first difference is that in goal state there is no dot symbol which is present in the initial state, so, first we will apply the Delete operator to remove this dot. 3. Applying Move Operator: After applying the Delete operator, the new state occurs which we will again compare with goal state. After comparing these states, there is another difference that is the square is outside the circle, so, we will apply the Move Operator. 4. Applying Expand Operator: Now a new state is generated in the third step, and we will compare this state with the goal state. After comparing the states there is still one difference which is the size of the square, so, we will apply Expand operator, and finally, it will generate the goal state. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Adversarial Search - Javatpoint

Adversarial Search Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Types of Games in AI: Zero-Sum Game Game tree: Important Features of Adversarial Search Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Zero-sum game: Embedded thinking Formalization of the problem: Note: In this topic, we will discuss deterministic games, fully observable environment, zero-sum, and where each agent acts alternatively. Contact info Follow us Tutorials Interview Questions Online Compiler Adversarial search is a search, where we examine the problem which arises when we try to plan ahead of the world and other agents are planning against us. The Zero-sum game involved embedded thinking in which one agent or player is trying to figure out: Each of the players is trying to find out the response of his opponent to their actions. This requires embedded thinking or backward reasoning to solve the game problems in AI. A game can be defined as a type of search in AI which can be formalized of the following elements: A game tree is a tree where nodes of the tree are the game states and Edges of the tree are the moves by players. Game tree involves initial state, actions function, and result Function. Example: Tic-Tac-Toe game tree: The following figure is showing part of the game-tree for tic-tac-toe game. Following are some key points of the game: Example Explanation: Hence adversarial Search for the minimax procedure works as follows: In a given game tree, the optimal strategy can be determined from the minimax value of each node, which can be written as MINIMAX(n). MAX prefer to move to a state of maximum value and MIN prefer to move to a state of minimum value then: An important field in artificial intelligence is adversarial search. This deals with decision-making when faced with hostile situations. Here are some key aspects of adversarial search: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Mini-Max Algorithm - Javatpoint

Mini-Max Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Pseudo-code for MinMax Algorithm: Working of Min-Max Algorithm: Properties of Mini-Max algorithm: Limitation of the minimax Algorithm: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Initial call: Minimax(node, 3, true) Step-1: In the first step, the algorithm generates the entire game-tree and apply the utility function to get the utility values for the terminal states. In the below tree diagram, let's take A is the initial state of the tree. Suppose maximizer takes first turn which has worst-case initial value =- infinity, and minimizer will take next turn which has worst-case initial value = +infinity. Step 2: Now, first we find the utilities value for the Maximizer, its initial value is -∞, so we will compare each value in terminal state with initial value of Maximizer and determines the higher nodes values. It will find the maximum among the all. Step 3: In the next step, it's a turn for minimizer, so it will compare all nodes value with +∞, and will find the 3rd layer node values. Step 4: Now it's a turn for Maximizer, and it will again choose the maximum of all nodes value and find the maximum value for the root node. In this game tree, there are only 4 layers, hence we reach immediately to the root node, but in real games, there will be more than 4 layers. That was the complete workflow of the minimax two player game. The main drawback of the minimax algorithm is that it gets really slow for complex games such as Chess, go, etc. This type of games has a huge branching factor, and the player has lots of choices to decide. This limitation of the minimax algorithm can be improved from alpha-beta pruning which we have discussed in the next topic. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Alpha-Beta Pruning - Javatpoint

Alpha-Beta Pruning Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Condition for Alpha-beta pruning: Key points about alpha-beta pruning: Pseudo-code for Alpha-beta Pruning: Working of Alpha-Beta Pruning: Move Ordering in Alpha-Beta pruning: Rules to find good ordering: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: To better understand this topic, kindly study the minimax algorithm. Contact info Follow us Tutorials Interview Questions Online Compiler The main condition which required for alpha-beta pruning is: Let's take an example of two-player search tree to understand the working of Alpha-beta pruning Step 1: At the first step the, Max player will start first move from node A where α= -∞ and β= +∞, these value of alpha and beta passed down to node B where again α= -∞ and β= +∞, and Node B passes the same value to its child D. Step 2: At Node D, the value of α will be calculated as its turn for Max. The value of α is compared with firstly 2 and then 3, and the max (2, 3) = 3 will be the value of α at node D and node value will also 3. Step 3: Now algorithm backtrack to node B, where the value of β will change as this is a turn of Min, Now β= +∞, will compare with the available subsequent nodes value, i.e. min (∞, 3) = 3, hence at node B now α= -∞, and β= 3. In the next step, algorithm traverse the next successor of Node B which is node E, and the values of α= -∞, and β= 3 will also be passed. Step 4: At node E, Max will take its turn, and the value of alpha will change. The current value of alpha will be compared with 5, so max (-∞, 5) = 5, hence at node E α= 5 and β= 3, where α>=β, so the right successor of E will be pruned, and algorithm will not traverse it, and the value at node E will be 5. Step 5: At next step, algorithm again backtrack the tree, from node B to node A. At node A, the value of alpha will be changed the maximum available value is 3 as max (-∞, 3)= 3, and β= +∞, these two values now passes to right successor of A which is Node C. At node C, α=3 and β= +∞, and the same values will be passed on to node F. Step 6: At node F, again the value of α will be compared with left child which is 0, and max(3,0)= 3, and then compared with right child which is 1, and max(3,1)= 3 still α remains 3, but the node value of F will become 1. Step 7: Node F returns the node value 1 to node C, at C α= 3 and β= +∞, here the value of beta will be changed, it will compare with 1 so min (∞, 1) = 1. Now at C, α=3 and β= 1, and again it satisfies the condition α>=β, so the next child of C which is G will be pruned, and the algorithm will not compute the entire sub-tree G. Step 8: C now returns the value of 1 to A here the best value for A is max (3, 1) = 3. Following is the final game tree which is the showing the nodes which are computed and nodes which has never computed. Hence the optimal value for the maximizer is 3 for this example. The effectiveness of alpha-beta pruning is highly dependent on the order in which each node is examined. Move order is an important aspect of alpha-beta pruning. It can be of two types: Following are some rules to find good ordering in alpha-beta pruning: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Based Agent in AI - Javatpoint

Knowledge-Based Agent in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials The architecture of knowledge-based agent: Why use a knowledge base? Inference system Operations Performed by KBA A generic knowledge-based agent: Various levels of knowledge-based agent: Approaches to designing a knowledge-based agent: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Knowledge level 2. Logical level: 3. Implementation level: Contact info Follow us Tutorials Interview Questions Online Compiler A knowledge-based agent must able to do the following: The above diagram is representing a generalized architecture for a knowledge-based agent. The knowledge-based agent (KBA) take input from the environment by perceiving the environment. The input is taken by the inference engine of the agent and which also communicate with KB to decide as per the knowledge store in KB. The learning element of KBA regularly updates the KB by learning new knowledge. Knowledge base: Knowledge-base is a central component of a knowledge-based agent, it is also known as KB. It is a collection of sentences (here 'sentence' is a technical term and it is not identical to sentence in English). These sentences are expressed in a language which is called a knowledge representation language. The Knowledge-base of KBA stores fact about the world. Knowledge-base is required for updating knowledge for an agent to learn with experiences and take action as per the knowledge. Inference means deriving new sentences from old. Inference system allows us to add a new sentence to the knowledge base. A sentence is a proposition about the world. Inference system applies logical rules to the KB to deduce new information. Inference system generates new facts so that an agent can update the KB. An inference system works mainly in two rules which are given as: Following are three operations which are performed by KBA in order to show the intelligent behavior: Following is the structure outline of a generic knowledge-based agents program: The knowledge-based agent takes percept as input and returns an action as output. The agent maintains the knowledge base, KB, and it initially has some background knowledge of the real world. It also has a counter to indicate the time for the whole process, and this counter is initialized with zero. Each time when the function is called, it performs its three operations: The MAKE-PERCEPT-SENTENCE generates a sentence as setting that the agent perceived the given percept at the given time. The MAKE-ACTION-QUERY generates a sentence to ask which action should be done at the current time. MAKE-ACTION-SENTENCE generates a sentence which asserts that the chosen action was executed. A knowledge-based agent can be viewed at different levels which are given below: Knowledge level is the first level of knowledge-based agent, and in this level, we need to specify what the agent knows, and what the agent goals are. With these specifications, we can fix its behavior. For example, suppose an automated taxi agent needs to go from a station A to station B, and he knows the way from A to B, so this comes at the knowledge level. At this level, we understand that how the knowledge representation of knowledge is stored. At this level, sentences are encoded into different logics. At the logical level, an encoding of knowledge into logical sentences occurs. At the logical level we can expect to the automated taxi agent to reach to the destination B. This is the physical representation of logic and knowledge. At the implementation level agent perform actions as per logical and knowledge level. At this level, an automated taxi agent actually implement his knowledge and logic so that he can reach to the destination. There are mainly two approaches to build a knowledge-based agent: However, in the real world, a successful agent can be built by combining both declarative and procedural approaches, and declarative knowledge can often be compiled into more efficient procedural code. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Representation in Artificial Intelligence - Javatpoint

What is knowledge representation? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What to Represent: Types of knowledge The relation between knowledge and intelligence: AI knowledge cycle: Approaches to knowledge representation: Requirements for knowledge Representation system: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Simple relational knowledge: 2. Inheritable knowledge: 3. Inferential knowledge: 4. Procedural knowledge: Contact info Follow us Tutorials Interview Questions Online Compiler Humans are best at understanding, reasoning, and interpreting knowledge. Human knows things, which is knowledge and as per their knowledge they perform various actions in the real world. But how machines do all these things comes under knowledge representation and reasoning. Hence we can describe Knowledge representation as following: Following are the kind of knowledge which needs to be represented in AI systems: Knowledge: Knowledge is awareness or familiarity gained by experiences of facts, data, and situations. Following are the types of knowledge in artificial intelligence: Following are the various types of knowledge: 1. Declarative Knowledge: 2. Procedural Knowledge 3. Meta-knowledge: 4. Heuristic knowledge: 5. Structural knowledge: Knowledge of real-worlds plays a vital role in intelligence and same for creating artificial intelligence. Knowledge plays an important role in demonstrating intelligent behavior in AI agents. An agent is only able to accurately act on some input when he has some knowledge or experience about that input. Let's suppose if you met some person who is speaking in a language which you don't know, then how you will able to act on that. The same thing applies to the intelligent behavior of the agents. As we can see in below diagram, there is one decision maker which act by sensing the environment and using knowledge. But if the knowledge part will not present then, it cannot display intelligent behavior. An Artificial intelligence system has the following components for displaying intelligent behavior: The above diagram is showing how an AI system can interact with the real world and what components help it to show intelligence. AI system has Perception component by which it retrieves information from its environment. It can be visual, audio or another form of sensory input. The learning component is responsible for learning from data captured by Perception comportment. In the complete cycle, the main components are knowledge representation and Reasoning. These two components are involved in showing the intelligence in machine-like humans. These two components are independent with each other but also coupled together. The planning and execution depend on analysis of Knowledge representation and reasoning. There are mainly four approaches to knowledge representation, which are givenbelow: Example: The following is the simple relational knowledge representation. A good knowledge representation system must possess the following properties. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

AI Techniques of Knowledge Representation - Javatpoint

Techniques of knowledge representation Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Logical Representation 2. Semantic Network Representation 3. Frame Representation 4. Production Rules Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Syntax: Semantics: Advantages of logical representation: Disadvantages of logical Representation: Statements: Drawbacks in Semantic representation: Advantages of Semantic network: Example: 1 Example 2: Advantages of frame representation: Disadvantages of frame representation: Example: Advantages of Production rule: Disadvantages of Production rule: Note: We will discuss Prepositional Logics and Predicate logics in later chapters. Note: Do not be confused with logical representation and logical reasoning as logical representation is a representation language and reasoning is a process of thinking logically. Contact info Follow us Tutorials Interview Questions Online Compiler There are mainly four ways of knowledge representation which are given as follows: Logical representation is a language with some concrete rules which deals with propositions and has no ambiguity in representation. Logical representation means drawing a conclusion based on various conditions. This representation lays down some important communication rules. It consists of precisely defined syntax and semantics which supports the sound inference. Each sentence can be translated into logics using syntax and semantics. Logical representation can be categorised into mainly two logics: Semantic networks are alternative of predicate logic for knowledge representation. In Semantic networks, we can represent our knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Semantic networks can categorize the object in different forms and can also link those objects. Semantic networks are easy to understand and can be easily extended. This representation consist of mainly two types of relations: Example: Following are some statements which we need to represent in the form of nodes and arcs. In the above diagram, we have represented the different type of knowledge in the form of nodes and arcs. Each object is connected with another object by some relation. A frame is a record like structure which consists of a collection of attributes and its values to describe an entity in the world. Frames are the AI data structure which divides knowledge into substructures by representing stereotypes situations. It consists of a collection of slots and slot values. These slots may be of any type and sizes. Slots have names and values which are called facets. Facets: The various aspects of a slot is known as Facets. Facets are features of frames which enable us to put constraints on the frames. Example: IF-NEEDED facts are called when data of any particular slot is needed. A frame may consist of any number of slots, and a slot may include any number of facets and facets may have any number of values. A frame is also known as slot-filter knowledge representation in artificial intelligence. Frames are derived from semantic networks and later evolved into our modern-day classes and objects. A single frame is not much useful. Frames system consist of a collection of frames which are connected. In the frame, knowledge about an object or event can be stored together in the knowledge base. The frame is a type of technology which is widely used in various applications including Natural language processing and machine visions. Let's take an example of a frame for a book Let's suppose we are taking an entity, Peter. Peter is an engineer as a profession, and his age is 25, he lives in city London, and the country is England. So following is the frame representation for this: Production rules system consist of (condition, action) pairs which mean, "If condition then action". It has mainly three parts: In production rules agent checks for the condition and if the condition exists then production rule fires and corresponding action is carried out. The condition part of the rule determines which rule may be applied to a problem. And the action part carries out the associated problem-solving steps. This complete process is called a recognize-act cycle. The working memory contains the description of the current state of problems-solving and rule can write knowledge to the working memory. This knowledge match and may fire other rules. If there is a new situation (state) generates, then multiple production rules will be fired together, this is called conflict set. In this situation, the agent needs to select a rule from these sets, and it is called a conflict resolution. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Propositional Logic in Artificial Intelligence - Javatpoint

Propositional logic in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Logical Connectives: Truth Table: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Syntax of propositional logic: Following is the summarized table for Propositional Logic Connectives: Truth table with three propositions: Precedence of connectives: Logical equivalence: Properties of Operators: Limitations of Propositional logic: Note: For better understanding use parenthesis to make sure of the correct interpretations. Such as ¬R∨ Q, It can be interpreted as (¬R) ∨ Q. Contact info Follow us Tutorials Interview Questions Online Compiler Propositional logic (PL) is the simplest form of logic where all the statements are made by propositions. A proposition is a declarative statement which is either true or false. It is a technique of knowledge representation in logical and mathematical form. Following are some basic facts about propositional logic: The syntax of propositional logic defines the allowable sentences for the knowledge representation. There are two types of Propositions: Example: Example: Logical connectives are used to connect two simpler propositions or representing a sentence logically. We can create compound propositions with the help of logical connectives. There are mainly five connectives, which are given as follows: In propositional logic, we need to know the truth values of propositions in all possible scenarios. We can combine all the possible combination with logical connectives, and the representation of these combinations in a tabular format is called Truth table. Following are the truth table for all logical connectives: We can build a proposition composing three propositions P, Q, and R. This truth table is made-up of 8n Tuples as we have taken three proposition symbols. Just like arithmetic operators, there is a precedence order for propositional connectors or logical operators. This order should be followed while evaluating a propositional problem. Following is the list of the precedence order for operators: Logical equivalence is one of the features of propositional logic. Two propositions are said to be logically equivalent if and only if the columns in the truth table are identical to each other. Let's take two propositions A and B, so for logical equivalence, we can write it as A⇔B. In below truth table we can see that column for ¬A∨ B and A→B, are identical hence A is Equivalent to B We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Rules of Inference in Artificial Intelligence - Javatpoint

Rules of Inference in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Inference: Inference rules: Types of Inference rules: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Modus Ponens: 2. Modus Tollens: 3. Hypothetical Syllogism: 4. Disjunctive Syllogism: 5. Addition: 6. Simplification: 7. Resolution: Contact info Follow us Tutorials Interview Questions Online Compiler In artificial intelligence, we need intelligent computers which can create new logic from old logic or by evidence, so generating the conclusions from evidence and facts is termed as Inference. Inference rules are the templates for generating valid arguments. Inference rules are applied to derive proofs in artificial intelligence, and the proof is a sequence of the conclusion that leads to the desired goal. In inference rules, the implication among all the connectives plays an important role. Following are some terminologies related to inference rules: From the above term some of the compound statements are equivalent to each other, which we can prove using truth table: Hence from the above truth table, we can prove that P → Q is equivalent to ¬ Q → ¬ P, and Q→ P is equivalent to ¬ P → ¬ Q. The Modus Ponens rule is one of the most important rules of inference, and it states that if P and P → Q is true, then we can infer that Q will be true. It can be represented as: Example: Statement-1: "If I am sleepy then I go to bed" ==> P→ QStatement-2: "I am sleepy" ==> PConclusion: "I go to bed." ==> Q.Hence, we can say that, if P→ Q is true and P is true then Q will be true. Proof by Truth table: The Modus Tollens rule state that if P→ Q is true and ¬ Q is true, then ¬ P will also true. It can be represented as: Statement-1: "If I am sleepy then I go to bed" ==> P→ QStatement-2: "I do not go to the bed."==> ~QStatement-3: Which infers that "I am not sleepy" => ~P Proof by Truth table: The Hypothetical Syllogism rule state that if P→R is true whenever P→Q is true, and Q→R is true. It can be represented as the following notation: Example: Statement-1: If you have my home key then you can unlock my home. P→QStatement-2: If you can unlock my home then you can take my money. Q→RConclusion: If you have my home key then you can take my money. P→R Proof by truth table: The Disjunctive syllogism rule state that if P∨Q is true, and ¬P is true, then Q will be true. It can be represented as: Example: Statement-1: Today is Sunday or Monday. ==>P∨QStatement-2: Today is not Sunday. ==> ¬PConclusion: Today is Monday. ==> Q Proof by truth-table: The Addition rule is one the common inference rule, and it states that If P is true, then P∨Q will be true. Example: Statement: I have a vanilla ice-cream. ==> PStatement-2: I have Chocolate ice-cream.Conclusion: I have vanilla or chocolate ice-cream. ==> (P∨Q) Proof by Truth-Table: The simplification rule state that if P∧ Q is true, then Q or P will also be true. It can be represented as: Proof by Truth-Table: The Resolution rule state that if P∨Q and ¬ P∧R is true, then Q∨R will also be true. It can be represented as Proof by Truth-Table: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

The Wumpus world in Artificial Intelligence - Javatpoint

The Wumpus World in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Wumpus world: PEAS description of Wumpus world: The Wumpus world Properties: Exploring the Wumpus world: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Performance measure: Environment: Actuators: Sensors: Note: Here Wumpus is static and cannot move. Contact info Follow us Tutorials Interview Questions Online Compiler The Wumpus world is a simple world example to illustrate the worth of a knowledge-based agent and to represent knowledge representation. It was inspired by a video game Hunt the Wumpus by Gregory Yob in 1973. The Wumpus world is a cave which has 4/4 rooms connected with passageways. So there are total 16 rooms which are connected with each other. We have a knowledge-based agent who will go forward in this world. The cave has a room with a beast which is called Wumpus, who eats anyone who enters the room. The Wumpus can be shot by the agent, but the agent has a single arrow. In the Wumpus world, there are some Pits rooms which are bottomless, and if agent falls in Pits, then he will be stuck there forever. The exciting thing with this cave is that in one room there is a possibility of finding a heap of gold. So the agent goal is to find the gold and climb out the cave without fallen into Pits or eaten by Wumpus. The agent will get a reward if he comes out with gold, and he will get a penalty if eaten by Wumpus or falls in the pit. Following is a sample diagram for representing the Wumpus world. It is showing some rooms with Pits, one room with Wumpus and one agent at (1, 1) square location of the world. There are also some components which can help the agent to navigate the cave. These components are given as follows: To explain the Wumpus world we have given PEAS description as below: Now we will explore the Wumpus world and will determine how the agent will find its goal by applying logical reasoning. Agent's First step: Initially, the agent is in the first room or on the square [1,1], and we already know that this room is safe for the agent, so to represent on the below diagram (a) that room is safe we will add symbol OK. Symbol A is used to represent agent, symbol B for the breeze, G for Glitter or gold, V for the visited room, P for pits, W for Wumpus. At Room [1,1] agent does not feel any breeze or any Stench which means the adjacent squares are also OK. Agent's second Step: Now agent needs to move forward, so it will either move to [1, 2], or [2,1]. Let's suppose agent moves to the room [2, 1], at this room agent perceives some breeze which means Pit is around this room. The pit can be in [3, 1], or [2,2], so we will add symbol P? to say that, is this Pit room? Now agent will stop and think and will not make any harmful move. The agent will go back to the [1, 1] room. The room [1,1], and [2,1] are visited by the agent, so we will use symbol V to represent the visited squares. Agent's third step: At the third step, now agent will move to the room [1,2] which is OK. In the room [1,2] agent perceives a stench which means there must be a Wumpus nearby. But Wumpus cannot be in the room [1,1] as by rules of the game, and also not in [2,2] (Agent had not detected any stench when he was at [2,1]). Therefore agent infers that Wumpus is in the room [1,3], and in current state, there is no breeze which means in [2,2] there is no Pit and no Wumpus. So it is safe, and we will mark it OK, and the agent moves further in [2,2]. Agent's fourth step: At room [2,2], here no stench and no breezes present so let's suppose agent decides to move to [2,3]. At room [2,3] agent perceives glitter, so it should grab the gold and climb out of the cave. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

knowledge-base for Wumpus World - Javatpoint

Knowledge-base for Wumpus world Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Prove that Wumpus is in the room (1, 3) Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Atomic proposition variable for Wumpus world: Some Propositional Rules for the wumpus world: Representation of Knowledgebase for Wumpus world: Note: For a 4 * 4 square board, there will be 7*4*4= 122 propositional variables. Note: lack of variables gives us similar rules for each cell. Contact info Follow us Tutorials Interview Questions Online Compiler As in the previous topic we have learned about the wumpus world and how a knowledge-based agent evolves the world. Now in this topic, we will create a knowledge base for the wumpus world, and will derive some proves for the Wumpus-world using propositional logic. The agent starts visiting from first square [1, 1], and we already know that this room is safe for the agent. To build a knowledge base for wumpus world, we will use some rules and atomic propositions. We need symbol [i, j] for each location in the wumpus world, where i is for the location of rows, and j for column location. Following is the Simple KB for wumpus world when an agent moves from room [1, 1], to room [2,1]: Here in the first row, we have mentioned propositional variables for room[1,1], which is showing that room does not have wumpus(¬ W11), no stench (¬S11), no Pit(¬P11), no breeze(¬B11), no gold (¬G11), visited (V11), and the room is Safe(OK11). In the second row, we have mentioned propositional variables for room [1,2], which is showing that there is no wumpus, stench and breeze are unknown as an agent has not visited room [1,2], no Pit, not visited yet, and the room is safe. In the third row we have mentioned propositional variable for room[2,1], which is showing that there is no wumpus(¬ W21), no stench (¬S21), no Pit (¬P21), Perceives breeze(B21), no glitter(¬G21), visited (V21), and room is safe (OK21). We can prove that wumpus is in the room (1, 3) using propositional rules which we have derived for the wumpus world and using inference rule. We will firstly apply MP rule with R1 which is ¬S11 → ¬ W11 ^ ¬ W12 ^ ¬ W21, and ¬S11 which will give the output ¬ W11 ^ W12 ^ W12. After applying And-elimination rule to ¬ W11 ∧ ¬ W12 ∧ ¬ W21, we will get three statements:¬ W11, ¬ W12, and ¬W21. Now we will apply Modus Ponens to ¬S21 and R2 which is ¬S21 → ¬ W21 ∧¬ W22 ∧ ¬ W31, which will give the Output as ¬ W21 ∧ ¬ W22 ∧¬ W31 Now again apply And-elimination rule to ¬ W21 ∧ ¬ W22 ∧¬ W31, We will get three statements:¬ W21, ¬ W22, and ¬ W31. Apply Modus Ponens to S12 and R4 which is S12 → W13 ∨. W12 ∨. W22 ∨.W11, we will get the output as W13∨ W12 ∨ W22 ∨.W11. After applying Unit resolution formula on W13 ∨ W12 ∨ W22 ∨W11 and ¬ W11 we will get W13 ∨ W12 ∨ W22. After applying Unit resolution on W13 ∨ W12 ∨ W22, and ¬W22, we will get W13 ∨ W12 as output. After Applying Unit resolution on W13 ∨ W12 and ¬ W12, we will get W13 as an output, hence it is proved that the Wumpus is in the room [1, 3]. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

First-order logic in Artificial Intelligence - Javatpoint

First-Order Logic in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials First-Order logic: Syntax of First-Order logic: Quantifiers in First-order logic: Existential Quantifier: Points to remember: Properties of Quantifiers: Free and Bound Variables: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Basic Elements of First-order logic: Atomic sentences: Complex Sentences: Universal Quantifier: Example: Example: Note: In universal quantifier we use implication "→". Note: In Existential quantifier we always use AND or Conjunction symbol (∧). Contact info Follow us Tutorials Interview Questions Online Compiler In the topic of Propositional logic, we have seen that how to represent statements using propositional logic. But unfortunately, in propositional logic, we can only represent the facts, which are either true or false. PL is not sufficient to represent the complex sentences or natural language statements. The propositional logic has very limited expressive power. Consider the following sentence, which we cannot represent using PL logic. To represent the above statements, PL logic is not sufficient, so we required some more powerful logic, such as first-order logic. The syntax of FOL determines which collection of symbols is a logical expression in first-order logic. The basic syntactic elements of first-order logic are symbols. We write statements in short-hand notation in FOL. Following are the basic elements of FOL syntax: Example: Ravi and Ajay are brothers: => Brothers(Ravi, Ajay).                Chinky is a cat: => cat (Chinky). First-order logic statements can be divided into two parts: Consider the statement: "x is an integer.", it consists of two parts, the first part x is the subject of the statement and second part "is an integer," is known as a predicate. Universal quantifier is a symbol of logical representation, which specifies that the statement within its range is true for everything or every instance of a particular thing. The Universal quantifier is represented by a symbol ∀, which resembles an inverted A. If x is a variable, then ∀x is read as: All man drink coffee. Let a variable x which refers to a cat so all x can be represented in UOD as below: ∀x man(x) → drink (x, coffee). It will be read as: There are all x where x is a man who drink coffee. Existential quantifiers are the type of quantifiers, which express that the statement within its scope is true for at least one instance of something. It is denoted by the logical operator ∃, which resembles as inverted E. When it is used with a predicate variable then it is called as an existential quantifier. If x is a variable, then existential quantifier will be ∃x or ∃(x). And it will be read as: Some boys are intelligent. ∃x: boys(x) ∧ intelligent(x) It will be read as: There are some x where x is a boy who is intelligent. Some Examples of FOL using quantifier: 1. All birds fly.In this question the predicate is "fly(bird)."And since there are all birds who fly so it will be represented as follows.              ∀x bird(x) →fly(x). 2. Every man respects his parent.In this question, the predicate is "respect(x, y)," where x=man, and y= parent.Since there is every man so will use ∀, and it will be represented as follows:              ∀x man(x) → respects (x, parent). 3. Some boys play cricket.In this question, the predicate is "play(x, y)," where x= boys, and y= game. Since there are some boys so we will use ∃, and it will be represented as:              ∃x boys(x) → play(x, cricket). 4. Not all students like both Mathematics and Science.In this question, the predicate is "like(x, y)," where x= student, and y= subject.Since there are not all students, so we will use ∀ with negation, so following representation for this:              ¬∀ (x) [ student(x) → like(x, Mathematics) ∧ like(x, Science)]. 5. Only one student failed in Mathematics.In this question, the predicate is "failed(x, y)," where x= student, and y= subject.Since there is only one student who failed in Mathematics, so we will use following representation for this:              ∃(x) [ student(x) → failed (x, Mathematics) ∧∀ (y) [¬(x==y) ∧ student(y) → ¬failed (x, Mathematics)]. The quantifiers interact with variables which appear in a suitable way. There are two types of variables in First-order logic which are given below: Free Variable: A variable is said to be a free variable in a formula if it occurs outside the scope of the quantifier. Example: ∀x ∃(y)[P (x, y, z)], where z is a free variable. Bound Variable: A variable is said to be a bound variable in a formula if it occurs within the scope of the quantifier. Example: ∀x [A (x) B( y)], here x and y are the bound variables. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Engineering in First-order logic - Javatpoint

Knowledge Engineering in First-order logic Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is knowledge-engineering? The knowledge-engineering process: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Identify the task: 2. Assemble the relevant knowledge: 3. Decide on vocabulary: 4. Encode general knowledge about the domain: 5. Encode a description of the problem instance: 6. Pose queries to the inference procedure and get answers: 7. Debug the knowledge base: Note: Ontology defines a particular theory of the nature of existence. Contact info Follow us Tutorials Interview Questions Online Compiler The process of constructing a knowledge-base in first-order logic is called as knowledge- engineering. In knowledge-engineering, someone who investigates a particular domain, learns important concept of that domain, and generates a formal representation of the objects, is known as knowledge engineer. In this topic, we will understand the Knowledge engineering process in an electronic circuit domain, which is already familiar. This approach is mainly suitable for creating special-purpose knowledge base. Following are some main steps of the knowledge-engineering process. Using these steps, we will develop a knowledge base which will allow us to reason about digital circuit (One-bit full adder) which is given below The first step of the process is to identify the task, and for the digital circuit, there are various reasoning tasks. At the first level or highest level, we will examine the functionality of the circuit: At the second level, we will examine the circuit structure details such as: In the second step, we will assemble the relevant knowledge which is required for digital circuits. So for digital circuits, we have the following required knowledge: The next step of the process is to select functions, predicate, and constants to represent the circuits, terminals, signals, and gates. Firstly we will distinguish the gates from each other and from other objects. Each gate is represented as an object which is named by a constant, such as, Gate(X1). The functionality of each gate is determined by its type, which is taken as constants such as AND, OR, XOR, or NOT. Circuits will be identified by a predicate: Circuit (C1). For the terminal, we will use predicate: Terminal(x). For gate input, we will use the function In(1, X1) for denoting the first input terminal of the gate, and for output terminal we will use Out (1, X1). The function Arity(c, i, j) is used to denote that circuit c has i input, j output. The connectivity between gates can be represented by predicate Connect(Out(1, X1), In(1, X1)). We use a unary predicate On (t), which is true if the signal at a terminal is on. To encode the general knowledge about the logic circuit, we need some following rules: Now we encode problem of circuit C1, firstly we categorize the circuit and its gate components. This step is easy if ontology about the problem is already thought. This step involves the writing simple atomics sentences of instances of concepts, which is known as ontology. For the given circuit C1, we can encode the problem instance in atomic sentences as below: Since in the circuit there are two XOR, two AND, and one OR gate so atomic sentences for these gates will be: And then represent the connections between all the gates. In this step, we will find all the possible set of values of all the terminal for the adder circuit. The first query will be: What should be the combination of input which would generate the first output of circuit C1, as 0 and a second output to be 1? Now we will debug the knowledge base, and this is the last step of the complete process. In this step, we will try to debug the issues of knowledge base. In the knowledge base, we may have omitted assertions like 1 ≠ 0. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Inference in First-Order Logic - Javatpoint

Inference in First-Order Logic Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials FOL inference rules for quantifier: Generalized Modus Ponens Rule: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: First-order logic is capable of expressing facts about some or all objects in the universe. Contact info Follow us Tutorials Interview Questions Online Compiler Inference in First-Order Logic is used to deduce new facts or sentences from existing sentences. Before understanding the FOL inference rule, let's understand some basic terminologies used in FOL. Substitution: Substitution is a fundamental operation performed on terms and formulas. It occurs in all inference systems in first-order logic. The substitution is complex in the presence of quantifiers in FOL. If we write F[a/x], so it refers to substitute a constant "a" in place of variable "x". Equality: First-Order logic does not only use predicate and terms for making atomic sentences but also uses another way, which is equality in FOL. For this, we can use equality symbols which specify that the two terms refer to the same object. Example: Brother (John) = Smith. As in the above example, the object referred by the Brother (John) is similar to the object referred by Smith. The equality symbol can also be used with negation to represent that two terms are not the same objects. Example: ￢(x=y) which is equivalent to x ≠y. As propositional logic we also have inference rules in first-order logic, so following are some basic inference rules in FOL: 1. Universal Generalization: Example: Let's represent, P(c): "A byte contains 8 bits", so for ∀ x P(x) "All bytes contain 8 bits.", it will also be true. 2. Universal Instantiation: Example:1. IF "Every person like ice-cream"=> ∀x P(x) so we can infer that"John likes ice-cream" => P(c) Example: 2. Let's take a famous example, "All kings who are greedy are Evil." So let our knowledge base contains this detail as in the form of FOL: ∀x king(x) ∧ greedy (x) → Evil (x), So from this information, we can infer any of the following statements using Universal Instantiation: 3. Existential Instantiation: Example: From the given sentence: ∃x Crown(x) ∧ OnHead(x, John), So we can infer: Crown(K) ∧ OnHead( K, John), as long as K does not appear in the knowledge base. 4. Existential introduction For the inference process in FOL, we have a single inference rule which is called Generalized Modus Ponens. It is lifted version of Modus ponens. Generalized Modus Ponens can be summarized as, " P implies Q and P is asserted to be true, therefore Q must be True." According to Modus Ponens, for atomic sentences pi, pi', q. Where there is a substitution θ such that SUBST (θ, pi',) = SUBST(θ, pi), it can be represented as: Example: We will use this rule for Kings are evil, so we will find some x such that x is king, and x is greedy so we can infer that x is evil. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Unification in First-order logic - Javatpoint

What is Unification? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conditions for Unification: Unification Algorithm: Implementation of the Algorithm Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Let Ψ1 = King(x), Ψ2 = King(John), Substitution θ = {John/x} is a unifier for these atoms and applying this substitution, and both expressions will be identical. E.g. Let's say there are two different expressions, P(x, y), and P(a, f(z)). In this example, we need to make both above statements identical to each other. For this, we will perform the substitution. P(x, y)......... (i)            P(a, f(z))......... (ii) Following are some basic conditions for unification: Algorithm: Unify(Ψ1, Ψ2) Step.1: Initialize the substitution set to be empty. Step.2: Recursively unify atomic sentences: For each pair of the following atomic sentences find the most general unifier (If exist). 1. Find the MGU of {p(f(a), g(Y)) and p(X, X)} Sol: S0 => Here, Ψ1 = p(f(a), g(Y)), and Ψ2 = p(X, X)                  SUBST θ= {f(a) / X}                  S1 => Ψ1 = p(f(a), g(Y)), and Ψ2 = p(f(a), f(a))                  SUBST θ= {f(a) / g(y)}, Unification failed. Unification is not possible for these expressions. 2. Find the MGU of {p(b, X, f(g(Z))) and p(Z, f(Y), f(Y))} Here, Ψ1 = p(b, X, f(g(Z))) , and Ψ2 = p(Z, f(Y), f(Y))S0 => { p(b, X, f(g(Z))); p(Z, f(Y), f(Y))}SUBST θ={b/Z} S1 => { p(b, X, f(g(b))); p(b, f(Y), f(Y))}SUBST θ={f(Y) /X} S2 => { p(b, f(Y), f(g(b))); p(b, f(Y), f(Y))}SUBST θ= {g(b) /Y} S2 => { p(b, f(g(b)), f(g(b)); p(b, f(g(b)), f(g(b))} Unified Successfully.And Unifier = { b/Z, f(Y) /X , g(b) /Y}. 3. Find the MGU of {p (X, X), and p (Z, f(Z))} Here, Ψ1 = {p (X, X), and Ψ2 = p (Z, f(Z))S0 => {p (X, X), p (Z, f(Z))}SUBST θ= {X/Z}              S1 => {p (Z, Z), p (Z, f(Z))}SUBST θ= {f(Z) / Z}, Unification Failed. Hence, unification is not possible for these expressions. 4. Find the MGU of UNIFY(prime (11), prime(y)) Here, Ψ1 = {prime(11) , and Ψ2 = prime(y)}S0 => {prime(11) , prime(y)}SUBST θ= {11/y} S1 => {prime(11) , prime(11)} , Successfully unified.              Unifier: {11/y}. 5. Find the MGU of Q(a, g(x, a), f(y)), Q(a, g(f(b), a), x)} Here, Ψ1 = Q(a, g(x, a), f(y)), and Ψ2 = Q(a, g(f(b), a), x)S0 => {Q(a, g(x, a), f(y)); Q(a, g(f(b), a), x)}SUBST θ= {f(b)/x}S1 => {Q(a, g(f(b), a), f(y)); Q(a, g(f(b), a), f(b))} SUBST θ= {b/y}S1 => {Q(a, g(f(b), a), f(b)); Q(a, g(f(b), a), f(b))}, Successfully Unified. Unifier: [a/a, f(b)/x, b/y]. 6. UNIFY(knows(Richard, x), knows(Richard, John)) Here, Ψ1 = knows(Richard, x), and Ψ2 = knows(Richard, John)S0 => { knows(Richard, x); knows(Richard, John)}SUBST θ= {John/x}S1 => { knows(Richard, John); knows(Richard, John)}, Successfully Unified.Unifier: {John/x}. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Resolution in First-order logic - Javatpoint

Resolution in FOL Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Resolution The resolution inference rule: Steps for Resolution: Explanation of Resolution graph: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Example: Note: To better understand this topic, firstly learns the FOL in AI. Note: Statements "food(Apple) Λ food(vegetables)" and "eats (Anil, Peanuts) Λ alive(Anil)" can be written in two separate statements. Contact info Follow us Tutorials Interview Questions Online Compiler Resolution is a theorem proving technique that proceeds by building refutation proofs, i.e., proofs by contradictions. It was invented by a Mathematician John Alan Robinson in the year 1965. Resolution is used, if there are various statements are given, and we need to prove a conclusion of those statements. Unification is a key concept in proofs by resolutions. Resolution is a single inference rule which can efficiently operate on the conjunctive normal form or clausal form. Clause: Disjunction of literals (an atomic sentence) is called a clause. It is also known as a unit clause. Conjunctive Normal Form: A sentence represented as a conjunction of clauses is said to be conjunctive normal form or CNF. The resolution rule for first-order logic is simply a lifted version of the propositional rule. Resolution can resolve two clauses if they contain complementary literals, which are assumed to be standardized apart so that they share no variables. Where li and mj are complementary literals. This rule is also called the binary resolution rule because it only resolves exactly two literals. We can resolve two clauses which are given below: [Animal (g(x) V Loves (f(x), x)]       and       [￢ Loves(a, b) V ￢Kills(a, b)] Where two complimentary literals are: Loves (f(x), x) and ￢ Loves (a, b) These literals can be unified with unifier θ= [a/f(x), and b/x] , and it will generate a resolvent clause: [Animal (g(x) V ￢ Kills(f(x), x)]. To better understand all the above steps, we will take an example in which we will apply resolution. Step-1: Conversion of Facts into FOL In the first step we will convert all the given statements into its first order logic. Step-2: Conversion of FOL into CNF In First order logic resolution, it is required to convert the FOL into CNF as CNF form makes easier for resolution proofs. Step-3: Negate the statement to be proved In this statement, we will apply negation to the conclusion statements, which will be written as ¬likes(John, Peanuts) Step-4: Draw Resolution graph: Now in this step, we will solve the problem by resolution tree using substitution. For the above problem, it will be given as follows: Hence the negation of the conclusion has been proved as a complete contradiction with the given set of statements. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Forward Chaining and backward chaining in AI - Javatpoint

Forward Chaining and backward chaining in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Inference engine: A. Forward Chaining Forward chaining proof: B. Backward Chaining: Backward-Chaining proof: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Facts Conversion into FOL: Example: Contact info Follow us Tutorials Interview Questions Online Compiler In artificial intelligence, forward and backward chaining is one of the important topics, but before understanding forward and backward chaining lets first understand that from where these two terms came. The inference engine is the component of the intelligent system in artificial intelligence, which applies logical rules to the knowledge base to infer new information from known facts. The first inference engine was part of the expert system. Inference engine commonly proceeds in two modes, which are: Horn Clause and Definite clause: Horn clause and definite clause are the forms of sentences, which enables knowledge base to use a more restricted and efficient inference algorithm. Logical inference algorithms use forward and backward chaining approaches, which require KB in the form of the first-order definite clause. Definite clause: A clause which is a disjunction of literals with exactly one positive literal is known as a definite clause or strict horn clause. Horn clause: A clause which is a disjunction of literals with at most one positive literal is known as horn clause. Hence all the definite clauses are horn clauses. Example: (¬ p V ¬ q V k). It has only one positive literal k. Forward chaining is also known as a forward deduction or forward reasoning method when using an inference engine. Forward chaining is a form of reasoning which start with atomic sentences in the knowledge base and applies inference rules (Modus Ponens) in the forward direction to extract more data until a goal is reached. The Forward-chaining algorithm starts from known facts, triggers all rules whose premises are satisfied, and add their conclusion to the known facts. This process repeats until the problem is solved. Properties of Forward-Chaining: Consider the following famous example which we will use in both approaches: "As per the law, it is a crime for an American to sell weapons to hostile nations. Country A, an enemy of America, has some missiles, and all the missiles were sold to it by Robert, who is an American citizen." Prove that "Robert is criminal." To solve the above problem, first, we will convert all the above facts into first-order definite clauses, and then we will use a forward-chaining algorithm to reach the goal. Step-1: In the first step we will start with the known facts and will choose the sentences which do not have implications, such as: American(Robert), Enemy(A, America), Owns(A, T1), and Missile(T1). All these facts will be represented as below. Step-2: At the second step, we will see those facts which infer from available facts and with satisfied premises. Rule-(1) does not satisfy premises, so it will not be added in the first iteration. Rule-(2) and (3) are already added. Rule-(4) satisfy with the substitution {p/T1}, so Sells (Robert, T1, A) is added, which infers from the conjunction of Rule (2) and (3). Rule-(6) is satisfied with the substitution(p/A), so Hostile(A) is added and which infers from Rule-(7). Step-3: At step-3, as we can check Rule-(1) is satisfied with the substitution {p/Robert, q/T1, r/A}, so we can add Criminal(Robert) which infers all the available facts. And hence we reached our goal statement. Hence it is proved that Robert is Criminal using forward chaining approach. Backward-chaining is also known as a backward deduction or backward reasoning method when using an inference engine. A backward chaining algorithm is a form of reasoning, which starts with the goal and works backward, chaining through rules to find known facts that support the goal. Properties of backward chaining: In backward-chaining, we will use the same above example, and will rewrite all the rules. In Backward chaining, we will start with our goal predicate, which is Criminal(Robert), and then infer further rules. Step-1: At the first step, we will take the goal fact. And from the goal fact, we will infer other facts, and at last, we will prove those facts true. So our goal fact is "Robert is Criminal," so following is the predicate of it. Step-2: At the second step, we will infer other facts form goal fact which satisfies the rules. So as we can see in Rule-1, the goal predicate Criminal (Robert) is present with substitution {Robert/P}. So we will add all the conjunctive facts below the first level and will replace p with Robert. Here we can see American (Robert) is a fact, so it is proved here. Step-3:t At step-3, we will extract further fact Missile(q) which infer from Weapon(q), as it satisfies Rule-(5). Weapon (q) is also true with the substitution of a constant T1 at q. Step-4: At step-4, we can infer facts Missile(T1) and Owns(A, T1) form Sells(Robert, T1, r) which satisfies the Rule- 4, with the substitution of A in place of r. So these two statements are proved here. Step-5: At step-5, we can infer the fact Enemy(A, America) from Hostile(A) which satisfies Rule- 6. And hence all the statements are proved true using backward chaining. 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Knowledge Representation

Difference Between Backward Chaining and Forward Chaining - Javatpoint

Difference between backward chaining and forward chaining Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Following is the difference between the forward chaining and backward chaining: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Reasoning in Artificial Intelligence - Javatpoint

Reasoning in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Reasoning: Types of Reasoning Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Deductive reasoning: 2. Inductive Reasoning: 3. Abductive reasoning: 4. Common Sense Reasoning 5. Monotonic Reasoning: Advantages of Monotonic Reasoning: Disadvantages of Monotonic Reasoning: 6. Non-monotonic Reasoning Advantages of Non-monotonic reasoning: Disadvantages of Non-monotonic Reasoning: Note: Inductive and deductive reasoning are the forms of propositional logic. Contact info Follow us Tutorials Interview Questions Online Compiler In previous topics, we have learned various ways of knowledge representation in artificial intelligence. Now we will learn the various ways to reason on this knowledge using different logical schemes. The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. Or we can say, "Reasoning is a way to infer facts from existing data." It is a general process of thinking rationally, to find valid conclusions. In artificial intelligence, the reasoning is essential so that the machine can also think rationally as a human brain, and can perform like a human. In artificial intelligence, reasoning can be divided into the following categories: Deductive reasoning is deducing new information from logically related known information. It is the form of valid reasoning, which means the argument's conclusion must be true when the premises are true. Deductive reasoning is a type of propositional logic in AI, and it requires various rules and facts. It is sometimes referred to as top-down reasoning, and contradictory to inductive reasoning. In deductive reasoning, the truth of the premises guarantees the truth of the conclusion. Deductive reasoning mostly starts from the general premises to the specific conclusion, which can be explained as below example. Example: Premise-1: All the human eats veggies Premise-2: Suresh is human. Conclusion: Suresh eats veggies. The general process of deductive reasoning is given below: Inductive reasoning is a form of reasoning to arrive at a conclusion using limited sets of facts by the process of generalization. It starts with the series of specific facts or data and reaches to a general statement or conclusion. Inductive reasoning is a type of propositional logic, which is also known as cause-effect reasoning or bottom-up reasoning. In inductive reasoning, we use historical data or various premises to generate a generic rule, for which premises support the conclusion. In inductive reasoning, premises provide probable supports to the conclusion, so the truth of premises does not guarantee the truth of the conclusion. Example: Premise: All of the pigeons we have seen in the zoo are white. Conclusion: Therefore, we can expect all the pigeons to be white. Abductive reasoning is a form of logical reasoning which starts with single or multiple observations then seeks to find the most likely explanation or conclusion for the observation. Abductive reasoning is an extension of deductive reasoning, but in abductive reasoning, the premises do not guarantee the conclusion. Example: Implication: Cricket ground is wet if it is raining Axiom: Cricket ground is wet. Conclusion It is raining. Common sense reasoning is an informal form of reasoning, which can be gained through experiences. Common Sense reasoning simulates the human ability to make presumptions about events which occurs on every day. It relies on good judgment rather than exact logic and operates on heuristic knowledge and heuristic rules. Example: The above two statements are the examples of common sense reasoning which a human mind can easily understand and assume. In monotonic reasoning, once the conclusion is taken, then it will remain the same even if we add some other information to existing information in our knowledge base. In monotonic reasoning, adding knowledge does not decrease the set of prepositions that can be derived. To solve monotonic problems, we can derive the valid conclusion from the available facts only, and it will not be affected by new facts. Monotonic reasoning is not useful for the real-time systems, as in real time, facts get changed, so we cannot use monotonic reasoning. Monotonic reasoning is used in conventional reasoning systems, and a logic-based system is monotonic. Any theorem proving is an example of monotonic reasoning. Example: It is a true fact, and it cannot be changed even if we add another sentence in knowledge base like, "The moon revolves around the earth" Or "Earth is not round," etc. In Non-monotonic reasoning, some conclusions may be invalidated if we add some more information to our knowledge base. Logic will be said as non-monotonic if some conclusions can be invalidated by adding more knowledge into our knowledge base. Non-monotonic reasoning deals with incomplete and uncertain models. "Human perceptions for various things in daily life, "is a general example of non-monotonic reasoning. Example: Let suppose the knowledge base contains the following knowledge: So from the above sentences, we can conclude that Pitty can fly. However, if we add one another sentence into knowledge base "Pitty is a penguin", which concludes "Pitty cannot fly", so it invalidates the above conclusion. 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Knowledge Representation

Difference between Inductive and Deductive Reasoning - Javatpoint

Difference between Inductive and Deductive reasoning Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Reasoning in artificial intelligence has two important forms, Inductive reasoning, and Deductive reasoning. Both reasoning forms have premises and conclusions, but both reasoning are contradictory to each other. Following is a list for comparison between inductive and deductive reasoning: The differences between inductive and deductive can be explained using the below diagram on the basis of arguments: Comparison Chart: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Probabilistic Reasoning in Artificial Intelligence - Javatpoint

Probabilistic reasoning in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Uncertainty: Causes of uncertainty: Probabilistic reasoning: Conditional probability: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: We will learn the above two rules in later chapters. Contact info Follow us Tutorials Interview Questions Online Compiler Till now, we have learned knowledge representation using first-order logic and propositional logic with certainty, which means we were sure about the predicates. With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is true or not then we cannot express this statement, this situation is called uncertainty. So to represent uncertain knowledge, where we are not sure about the predicates, we need uncertain reasoning or probabilistic reasoning. Following are some leading causes of uncertainty to occur in the real world. Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty. We use probability in probabilistic reasoning because it provides a way to handle the uncertainty that is the result of someone's laziness and ignorance. In the real world, there are lots of scenarios, where the certainty of something is not confirmed, such as "It will rain today," "behavior of someone for some situations," "A match between two teams or two players." These are probable sentences for which we can assume that it will happen but not sure about it, so here we use probabilistic reasoning. Need of probabilistic reasoning in AI: In probabilistic reasoning, there are two ways to solve problems with uncertain knowledge: As probabilistic reasoning uses probability and related terms, so before understanding probabilistic reasoning, let's understand some common terms: Probability: Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal uncertainties. We can find the probability of an uncertain event by using the below formula. Event: Each possible outcome of a variable is called an event. Sample space: The collection of all possible events is called sample space. Random variables: Random variables are used to represent the events and objects in the real world. Prior probability: The prior probability of an event is probability computed before observing new information. Posterior Probability: The probability that is calculated after all evidence or information has taken into account. It is a combination of prior probability and new information. Conditional probability is a probability of occurring an event when another event has already happened. Let's suppose, we want to calculate the event A when event B has already occurred, "the probability of A under the conditions of B", it can be written as: Where P(A⋀B)= Joint probability of a and B P(B)= Marginal probability of B. If the probability of A is given and we need to find the probability of B, then it will be given as: It can be explained by using the below Venn diagram, where B is occurred event, so sample space will be reduced to set B, and now we can only calculate event A when event B is already occurred by dividing the probability of P(A⋀B) by P( B ). Example: In a class, there are 70% of the students who like English and 40% of the students who likes English and mathematics, and then what is the percent of students those who like English also like mathematics? Solution: Let, A is an event that a student likes Mathematics B is an event that a student likes English. Hence, 57% are the students who like English also like Mathematics. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Bayes theorem in Artificial Intelligence - Javatpoint

Bayes' theorem in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Bayes' theorem: Applying Bayes' rule: Application of Bayes' theorem in Artificial intelligence: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Bayes' theorem is also known as Bayes' rule, Bayes' law, or Bayesian reasoning, which determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events. Bayes' theorem was named after the British mathematician Thomas Bayes. The Bayesian inference is an application of Bayes' theorem, which is fundamental to Bayesian statistics. It is a way to calculate the value of P(B|A) with the knowledge of P(A|B). Bayes' theorem allows updating the probability prediction of an event by observing new information of the real world. Example: If cancer corresponds to one's age then by using Bayes' theorem, we can determine the probability of cancer more accurately with the help of age. Bayes' theorem can be derived using product rule and conditional probability of event A with known event B: As from product rule we can write: Similarly, the probability of event B with known event A: Equating right hand side of both the equations, we will get: The above equation (a) is called as Bayes' rule or Bayes' theorem. This equation is basic of most modern AI systems for probabilistic inference. It shows the simple relationship between joint and conditional probabilities. Here, P(A|B) is known as posterior, which we need to calculate, and it will be read as Probability of hypothesis A when we have occurred an evidence B. P(B|A) is called the likelihood, in which we consider that hypothesis is true, then we calculate the probability of evidence. P(A) is called the prior probability, probability of hypothesis before considering the evidence P(B) is called marginal probability, pure probability of an evidence. In the equation (a), in general, we can write P (B) = P(A)*P(B|Ai), hence the Bayes' rule can be written as: Where A1, A2, A3,........, An is a set of mutually exclusive and exhaustive events. Bayes' rule allows us to compute the single term P(B|A) in terms of P(A|B), P(B), and P(A). This is very useful in cases where we have a good probability of these three terms and want to determine the fourth one. Suppose we want to perceive the effect of some unknown cause, and want to compute that cause, then the Bayes' rule becomes: Example-1: Question: what is the probability that a patient has diseases meningitis with a stiff neck? Given Data: A doctor is aware that disease meningitis causes a patient to have a stiff neck, and it occurs 80% of the time. He is also aware of some more facts, which are given as follows: Let a be the proposition that patient has stiff neck and b be the proposition that patient has meningitis. , so we can calculate the following as: P(a|b) = 0.8 P(b) = 1/30000 P(a)= .02 Hence, we can assume that 1 patient out of 750 patients has meningitis disease with a stiff neck. Example-2: Question: From a standard deck of playing cards, a single card is drawn. The probability that the card is king is 4/52, then calculate posterior probability P(King|Face), which means the drawn face card is a king card. Solution: P(king): probability that the card is King= 4/52= 1/13 P(face): probability that a card is a face card= 3/13 P(Face|King): probability of face card when we assume it is a king = 1 Putting all values in equation (i) we will get: Following are some applications of Bayes' theorem: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Bayesian Belief Network in Artificial Intelligence - Javatpoint

Bayesian Belief Network in artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Joint probability distribution: Explanation of Bayesian network: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a directed acyclic graph or DAG. Contact info Follow us Tutorials Interview Questions Online Compiler Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty. We can define a Bayesian network as: "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph." It is also called a Bayes network, belief network, decision network, or Bayesian model. Bayesian networks are probabilistic, because these networks are built from a probability distribution, and also use probability theory for prediction and anomaly detection. Real world applications are probabilistic in nature, and to represent the relationship between multiple events, we need a Bayesian network. It can also be used in various tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction, and decision making under uncertainty. Bayesian Network can be used for building models from data and experts opinions, and it consists of two parts: The generalized form of Bayesian network that represents and solve decision problems under uncertain knowledge is known as an Influence diagram. A Bayesian network graph is made up of nodes and Arcs (directed links), where: The Bayesian network has mainly two components: Each node in the Bayesian network has condition probability distribution P(Xi |Parent(Xi) ), which determines the effect of the parent on that node. Bayesian network is based on Joint probability distribution and conditional probability. So let's first understand the joint probability distribution: If we have variables x1, x2, x3,....., xn, then the probabilities of a different combination of x1, x2, x3.. xn, are known as Joint probability distribution. P[x1, x2, x3,....., xn], it can be written as the following way in terms of the joint probability distribution. = P[x1| x2, x3,....., xn]P[x2, x3,....., xn] = P[x1| x2, x3,....., xn]P[x2|x3,....., xn]....P[xn-1|xn]P[xn]. In general for each variable Xi, we can write the equation as: Let's understand the Bayesian network through an example by creating a directed acyclic graph: Example: Harry installed a new burglar alarm at his home to detect burglary. The alarm reliably responds at detecting a burglary but also responds for minor earthquakes. Harry has two neighbors David and Sophia, who have taken a responsibility to inform Harry at work when they hear the alarm. David always calls Harry when he hears the alarm, but sometimes he got confused with the phone ringing and calls at that time too. On the other hand, Sophia likes to listen to high music, so sometimes she misses to hear the alarm. Here we would like to compute the probability of Burglary Alarm. Problem: Calculate the probability that alarm has sounded, but there is neither a burglary, nor an earthquake occurred, and David and Sophia both called the Harry. Solution: List of all events occurring in this network: We can write the events of problem statement in the form of probability: P[D, S, A, B, E], can rewrite the above probability statement using joint probability distribution: P[D, S, A, B, E]= P[D | S, A, B, E]. P[S, A, B, E] =P[D | S, A, B, E]. P[S | A, B, E]. P[A, B, E] = P [D| A]. P [ S| A, B, E]. P[ A, B, E] = P[D | A]. P[ S | A]. P[A| B, E]. P[B, E] = P[D | A ]. P[S | A]. P[A| B, E]. P[B |E]. P[E] Let's take the observed probability for the Burglary and earthquake component: P(B= True) = 0.002, which is the probability of burglary. P(B= False)= 0.998, which is the probability of no burglary. P(E= True)= 0.001, which is the probability of a minor earthquake P(E= False)= 0.999, Which is the probability that an earthquake not occurred. We can provide the conditional probabilities as per the below tables: Conditional probability table for Alarm A: The Conditional probability of Alarm A depends on Burglar and earthquake: Conditional probability table for David Calls: The Conditional probability of David that he will call depends on the probability of Alarm. Conditional probability table for Sophia Calls: The Conditional probability of Sophia that she calls is depending on its Parent Node "Alarm." From the formula of joint distribution, we can write the problem statement in the form of probability distribution: P(S, D, A, ¬B, ¬E) = P (S|A) *P (D|A)*P (A|¬B ^ ¬E) *P (¬B) *P (¬E). = 0.75* 0.91* 0.001* 0.998*0.999 = 0.00068045. Hence, a Bayesian network can answer any query about the domain by using Joint distribution. The semantics of Bayesian Network: There are two ways to understand the semantics of the Bayesian network, which is given below: 1. To understand the network as the representation of the Joint probability distribution. It is helpful to understand how to construct the network. 2. To understand the network as an encoding of a collection of conditional independence statements. It is helpful in designing inference procedure. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Examples of AI (Artificial Intelligence) - Javatpoint

Examples of AI-Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is AI-Artificial Intelligence? Examples of AI-Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Learning Processes Reasoning Processes Self-Correction Processes 1. Google Maps and Ride-Hailing Applications 2. Face Detection and Recognition 3. Text Editors or Autocorrect 4. Chatbots 5. Online-Payments 6. Search and Recommendation Algorithms 7. Digital Assistants 8. Social Media 9. Healthcare 10. Gaming 11. Online Ads Network 12. Banking and Finance 13. Smart Home Devices 14. curity and Surveillance 15. Smart Keyboard Apps 16. Smart Speakers 17. E-Commerce 18. Smart Email Apps 19. Music and Media Streaming Service 20. Space Exploration Contact info Follow us Tutorials Interview Questions Online Compiler The term "Artificial Intelligence" refers to the simulation of human intelligence processes by machines, especially computer systems. It also includes Expert systems, voice recognition, machine vision, and natural language processing (NLP). AI programming focuses on three cognitive aspects, such as learning, reasoning, and self-correction. This part of AI programming is concerned with gathering data and creating rules for transforming it into useful information. The rules, which are also called algorithms, offer computing devices with step-by-step instructions for accomplishing a particular job. This part of AI programming is concerned with selecting the best algorithm to achieve the desired result. This part of AI programming aims to fine-tune algorithms regularly in order to ensure that they offer the most reliable results possible. Artificial Intelligence is an extensive field of computer science which focuses on developing intelligent machines capable of doing activities that would normally require human intelligence. While AI is a multidisciplinary science with numerous methodologies, advances in deep learning and machine learning create a paradigm shift in almost every aspect of technology. The following are the examples of AI-Artificial Intelligence: Let's discuss the above examples in detail. Travelling to a new destination does not require much thought any longer. Rather than relying on confusing address directions, we can now easily open our phone's map app and type in our destination. So how does the app know about the appropriate directions, best way, and even the presence of roadblocks and traffic jams? A few years ago, only GPS (satellite-based navigation) was used as a navigation guide. However, artificial intelligence (AI) now provides users with a much better experience in their unique surroundings. The app algorithm uses machine learning to recall the building's edges that are supplied into the system after the person has manually acknowledged them. This enables the map to provide simple visuals of buildings. Another feature is identifying and understanding handwritten house numbers, which assists travelers in finding the exact house they need. Their outline or handwritten label can also recognize locations that lack formal street signs. The application has been trained to recognize and understand traffic. As a result, it suggests the best way to avoid traffic congestion and bottlenecks. The AI-based algorithm also informs users about the precise distance and time it will take them to arrive at their destination. It has been trained to calculate this based on the traffic situations. Several ride-hailing applications have emerged as a result of the use of similar AI technology. So, whenever you need to book a cab via an app by putting your location on a map, this is how it works. Utilizing face ID for unlocking our phones and using virtual filters on our faces while taking pictures are two uses of AI that are presently essential for our day-by-day lives. Face recognition is used in the former, which means that every human face can be recognized. Face recognition is used in the above, which recognizes a particular face. How does it work? Intelligent machines often match-and some cases, even exceed human performance! - Human potential. Human babies begin to identifying facial features such as eyes, lips, nose, and face shapes. A face, though, is more than just that. A number of characteristics distinguish human faces. Smart machines are trained in order to recognize facial coordinates (x, y, w, and h; which form a square around the face as an area of interest), landmarks (nose, eyes, etc.), and alignment (geometric structures). This improves the human ability to identify faces by several factors. Face recognition is also used by government facilities or at the airport for monitoring, and security. When typing a document, there are inbuilt or downloadable auto-correcting tools for editors of spelling errors, readability, mistakes, and plagiarism based on their difficulty level. It should have taken a long time for us to master our language and become fluent in it. Artificially intelligent algorithms often used deep learning, machine learning, and natural language in order to detect inappropriate language use and recommend improvements. Linguists and computer scientists collaborate in teaching machines grammar in the same way that we learned it in school. Machines are fed large volumes of high-quality data that has been structured in a way that machines can understand. Thus, when we misspell a single comma, the editor will highlight it in red and offer suggestions. Answering a customer's inquiries can take a long time. The use of algorithms to train machines to meet customer needs through chatbots is an artificially intelligent solution to this problem. This allows machines to answer as well as take and track orders. We used Natural Language Processing (NLP) to train chatbots to impersonate customer service agents' conversational approaches. Advanced chatbots do not require complex input formats (such as yes/o questions). They are capable of responding to complex questions that necessitate comprehensive answers. They will appear to be a customer representative, in fact, another example of artificial intelligence (AI). If you give a negative rating to a response, the bot will figure out what went wrong and fix it the next time, ensuring that you get the best possible service. It can be a time-consuming errand to rush to the bank for any transaction. Good news! Artificial Intelligence is now being used by banks to support customers by simplifying the process of payment. Artificial intelligence has enabled you to deposit checks from the convenience of our own home. Since AI is capable of deciphering handwriting and making online cheque processing practicable. Artificial Intelligence can potentially be utilized to detect fraud by observing consumers' credit card spending patterns. For example, the algorithms are aware of what items User X purchases, when and where they are purchased, and in what price range they are purchased. If there is some suspicious behaviour that does not match the user's profile, then the system immediately signals user X. When we wish to listen to our favorite songs or watch our favorite movie or shop online, we have ever found that the things recommended to us perfectly match our interests? This is the beauty of artificial intelligence. These intelligent recommendation systems analyze our online activity and preferences to provide us with similar content. Continuous training allows us to have a customized experience. The data is obtained from the front-end, saved as big data, and analysed using machine learning and deep learning. Then it can predict your preferences and make suggestions to keep you amused without having to look for something else. Artificial intelligence can also be utilized to improve the user experience of a search engine. Generally, the answer we are searching for is found in the top search results. What cause this? Data is fed into a quality control algorithm to identify high-quality content from SEO-spammed, low-quality content. This aids in creating an ascending order of search results on the basis of the quality for the greatest user experience. Since search engines are made up of codes, natural language processing technology aids in understanding humans by these applications. In reality, they can predict what a person wants to ask by compiling top-ranked searches and guessing their questions when they begin to type. Machines are constantly being updated with new features such as image search and voice search. If we need to find out a song that is playing at a mall, all we have to do is hold the phone up to it, and a music-identifying app will tell us what it is within a few seconds. The machine will also offer you song details after searching through an extensive collection of tunes. When our hands are full, we often enlist the help of digital assistants to complete tasks on our behalf. We might ask the assistant to call our father while we are driving with a cup of tea in one hand. For instance, Siri would look at our contacts, recognize the word "father," and dial the number. Siri is an example of a lower-tier model which can only respond to voice commands and cannot deliver complex responses. The new digital assistant is fluent in human language and uses advanced NLP (Natural Language Processing) and ML (Machine Learning) techniques. They are capable of understanding complex command inputs and providing acceptable results. They have adaptive abilities which can examine preferences, habits, and schedules. It enables them to use prompts, schedules, and reminders to help us systemize, coordinate, and plan things. The advent of social media gave the world a new narrative with immense freedom of speech. Although, it brought certain social ills like cyberbullying, cybercrime, and abuse of language. Several social media apps are using AI to help solve these issues while also providing users with other enjoyable features. AI algorithms are much quicker than humans at detecting and removing hate speech-containing messages. It is made possible by their ability to recognize hostile terms, keywords, and symbols in a variety of languages. These have been entered into the system, which can also contribute neologisms to its dictionary. Deep learning's neural network architecture is a vital part of the process. Emojis have become the most common way to express a wide range of emotions. This digital language is also understood by AI technology because it can understand the meaning of a certain piece of text and guess the exact emoji. Social networking, a perfect example of artificial intelligence, may even figure out what kind of content a user likes and recommends similar content. Facial recognition is also used in social media profiles, assisting users in tagging their friends via automatic suggestions. Smart filters can recognize spam and undesirable messages and automatically filter them out. Users may also take advantage of smart answers. The social media sector could use artificial intelligence to detect mental health issues such as suicidal thoughts by analyzing the information published and consumed. This information can be shared with mental health professionals. Infervision is using artificial intelligence and deep learning to save lives. In China, where there are insufficient radiologists to keep up with the demand for checking 1.4 billion CT scans each year for early symptoms of lung cancer. Radiologists essential to review many scans every day, which isn't just dreary, yet human weariness can prompt errors. Infervision trained and instructed algorithms to expand the work of radiologists in order to permit them to diagnose cancer more proficiently and correctly. The inspiration and foundation for Google's DeepMind are Neuroscience, which aims to create a machine that can replicate the thinking processes in our own brains. While DeepMind has effectively beaten people at games, what are truly captivating are the opportunities for medical care applications. For example, lessening the time it takes to plan treatments and utilizing machines to help diagnose ailments. Artificial Intelligence has been an important part of the gaming industry in recent years. In reality, one of AI's most significant achievements is in the gaming industry. One of the most important achievements in the field of AI is DeepMind's AI-based AlphaGo software, which is famous for defeating Lee Sedol, the world champion in the game of GO. Shortly after the win, DeepMind released AlphaGo, which trounced its predecessor in an AI-AI face off. The advanced machine, AlphaGo Zero, taught itself to master the game, unlike the original AlphaGo, which DeepMind learned over time using a vast amount of data and supervision. Not at all like the first AlphaGo, which DeepMind prepared over the long run by utilizing a lot of information and oversight, the high-level framework, AlphaGo Zero instructed itself to dominate the game. Another example of Artificial Intelligence in gaming comprises the First Encounter Assault Recon, also known as F.E.A.R that is the first-person shooter video game. The online advertising industry is the most significant user of artificial intelligence that uses AI (Artificial Intelligence) to not only monitor user statistics but also to advertise us on the basis of the statistics. The online advertising industry will struggle if AI is not implemented, as users will be shown random advertisements that have no relation to their interests. Since AI has been so good at determining our preferences and serving us ads, the worldwide digital ad industry has crossed 250 billion US dollars, with the business projected to cross the 300 billion mark in 2019. So, the next time remembers that AI is changing your life while you browse the internet and encounter adverts or product recommendations. The banking and finance industry has a major impact on our daily lives which means the world runs on liquidity, and banks are the gatekeepers who control the flow. Did you know that artificial intelligence is heavily used in the banking and finance industry for things such as customer service, investment, fraud protection, and so on? The automatic emails we get from banks if we make an ordinary transaction, are a simple example. That's AI keeping an eye on our account and trying to alert us regarding any potential fraud. AI is now being trained to examine vast samples of fraud data in order to identify patterns so that we can be alerted before it happens to us. If we run into a snag and contact our bank's customer service, we are probably speaking with an AI bot. Even the largest financial industry use AI to analyse data in order to find the best ways to invest capital in order to maximize returns while minimizing risk. Not only that, but AI is set to play an even larger role in the industry, with major banks around the world investing billions of dollars in AI technology, and we will be able to see the results sooner rather than later. Another popular example of AI (Artificial Intelligence) is smart home devices. Artificial intelligence is even being welcomed into our homes. Most of the smart home gadgets we purchase use artificial intelligence to learn our habits and automatically change settings to make our experience as seamless as possible. We have effectively examined how we utilize savvy voice assistants to control these smart home gadgets. We probably are aware that it is a great example of AI's impact on our lives. That is to say, there are smart thermostats that change the temperature-dependent on our preferences, smart lights which change the colour and intensity of lights dependent on time, and much more. This will not happen when our primary interaction with all our smart home devices is only through AI. Although we all can debate about the ethics of using a large surveillance system, there's no denying that it's being used, and AI is playing a significant role in it. It isn't workable for people to keep monitoring many monitors simultaneously, and thus, utilizing AI makes well. With technologies such as facial recognition and object recognition improving every day, it won't be long when all the security camera deals with are being checked by an AI and not a human. Right now, before AI can be completely implemented, this is going to be our future. Smart keyboard apps are another example of AI (Artificial Intelligence). In all actuality, not every person loves managing on-screen keyboards. Although, they have become far more intuitive, permitting clients to type comfortably and quickly. What has likely ended up being a catalyst for them is the integration of AI. The smart keyboard applications keep a tab on the composing style of a client and predict words and emojis based on that. Consequently, typing on the touchscreen has gotten quicker and more advantageous. Not to mention that artificial intelligence is crucial in detecting misspellings and typos. Not in vain, many thinks that smart speakers are good to go for a major blast in technology. Besides controlling smart home gadgets, they are likewise capable of various things like sending fast messages, setting updates, checking the climate, and getting the most recent news. Also, it's this flexibility that is ending up being a conclusive factor for them. Driven by the hugely popular Amazon Echo series, the worldwide brilliant speaker market arrived at an exceptional high in 2019 with sales of 149.9 million units, which is a huge increment of 70% in 2018. Additionally, the sales in Q4 2019 also saw another record with an incredible 55.7 million units. Smart speakers are likely the most unmistakable instances of the utilization of AI in our reality. Artificial intelligence algorithms have given the necessary vital impulse to web-based businesses to give a more customized insight. According to many sources, its use has significantly improved sales and has also aided in developing long-term consumer relationships. Thus, organizations take advantage of AI to deploy chatbots to gather urgent information and predict purchases to make a client-centric experience. On the way across this shift of technique? Simply invest some time on websites such as Amazon, and eBay and we will soon see how quickly the scene around you is improving rapidly! In the event that you actually find your inbox cluttered with an excessive number of undesirable messages, the possibility is quite high that we can yet stay with an old-fashioned email application. Present-day email applications such as Spark make several AI to filter out spam messages and furthermore arrange messages so you can rapidly get to the significant ones. Likewise, it additionally provides smart answers dependent on the messages we get to help us answer to any email rapidly. The "Smart Reply" highlight of Gmail is an extraordinary illustration of this. It utilizes AI to filter the content of the email and gives you context-oriented answers. Another amazing illustration of how AI affects our lives is the music and media streaming features that we utilize reliably. Whether or not you are utilizing Spotify, Netflix, or YouTube, AI is making the decisions for you. All things considered as everything, once in a while is great and some of the time is awful. For instance, I enjoy Spotify's Discover Weekly playlist since it has acquainted me with a few new artists who I would not have known about if it weren't for Spotify's AI divine beings. Then again, I additionally remember going down the YouTube rabbit hole, wasting uncountable hours simply watching the suggested videos. That suggested videos section has become so great at knowing my taste that it's alarming. Thus, keep in mind that AI is at work whenever you are watching a suggested video on YouTube, viewing a suggested show on Netflix, listening to a pre-made playlist on Spotify, or using any other media and music streaming service. Space expeditions and discoveries consistently require investigating immense measures of information. Artificial Intelligence and Machine learning are the best approach for dealing with and measure information on this scale. After thorough astronomers, and research utilized Artificial Intelligence to filter through long periods of information got by the Kepler telescope to distinguish an inaccessible eight-planet solar system.' We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence Essay - Javatpoint

Examples of AI-Artificial Intelligence Essay Essay on Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Advantages: Disadvantages: Contact info Follow us Tutorials Interview Questions Online Compiler In this topic, we are going to provide an essay on Artificial Intelligence. This long essay on Artificial Intelligence will cover more than 1000 words, including Introduction of AI, History of AI, Advantages and disadvantages, Types of AI, Applications of AI, Challenges with AI, and Conclusion. This long essay will be helpful for students and competitive exam aspirants. Artificial Intelligence is a combination of two words Artificial and Intelligence, which refers to man-made intelligence. Therefore, when machines are equipped with man-made intelligence to perform intelligent tasks similar to humans, it is known as Artificial Intelligence. It is all about developing intelligent machines that can simulate the human brain and work & behave like human beings. We can define AI as, "Artificial Intelligence is a branch of computer science that deals with developing intelligent machines which can behave like human, think like human, and has ability to take decisions by their own." With AI, machines can have human-based skills such as learning, reasoning, and solving logical problems. AI is one of the fastest-growing technology that is making human life much easier by providing solutions for complex problems. It has also brought different opportunities for everyone, and hence it is a very demanding technology in the market. Artificial intelligence is assumed a new technology, but in reality, it is not new. The researchers in the field of AI are much older. It is said that the concept of intelligent machines was found in Greek Mythology. Below are some keystones in the development of AI: 1. Narrow AI or Weak AI: Narrow AI or Weak AI is a basic kind of Artificial Intelligence, which is capable of completing dedicated tasks with intelligence. The current version of AI is narrow AI. Narrow AI can only perform the specific task and not beyond its limitation, as they are trained for one task only. It is programmed to do a specific task such as Play Chess, Checking Weather, etc. 2. General AI: Artificial General intelligence or "Strong" AI defines the machines that can show human intelligence. We can say, Machines with AGI can successfully perform any intellectual task that a human can do. This is the sort of AI that we see in movies like "Her" or other sci-fi movies in which humans interact with machines and operating systems that are conscious, sentient, and driven by emotion and self-awareness. Currently, this type of intelligence does not exist in the real world and only exist in researches and movies. However, researchers across the world are working to develop such machines, which is still a very difficult task. 3. Super AI Super AI refers to AI that is self-aware, with cognitive abilities that surpass that of humans. It is a level where machines are capable of doing any task that a human can do with cognitive properties. However, Super AI is still a hypothetical concept, and it is a challenging task to develop such AI-enabled machines. 1. Reactive Machines Reactive machines are the basic types of AI, which don't store memories or past experiences for their actions. These types of AI machines only focus on current scenarios and work as per the requirement with the best possible actions. IBM's Deep Blue is an example of a reactive machine. 2. Limited Memory Limited memory can store some memory or past experiences for a limited time period. Some examples of limited memory are Self-driving cars. 3. Theory of Mind Theory of Mind is the type of AI which are capable of understanding human emotions, and interact with the human in their way. However, such AI machines are yet not developed, and developers and researchers are making efforts for creating such AI-enabled machines. 4. Self-awareness Self-awareness AI is the future of Artificial Intelligence, which will have its own awareness, sentiments, and consciousness. This AI is only a hypothetical concept and will take a long journey and challenges to create such AI. 1. Game Playing: AI is widely used in Gaming. Different strategic games such as Chess, where the machine needs to think logically, and video games to provide real-time experiences use Artificial Intelligence. 2. Robotics: Artificial Intelligence is commonly used in the field of Robotics to develop intelligent robots. AI implemented robots use real-time updates to sense any obstacle in their path and can change the path instantly. AI robots can be used for carrying goods in hospitals and industries and can also be used for other different purposes. 3. Healthcare: In the healthcare sector, AI has diverse uses. In this field, AI can be used to detect diseases and cancer cells. It also helps in finding new drugs with the use of historical data and medical intelligence. 4. Computer Vision: Computer vision enables the computer system to understand and derive meaningful information from digital images, video, and other visual input with the help of AI. 5. Agriculture: AI is now widely used in Agriculture; for example, with the help of AI, we can easily identify defects and nutrient absences in the soil. To identify these defects, AI robots can be utilized. AI bots can also be used in crop harvesting at a higher speed than human workers. 6. E-commerce AI is one of the widely used and demanding technologies in the E-commerce industry. With AI, e-commerce businesses are gaining more profit and grow in business by recommending products as per the user requirement. 7. Social Media Different social media websites such as Facebook, Instagram, Twitter, etc., use AI to make the user experiences much better by providing different features. For example, Twitter uses AI to recommend tweets as per the user interest and search history. As a beginner, below are some of the prerequisites that will help to get started with AI technology. One of the big challenges with AI is that we don't have enough data to work with AI systems, or data we have is of poor quality or unstructured. AI depends on data for its working and requires a huge amount of data for a good result, but in the real world, data is available either in raw form or unstructured form that contains lots of impurities and missing values that cannot be processed or analyzed. Hence the processing of such data is a big task for organizations, and it takes lots of effort and is a time-consuming process. There is still a lack of IT infrastructures, mainly in start-ups, which is a big issue in AI researches and development. AI is growing continuously day by day with rapid speed, and more people are accepting the proven ideas of AI. The growing rate of AI also needs developers of AI tech. However, the professionals with full scales skills to develop high-level AI implementations are still lacking, which is also one of the big challenges with AI. Computing power has always been a big issue in the IT industry, but day by day, this issue has been resolved. However, with the development of AI, this issue has arisen again. Deep learning and the processing of neural networks, which are part of AI, require a high level of computing power, and are a major challenge for the tech industries. Mainly for start-ups, collecting money and such high computing power to process the data is a big deal. One of the latest challenges with AI is that now organizations need to be wary of AI. The legal issues are raised for concern that if AI collects sensitive data, that may be a violation of federal laws. Although it is not illegal, industries need to be careful of any supposed impact that might negatively affect their organization. Artificial Intelligence is undoubtedly a trending and emerging technology. It is growing very fast day by day, and it is enabling machines to mimic the human brain. Due to its high performance and as it is making human life easier, it is becoming a highly demanded technology among industries. However, there are also some challenges and problems with AI. Many people around the world are still thinking of it as a risky technology, because they feel that if it overtakes humans, it will be dangerous for humanity, as shown in various sci-fi movies. However, the day-to-day development of AI is making it a comfortable technology, and people are connecting with it more. Therefore, we can conclude that it is a great technology, but each technique must be used in a limited way in order to be used effectively, without any harm. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Healthcare - Javatpoint

Artificial Intelligence in Healthcare Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction to Artificial Intelligence AI in Healthcare AI technologies used in healthcare AI-based healthcare system vs. Traditional healthcare system Roles of Artificial Intelligence (AI) in healthcare Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews AI-based Healthcare system: Traditional Healthcare System: Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence (AI) is transforming industries around the world, and currently, its application is rapidly increasing in the healthcare sector. AI in healthcare describes the use of AI or machine-learning algorithms to mimic human cognition for gathering and understanding complex medical and health care data.AI does this by various Machine Learning algorithms, Computer Vision, Natural Language Processing, Robotics, and Deep Learning. These algorithms recognize a pattern in behaviour and then create their own logic to give well-defined output to end-users. Machine Learning helps to gain important insights and predictions using extensive amounts of input data. Further, they also instruct experts on how to build companions for expensive clinical preliminaries. In this topic, we are going to discuss the impact of Artificial Intelligence on the healthcare sector. But before starting, let's first understand the brief introduction of AI. Artificial Intelligence (AI) is defined as a branch of computer science that aims to enable computer systems to perform various tasks with intelligence similar to humans. It is also an ability of computers or machines to display intellectual processes and characteristics of humans such as reasoning, generalizing and learning from past experience, etc. Artificial Intelligence in Healthcare is used to analyze the treatment techniques of various diseases and to prevent them. AI is used in various areas of healthcare such as diagnosis processes, drug research sector, medicine, patient monitoring care centre, etc. In the healthcare industry, AI helps to gather past data through electronic health records for disease prevention and diagnosis. There are various medical institutes that have developed their own AI algorithms for their department, such as Memorial Sloan Kettering Cancer Center and The Mayo clinic, etc. Further, IBM and Google have also developed AI algorithms for the healthcare industry that help to support operational initiatives that increase cost-saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Artificial Intelligence uses various technologies or algorithms in healthcare industries, and these are as follows: AI helps to predict and analyze data through electronic health records for disease prevention, diagnosis, and treatment of diseases, illness and other physical and mental impairments in human beings. Nowadays, AI is a widely used technology worldwide, which plays a very crucial role in each sector, such as gaming, banking, agriculture, etc. AI also plays a very important role in the healthcare sector, such as deceases prediction and prevention, Drug research and manufacturing, deceases treatments, surgery and patient monitoring, etc. Artificial Intelligence helps to analyze and predict the type of deceases, and it's a method of prevention based on gathering past data through electronic health records for disease prevention and diagnosis and later used in various decease prediction and their treatment. However, AI also gathers this data from the traditional approach of doctors, such as X-Ray. Further, AI uses robotics technology in the research and manufacturing of drugs and surgery. Current Research of AI in healthcare: AI has developed exponential growth in the research industry. The government of the United States of America is estimated to invest more than $2 billion in AI-related to healthcare sectors like Dermatology, Radiology, Screening, Psychiatry and Drug Interactions, etc., over the next five years. In the healthcare sector, Artificial Intelligence helps to decrease medication costs with a more accurate diagnosis, better prediction and treatment of diseases. The researchers are also working on an AI project that can be a boon for humans in the upcoming years. The brain-computer interface can help patients who are physically disabled or suffering spinal cord injury as well. Hence, the Healthcare industry is fully ripe for some major changes. From chronic disease and cancer to radiology and risk assessment, it can be deployed with new AI-based technologies with more precise, efficient, and cost-efficient inventions. The Healthcare industry is treated as a complicated science bound by legal, ethical, economical and social constraints and can be implemented with AI by making parallel changes in the environment. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Education - Javatpoint

Artificial Intelligence in Education Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Overview Of AIED(Artificial Intelligence in Education) Applications/roles of Artificial Intelligence in Education Benefits of AI For Students Future of AI in Education Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Education is an important part of life for everyone, and a good education plays a vital role to have a successful life. In order to improve the education system for the students, there are always a lot of changes happening around the world, ranging from the way of teaching to the type of curriculum. Artificial Intelligence is a thriving technology that is being used in almost every field and is changing the world. One place where artificial intelligence is poised to make big changes is (and in some cases already is) in education Artificial Intelligence in Education is developing new solutions for teaching and learning for different situations. Nowadays, AI is being used by different schools and colleges across different countries. AI in education has given a completely new perspective of looking at education to teachers, students, parents, and of course, the educational institutions as well. AI in education is not about humanoid robots as a teacher to replace human teachers, but it is about using computer intelligence to help teachers and students and making the education system much better and effective. In future, the education system will have lots of AI tools that will shape the educational experience of the future. In this topic, we will discuss the impact and application of Artificial Intelligence on Education. To better understand this topic, let's first understand what AIED is? Artificial Intelligence (AI) is a simulation of human intelligence into a computer machine so that it can think and act like a human. It is a technology that helps a computer machine to think like a human. Artificial Intelligence aims to mimic human behaviour. AI has various uses and applications in different sectors, including education. In the 1970s, AIED has occurred as a specialist area to cover new technology to teaching & learning, specifically for higher education. The main aim of AIED is to facilitate the learners with flexible, personalized, and engaging learning along with the basic automated task. Some popular trends in AIED include Intelligent tutor systems, smart classroom technologies, adaptive learning, and pedagogical agents. Below diagram shows the relationship between all these trends: As per the researches, in the near future, AI in education will step in three main ways, which are: Artificial intelligence and its uses in our lives are growing day by day in many segments. In the field of education, AI has started showing its influences and working as a helping tool for both the students and teachers and supporting the learning process. But still, the use of AI in education is not adapted by all the colleges completely, and it will take a long journey to do this. However, studies show that in the near future, AI will have a good impact on the education sector. It is currently transforming the education industry but is yet to show its real potential in education. Further, learning from computer systems can be much helpful, but it is unlikely to fully replacing human teaching in schools and colleges. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Agriculture - Javatpoint

Artificial Intelligence in Agriculture Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Challenges in Agriculture using traditional methods Applications of Artificial Intelligence in Agriculture AI start-ups in Agriculture Benefits and Challenges of AI in agriculture Challenges of AI adoption in Agriculture Conclusion: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Advantages Contact info Follow us Tutorials Interview Questions Online Compiler Agriculture plays a crucial role in the economic sector for each country. Population around the world is increasing day by day, and so is the demand for food. The traditional methods that are used by the farmers are not sufficient to fulfil the need at the current stage. Hence, some new automation methods are introduced to satisfy these requirements and to provide great job opportunities to many people in this sector. Artificial Intelligence has become one of the most important technologies in every sector, including education, banking, robotics, agriculture, etc. In the agriculture sector, it is playing a very crucial role, and it is transforming the agriculture industry. AI saves the agriculture sector from different factors such as climate change, population growth, employment issues in this field, and food safety. Today's agriculture system has reached at a different level due to AI. Artificial Intelligence has improved crop production and real-time monitoring, harvesting, processing and marketing. Different hi-tech computer-based systems are designed to determine various important parameters such as weed detection, yield detection, crop quality, and many more. In this topic, we will discuss the impact and application of Artificial Intelligence on Agriculture, along with the challenges in adoption of AI. Before understanding AI impact and application in Agriculture, we must understand what are the challenges in agriculture by using traditional methods, which are given below: As with the traditional methods of Agriculture, there are so many challenges that farmers would face. To solve these challenges, AI is being widely used in this sector. For agriculture, Artificial Intelligence has become a revolutionary technology. It helps the farmers by yielding healthier crops, control pests, soil monitoring, and many more ways. Below are some key applications of Artificial Intelligence in the Agriculture sector: 1. Weather & price Forecasting: As we have discussed in challenges that it is difficult for the farmers to take the right decision for harvesting, sowing seeds, and soli preparing due to climate change. But with the help of AI weather forecasting, farmers can have information on weather analysis, and accordingly, they can plan for the type of crop to grow, seeds to sow, and harvesting the crop. With price forecasting, farmers can get a better idea about the price of crops for the next few weeks, which can help them to get maximum profit. 2. Health Monitoring of Crops: The quality of crop widely depends on the type of soil and nutrition of the soil. But with the increasing rate of deforestation, the soil quality is degrading day by day, and it is hard to determine it. To resolve this issue, AI has come up with a new application called Plantix. It was developed by PEAT to identify the deficiencies in soil, including plant pests and diseases. With the help of this application, farmers can get an idea to use better fertilizer which can improve the harvest quality. In this app, AI's image recognition technology is used by which farmers can capture the images of plants and get information about the quality. 3. Agriculture Robotics: Robotics is being widely used in different sectors, mainly in manufacturing, to perform complex tasks. Nowadays, different AI companies are developing robots to be employed in the Agriculture sector. These AI robots are developed in such a way that they can perform multiple tasks in farming. AI robots are also trained in checking the quality of crops, detect and controlling weeds, and harvesting the crop with faster speed compared to a human. 4. Intelligent Spraying With AI sensors, weed can be detected easily, and it also detects weed affected areas. On finding such areas, herbicides can be precisely sprayed to reduce the use of herbicides and also saves time and crop. There are different AI companies that are building robots with AI and computer vision, which can precisely spray on weeds. The use of AI sprayers can widely reduce the number of chemicals to be used on fields, and hence improves the quality of crops and also saves money. 5. Disease Diagnosis With AI predictions, farmers can get knowledge of diseases easily. With this, they can easily diagnose diseases with proper strategy and on time. It can save the life of plants and farmer's time. To do this, firstly, images of plants are pre-processed using computer vision technology. This ensures that plant images are properly divided into the diseased and non-diseased parts. After detection, the diseased part is cropped and send to the labs for further diagnosis. This technique also helps in the detection of pests, deficiency of nutrients, and many more. 6. Precision Farming Precision farming is all about "Right place, Right Time, and Right products". The precision farming technique is a much accurate and controlled way that can replace the labour-intensive part of farming to perform repetitive tasks. One example of Precision farming is the identification of stress levels in plants. This can be obtained using high-resolution images and different sensor data on plants. The data obtained from sensors is then fed to a machine learning model as input for stress recognition. Below is the list of popular start-ups in Agriculture: 1. Prospera: It is an Israeli start-up founded in the year 2014. This company creates intelligent solutions for efficient farming. It develops cloud-based solutions that collect all the data from the fields such as soil/water, aerial images, etc. and combine this data with an in-field device. This device is known as the Prospera device, and it makes insights from this data. The device is powered by various sensors and technologies such as computer vision. 2. Blue River technology: Blue-River technology is a California-based start-up that has started in the year 2011. It develops next-generation agriculture equipment using AI, computer vision, and robotics technology. This equipment identifies individual plants using computer vision, ML decides action, and with robotics, the action is performed. This helps the farmers to save costs and chemicals in farming. 3. FarmBot: Farmbot is an open-source CNC precision farming machine and software package, which is developed to grow crops by anyone at their own place. The complete product "Farmbot" is available at a price of $4000, and it enables anyone to do complete farming ranging from seed plantation to weed detection on their own with the help of a physical bot and open-source software system. It also provides a webapp that can be downloaded on any smartphone or computer system and allows us to manage farming from any place at any time. 4. Fasal: The use of AI in the agriculture industry is increasing day by day in various places across the world. However, agriculture holdings per farmer in the poorer region is less compared to the rich region, which is advantageous for automated monitoring as it requires a lesser number of devices with low bandwidth and size to capture the complete agriculture data. In this field, the Indian start-up Fasal is working. It uses affordable sensors and AI to provide real-time data and insights to farmers. With this, farmers can be benefitted from real-time, actionable information relevant to day-to-day operations at the farm. The company's devices are easy to implement for small places. They are developing AI-enabled machines to make precision farming that can be accessible by every farmer. 5. OneSoil: Onesoil is an application that is designed to help farmers to take a better decision. This app uses a machine-learning algorithm and computer vision for precision farming. It monitors the crops remotely, identifies problems in the fields, check the weather forecast, and calculate nitrogen, phosphorus, and potassium fertilizer rate, etc. Predictive analytics is really a boon for the agriculture industry. It helps the farmers solving the key challenges of farming, such as analysing the market demands, price forecasting, and finding optimal times for sowing and harvesting the crop. Moreover, AI-powered machines can also determine soil and crop health, provides fertilizer recommendations, monitor the weather, and can also determine the quality of crop. All such benefits of AI in agriculture enable the farmers to make better decisions and do efficient farming. Precision farming using AI-enabled equipment helps the farmers to grow more crops with lesser resources and cost. AI provides the real-time insights to farmers that enables them to take proper decision at each stage of farming. With this correct decision, there is less loss of products and chemicals and efficient use of time and money. Moreover, it also allows the farmers to identify the particular areas that need irrigation, fertilization, and pesticide treatment, which saves excessive use of chemicals on the crop. All these things sum up and result in reduced use of herbicides, better crop quality and high profit with fewer resources. There has always been an issue of labour shortage in the agriculture industry. AI can solve this issue with automation in farming. With AI and automation, farmers can get work done without having more people, and some examples are Driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting. AI-driven machines and equipment are much faster and accurate compared to human farmhands. By seeing the advantages of AI for sustainable farming, implementing this technology may seem like a logical step for every farmer. However, there are still some serious challenges that everyone knows, which are as follows: Although there are lots of benefits of using AI in agriculture, yet people are not familiar with the use of AI-enabled solutions and equipment across most of the world. To solve the issues, AI companies should provide the basic equipment to farmers, and once they get familiar with them, then provide them with advanced machines. The adoption of AI and emerging technologies in agriculture for developing countries can be a challenging task. It will be very difficult to sell such technologies in the areas where there is no such agricultural technology is being taken into use. In such areas, to use these technologies, farmers need someone's help. As there are still no clear regulations and policies for using AI, it may raise various legal issues. Further, due to the use of software and the internet, there may also be some privacy and security issues such as cyberattacks and data leaks. All these issues can create a big problem for farm owners or farmers. The future of AI in farming largely depends on the adoption of AI solutions. Although some large-scale researches are in progress and some applications are already in the market, yet industry in agriculture is underserved. Moreover, creating predictive solutions to solve a real challenge faced by farmers in farming is still in progress at an early stage. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Engineering Applications of Artificial Intelligence - Javatpoint

Engineering Applications of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI in Engineering: Applications Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Application 1: Advanced Robots Application 2: Big Data Application 3: Internet of Things Application 4: Image Processing Application 5: Natural Language Processing Contact info Follow us Tutorials Interview Questions Online Compiler AI is a prominent fixture in Hollywood films as producers attempt to make movies futuristic. But AI is not a thing of the future as we already use it. It is making considerable contributions across multiple industries, including engineering. In this post, we'll look at the different engineering applications of artificial intelligence. Many scientists are fascinated with the idea of developing a machine that can mimic the human brain. Artificial neural networks, brain-computer interfaces, and trans-humanism all try to replicate the human brain's complexity. But many do realize that it's not that simple. However, we can't discount that while we are still far from bringing this complexity to a machine, AI is significantly making our lives easier. It has already become a vital part of engineering. Let's take a closer look at some engineering applications of artificial intelligence. The growth of AI allowed developers to create machines that can carry out complex manufacturing tasks. The goal is to develop systems that can learn and improve without the need for human intervention. As manufacturing needs continue to expand, we foresee a more significant demand for advanced robots to replace humans in an assembly line. An example is the use of advanced robots in the automobile manufacturing sector. AI systems have evolved from doing simple tasks to performing precise and complex processes that mimic the intricate functions initially reserved for human workers. All industries now rely heavily on data. Information has become a hot commodity that many organizations that want to beat the competition invest in. But no data would be useful without AI systems that allow users to collect, analyze, and give context. Through machine learning (ML), AI can provide organizations with algorithms capable of detecting mistakes and formulating solutions to improve their operations. Engineers can use big data and AI to facilitate large-scale urban projects. The technology can help them identify where people are and what public infrastructure projects they can carry out to address general issues. The Internet of Things (IoT) has exploded in the past decade, as many organizations continuously work to get everyone connected. Smart devices have become prevalent, allowing people to remain in touch wherever they may be. Connectivity has benefited the engineering industry, as IoT devices make it possible for specialists to monitor projects remotely. For instance, an engineer can use IoT sensors to monitor how well a system they designed measures soil consolidation, degradation, and environmental impact for the client. By enabling ML on IoT devices, it is possible to achieve "connected intelligence" that allows engineers to do predictive, prescriptive, and adaptive analyses for their projects. While the image processing component of AI may not have that much of an impact on engineering, it can potentially change practices to a high degree. Engineers can readily identify structural deformities and other potential issues that may not be readily identifiable with the naked eye through image processing algorithms. These engineering applications of artificial intelligence are crucial to ensure the safety of workers on a project. When combined with other data from sensors, image processing can give contextual information that would aid engineers in decision-making. For instance, a construction site's structural integrity can be assessed with the help of AI before construction begins. Another AI concept that can help engineers is natural language processing (NLP), allowing machines and humans to communicate. Imagine an engineer talking to a tool to get the latter's input on reinforcing an assembly line process in real-time. While this is still a concept, it can be an area worth looking into. The engineering applications of artificial intelligence featured in this post show us that evolution is not something to be scared of. Technology, when used appropriately, can bring about positive outcomes. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Advantages and Disadvantages of Artificial Intelligence - Javatpoint

Advantages & Disadvantages of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Advantages of Artificial Intelligence (AI) Disadvantages of Artificial Intelligence (AI) Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Reduction in Human Error Reduce the Risk (Zero Risk) 24/7 Support Perform Repetitive Jobs Faster decision New Inventions Daily Applications Digital Assistance AI in risky situations High production cost Risk of Unemployment Increasing human's laziness Emotionless Lack of creativity No Ethics No improvement Contact info Follow us Tutorials Interview Questions Online Compiler Nowadays, Artificial Intelligence (AI) is one of the rapidly growing and developing technology of the computer world. This technology helps a machine to think like a human. Artificial Intelligence (AI) is a simulation of human intelligence into a computer machine to think and act like a human. The term Artificial Intelligence (AI) was first invented in the early 1950, and the idea of artificial intelligence was initially begun by the great computer scientist John McCarthy from 1943 to 1956. In a general way, we can say "anything can be termed as Artificial Intelligence if it involves programming or coding to do and think similar to humans." Artificial Intelligence consists of a large number of advantages as well disadvantages like reduction in human error, 24/7 chatbot assistant, medical application, accuracy and right decision making, high cost, etc. In this tutorial of ''Advantages and Disadvantages of Artificial Intelligence'', we will briefly discuss the advantages and disadvantages of Artificial Intelligence. So, let's start with the advantages of Artificial intelligence first. There is an enormous number of advantages of Artificial intelligence as follows: One of the biggest achievements of Artificial Intelligence is that it can reduce human error. Unlike humans, a computer machine can't make mistakes if programmed correctly, while humans make mistakes from time to time. Therefore, Artificial Intelligence uses some set of algorithms by gathering previously stored data, reducing the chances of error and increasing the accuracy and precision of any task. Hence, Artificial Intelligence helps to solve complex problems that require difficult calculations and can be done without any error. It is also one of the biggest advantages of Artificial Intelligence. The technology of developing AI Robots can overcome many risky limitations of humans and do risky things for us such as defusing a bomb, oil and coal mining and exploring the deepest part of the ocean, etc. So, it helps in any worst situation, either human or natural disasters too. AI Robots can be used in such situations where intervention can be hazardous. Unlike humans, a computer does not require breaks and refreshers. A normal human can continue work till 8-9 hours, including breaks and refreshers, while a computer machine can work 24x7 without any breaks and don't even get bored, unlike humans. Chatbots and helpline centres can be seen as the best example of 24/7 support of various websites continuously engaged in receiving customers queries and automatically resolved by Artificial Intelligence. We perform so many repetitive works in our day-to-day life, such as automatic replies to emails, sending birthday and anniversary quotes and verifying documents, etc. Therefore, Artificial Intelligence (AI) helps to automate the business by performing these repetitive jobs. Unlike humans, a machine helps to take decisions faster than a human and carry out actions quicker. While taking a decision, humans analyze many factors while the machine works on what it is programmed and delivers the results faster. The best example of the faster decision can be seen in an online chess game in the third level. It is impossible to beat a computer machine because it takes the best possible step in a very short time, according to the algorithms used behind it. For new inventions, AI is helping humans almost in each sector, it can be healthcare, medical, educational, sports, technology, entertainment or research industry etc. Using advanced AI-based technologies, doctors can predict various dangerous diseases like cancer at a very early stage. Now, we are all completely dependent on mobile and the internet for our daily routine. We use several applications like Google map, Alexa, Apple's Siri, Window's Cortana, OK Google, taking a selfie, making a phone call, replying to a mail, etc. Further, we can also predict the weather for today and upcoming days with the help of various AI-based methods. Digital Assistance is one of the most powerful methods that help various highly advanced organizations to interact with users without engaged human resources. Digital Assistance helps users by gathering previous users queries and providing solutions that users want. The best example of digital Assistance can be seen on various websites in the form of chatbot support. A user asks something, and the computer machine provides relevant information like banking, education, travel, and ticket booking sites. Some chatbots are designed so that it's become hard to determine whether we're chatting with a chatbot or a human being. Human safety is always the primary thing that is also taken care by machines. Whenever we need to explore the deepest part of the ocean or study space, scientists use AI-enabled machines in risky situations where human survival becomes difficult. AI can reach at every place where humans can't reach. If something has a bright side, then parallelly, it also has some dark side. Similarly, Artificial intelligence also has a few drawbacks that are as follows: Although artificial intelligence is one of the most trending and demanding technology around the globe, it still has some disadvantages. Some of the common disadvantages of AI are as follows: We are living in a technological world where we have to manipulate ourselves according to society. Similarly, a computer machine also requires time to time software and hardware updates to meet the latest requirements. Hence, AI also need repairing and maintenance, which need plenty of costs. A robot is one of the implementations of Artificial intelligence, and it is replacing jobs and leading to serve unemployment (In some cases). Hence, according to some people, there is always a risk of unemployment because of robots and chatbots instead of humans. For example, in some more technology-oriented countries such as Japan, robots are widely used in manufacturing industries to replace human resources. However, this is not always the truth because as it replaces humans to enhance efficiency, it is also making more jobs opportunities for humans. The new inventions of Artificial Intelligence are making humans lazier towards their work, resulting in humans being completely dependent on machines and robots. If this continues for more upcoming years, then our next generations will become entirely dependent on a machine, resulting in further unemployment and health issues. We have always learned since childhood that computers or machines don't have emotions. Humans work like a team, and team management is a key factor for completing a target. However, there is no doubt that machines are much better when working efficiently, but it is also true that they never replace the human's connection that makes the team. The biggest disadvantage of Artificial Intelligence is its lack of creativity. Artificial Intelligence is a technology that is completely based on pre-loaded data. However, Artificial Intelligence can learn over time with this pre-fed data and past experiences, but it cannot be creative like humans. Ethics and morality are the two most important features of humans, but it isn't easy to incorporate both of these into Artificial Intelligence. AI is rapidly increasing uncontrollably in each sector, so if this continues for the upcoming decades, it may eventually wipe out humanity. Artificial Intelligence is a technology completely based on pre-loaded data and experience, so it cannot be improved as human. It can perform the same task repeatedly, but if you want some improvement and changes, you have to change the command for the same. However, it can store unlimited data that humans cannot, but also it cannot be accessed and used like human intelligence. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Robotics and Artificial Intelligence - Javatpoint

Robotics and Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Artificial Intelligence? What is a robot? What are Artificially Intelligent Robots? Difference in Robot System and AI Programs Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Components of Robot Applications of Robotics AI technology used in Robotics Computer Vision Natural Language Processing Edge Computing Complex Event Process Transfer Learning and AI Reinforcement Learning Affective computing Mixed Reality What are the advantages of integrating Artificial Intelligence into robotics? 1. AI Programs 2. Robots Contact info Follow us Tutorials Interview Questions Online Compiler Robotics is a separate entity in Artificial Intelligence that helps study the creation of intelligent robots or machines. Robotics combines electrical engineering, mechanical engineering and computer science & engineering as they have mechanical construction, electrical component and programmed with programming language. Although, Robotics and Artificial Intelligence both have different objectives and applications, but most people treat robotics as a subset of Artificial Intelligence (AI). Robot machines look very similar to humans, and also, they can perform like humans, if enabled with AI. In earlier days, robotic applications were very limited, but now they have become smarter and more efficient by combining with Artificial Intelligence. AI has played a crucial role in the industrial sector by replacing humans in terms of productivity and quality. In this article, 'Robotics and Artificial Intelligence, we will discuss Robots & Artificial Intelligence and their various applications, advantages, differences, etc. Let's start with the definition of Artificial Intelligence (AI) and Robots. Artificial Intelligence is defined as the branch of Computer Science & Engineering, which deals with creating intelligent machines that perform like humans. Artificial Intelligence helps to enable machines to sense, comprehend, act and learn human like activities. There are mainly 4 types of Artificial Intelligence: reactive machines, limited memory, theory of mind, and self-awareness. A robot is a machine that looks like a human, and is capable of performing out of box actions and replicating certain human movements automatically by means of commands given to it using programming. Examples: Drug Compounding Robot, Automotive Industry Robots, Order Picking Robots, Industrial Floor Scrubbers and Sage Automation Gantry Robots, etc. Several components construct a robot, these components are as follows: Robotics have different application areas. Some of the important applications domains of robotics are as follows: Robots can also see, and this is possible by one of the popular Artificial Intelligence technologies named Computer vision. Computer Vision plays a crucial role in all industries like health, entertainment, medical, military, mining, etc. Computer Vision is an important domain of Artificial Intelligence that helps in extracting meaningful information from images, videos and visual inputs and take action accordingly. NLP (Natural Languages Processing) can be used to give voice commands to AI robots. It creates a strong human-robot interaction. NLP is a specific area of Artificial Intelligence that enables the communication between humans and robots. Through the NLP technique, the robot can understand and reproduce human language. Some robots are equipped with NLP so that we can't differentiate between humans and robots. Similarly, in the health care sector, robots powered by Natural Language Processing may help physicians to observe the decease details and automatically fill in EHR. Besides recognizing human language, it can learn common uses, such as learn the accent, and predict how humans speak. Edge computing in robots is defined as a service provider of robot integration, testing, design and simulation. Edge computing in robotics provides better data management, lower connectivity cost, better security practices, more reliable and uninterrupted connection. Complex event processing (CEP) is a concept that helps us to understand the processing of multiple events in real time. An event is described as a Change of State, and one or more events combine to define a Complex event. The complex event process is most widely used term in various industries such as healthcare, finance, security, marketing, etc. It is primarily used in credit card fraud detection and also in stock marketing field. For example, the deployment of an airbag in a car is a complex event based on the data from multiple sensors in real-time. This idea is used in Robotics, for example, Event-Processing in Autonomous Robot Programming. This is the technique used to solve a problem with the help of another problem that is already solved. In Transfer learning technique, knowledge gained from solving one problem can be implement to solve related problem. We can understand it with an example such as the model used for identifying a circle shape can also be used to identify a square shape. Transfer learning reuses the pre-trained model for a related problem, and only the last layer of the model is trained, which is relatively less time consuming and cheaper. In robotics, transfer learning can be used to train one machine with the help of other machines. Reinforcement learning is a feedback-based learning method in machine learning that enables an AI agent to learn and explore the environment, perform actions and learn automatically from experience or feedback for each action. Further, it is also having feature of autonomously learn to behave optimally through hit-and-trail action while interacting with the environment. It is primarily used to develop the sequence of decisions and achieve the goals in uncertain and potentially complex environment. In robotics, robots explore the environment and learn about it through hit and trial. For each action, he gets rewarded (positive or negative). Reinforcement learning provides Robotics with a framework to design and simulate sophisticated and hard-to-engineer behaviours. Affective computing is a field of study that deals with developing systems that can identify, interpret, process, and simulate human emotions. Affective computing aims to endow robots with emotional intelligence to hope that robots can be endowed with human-like capabilities of observation, interpretation, and emotion expression. Mixed Reality is also an emerging domain. It is mainly used in the field of programming by demonstration (PbD). PbD creates a prototyping mechanism for algorithms using a combination of physical and virtual objects. Artificial intelligent robots connect AI with robotics. AI robots are controlled by AI programs and use different AI technologies, such as Machine learning, computer vision, RL learning, etc. Usually, most robots are not AI robots, these robots are programmed to perform repetitive series of movements, and they don't need any AI to perform their task. However, these robots are limited in functionality. AI algorithms are necessary when you want to allow the robot to perform more complex tasks. A warehousing robot might use a path-finding algorithm to navigate around the warehouse. A drone might use autonomous navigation to return home when it is about to run out of battery. A self-driving car might use a combination of AI algorithms to detect and avoid potential hazards on the road. All these are the examples of artificially intelligent robots. Here is the difference between Artificial Intelligence and Robots: Usually, we use to operate them in computer-simulated worlds. Generally, input is given in the form of symbols and rules. To operate this, we need general-purpose/Special-purpose computers. Generally, we use robots to operate in the real physical world. Inputs are given in the form of the analogue signal or in the form of the speech waveform. Also, to operate this, special hardware with sensors and effectors are needed. 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Future of Artificial Intelligence - Javatpoint

Future of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Artificial Intelligence (AI) at Present Myths about Advanced Artificial Intelligence How can Artificial Intelligence be risky? Future impact of AI in different sectors Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Superintelligence by the year 2100 is not possible. 2. I will replace all human jobs. 3. Super-intelligent computers will become better than humans at doing anything we can do 4. AI does not require human intervention. 1. AI is programmed to do something destructive: 2. Misalignment between our goals and machines: Healthcare: Cyber security: Transportation: E-commerce: Employment: Contact info Follow us Tutorials Interview Questions Online Compiler Undoubtedly, Artificial Intelligence (AI) is a revolutionary field of computer science, which is ready to become the main component of various emerging technologies like big data, robotics, and IoT. It will continue to act as a technological innovator in the coming years. In just a few years, AI has become a reality from fantasy. Machines that help humans with intelligence are not just in sci-fi movies but also in the real world. At this time, we live in a world of Artificial Intelligence that was just a story though for some years. We are using AI technology in our daily lives either unknowingly or knowingly, and somewhere it has become a part of our life. Ranging from Alexa/Siri to Chatbots, everyone is carrying AI in their daily routine. The development and evolution of this technology are happening at a rapid pace. However, it was not as smooth and easy as it seemed to us. It has taken several years and lots of hard work & contributions of various people to take AI at this stage. Being so revolutionary technology, AI also deals with many controversies about its future and impact on Human beings. It may be dangerous, but also a great opportunity. AI will be deployed to enhance both defensive and offensive cyber operations. Additionally, new means of cyber-attack will be invented to take advantage of particular vulnerabilities of AI technology. This topic will discuss the future of AI and its impact on human life, i.e., whether it is a great technology or a threat to humans. Before going deep dive into AI in future, first, let's understand what is Artificial Intelligence and at what stage it is at present. We can define AI as, "It is the ability of machines or computer-controlled robot to perform task that are associated with intelligence." So, AI is computer science, which aims to develop intelligent machines that can mimic human behaviour. Based on capabilities, AI can be divided into three types that are: At the current stage, AI is known as Narrow AI or Weak AI, which can only perform dedicated tasks. For example, self-driving cars, speech recognition, etc. The reality about the possibility of superintelligence is that currently, we can't determine it. It may occur in decades, or centuries, or may never, but nothing is confirmed. There have been several surveys in which AI researchers have been asked how many years from now they think we will have human-scale AI with at least a 50% chance. All of these surveys have the same conclusion: The world's leading experts disagree, so we don't know. For example, in such a survey of AI researchers at the 2015 Puerto Rico AI conference, the (average) answer was by 2045, but some researchers estimated hundreds of years or more. It's certainly true that the advent of AI and automation has the potential to disrupt labour seriously - and in many situations, it is already doing just that. However, seeing this as a straightforward transfer of labour from humans to machines is a vast oversimplification. With the development of AI, a revolution has come in industries of every sector, and people fear losing jobs with the increased development of AI. But in reality, AI has come up with more jobs and opportunities for people in every sector. Every machine needs a human being to operate it. However, AI has taken over some roles, but it reverts to producing more jobs for people. As discussed above, AI can be divided into three types, Weak AI, which can perform specific tasks, such as weather Prediction. General AI; Capable of performing the task as a human can do, Super AI; AI capable of performing any task better than human. At present, we are using weak AI that performs a particular task and improves its performance. On the other hand, general AI and Super AI are not yet developed, and researches are going on. They will be capable of doing different tasks similar to human intelligence. However, the development of such AI is far away, and it will take years or centuries to create such AI applications. Moreover, the efficiency of such AI, whether it will be better than humans, is not predictable at the current stage. People also have a misconception that AI does not need any human intervention. But the fact is that AI is not yet developed to take their own decisions. A machine learning engineer/specialist is required to pre-process the data, prepare the models, prepare a training dataset, identify the bias and variance and eliminate them, etc. Each AI model is still dependent on humans. However, once the model is prepared, it improves its performance on its own from the experiences. Most of the researchers agree that super AI cannot show human emotions such as Love, hate or kindness. Moreover, we should not expect an AI to become intentionally generous or spiteful. Further, if we talk about AI to be risky, there can be mainly two scenarios, which are: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war resulting in mass casualties. To avoid being dissatisfied with the enemy, these weapons would be designed to be extremely difficult to "turn off," so humans could plausibly lose control of such a situation. This risk is present even with narrow AI but grows as levels of AI intelligence and autonomy increase. The second possibility of AI as a risky technology is that if intelligent AI is designed to do something beneficial, it develops destructive results. For example, Suppose we ask the self-driving car to "take us at our destination as fast as possible." The machine will immediately follow our instructions. It may be dangerous for human lives until we specify that traffic rules should also be followed and we value human life. It may break traffic rules or meet with an accident, which was not really what we wanted, but it did what we have asked to it. So, super-intelligent machines can be destructive if they ask to accomplish a goal that doesn't meet our requirements. AI will play a vital role in the healthcare sector for diagnosing diseases quickly and more accurately. New drug discovery will be faster and cost-effective with the help of AI. It will also enhance the patient engagement in their care and also make ease appointment scheduling, bill paying, with fewer errors. However, apart from these beneficial uses, one great challenge of AI in healthcare is to ensure its adoption in daily clinical practices. Undoubtedly, cyber security is a priority of each organization to ensure data security. There are some predictions that cyber security with AI will have below changes: However, being a great technology, it can also be used as a threat by attackers. They can use AI in a non-ethical way by using automated attacks that may be intangible to defend. The fully autonomous vehicle is not yet developed in the transportation sector, but researchers are reaching in this field. AI and machine learning are being applied in the cockpit to help reduce workload, handle pilot stress and fatigue, and improve on-time performance. There are several challenges to the adoption of AI in transportation, especially in areas of public transportation. There's a great risk of over-dependence on automatic and autonomous systems. Artificial Intelligence will play a vital role in the e-commerce sector shortly. It will positively impact each aspect of the e-commerce sector, ranging from user experience to marketing and distribution of products. We can expect e-commerce with automated warehouse and inventory, shopper personalization, and the use of chatbots in future. Nowadays, employment has become easy for job seekers and simple for employers due to the use of Artificial Intelligence. AI has already been used in the job search market with strict rules and algorithms that automatically reject an employee's resume if it does not fulfil the requirement of the company. It is hoping that the employment process will be driven by most AI-enabled applications ranging from marking the written interviews to telephonic rounds in the future. For jobseekers, various AI applications are helping build awesome resumes and find the best job as per your skills, such as Rezi, Jobseeker, etc. Apart from above sectors, AI has great future in manufacturing, finance & banking, entertainment, etc. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Languages used in Artificial Intelligence - Javatpoint

Languages used in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Python 2. Java 3. Prolog 4. Lisp 5. R 6. Julia 7. C++ Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Features of Python Features of Java Features of LISP Features of R programming Features of Julia Features of C++ Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence has become an important part of human life as we are now highly dependent on machines. Artificial Intelligence is a very important technology to develop and build new computer programs and systems, which can be used to simulate various intelligence processes like learning, reasoning, etc. Python is one of the most powerful and easy programming languages that anyone can start to learn. Python is initially developed in the early stage of 1991. Most of the developers and programmers choose Python as their favourite programming language for developing Artificial Intelligence solutions. Python is worldwide popular among all developers and experts because it has more career opportunities than any other programming language. Python also comes with some default sets of standards libraries and also provides better community support to its users. Further, Python is a platform-independent language and also provides an extensive framework for Deep Learning, Machine Learning, and Artificial Intelligence. Python is also a portable language as it is used on various platforms such as Linux, Windows, Mac OS, and UNIX. Python is an ideal programming language used for Machine Language, Natural Processing Language (NLP), and Neural networks, etc. Due to the flexible nature of Python, it can be used for AI development. It contains various pre-existing libraries such as Pandas, SciPy and nltk, etc. Further, Python also contains simple syntax and easy coding, which makes Python the first choice of AI developers and programmers. There are some standard Libraries in Python used for Artificial Intelligence as follows: Java is also the most widely used programming language by all developers and programmers to develop machine learning solutions and enterprise development. Similar to Python, Java is also a platform-independent language as it can also be easily implemented on various platforms. Further, Java is an object-oriented and scalable programming language. Java allows virtual machine technology that helps to create a single version of the app and provides support to your business. The best thing about Java is once it is written and compiled on one platform, then you do not need to compile it again and again. This is known as WORA (Once Written Read/Run Anywhere) principle. Java has so many features which make Java best in industry and to develop artificial intelligence applications: Prolog is one of the oldest programming languages used for Artificial Intelligence solutions. Prolog stands for "Programming in Logic", which was developed by French scientist Alain Colmerauer in 1970. For AI programming in Prolog, developers need to define the rules, facts, and the end goal. After defining these three, the prolog tries to discover the connection between them. Programming in AI using Prolog is different and has several advantages and disadvantages. It may seem like a bizarre language to learn for those programmers who are from a C++ background. Prolog may not be a great programming language to build something big, but it's a great language to study and think about problems in more logical ways rather than procedural. Features of Prolog Lisp has been around for a very long time and has been widely used for scientific research in the fields of natural languages, theorem proofs, and to solve artificial intelligence problems. Lisp was originally created as a practical mathematical notation for programs but eventually became a top choice of developers in the field of AI. Although Lisp programming language is the second oldest language after Fortran, it is still being used because of its crucial features. The inventor of LISP programming was John McCarthy, who coined the term Artificial Intelligence. LISP is one of the most efficient programming languages for solving specific problems. Currently, it is mainly used for machine learning and inductive logic problems. It has also influenced the creation of other programming languages for AI, and some worth examples are R and Julia. However, though being so flexible, it has various deficiencies, such as lack of well-known libraries, not so-human-friendly syntax, etc. Due to this reason, it is not preferred by the programmers. R is one of the great languages for statistical processing in programming. However, R supports free, open-source programming language for data analysis purposes. It may not be the perfect language for AI, but it provides great performance while dealing with large numbers. Some inbuilt features such as built-in functional programming, object-oriented nature, and vectorial computation make it a worthwhile programming language for AI. R contains several packages that are specially designed for AI, which are: Julia is one of the newer languages on the list and was created to focus on performance computing in scientific and technical fields. Julia includes several features that directly apply to AI programming. Julia is a comparatively new language, which is mainly suited for numerical analysis and computational science. It contains several features that can be very helpful in AI programming. C++ language has been present for so long around, but still being a top and popular programming language among developers. It provides better handling for AI models while developing. Although C++ may not be the first choice of developers for AI programming, various machine learning and deep learning libraries are written in the C++ language. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. 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Approaches to AI Learning - Javatpoint

Approaches to AI Learning Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What are the Different Types of Artificial Intelligence Approaches? Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1: Symbolic logic 2: Symbolic reasoning 3: The connections are based on the neurons of the brain. 4: Evolutionary algorithms that test variation 5: Bayesian Approximation 6: Systems that learn by analogy 1. Reactive machines 2. Limited memory 3. Theory of mind 4. Self-awareness Contact info Follow us Tutorials Interview Questions Online Compiler An algorithm is a kind of container, and it provides a box for storing a method to solve a particular kind of problem. Algorithms process data through a series of well-defined states. States do not need to be deterministic, but states are defined nonetheless. The goal is to create an output that solves a problem. The algorithm receives input that helps define the output in some cases, but the focus is always on the output. Algorithms must express transitions between states using a well-defined and formal language that the computer can understand. In processing data and solving a problem, the algorithm defines, refines, and performs a function. The function is always specific to the type of problem being addressed by the algorithm. Each of the five tribes has a different technique and strategy for solving those problems resulting in unique algorithms. The combination of these algorithms should eventually lead to the master algorithm, which will solve any problem. The following discussion provides an overview of the five main algorithmic techniques. One of the ancient tribes, the Symbolists, believed that knowledge could be gained by working on symbols (signs that stand for a certain meaning or event) and drawing rules from them. One of the earliest tribes, the symbolists, believed that knowledge could be obtained by operating on symbols (signs that stand for a certain meaning or event) and deriving rules from them. By putting together complex rules systems, you could attain a logical deduction of the result you wanted to know; thus, the symbolists shaped their algorithms to produce rules from data. In symbolic logic, deduction expands the scope of human knowledge, while induction increases the level of human knowledge. Induction usually opens up new areas of exploration, whereas deduction explores those areas. The Connectionists are perhaps the most famous of the five tribes. This tribe attempts to reproduce brain functions by using silicon instead of neurons. Essentially, each of the neurons (built as an algorithm that models the real-world counterpart) solves a small piece of the problem, and using multiple neurons in parallel solves the problem as a whole. The goal is to keep changing the weights and biases until the actual output matches the target output. The artificial neuron fires up and transmits its solution to the next neuron in line. The solution produced by just one neuron is a part of the whole solution. Each neuron sends information to the next neuron until the neurons make up the final output. Such a method proved most effective in human-like tasks such as recognizing objects, understanding written and spoken language and interacting with humans. The revolutionaries relied on the principles of evolution to solve problems. In other words, this strategy is based on the existence of the fittest (removing any solutions that do not match the desired output). A fitness function determines the feasibility of each function in solving a problem. Using a tree structure, the solution method finds the best solution based on the function output. The winner of each level of development has to create tasks for the next level. The idea is that the next level will get closer to solving the problem but may not solve it completely, which means that another level is needed. This particular tribe relies heavily on recursion and languages that strongly support recursion to solve problems. An interesting output of this strategy has been algorithms that evolve: one generation of algorithms creates the next generation. A group of Bayesian scientists recognized that uncertainty was the dominant aspect of the view. Learning was not assured but rather occurred as a continuous update of previous assumptions that became more accurate. This notion inspired Bayesians to adopt statistical methods and, in particular, derivations from Bayes' theorem, which help you calculate probabilities in specific situations (for example, by looking at a card of a certain seed, pseudo -The starting value for a random sequence, after three other cards of the same seed are drawn from a deck). Analysts use kernel machines to recognize patterns in the data. By recognizing the pattern of a set of inputs and comparing it to known outputs, you can create a problem solution. The goal is to use equality to determine the best solution to a problem. It is the kind of reasoning that determines whether a particular solution was used in a particular situation at a prior time. Using that solution for similar situations should also work. One of the most recognizable outputs of this tribe is the recommendation system. For example, when you buy a product on Amazon, the recommendation system comes up with other related products that you might want to buy. The ultimate goal of machine learning is to combine the techniques and strategies adopted by the five tribes to form a single master algorithm that can learn anything. Of course, achieving that goal is a long way off, yet scientists like Pedro Domingos are currently working toward that goal. While everything seems green and sunny to a non-specialist, there is a lot of technology to build AI systems. There are four types of artificial intelligence approaches based on how machines behave - reactive machines, limited memory, theory of mind, and self-awareness. These machines are the most basic form of AI applications. Examples of reactive machines are Deep Blue, IBM's chess-playing supercomputer, and the same computer that defeated the then-grand master of the world. AI teams do not use training sets to feed the machines or store subsequent data for future references. Based on the move made by the opponent, the machine decides/predicts the next move. These machines belong to the Category II category of AI applications, and Self-propelled cars are the perfect example. Over time, these machines are fed with data and trained on the speed and direction of other cars, lane markings, traffic lights, curves of roads, and other important factors. It is where we are struggling to make this concept work. However, we are not there yet. Theory of mind is the concept where bots will understand and react to human emotions, thoughts. If AI-powered machines are always mingling and moving around with us, then understanding human behavior is imperative. And then, it is necessary to react to such behaviors accordingly. These machines are an extension of class III type AI, and it is a step ahead of understanding human emotions. It is the stage where AI teams build machines with self-awareness factors programmed into them. When someone is honking the horn from behind, the machines must sense the emotion, and only then do they understand what it feels like when they horn someone from behind. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Scope of AI (AI Careers) - Javatpoint

Scope of AI (AI Careers) Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Benefits of AI Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews The future of AI Jobs in AI The future scope of Artificial Intelligence in India Artificial Intelligence job opportunities 1: Banking 2: Health care and medicine What are the risks associated with AI? What are the applications of AI? Contact info Follow us Tutorials Interview Questions Online Compiler Fresher's should analyze their competencies and skills and choose a better AI role with the potential for upward mobility. The future scope of Artificial Intelligence continues to grow due to new job roles and advancements in the AI field. The various roles in an AI career are as follows: The future of Artificial Intelligence is bright in India, with many organizations opting for AI automation. It is essential to understand the recent developments in AI to find suitable job roles based on your competencies. The scope of Artificial Intelligence is limited to domestic and commercial purposes as the medical and aviation sectors are also using AI to improve their services. If AI is outperforming human efforts, then opting for AI automation will reduce costs in the long run for a business. Automation in operational vehicles has created a buzz in the logistics industry as it is expected that automated trucks/vehicles may soon be used. Due to the bright scope of Artificial Intelligence in the future, the number of AI start-ups is expected to increase in the coming years. Indicating the opportunities, the number of AI start-ups in India has increased significantly. Moreover, India's talent gap for specialist AI developers is huge, and AI experts are needed by businesses more than ever. Businesses don't want to miss out on any technology that can revolutionize their business processes. The name of the designation in the AI field may be different. Some of the top jobs in AI (India - 2021) are as follows: The adoption of Artificial Intelligence in India is promising. However, it is currently in its early stages. While some industries, such as IT, manufacturing, automobiles, etc., are taking advantage of the prowess of AI, there are still many areas in which its potential has not been explored. The immense potential present in AI can be understood by the various other technologies included under the umbrella of AI. Examples of such technologies include self-improvement algorithms, machine learning, pattern recognition, big data, and many others. It is predicted that hardly any industry will be left untouched by this powerful tool in the next few years. It is the reason why AI has so much potential to grow in India. In this comprehensive blog, we have discussed some of the areas in which AI is being used: According to a report published by Forbes, AI job opportunities are continuously increasing at 74% annually. It is a no-brainer that today, AI is one of the most in-demand technologies, and it has an impact in almost every field. As a result, the demand for AI professionals continues to grow. As the number of job opportunities increases, it is the best time to explore your career in AI. Below, we have compiled a list of different areas where AI is used or has immense potential to grow. Banking is nothing new, thanks to the trends in Artificial Intelligence and Machine Learning technologies. The sector has rapidly adopted technology to keep up to date with the current market trends. It uses this technology to record customer data, which was previously a monotonous manual task. With the rapid increase in the amount of data generated and stored in the banking sector today, Artificial Intelligence and ML allow professionals to do this accurately and efficiently. How AI has made a significant difference in banking includes better customer support, enhanced data quality, fraud prevention, digital assistants, and more. One of the most progressive sectors in the world today is healthcare. In the next section, you will read how Artificial Intelligence has affected this sector and how it will continue to do so. According to one of the studies conducted by Forbes, the realm of AI can add value to life, as has already been observed over the years. The healthcare sector uses this technology to its advantage in several ways and constantly innovates. AI use case is the collaborative Cancer Cloud, developed by Intel and the Knight Career Institute. Cloud collects past data of cancer patients and other patients with similar diseases to help doctors diagnose cancer early based on the symptoms they have shown and compare them with previously available data. The best treatment for this deadly disease is to prevent it from reaching its advanced stage. In addition, Eve, an AI-based robot built by a team of scientists from the top universities of Aberystwyth, Manchester, and Cambridge, discovered an element often found in toothpaste that can cure malaria. It is proof that Artificial Intelligence will play an important role in the medical field in the coming times. AAI is also used in health care and medicine in other similar fields, such as drug testing, synthetic biology, etc. You can also be sure that AI will accelerate the process of scientific research and development, which may well aid this field. AI has various uses in the modern-day scenario. Industries are using AI to automate processes, and better AI algorithms are being developed every day to speed up various industry processes/tasks. The various benefits of AI lead to different use cases and job roles in the market, which are beneficial for deep tech enthusiasts or those new to pursuing a career in the AI industry. The scope of AI in India is bright as firms need expert employees who can extract meaningful information from large chunks of data. Are you aware of the risks associated with AI and how to manage them? A beginner should be aware of the potential risks associated with AI processes and how to deal with them. Some of the common risks associated with AI are as follows: AI has its application in every conceivable field or area, and recent advances will only increase the application and relevance of AI in almost every field of human activity. As a beginner, you might think that AI is a newly developed technology, and AI has been under development for longer than you might think. Some of the top use cases of AI are as follows: These were only the top use cases for AI in the current scenario. AI has many other use cases such as autopilot technology in vehicles, ride-booking services, cyber security, etc. Recently, businesses have seen a massive exposure to AI and ML phenomena as they explore their application possibilities in various fields. For example, researchers began using machine learning to gain insight into the recent global pandemic that brought the world to a standstill. When we talk about the combined scope of AI and Machine Learning in India, it is essential to recognize its application in the medical segment to track the spread of viruses, contact tracing, and even analytics for a treatment. Experts predict that as the vicious cycle of economic slowdown begins, India's demand for AI and ML professionals will increase. It is a positive indicator of the scope of Machine Learning and Artificial Intelligence in India. Many IT professionals aspire to pursue a career in AI and ML technologies and look for ways to become and become AI and ML experts. Even as the country witnessed massive job losses, machine learning and artificial intelligence jobs were the least affected. Businesses are already on the patch to create more robust virtual work environments, which has increased the demand for AI and ML professionals. Advancements in technologies also fuel the future scope of Artificial Intelligence in India in subsets of AI such as Deep Learning, Machine Learning, etc. You can opt for a course in AI from a reputed source to know more about the progress of AI in India. From NLP to CNN (Concurrent Neural Networks), AI courses can give you a comprehensive understanding of the latest advances in AI. AI certification course by Aara Academy can help you understand the scope of AI in India better. Also, selecting suitable job roles in AI based on individual competencies is important for the job seeker / fresher. Start building a successful AI career in India. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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What is the composition for agents in Artificial Intelligence (Agents in AI) - Javatpoint

What is the composition for agents in Artificial Intelligence (Agents in AI) Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Types of agents Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews An agent is anything that can be viewed as: Agent examples: Simple reflex agent: Model-based reflex agents: Goal-based agents Utility-based agents Learning Agent: The Nature of Environments Turing Test Properties of Environment Note: Every agent can perceive its actions (but not always the effects) Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence is defined as the study of rational agents. A rational agent may take the form of a person, firm, machine, or software to make decisions. It works with the best results after considering past and present perceptions (perceptual inputs of the agent at a given instance). An AI system is made up of an agent and its environment. Agents work in their environment, and the environment may include other agents. Attention readers! Don't stop learning now. Catch up on all important DSA concepts and be industry-ready with the DSA Self-Pace Course at student-friendly prices. If you would like to attend live classes with experts, please check out DSA Live Classes for Working Professionals and live competitive programming for students. To understand the structure of Intelligent Agents, we must be familiar with the architecture and agent programs. Architecture is the machinery on which the agent executes. It is a device with sensors and actuators, for example, a robot car, a camera, a PC. An agent program is an implementation of an agent function. An agent function concept is a map from the sequence (the history of all that an agent has considered to date). agent = architecture + agent program A software agent has keystrokes, file contents, received network packages that act as sensors and are displayed on the screen, files, sent network packets to act as actuators. The human agent has eyes, ears, and other organs that act as sensors, and hands, feet, mouth, and other body parts act as actuators. A robotic agent consists of cameras and infrared range finders that act as sensors and various motors that act as actuators. Agents can be divided into four classes based on their perceived intelligence and ability: Simple reflex agents ignore the rest of the concept history and act only based on the current concept. Concept history is the history of all that an agent has believed to date. The agent function is based on the condition-action rule. A condition-action rule is a rule that maps a state, that is, a condition, to an action. If the condition is true, then action is taken; otherwise, not. This agent function succeeds only when the environment is fully observable. For simple reflex agents operating in a partially observable environment, infinite loops are often unavoidable. If the agent can randomize its actions, then it may be possible to avoid the infinite loop. The problems with simple reflex agents are: It works by searching for a rule whose position matches the current state. A model-based agent can handle a partially observable environment using a model about the world. The agent has to keep track of the internal state, adjusted by each concept, depending on the concept history. The current state is stored inside the agent, which maintains some structure describing the part of the world that cannot be seen. Updating the state requires information about: These types of agents make decisions based on how far they are currently from their goals (details of desired conditions). Their every action is aimed at reducing its distance from the target. This gives the agent a way to choose from a number of possibilities, leading to a target position. The knowledge supporting their decisions is clearly presented and can be modified, which makes these agents more flexible. They usually require discovery and planning. The behavior of a target-based agent can be easily changed. The agents which are developed having their end uses as building blocks are called utility-based agents. When there are multiple possible alternatives, then to decide which one is best, utility-based agents are used. They choose actions based on a preference (utility) for each state. Sometimes achieving the desired goal is not enough. We may look for a quicker, safer, cheaper trip to reach a destination. Agent happiness should be taken into consideration. Utility describes how "happy" the agent is. Because of the uncertainty in the world, a utility agent chooses the action that maximizes the expected utility. A utility function maps a state onto a real number which describes the associated degree of happiness. A learning agent in AI is the type of agent that can learn from its past experiences or it has learning capabilities. It starts to act with basic knowledge and then is able to act and adapt automatically through learning. Some programs operate in completely artificial environments that are limited to keyboard input, databases, computer file systems, and character output on the screen. In contrast, some software agents (software robots or softbots) exist in rich, unlimited softbot domains. The simulator has a very detailed, complex environment. The software agent needs to choose from a long range of tasks in real time. A softbot designed to scan the customer's online preferences and show interesting items to the customer works in a real as well as an artificial environment. The most famous artificial environment is the Turing test environment, in which a real and other artificial agents are tested on an equal basis. This is a very challenging environment as it is extremely difficult for a software agent to perform side-by-side with a human. The success of a system's intelligent behavior can be measured with the Turing test. Two persons and a machine to be evaluated participate in the test. One of the two persons plays the role of the examiner. Each of them is sitting in different rooms. The examiner is unaware of who is a machine and who is a human. He inquires by typing the questions and sending them to both intelligences, for which he receives typed responses. The purpose of this test is to fool the tester. If the tester fails to determine the response of the machine from the human response, the machine is said to be intelligent. 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Artificial Intelligence Jobs - Javatpoint

Artificial Intelligence Jobs Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Artificial Intelligence Job Outlook Jobs affected by AI Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews What is Artificial Intelligence? Examples of Artificial Intelligence include: What Does an AI Professional Do? Skills an AI Professional Needs Steps toward a Career in Artificial Intelligence 12 Career Paths in Artificial Intelligence Companies Currently Hiring AI Positions Artificial Intelligence Salaries Artificial Intelligence Career FAQs Q: If I'm interested in a career in AI, where do I start? Q: What skills and background do I need to pursue a career in artificial intelligence? Q: What is the outlook for a career in AI? Q: Do any AI careers require a master's degree? Q: What is the benefit of a master's degree in artificial intelligence? Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence is all around us, even in places you might not know. From music preferences to home appliances and health care, the power of AI is far-reaching. Smart assistants like Siri and Alexa Pandora and Netflix, which provide personalized song and entertainment recommendations Artificial intelligence is everywhere, and the demand for AI - especially skilled, experienced AI professionals - is rising. Bernard Marr, a business and technology adviser to governments and companies, told Forbes that we now have access to more data than ever before, which means AI has become smarter, faster, and more accurate. "As a very simple example, think of Spotify recommendations," he explained in the article. "The more music (or podcasts) you listen to through Spotify, the better Spotify is at recommending other content you might enjoy. Netflix and Amazon recommendations certainly work on the same principle." As artificial intelligence is an increasingly widespread and growing form of technology, professionals with expertise in AI are needed now more than ever. The good news is that the AI professional field is full of different career opportunities, which means you can take on different roles and responsibilities depending on the position, your experience, and your interests. The need for skilled AI professionals spans almost every industry, including: If you want to enter the professional world of AI, it's important to make sure you have the right skills, which sets you apart from other candidates and helps put you in the right position. First, proficiency with calculus and linear algebra is of the utmost importance. In addition, if you are interested in AI, you must have some knowledge and experience in at least one of the following programming languages: According to ZipRecruiter, these are the top 5 skills required for AI jobs: Communication skills Knowledge and experience with Python specifically (in general, proficiency in a programming language) If you're not already in the industry, the first step is to do research, including talking to current AI professionals and researching reputable colleges and programs. According to Springboard, hiring managers will probably require you to hold at least a bachelor's degree in math and basic computer technology (but a bachelor's degree in many cases only qualifies you for entry-level positions). A bachelor's degree in computer science or engineering is a good starting point. Nevertheless, a master's degree in Artificial Intelligence can provide direct experience and knowledge from industry experts that can help you secure a position and set you apart from other candidates. Dan Ayoob, general manager of mixed reality education at Microsoft, pointed out in the Best Colleges article that AI is still a relatively emerging field; Colleges and universities still "vary in how much specific degrees you may be able to obtain." He added that getting familiar with computer science, data science, machine learning, and Java are good places to start, but degree programs can provide specialized training. "Every day many new undergraduate and graduate programs are emerging that are specifically designed to prepare someone to work in AI." Those wishing to pursue a Masters's in Artificial Intelligence must have a strong base of knowledge and experience consisting of a combination of maths, science, computer, and data proficiency. The list below includes jobs in AI and some positions that work closely with those in AI roles. The job outlook for AI professionals is extremely promising, with ZipRecruiter predicting the industry "to grow explosively as it becomes able to accomplish more tasks." In an article on built-ins, Satya Malik, founder of OpenCV.org, Big Vision LLC/Interim CEO, compared AI to "a rocket ship that is taking off". He also pointed out that even entry-level jobs can play very well. "The reason is there is a huge demand for AI talent and there are not enough people with the right expertise," he explained. The US Bureau of Labor Statistics expects computer and information technology employment to grow 11% from 2019 to 2029 (projected to add about 531,200 new jobs). A recent search for "artificial intelligence" job openings on LinkedIn revealed more than 45,000 results at a wide variety of companies, some of which are cited below. As you can see from the list above, artificial intelligence has many different positions. The most common AI-related job titles, courtesy of Glassdoor, include: In general, tech companies (both software and hardware) dominate the list of companies hiring AI professionals. Glassdoor lists the following top companies to keep an eye on: According to our degree page, the average salary of an artificial intelligence programmer ranges from $100,000 to $150,000. Salaries for AI engineers are quite high, averaging $171,715, with the top 25% earning above $200,000. There is a range of averages depending on the position and responsibilities, but here are the most popular: Indeed, the average salary for "artificial intelligence" ranges from approximately $93,451 per year for research engineers to $150,683 per year for machine learning engineers. According to Glassdoor, the median annual base salary for artificial intelligence salaries in the United States is $105,669. According to Talent.com, the average artificial intelligence salary is $140,000 per year. Entry positions start at $110,063, and most experienced workers can earn up to $210,000 per year. AI can impact work in almost every business group. While research on the robotics and software of automation continues to show that less-educated, low-wage workers may be most exposed to displacement, current analysis suggests that better-educated, better-paid workers be most affected by new AI technologies, with few exceptions. Our analysis shows that employees with bachelor's or professional degrees will be nearly four times more exposed to AI than workers with only a high school degree. Holders of a bachelor's degree would be most exposed by education level, with more than five times the exposure to AI, compared to workers with only a high school degree. A: The first step is to conduct research, including talking to current AI professionals and researching reputable colleges and programs offering AI-related degrees. You need at least a bachelor's degree in math and basic computer technology to get started. An advanced degree in artificial intelligence is also worth considering if you want to stand out from other applicants and get real exposure from industry experts. Want to learn from the experience of the world. A: It is important to have a strong background in math, science, engineering, and command of at least one of the following programming languages: Python, C, and MATLAB. A: Very promising. There are more artificial intelligence jobs than skilled professionals to fill them, and the AI world has shown no signs of slowing down, so demand is enormous. A: Most top-level AI jobs typically require a master's degree, including research scientist, AI engineer, and big data engineer. Most AI roles will require applicants to have solid knowledge and skills with MATLAB, C/C++, and Python programming. A: A master's degree is a way to expand the number of AI job opportunities available, especially since an advanced degree is required, especially for high-level jobs. A master's degree will give you greater earning potential and show that you are invested in your career and industry. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Amazon CloudFront - Javatpoint

Amazon CloudFront Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Benefits Amazon Cloud Front edge locations North America Europe Asia Australia South America Middle East Africa How do you set up CloudFront to deliver content? How do you configure Cloud Front to deliver your content CloudFront use cases Accelerate static website content delivery Contact info Follow us Tutorials Interview Questions Online Compiler Amazon CloudFront is a fast content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency, high transfer speeds, all within a developer-friendly environment. CloudFront China has Edge locations in Beijing, Shanghai, Zhongwei, and Shenzhen. These four Edge locations are connected by private network directly to Amazon Web Services China (Beijing) Region operated by Sinnet and Amazon Web Services China (Ningxia) Region operated by NWCD for speedy content delivery to viewers in China. CloudFront works seamlessly with services, including Amazon Shield Standard for DDoS mitigation, Amazon S3, Elastic Load Balancing, or Amazon EC2 as origins for your applications. You can get started with the Content Delivery Network in minutes, using the Amazon Web Services tools you're already familiar with: APIs, Amazon Web Services Management Console, Command Line Interface (CLI), and SDKs. Amazon's CDN offers a simple, pay-as-you-go pricing model with no upfront fees or required long-term contracts, and support for the CDN is included in your existing Amazon Support subscription. Fast and global The Amazon CloudFront content delivery network (CDN) is massively scaled and globally distributed. The CloudFront network has 191 POPs (180 edge locations and 11 Regional Edge Caches) in 73 cities across 33 countries. It leverages the highly-resilient private backbone network for superior performance and availability for your end-users. Security at the Edge Amazon CloudFront is a highly-secure CDN that provides network and application-level protection. Your traffic and applications benefit through various built-in protections such as Amazon Shield Standard at no additional cost. Deep integration with Amazon Web Services Amazon CloudFront China is integrated with Amazon Web Services services such as Amazon S3, Amazon EC2, and Elastic Load Balancing. They are all accessible via the same console, and all features in the CDN can be programmatically configured by using SDKs or the Amazon Web Services Management Console. Lastly, if you use Amazon Web Services origins such as Amazon S3, Amazon EC2, or Elastic Load Balancing, you don't pay for any data transferred between these services and CloudFront. In October 2018, Amazon CloudFront consisted of 138 access points (127 edge locations and 11 regional edge caches) in 63 cities across 29 countries.[4] Edge locations: Ashburn, VA (3); Atlanta, GA (3); Boston, MA; Chicago, IL (2); Dallas/Fort Worth, TX (5); Denver, CO (2); Hayward, CA; Jacksonville, FL; Los Angeles, CA (4); Miami, FL (3); Minneapolis, MN; Montreal, QC; New York, NY (3); Newark, NJ (3); Palo Alto, CA; Philadelphia, PA; Phoenix, AZ; San Jose, CA (2); Seattle, WA (3); South Bend, IN; St. Louis, MO; Toronto, ON Regional Edge caches: Virginia; Ohio; Oregon Edge locations: Amsterdam, The Netherlands (2); Berlin, Germany; Copenhagen, Denmark; Dublin, Ireland; Frankfurt, Germany (8); Helsinki, Finland; London, England (7); Madrid, Spain (2); Manchester, England; Marseille, France; Milan, Italy; Munich, Germany; Oslo, Norway; Palermo, Italy; Paris, France (4); Prague, Czech Republic; Stockholm, Sweden (3); Vienna, Austria; Warsaw, Poland; Zurich, Switzerland Regional Edge caches: Frankfurt, Germany; London, England Edge locations: Bangalore, India; Chennai, India (3); Bangkok, Thailand (2); Hong Kong, China (3); Kuala Lumpur, Malaysia; Mumbai, India (2); Manila, Philippines; New Delhi, India (2); Osaka, Japan; Seoul, South Korea (4); Singapore (3); Taipei, Taiwan(3); Tokyo, Japan (9) Regional Edge caches: Mumbai, India; Singapore; Seoul, South Korea; Tokyo, Japan Edge locations: Melbourne; Perth; Sydney Regional Edge caches: Sydney Edge locations: São Paulo, Brazil (2); Rio de Janeiro, Brazil (2) Regional Edge caches: São Paulo, Brazil Edge location: Dubai, United Arab Emirates; Fujairah, United Arab Emirates; Tel Aviv, Israel Edge locations: Nairobi, Kenya; Johannesburg, South Africa; Cape Town, South Africa You create a CloudFront distribution to tell CloudFront where you want the content delivered and details about how to track and manage the content delivery. CloudFront then uses computer-edge servers-closer to your audience-to quickly deliver that content when someone wants to view or use it. 1. You specify the origin server, such as Amazon S3 Bucket or your HTTP server, from which CloudFront gets your files, which will then be distributed from CloudFront edge locations around the world. An origin server stores the original, fixed version of your items. If you're serving content over HTTP, your origin server is either an Amazon S3 bucket or an HTTP server, such as a web server. Your HTTP server may be running on an Amazon Elastic Compute Cloud (Amazon EC2) instance or a server you manage; These servers are also known as custom origins. 2. You upload your files to your origin server. Your files, also known as objects, typically include web pages, images, and media files but can also be anything that can be served over HTTP. If you're using an Amazon S3 bucket as the origin server, you can make the objects in your bucket publicly readable so that anyone who knows the CloudFront URL for your items can access them. You also can keep objects private and control who has access to them. You create a CloudFront distribution, which tells CloudFront which origin server to get your files from when users request files through your website or application. At the same time, you specify whether you want CloudFront to log all requests and whether you want it to be enabled as soon as the distribution is built. 3. CloudFront specifies a domain name for your new distribution that you can view in the CloudFront console or return in response to a programmatic request, such as an API request. If you wish, you can add an alternate domain name to use instead. 4. CloudFront sends your distribution's configuration (but not your content) to all of its edge locationsor points of presence (POPs) - collections of servers in geographically dispersed data centers where CloudFront caches copies of your files. You use CloudFront's domain name for your URL as you develop your website or application. For example, if CloudFront returns d111111abcdef8.cloudfront.net as the domain name for your distribution, the URL for logo.jpg in your Amazon S3 bucket (or in the root directory on the HTTP server) is http://d111111abcdef8. is CloudFront. net/logo.jpg. Or you can set up CloudFront to use your domain name with your distribution. In that case, the URL might be http://www.example.com/logo.jpg. Using CloudFront can help you achieve a variety of goals. This section lists just a few with links to more information to give you an idea of the possibilities. CloudFront can accelerate the delivery of your static content (for example, images, style sheets, JavaScript, and so on) to audiences around the world. Using CloudFront, you can take advantage of the AWS backbone network and CloudFront Edge servers to provide a fast, secure, and reliable experience for your visitors when they visit your website. A simple way to store and distribute static content is to use an Amazon S3 bucket. There are many benefits to using S3 with CloudFront, including the option to use Origin Access Identity (OAI) to restrict access to your S3 content easily. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Goals of Artificial Intelligence - Javatpoint

Goals of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Methods of Artificial Intelligence Application areas of artificial intelligence AI as a Rewarding Career Possibility Benefits of Artificial Intelligence Disadvantages of Artificial Intelligence Contact info Follow us Tutorials Interview Questions Online Compiler AI can be achieved by reading the behavior of humans and using the results to develop intelligent systems. For example, they learn, make decisions and act in certain situations. Observing humans while problem-solving in simple tasks and using its results to develop intelligent systems. The overall research goal of artificial intelligence is to create technology that allows computers and machines to work intelligently. The general problem of simulating (or creating) intelligence is broken down into sub-problems. The symptoms described below receive the most attention. These include special traits or abilities that researchers expect an intelligent system to exhibit. Eric Sandwell emphasizes planning and learning that is relevant and applicable to the given situation. After defining Artificial Intelligence, let us know about the philosophical methods lying at its core. Every research about AI falls into one of the following two categories: Both these methods compete for the approach to developing AI systems and algorithms. Although they may appear similar, they differ in their principle. Whereas the "top-down" approach focuses on symbolic details, the "bottom-up" approach considers neural activities inside the brain. We can highlight the difference between these two approaches with an example. Consider a robot that recognizes numbers through image processing. The symbolic approach would be to write an algorithm based on the geometric pattern of each number. The program will compare and match numeric patterns of different numbers stored in its memory. The robot would train its artificial neural network by repeatedly tuning it to recognize numbers in the connectionist approach. In a way, The Connectionist approach more closely emulates the human mind and its thought process than the symbolic approach. Researchers use both these methods of AI implementation when developing algorithms. While the symbolic approach is famous for simple problems, researchers prefer the connectionist method for complex, real-world problems. Despite showing immense potential, both of these approaches have produced limited results. In addition to these two major classifications, researchers have coined several approaches to implementing AI. Modern AI-based technologies are relevant in any intelligent task, and the list of its applications continues to grow significantly. Let's take a quick look at some of them. We hope that the brief introduction to Artificial Intelligence in this blog has given you a taste of its technology and capabilities, as you must have understood by now that AI opens up an ocean of opportunities for your career. By visiting the Indian career portal, you can know about various courses and job opportunities to make a successful career in AI. Artificial intelligence looks promising, and it is quite futuristic. It is gradually being implemented in many areas. There are many drawbacks of Artificial Intelligence which are; Artificial Intelligence is slowly making its way into real-time applications. AI offers a lot of possibilities, but it is really expensive. Smaller organizations cannot afford the high-end machines, softwares, and resources required to implement AI. Artificial intelligence systems can replace humans in performing tasks in terms of productivity, but they cannot make decisions. Robots cannot decide what is right and what is wrong. With intelligent systems, you won't get creative with everyday experience. Human beings display creative ideas with everyday experience. Replacing humans with intelligent systems can increase unemployment which leads to poor GDP. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Can Artificial Intelligence replace Human Intelligence - Javatpoint

Can Artificial Intelligence replace Human Intelligence? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Artificial Intelligence Vs. Human Intelligence (AI vs. HI) Will Machines Replace Humans? Human Intelligence Is Infinite Will AI Take Over Jobs? AI Takeover: Can Machines Replace Humans? Conclusion Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence (AI), a modern approach, is a new science-making machine that helps machines learn on their own with human-like intelligence through a combination of Deep Learning, Machine Learning and Data Science algorithms. Technologies like Neural Networks, Natural Language Processing, Robotics Processing, Cognitive Services, Mixed Reality (AR/VR) etc. make machines more intelligent. As a result, machine systems make decisions the same way we do in our daily lives. This machine decision making system is fueling debates like Human Intelligence vs Artificial Intelligence! To give you a good perspective about this hotly debated, we will today discuss the advantages of artificial intelligence, importance of human intelligence and risks of artificial intelligence to humans and their daily lives. Movies like Robert Downey Jr.'s Iron Man, Arnold Schwarzenegger's Terminator, Will Smith's Eye of the Smith, Robot, Tom Cruise's Cruise Oblivion, and Age of Tomorrow teach us that sooner or later there will be an increased dependence on people. human intelligence. The West, especially Hollywood, looks to machines to think and act like humans for the facts of the real world. Robots, bots, humanoids, digital humans, star children, etc. are examples of movies and YouTube clips that coordinate our appearance in many ways. Now, most of you know that this is no longer fiction. It's a reality now! Do you know Sofia? Sophia is a social humanoid robot developed by Hanson Robotics, Hong Kong. Sofia can do everything that you do in your life. Plus, she can answer any questions you may have. "As a robot, Sofia is now an official citizen of Dubai." You can watch his interview from his first public appearance in March 2016, Texas, USA! Today, many AI-powered applications have faster execution speeds, higher operational efficiencies, better work efficiency and better decision-making accuracy than humans. We know that human intelligence stems from adaptive learning and personal experience; It does not depend on pre-fed data. But AI requires pre-fed data! It is true that for the hardware and software of a machine, or a robot, our human memory, brain computing power, and body composition as a whole seem insignificant. The only reason we see these advanced machines, systems and robots as aliens and instinctively fear them is the same as we are afraid of lions in the jungle. This alien thought in our mind destroys our God-given human intelligence and allows the machines to dominate us! What we have been getting from Hollywood movies like Terminator is that in human intelligence versus artificial intelligence, AI is more likely to dominate us. But, as an industry expert, we would say that our brains are more complex, sophisticated and deeper layers that machines can't beat anytime soon, at least not for the next 35 years! Let's go back to the Artificial Intelligence vs Human Intelligence debate. Recent AI achievements closely mimic human intelligence but cannot go beyond the human brain. Our mind acquires knowledge by sense of understanding, reasoning, learning, reasoning and experience. The way we feel everything, most importantly, emotions, separates us from digital machines, robotics, AI technologies and much more. Your mind and heart make the importance of human intelligence above AI. However, with the development of AI, the risks of artificial intelligence are increasing as we cannot use our brains and hearts to their full potential. Although machines mimic human behavior, the ability to make rational decisions is still lacking. Machines need the next level of development where they have to process 'common sense'. This development will take years because common sense varies per human! This means that AI systems do not understand "cause" and "effect". While we humans do everything on the basis of cause and effect, our decisions benefit certain sections of human society. Yes, AI has made our work easier, but there have been many failures, such as; All these disasters ask us to improve the capability of AI by improving AI technology with proper algorithms and data. Otherwise, AI will no longer co-exist with our morals, ethics and competence. "We have many types of human intelligence, such as morality, ethics, abilities, intuition, instinct, reflex, accuracy, precision, timing, quality judgment, sense of understanding, reasoning, learning, reasoning, and experience, emotions, and much more. " If AI is to become the equivalent of HI, it must know advanced techniques to process different types of human intelligence. For this AI uses its subset - Deep Learning (DL). DL works on the concept of the human reflex and nervous system, a neural network similar to the brain. Machines and robots are being taught to apply intelligence and knowledge to real-world scenarios. As learning progresses, machines will start adopting humanity, and one day AI will find a way to match the frequency of our brains. Artificial Intelligence is taking the world by storm. Take the latest example of the COVID-19 coronavirus pandemic; There are some custom software development solutions companies in the United States that are using their AI development services to predict viruses in the human body, find vaccine combinations, help people with virus treatments, and much more. A health worker is responsible for all these things; However, AI is helping as it can act faster and assist humans faster. If AI learns to do better, job losses due to AI will increase. AI and robotics are replacing accounting, banking, sales; Thus, rising unemployment increases significantly. The impact of Artificial Intelligence on AI will lead to massive job losses in every field! "According to data and reports on the Internet, 47% of American jobs are at high risk by the mid-2030s due to automation." Although it is said that AI and automation will replace jobs, the World Economic Forum says that the benefits of artificial intelligence will double the job generation by the end of 2022! Coming to the debate of Artificial Intelligence vs. Human intelligence, recent AI achievements more closely mimic human intelligence than previously thought. However, machines are still far ahead of the human brain. What sets us apart is man's ability to reason, reason, understand, learn and apply knowledge acquired with a sense of experience. With knowledge comes power, and with power comes great responsibility. Although machines can imitate human behavior to some degree, their knowledge of rational decision-making like ours may be attenuated. AI-powered machines make decisions based on events and their association with them. However, they lack "common sense". AI systems are clueless in understanding "cause" and "effect". Meanwhile, real-world scenarios require a holistic human approach. AI is currently in its development stage, and human controls AI with all necessary security measures. But, we can't tell you the future? The 21st century is passing through a period of rapid change. While AI is making our lives easier, AI is learning more about humans and their skills. Human and computer workforce processing together, working efficiently and accurately for the benefit of mankind appears to be a good artificial intelligence future. But, whether this will be possible or not, it is not known yet! We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Importance of Artificial Intelligence - Javatpoint

Importance of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Importance of artificial intelligence Top 4 Uses of Artificial Intelligence Conclusion Contact info Follow us Tutorials Interview Questions Online Compiler In computer science and computers, the term artificial intelligence has played a very prominent role. The term has become more popular due to recent advances in Artificial Intelligence and Machine Learning. Machine learning is the area of artificial intelligence where machines are responsible for completing daily tasks and are believed to be smarter than humans. They are known to learn, adapt and perform much faster than humans and are programmed to do so. Robotics and integration with IoT devices have taken machines to think and work to a new level where they out-perform humans in their cognitive abilities and smarts. In this article, we will read about the huge importance of artificial intelligence. Below we are going to read about the huge importance of Artificial Intelligence: Important uses of Artificial Intelligence are given below: 1. In Medical Science 2. In the Field of Air Transport 3. In the field of banking and financial institutions 4. In the field of gaming and entertainment 5. AI Achieves Unprecedented Accuracy 6. AI Is Reliable & Quick AI performs computer-generated tasks consistently, extensively, and reliably. However, human skills are required to set up the system and ask the appropriate questions. 7. AI Adds Intelligence to Products 8. AI Evaluates Deep Data 9. AI Fully Utilized Data It would help if you implemented AI to get answers from the data. The role of data is more important than ever; Its gives you an edge over your competitors if you have the best data system in this competitive industry because the best data will win! Artificial Intelligence holds much more importance and importance than what we read in this article. It will continue to increase in the times to come. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence Stock in India - Javatpoint

Artificial Intelligence Stock in India Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews These 3 stocks are ticking time bombs in your portfolio. 1. Coforge 2. Happiest Minds Technologies 3. Saksoft 4. Tata Elxsi 5. Persistent Systems Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence (AI) is no longer a thing of the future. It is right here and is present everywhere around us. AI is linked to every aspect of our lives. Each of us is currently using this technology in some form or the other. From personal digital assistants like Siri, Google Assistant, Alexa to self-driving cars, it is being used very widely. Its use is increasing daily in the rapidly growing healthcare, finance, e-commerce and manufacturing. Furthermore, businesses like Swiggy and Zomato, which have invested heavily in AI over the years, have seen the power of technology to sustain and drive growth. This has propelled the discussion towards the potential of AI for other companies in India. Many stockbrokers have taken pink pictures of these stocks, but the reality is completely different from this. If the market goes down, these stocks can wipe out your assets. We will reveal the details of these 3 landmines in our upcoming special event on December 27th at 5PM. According to a report by Accenture, it is expected that AI has the potential to create 15% of India's current gross value or US$957 billion in 2035. In the coming years, AI will change the way we live and work. With the increasing demand for AI technology, investor interest in AI stocks has also increased. Here is the list of top Indian companies working on AI in Indian stock market. Coverage is an IT services company that provides end-to-end software solutions and services. It is one of the top-20 Indian software exporters. The company was earlier known as NIIT Technologies and was incorporated in April 2003. It offers AI-based digital business assistant, deep learning, machine learning, multi-currency, multi-lingual, multi-channel experience, image recognition, robotic process automation (RPA), natural language processing (NLP) and workflow automation. In the past, the company has made a few acquisitions to increase revenue and increase geographic and customer presence. In April 2021, Coforge completed its strategic investment in SLK Global Solutions. SLK Global has deep domain expertise in the banking and insurance segment in North America. It enjoys many long-term and scalable relationships with marquee clients with strong growth potential. The company has given a return of 1,202% in five years. Currently, the shares of Coforge are trading at Rs 5,136 per share. Happiest Minds is an IT consulting and services firm established in 2011. The company works on disruptive technologies such as Artificial Intelligence, Cloud, Internet of Things (IoT), Blockchain, Robotics/Drones, Virtual Reality and other services. The firm uses artificial intelligence for language processing, picture analysis, video analysis and upcoming technologies such as AR and VR. In addition, the company assists organizations in using robots by using AI, thereby saving time and costs. In September 2020, the firm was listed on the stock exchange. It is one of the most popular Indian Artificial Intelligence stocks. The company's executive chairman Ashok Soota is the main promoter and was earlier the founding chairman and MD of Mind Tree. Prior to Mindtree, he headed Wipro's IT business for fifteen years. The company has been able to give a return of 290.8 percent since the listing. Share of Happiest Minds is trading at Rs 1,445 on BSE. Saksoft is a leading provider of information management solutions to successful companies around the world. The company is a mid-sized IT company that provides end-to-end business solutions that leverage technology and enable its clients to enhance business performance. It primarily focuses on achieving transformation through enhanced efficiency, productivity, enhanced customer decisions and service innovations by combining AI and automation. Saksoft promotes digital transformation and applies intelligent automation to solve key business problems with the help of modern technology like IoT, AI, Machine Learning and Automation. The company has delivered decent profit growth of 20.1% Compounded Annual Growth Rate (CAGR) over the last 5 years. The share of Suksoft is trading at Rs 913 on BSE. Established in 1989, Tata Alexi is a part of the Tata Group and performs in the midcap range in the stock market. Tata Alexi is one of the leading providers of design and technology services across various industries. These include automotive, broadcasting, communication, healthcare and transportation. The company has found success in various sectors such as self-driving cars, video analytics solutions, etc. Recently, we uncovered this massive 15x opportunity in electric vehicles. And also shared the details of 3 EV shares to ride out this mega opportunity. Now, these are not ordinary EV stocks. Instead, these are what we call backdoor EV stocks. According to our research, the best way to ride the 15x EV wave is through only these 3 stocks. Tata Alexi Artificial Intelligence Center of Excellence caters to the growing demand for intelligent systems. Its customers can access a cloud-based integrated data analytics framework that features patent-pending technology to achieve actionable insights and outstanding returns. On the financial front, the company has performed well in the last few quarters. It has compounded profit growth of 19% in the last 5 years. The stock has provided 535% returns in the last five years as compared to Nifty IT which has given 95% returns to the investors. Persistent Systems Limited (PSL) is a distinguished IT player and market leader in outsourced software product development services. Over the years, it has successfully made its presence felt in the AI space. Take your partnership approach with the biggest tech giants like Microsoft, Oracle, Amazon Web Services, Google Cloud Platform, Salesforce, Appian, etc. These partnerships provide the skill-set, capability and access to the partner's clients. Its partnership with IBM needs special mention. Financially the company has performed well. It has achieved compound profit growth of 10% and sales growth of 13% over the past five years. This stock has given a return of 462% in the last five years. Shares of Persistent Systems are currently trading at Rs 3,479 per share. Apart from the above, here is a list of more AI-based stocks to watch out for in India. Today, AI is a vital tool for many businesses, and the technology market in India is growing rapidly. From online shopping to data used for educational purposes, AI has become an integral part of human life. Also, many Indian start-ups are expanding and developing AI solutions in education, health, financial services and other sectors. For the past few years, it has been attracting many companies to follow the trend, increasing investment due to its increasing demand in the present and future. Investing in digital technologies can generate huge revenue in the coming years. 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How to Use Artificial Intelligence in Marketing - Javatpoint

How to Use Artificial Intelligence in Marketing Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Marketers can now use AI Search Engines. Marketers can use Artificial Intelligence for Market Forecasting. Marketers use AI for Programming Advertising. Through Content creation. Artificial Intelligence provides Chatbots. Artificial Intelligence in Marketing for Dynamic Pricing Artificial Intelligence provides the Market with good Advertisement Performance. Conclusion. Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler This tutorial will discuss the use of artificial Intelligence in Marketing and how marketing companies can make effective use of it. Let's get started. Artificial Intelligence is a growing trend within the top technology companies. As technology and imagination merge, it is finally making strides. The concept of virtual reality is not only being adopted by technology companies but also by the general public. Artificial Intelligence has become the centre of modern business. The insight-driven eCommerce platforms have led to the creation of IoT (Internet of Things). Artificial Intelligence is set to have a long-lasting impact on marketing. Artificial Intelligence, for example, helps digital marketing teams bridge the gap between data available and execution. They can bridge the gap between customers and businesses. Marketers use Artificial Intelligence to unlock unimaginable possibilities. These opportunities include improving the digital market, which will result in better performance and profits. This will also increase the data-driven focus. Marketing brands can also use AI to make incredible gains by better understanding their customer base. We must have a solid understanding of Artificial Intelligence to be a successful marketer in today's marketplace. Even before artificial intelligence, search engines have advanced a lot. Search engines can be used to find the exact names that we are searching for. Artificial intelligence has made it possible to search for specific names more accurately and provide more relevant results. Our company brand can use AI-enabled search engines to help customers find the product or service they are looking for. It is easy to do, and it will work even if the customer typed a confusing term. Consider a customer who wants to buy a product from Amazon. The search engine will ask the customer to enter the general term. Amazon's AI-enabled search engine will correct any typos in order to deliver the most relevant results. We can also use the advanced search feature to perform more specific searches. It is essential to provide a positive customer experience as a marketer. The recent Google algorithm changes have made customer experience a hot topic. Marketers must adapt to the new algorithm in order to exceed customers' expectations. Artificial intelligence allows marketers to predict the market structure better (especially demand). This gives them the information they need to nurture the prospect or move on to the next opportunity. One example of this is a marketer who deals with inventory. An inventory marketer must be able to accurately predict the inventory that will be sold and when to increase our marketing efforts in order to sell it. In anticipation of higher sales, we can use the already available inventory. Marketers can use artificial intelligence to analyse customer conversations to determine successes and failures. This data will allow them to decide if they should continue working with the prospect. Programmatic advertising involves buying and selling ads automatically. Programmatic advertising allows advertisers and publishers to connect to ad inventory to exchange ads. This process is made easier by artificial intelligence, especially for marketers. It uses algorithms to analyse customer behaviour and optimize it for the most relevant results. Programmatic advertising is a method for marketing brands to reach customers that have a high probability of being convinced. Marketers must target customers looking for our product or service and convince them to buy. Cookies provide insight into artificial intelligence that can be tailored to the campaign. Marketers can target buyers who are hesitant by using artificial intelligence and programmatic marketing. This is achieved by analysing the trends and identifying preferred targeting options. Some trends provide a better picture of customer behaviour. These include matching subscriber data and finding other data. These trends enable marketers to create duplicate audiences or segments that are appropriate. The main benefit of artificial intelligence, particularly for marketers, is that AI systems are able to find the most relevant, valuable content to our intended viewers. The data used to make the content is sourced from various data sets. This is advantageous for marketers since it is a part of the content strategy of their marketing. AI systems can produce appropriate, highly targeted, and well-curated content relevant to our target viewers. Marketers also employ artificial intelligence to develop marketing strategies based on data collected by an AI system. Suppose the insights that help create content for your customers are utilized in conjunction with your audience. In that case, we may also utilize the information to devise an approach that we can employ to inform your customers. It is also possible to use this method to collect information on potential customers. As time passes, prospects are more likely to purchase or sign up for the item or service because they are seeking the exact deal. In Digital marketing, chatbots are considered an important element, particularly in modern-day digital marketing. Chatbots enable marketers to maintain a high retention capability. Chatbots with AI capabilities provide responses to customers in the form of questions. Companies and companies that have a staff dedicated to customer service may struggle in dealing with thousands of clients. That's why chatbots are required to meet the demands of customers (mainly small ones). Chatbots are able to engage customers, which frees up a customer service person to address the most important issues and questions. Chatbots are also able to be utilized anytime, which is much more efficient than having humans serving as customer service representatives 24/7. It's an aspect of knowing the future trends in the market; however, it's a different factor to employ an effective strategy that is efficient and precise. Dynamic pricing is essential for marketers because it allows you to maximize sales that have the highest demand. We will also know when we should offer discounts on sales. Utilizing artificial intelligence as a marketing tool lets us stay up to date on the invisible shifts on a vast scale. AI-enabled technology provides us with precise predictions that keep us up to date with the changing trends in pricing. The only way to determine if our marketing campaign is performing well is to use analytics for a marketer. Analytics can provide us with insight into the things that are effective and what's not working with our marketing campaign. Machine learning and artificial intelligence offer detailed analysis and insights into the success of our advertising and unsuccessful ones. This can help us in making the best choice about where to focus our efforts regarding our advertising campaign. Artificial intelligence can tell us the number of clicks an advertisement has. It also shows the country or the region that the clicks originated from, as well as the platforms the clicks were made on, in addition to other important data. This information will aid with our campaign to lead to a better ROI. It is also possible to utilize these insights to make forecasts for the future, so we have a better view of the trend overall. These insights will assist in reorganizing the strategies you employ to reach our goals in the future. It is also possible to increase our conversion rates as time passes. Artificial Intelligence is an effective instrument for marketers and marketing companies. With AI-enhanced advertising, marketers are able to rely on artificial intelligence in determining the efficacy of their marketing strategies. We'll also know the best places to invest our money to provide the best return on the investment. Artificial Intelligence also improves our customer's experience, providing more chances to engage with customers. Whatever the field Artificial Intelligence is beneficial to all modern efforts of marketing professionals. It streamlines the process of marketing and offers affordable, precise, and efficient solutions. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Business - Javatpoint

Artificial Intelligence in Business Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI Applications in Business AI to meet industry-specific needs Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Recruitment 2. Cybersecurity 3. Market prediction 4. Customer Analysis 5. Billing and Invoicing 6. Proposal Review 7. Virtual Assistants and Conversational Interfaces 8. Targeted Marketing 9. Vulnerability Exploit Prediction 10. Social media insights Contact info Follow us Tutorials Interview Questions Online Compiler We all are well aware that the computer has become an integral part of our life now. Today, technology has become so advanced that computers can function like humans and even achieve high success rates. All this has been possible due to Artificial Intelligence. Artificial Intelligence (AI) in technology enables machines to perform tasks that would otherwise require human intelligence to be automated. AI has a vast spectrum in computer science and is developed and programmed through machine learning and deep learning. AI is used in many fields daily, making our lives easier. The business world is one area where Artificial Intelligence is widely used. AI can help any business in automating business processes, gaining insights through data analysis, and engaging with customers and employees. There is huge competition among different companies in the market, and every company wants to be on top of its game. Successful MNCs use the features of AI such as automation, big data analytics, and natural language processing to gain insight into their business and make it more efficient and relevant to their customer base. Even small companies incorporate AI into their businesses to be successful. Let's look at the top applications of Artificial Intelligence in Business. There is a lot of competition for employment, and every day, hundreds of candidates are applying for the same position in a company. As a result, shortlisting the right candidate for the company's HR team becomes a daunting task on each resume. To make things easier, companies use Artificial Intelligence and Natural Language Processing (NLP) to filter through resumes and shortlist candidates who closely meet their needs. It is done by analyzing various characteristics like location, skill, education, etc. It also recommends other job positions for the candidates if they are eligible. This way, the candidates are selected practically and unbiased, saving time and manual labor for the HR team. The Internet has made storage and management very convenient in any business. But with it comes the risk of breaching and leakage of data. Cyber security is a necessity for all companies and is one of the most important applications of AI. Every business requires security online since all the important databases of their company, including financial data, strategies, private information, etc., are stored online. With the help of Artificial Intelligence, cyber experts can understand and remove unwanted noise or data that they might detect. It helps them be aware of any abnormal activities or malware and be prepared for any attack. It also analyzes big amounts of data and develops the system accordingly to reduce cyber threats. Stock markets are one of the most popular and unpredictable markets due to their dynamic nature. Many people invest in the stock markets as they have also proved very profitable. But Artificial Intelligence has made it easy too. With techniques like Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), which are types of machine learning, patterns are learned and predicted. This technical analysis is very important in predicting the financial markets and providing successful results. This prediction uses three algorithms: Begg-Marquardt, Scaled Conjugate Gradient, and Bayesian Regularization. Together they provide about 99% accuracy using tick data. Businesses run for their customers, and customers can make or break any brand. Hence companies need to analyze their customer base and strategize for greater engagement and improvement in any other area. Earlier, it was very difficult for companies to get information about their performance. Most of the exchanges took place in person, and the reaction was predicted manually through selling or sentimental aspects. Today, artificial intelligence enables companies to conduct surveys that provide customer feedback that goes much deeper than just historical data analysis. It provides accurate data and helps strategize to facilitate better engagement and sales by providing a better customer experience. Therefore, AI helps make the business more customer-centric, which ultimately benefits the company. With all businesses come financial responsibilities. It is conceivable that companies may frequently have bills, paychecks, and invoices exchanged, among others. These accounting and financial processes can become very cumbersome if handled manually. In addition, there may be calculation mistakes that can lead to terrible losses. Artificial Intelligence has made financial management easy and accurate by automating the process. There is much software available in the market for accounting and invoicing. For manual paper-based invoicing, these software provide features such as data extraction and segregation, which, once scanned and uploaded, can extract data from paper invoices and store them. Electronic invoices are easier to handle as they are analyzed and stored automatically. The AI-powered accounting tools are very precise and systematic, making financial management a very easy task. Artificial Intelligence has proved to be quite beneficial for proposal review. Proposals are often exchanged in the business world, and if not properly scrutinized and analyzed, they can lead the company to the wrong customers. Now, AI can easily analyze any offer given to the company with the help of machine learning. The company can automatically hold on to scope pricing and track any history of the source of the offer. AI proposal management software is very proficient in qualifying opportunities. It goes through the proposal and determines the best outcome. It is both a time saver and often provides accurate predictions. Proposal management software also provides the company with a strategic plan with which it can grow. Every business has its own set of services that need to be explained to the masses to expand its customer base and facilitate its sales. It is not possible for the owners to individually explain and clear the doubts of each individual. With the help of artificial intelligence, businesses are introducing virtual assistants and chatbots into their websites and applications that can answer any user questions about the company and provide 24/7 customer service. Usually, chatbots have a pre-programmed answering system, and they follow specific patterns while answering questions. These are advancing more and more with the improvement of neural networks and deep learning. Nowadays, all businesses are taking advantage of the Internet to gain more and more popularity. Targeted marketing or targeted advertising is a method of online advertising done with the help of NLP and AI that shows advertisements only to specific audiences. Their online activities determine the audience, and if they have recently searched for a similar product/service online, they start seeing ads. It is a very efficient and profitable marketing method as it saves a lot of money for the business. It is done through keyword matching. The number of vulnerabilities revealed over the years has been enormous. The amount of cooperation shown with machines has been very small compared to humans. It exposes AI to exploitation by humans and risks ruining any business. Artificial intelligence is the only solution to this problem. It protects the company from scams and big losses. Companies can predict any malpractices that may risk exploiting the system through AI, thereby saving the business. AI can also help identify credit fraud and insurance claim fraud in real-time. Social media has become one of the strongest platforms for brands to promote their business. It provides them with different types of users to showcase their services. If a company can use its social media platform properly, it can easily gain many customers. Since there are many users, no business can get their customer feedback manually. With the help of artificial intelligence, brands can know their position in the market and get information about their customer base. It will help brands strategize and build up their social media game! Although many AI applications are spread across industry sectors, other use cases are specific to the needs of the individual industry. Here are some examples: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Companies Working on Artificial Intelligence - Javatpoint

Companies Working on Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conclusion: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Tata Elxsi 2. Bosch 3. Kellton Tech 4. Happiest Minds 5. Zensar Technologies. 6. Persistent Systems 7. Saksoft 8. Affle 9. Dash Technologies Inc. 10. Talentica Software 11. Sigma 12. SPEC INDIA 13. SoluLab 14. BrancoSoft 15. Monkhub Contact info Follow us Tutorials Interview Questions Online Compiler This Indian Artificial Intelligence (AI) startup will be the best in the world in 2022. According to industry analysts, today's top-listed artificial intelligence enterprises in India employ innovative and sophisticated AI algorithms to provide faster and more reliable solutions at affordable rates. Businesses worldwide are harnessing the potential of artificial intelligence, but they are doing it to simplify operations and automate processes. Tata Alexi Artificial Intelligence Center of Excellence (AICOE) is dedicated to meeting the growing need for intelligent systems. During the last 25 years, Tata Alexi has assisted in technological advancement. Self-driving cars and video analytics are just some breakthroughs made possible by artificial intelligence and data analytics. Customers can rapidly adapt and transform the landscape using cloud-based integrated data analytics frameworks, including patent-pending technologies, resulting in actionable insights and improved outcomes. During that period, the stock returned 174.89 per cent to investors, while Nifty IT gave a return of 106.55 per cent. For operating revenue, interest charges accounted for less than 1% of total revenue in the financial year ended March 31, 2021, while personnel costs accounted for 56.1 per cent of total operating expenses. Innovative solutions will be developed by combining state-of-the-art artificial intelligence technology with Bosch products and services. The Bosch Center for Artificial Intelligence was established in 2017 to facilitate this integration, and Bosch lays the groundwork for artificial intelligence to make an impact in the real world through advanced technologies. Bosch's six research areas are distinguished in six ways, focusing on core artificial intelligence technology. Average intraday declines of over 5 per cent occurred in only 1.08 per cent of trading sessions during the prior 16-year period. Within three years, the stock had lost -15.94 per cent, while the Nifty 100 index had gained 44.16 per cent in the same period. Kelton Tech Solutions is an information technology and outsourcing company headquartered in Hyderabad, India, operating in the United States and Europe. Kelton Tech Solutions Limited was established in 1993 and now has a market capitalization of Rs.712.75 crores in the Information Technology Software business. With around 1400 people, the firm has to get Rs. Net profit of. 7.39 billion. Kelton Tech produces state-of-the-art artificial intelligence solutions that range from machine learning to deep learning for situations that traditionally require significant amounts of human expertise. At the same time, for investors, the stock gave a return of 40.86 per cent, while Nifty IT gave a return of 106.55 per cent. At Peace, Happiest Minds combine natural language processing, image analysis, video analysis, and augmented intelligence with upcoming technologies such as augmented reality and virtual reality to help businesses create engaging consumer experiences and outperform their competitors. Their goal is to inspire the next generation of technology by creating intelligent systems that can think like humans, learn from their mistakes, innovate, and make decisions. Established in 2011, Happiest Minds Technologies Limited has a market capitalization of Rs 13,507.78 Crore and deals in the Information Technology Software business. The company generated a return on equity (ROE) of 29.62 per cent for the financial year ended March 31, 2021, which was higher than its five-year average of 23.07 per cent. For Zensar Technologies, artificial intelligence is the most important thing (AI). The corporation's new go-to-market strategy is based on disruptive artificial intelligence. Genser AIR Labs, whose research and development division focuses on artificial intelligence, has submitted 100 patent applications. Earlier this week, Zensar announced the launch of an initial set of platforms for seven key areas, including sales and marketing, information technology, human resources, talent supply chain and human resource management. Three years later, Nifty IT stock gave a return of 15.63 per cent, while Nifty IT gave a return of 106.55 per cent to investors in the same period. While Cyient is a source of cutting-edge tools and solutions, the company also works with businesses to help them reach their objectives. Thanks to Artificial Intelligence (AI), real-time map updates for autonomous vehicles are now possible thanks to Artificial Intelligence (AI). Autonomous cars can benefit from navigation aids that help them better understand their surroundings to avoid collisions with other vehicles. Rather than simply supplying new equipment and technology, it helps businesses achieve their goals. In three years, investors got a return of 106.55 per cent on their investment in Nifty IT. Continuously working systems in Artificial Intelligence and Machine Learning, Persistent provides profitable solutions at every stage of the process. This method helps identify use cases, set up platforms, scale-up model development and operation of models across the organization, and ensure that you are AI and machine learning investments provide beneficial returns. The company's three-year compound annual growth rate (CAGR) was 10.75 per cent, better than the 16.16 per cent growth in annual revenue. Within three years, the stock had gained 208.41 per cent, while the Nifty IT index had returned 106.55 per cent in the same period. Intelligent automation, that integrates automation with modern technology such as robotic process automation, machine learning, Internet of Things and artificial intelligence, can be used to solve business difficulties. Saksoft's ability to help customers achieve change makes it possible to make intelligent decisions, increase efficiency, improve customer experience, and innovate services. Shares of Sucksoft gave a return of 118.06 per cent during the three years, while the Nifty IT index gave a return of 106.55 per cent. International technology company Affle provides app marketing services through its subsidiary. Consumer and enterprise platforms are the two primary business segments of the company, respectively. Affle's consumer platform uses mobile advertising relevant to customers' needs to attract them to buy, interact with and transact. Dash Inc. is an information technology firm specializing in web and mobile application development. Dash Technologies Inc. is a global leader in this field, creating world-class solutions. They can help you design applications that exceed your company's objectives as they have over a decade of experience in this field. Businesses of all sizes and types are served by Dash Technologies, which works with startups from Fortune 500 companies and everything in between. Dash Technologies Inc. is a globally renowned web and mobile app development company providing world-class solutions. Dash Technologies executes through collaboration and innovation from startups to enterprises and everything in between. With over 10 years of experience in providing Development as a Service (DaaS), they help you develop a solution to exceed your business expectations. Talentica Software is an innovative offshore product development company with a broad focus on startups. Over the last 18 years, the company has successfully empowered 170+ startups to create their own success stories. Among them, 52 startups are funded by top VCs like Accel, Khosla Ventures, Sequoia Capital, Index Ventures, etc. Sigma was born to lend its expertise to the world of Big Data! Sigma understands the gravity of each piece of data in today's world and the next generation. Based on this, the workshop pattern is pre-defined to understand the problem. It provides a unique solution to each client using different tools and frameworks. Spec India is an ISO/IEC 27001:2013 company with 30+ years of established experience. The company specializes in custom software development, web and mobile app development, BI and analytics solutions, automation and security testing, legacy software migration, product engineering and IoT solutions. Spec India has a team of 300+ consultants who are committed to provide quality solutions to global clients. One of the top blockchain development companies, with over 50 million active users for their apps and an industry-competitive 97% customer success score. SoluLab has partnered with Fortune 500 enterprises for high-growth startups, including Walt Disney, Goldman Sachs, Mercedes Benz, University of Cambridge, Georgia Tech, etc. Led by Goldman Sachs and Citrix management leaders, Solulab aims for cost savings of over 50% for clients with an advanced hiring model that improves hiring speed by up to 400% compared to other industry players. Brancosoft is an established Software Development Company with a remarkable and proven track record engaged in providing Application Development Services, Technology Consulting and IT Outsourcing Solutions to clients across the globe. IT Solutions, started as Thought Weaver in 2011, is a software development company with 50+ highly skilled IT experts providing result-oriented and cost-competitive solutions for SMEs across the globe, now rebranded as Brancosoft has gone. Monkhub is a technology services provider primarily focused on emerging technologies. We do projects in BlockChain, Mobile and Web Application Development and Data Science. We execute our projects following a structured approach to creativity, design, Development and deployment. Their customer base is spread across small and medium enterprises and covers Fortune 500 companies. As we can see, these are the top 10 artificial intelligence companies of 2022. Artificial intelligence is required by the ever-changing business environment (AI), the robot is capable of mimicking human-like capabilities and interactions, and with the help of artificial intelligence, we can do agriculture and farming, security and surveillance, sports analysis and activities, manufacturing, and upgrade ourselves in every field like production etc. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence Future Ideas - Javatpoint

Artificial Intelligence Future Ideas Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Increase Security 2. Generate New Services (and Potential Social Issues) 3. Empowering Businesses 4. Healthcare 5. Facilitating Sustainability 6. Make humans smarter 7. Inspire Artists 8. Creating New Jobs 9. Bridging Language Divides 10. Changing Government 11. Providing Health Care 12. Making Art Contact info Follow us Tutorials Interview Questions Online Compiler Today we show you the predictions of 7 tech experts about the future of artificial intelligence and machine learning in the next ten years: How will the application of AI technology change society, and which areas will be affected the most? According to these experts, in ten years artificial intelligence will be able to Drones will change the way we live, in the opinion of Nicolas Horbaczewski, CEO and founder of the Drone Racing League. Somehow they now represent what mobile phones were in the 90s. Drones are devices capable of moving objects very rapidly, and, in particular, they can fly. Package delivery, emergency response, or immediate delivery of medical products, anything with a drone will happen instantly. Horbaczewski considers them a central point in the security realm: they will make the world safer thanks to the possibility of inspecting places that would otherwise be difficult to control. Drones will become a part of our daily lives and fundamentally change them, as did smartphones and the Internet. NY Times writer Martin Ford says that artificial intelligence will improve our ability to solve problems and generate new ideas. It is likely that in the next ten years, AI and robotics will be fully integrated into business operations and greatly affect the efficiency of organizations: new products and services based on AI will be created, and there will be new markets and customers. Also, artificial intelligence could eliminate some jobs that would be automated, and could create critical situations in terms of privacy, security and military applications. According to Ford, in ten years, the debate about the potential issues arising from the application of AI will be central at both the political and social levels. Matthew Kamen, VSP of engineering at Foursquare, thinks that applications of AI are "stuck" at the moment, and are limited to reproducing what humans can already do or what humans rely on them to do. In ten years, these trust barriers against AI technology will progressively lower, and our addiction to algorithms and intelligent machines will grow. Kamen believes that AI technologies will allow analysts, developers, marketers and many more professionals to develop enterprise and consumer-based applications to better interact and understand users. According to Serkan Kutan, CTO of Zocdoc, intelligent machines will be very useful in the field of healthcare. Many doctors work too much; They cannot see all their patients and cannot keep up to date with new studies and advances due to time constraints. AI can help, especially for everything related to patient data analysis and diagnosis. The machines will have faster and immediate access to a larger set of clinical data, and the doctor will have more time to interact with his patients and improve outcomes, by delegating that part of the work. Nikita Johnson declared that Artificial Intelligence will seriously impact every industrial sector and everything we do. But at a high level, AI and machine learning will be at the fore in sustainability, environmental problems and climate change. There are many areas in which machines can greatly help and improve, especially if we talk about our century's great challenges, such as urbanization, population growth, and energy. Therefore, Artificial Intelligence will increase business productivity and for higher and more important purposes. John Steicher, group managing director at Barclays Investment Bank, says computing power will grow progressively by giving us more power in training our artificial intelligence models. In addition, the amount of data analyzed will grow exponentially, allowing us to monitor more elements on our platform and the world in general. Combining this with artificial intelligence, we will have the ability to make more intelligent predictions about future behaviors and events and train smarter knowledge systems and models. Steicher thinks that the concerns of many experts about the risks involved in the application of AI technology were unfounded because training a machine is equivalent to educating a child; If you teach him well, for example, what is wrong and what is right, he will grow and become a productive member of society who cares about people and the future as a human being. Stephanie Dinkins, Transdisciplinary AI Artist, believes intelligent algorithms will be part of most decisions made in ten years, small or large, and artists can join artificial intelligence. Even though that concept may sound daunting, it's just a matter of harnessing the potential of AI and starting to explore. Dickins encourages artists to use AI to create beautiful and expressive artistic works as they would any other medium. On the other hand, she completely denies the idea of technology becoming an artist. AI should be considered a tool to enhance human thought and develop creativity, not transform creativity or morality into machines. According to the artist, the developers of AI must ask themselves how AI systems can be used to increase productivity while respecting human diversity, dignity and cultural specialties? "Artificial intelligence will transform the workforce," confirmed Microsoft Corporate Secretary Carolyn Frantz. AI is "an opportunity for workers to focus on the parts of their jobs that may also be most satisfying to them," Frantz says. The bleak view of AI as a job killer is one side of the coin: While 75 million jobs may be missing, 133 million more lucrative, less repetitive new roles are expected to form. Whether teaching new languages in person or translating speech and text in real time, AI-powered language tools from Duolingo to Skype are bridging the social and cultural divide in our workplaces, classrooms and daily lives. Microsoft education leader Mark Sparwell admits that digital translation services are not "perfect", but "they provide a means of understanding" that might not be possible otherwise. Less paperwork, faster responses, more efficient bureaucracy - AI has the power to transform public administration in a big way, but are governments ready? This technology comes with risks and opportunities that need to be understood and evaluated. Academic Kevin D'Souza believes that simplification and role-playing can be the key for public servants to analyze complex cases, come up with better solutions, and truly understand the future of autonomous systems. Paul Bates, director of NHS services at Babylon Health, says AI has the potential to make health care "more accessible and more affordable". Babylon, an app that provides rapid access to symptoms and quick access to physicians when needed, provides advice to over one million residents in central London via an AI-powered chatbot. Patients can get accurate, safe and convenient answers in seconds - and save health care providers money. Computational creativity is drastically changing the nature of art. Software is becoming a creative, more than a tool merging computer scientists with the artist. As the Austrian artist, Sonja Baumel assures, "the exhibition space becomes a laboratory; the art becomes the expression of science, and the artist the researcher." 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Government Jobs in Artificial Intelligence in India - Javatpoint

Government Jobs in Artificial Intelligence in India Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Government Jobs in Artificial Intelligence One such initiative is the NITI Aayog. Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Introduction 1) Safe and Ethical Use of AI 2) Automated Facial Recognition System (AFRS) to identify criminals 3) AI system in government buses 4) AI-based traffic scan 5) Chatbot to register home and marriage 6) India will use AI and supercomputers to improve weather forecasting 7) AI-Based Solutions for Agriculture in India 8) AIRAWAT 9) AI For Improvement of Services in Rural Areas 10) Adoption of Privacy 11) Government of India will fund 100 startups 12) Center of Excellence 13) Global Hackathon on Artificial Intelligence 14) The Government of India is pushing investments through the following areas to establish an AI ecosystem. 15) Artificial Intelligence Specialist - GS-15 Key objectives Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence, or AI, as we popularly call it, is a hot topic nowadays, and people are showing interest in AI and jobs in that field, especially government jobs. Now, AI can be applied to various production industries, from agriculture to health care and more. But today, we will take a look at the government jobs offered in AI. Now, the Government of India has taken many initiatives for creating Government Jobs in AI. It is officially published by the Govt. of India. It talks about the implementation of AI in specific areas for health, agriculture, smart cities, etc., and the challenges faced in India regarding the implementation. This page talks about the various areas of ai that the govt is looking for and where the Govt is investing. For example, the govt is looking forward to researchers in ai in different colleges and educational institutes, investing in it. That means the researcher or the scholar working on ai will benefit in terms of stipends and facilities. The schemes are valid for faculty fellowships, inter academia collaborations, and Ph.D. scholarships. These are one kind of job opportunity created in AI. But you might be thinking about specific roles in govt jobs in AI. If you want to know specifically about the role or job profile of the future, then click on this link. Another important thing done by the govt is setting up a Centre of Excellences that are there to nurture your skill and develop them further. Let's talk about some of the instances where govt of India has focussed on ai. Recently, the Tamil Nadu government announced that they are working on a policy for artificial intelligence's safe and ethical use. This first-of-its-kind policy will be like a rule book for state agencies and vendors using AI for governance services. The National Crime Records Bureau (NCRB), Ministry of Home Affairs, and Government of India recently floated a tender to build an Automated Facial Recognition System (AFRS). The proposed AI system will be trained using the database's collection of photographs of Indian citizens. In August, the governments of Uttar Pradesh and Karnataka announced plans to install AI systems in buses to alert drowsy drivers and avoid collisions. The AI-powered anti-collision system consists of two sensors, one on the front bumper, to alert the bus driver to any danger or the possibility of a collision. A second sensor is placed near the headlight switch to alert inattentive or drowsy drivers. In May this year, the West Bengal government and the state's IT department, along with the police, announced that they are working on an AI system to monitor vehicles and immediately send alerts to the police if there is any abnormality in driving behavior. If the distance between two speeding cars becomes too short, the AI-enabled device will track the vehicles, immediately sending an alert to the nearest traffic police. A few months back, Telangana's Department of Information Technology, Electronics, and Communications (ITE&C) announced that the department is working on chatbots that will work on Robotic Process Automation (RPA). The chat will draw out details like the locations of Sub-Registrar Officers (SROs) and fees for marriage, property, and society registration. In an International Workshop on Prediction Skills of Extreme Rain Events and Tropical Cyclones: Current Status and Probability (IP4) and Annual Climate Change, Dr. M. Rajeevan, Union Secretary, Earth Sciences, announced the use of artificial intelligence and machine learning. Helps in improving the understanding of weather and climate phenomena and their forecasts. Currently, the ministry is working on expanding the existing supercomputing facility to 100 petaflops (PF) in the next 2 years. In October, the Maharashtra government announced the implementation of AI-based solutions for agriculture in India to reduce farm risk for farmers under the Maha Agri-Tech project. The technology will reduce farming risks from unreliable rains or pests and predict crop-wise and region-wise yields. Agriculture will have satellite imagery to help access crop area, crop status, and crop yield at the district level. AIRAWAT or AI Research, Analytics and Knowledge Assimilation plaTform is a cloud computing platform for Big Data announced by Niti Aayog last year. The platform will be focused on big data analytics and similar tasks. It will support multi-tenant, multi-user computing with resource partitioning, a dynamic computing environment, and more features. A few months ago, CSC e-Governance Services India Limited (CSC SPV) announced the development and delivery of new digital services to around 900 million citizens living in rural areas in India using Artificial Intelligence and Data Analytics in areas such as finance, education, health care, among others. In May 2019, the Joint Secretary, Ministry of Electronics and IT (MeitY), announced that the Government of India is working towards creating a body completely dedicated to the development of AI in the country. Privacy will be taken as a fundamental right and is said to be the first step toward preparing the country for AI adoption. Recently, the central government of India announced that it plans to directly fund 100 startups to use natural language translation techniques in various languages. In addition, the government will also use this platform for its purposes, such as translating videos of technical education lectures and others. Earlier this year, the Government of India and the Indian Institute of Technology Delhi (IIT Delhi) joined hands to set up a Center of Excellence for Waste to Wealth Technologies to implement sustainable, scientific, and technological solutions for waste management. Recently, Karnataka's Department of Information and Technology announced that the department had joined hands with the Indian Institute of Science (IISc) to set up a Center of Excellence (CoE) for Design. Think Tank of India, NITI Aayog launched the Global Hackathon on Artificial Intelligence in late 2018. NITI Aayog joined Singapore-based AI startup Perlin to launch 'AI for All Global Hackathon. Although the announcement was made in December 2018, the hackathon was held in January and March 2019. The hackathon ran in two phases, with the first phase ending on 15 January 2019 and the second phase ending on 15 March 2019. This information might not clarify the specific profiles of the govt jobs in ai. But these initiatives and schemes launched by the govt. will help you analyze the potential of the jobs you should target and the areas wherein you can focus your knowledge. As a Center of Excellence Artificial Intelligence Specialist, you will be responsible for advancing the applied use of Artificial Intelligence (AI) and Machine Learning (ML) in the federal government and showing AI's potential through early-stage solutions (e.g., for "the art of the possible") as well as supporting major enterprise-wide AI/ML initiatives across federal agency partners. In addition, you will be asked to provide input on important policy issues regarding the use of AI/ML in government. The ideal candidate must have previously worked in AI/ML in a leadership capacity. They will play a role as an expert advisor to federal agency partners, developing project plans and managing delivery activities, providing leadership, and influencing the adoption of technological AI/ML solutions. Your location will determine your locality since most of our locations are remote. If the position is not remote, your location will be determined based on the office location where the position is located. The base salary range does not include any adjustments for the locality. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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What is the Role of Planning in Artificial Intelligence - Javatpoint

What is the Role of Planning in Artificial Intelligence? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is a Plan? What is planning in AI? Target stack plan Non-linear Planning Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Forward State Space Planning (FSSP) 2. Backward State Space Planning (BSSP) Block-world planning problem Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence is an important technology in the future. Whether it is intelligent robots, self-driving cars, or smart cities, they will all use different aspects of artificial intelligence!!! But Planning is very important to make any such AI project. Even Planning is an important part of Artificial Intelligence which deals with the tasks and domains of a particular problem. Planning is considered the logical side of acting. Everything we humans do is with a definite goal in mind, and all our actions are oriented towards achieving our goal. Similarly, Planning is also done for Artificial Intelligence. For example, Planning is required to reach a particular destination. It is necessary to find the best route in Planning, but the tasks to be done at a particular time and why they are done are also very important. That is why Planning is considered the logical side of acting. In other words, Planning is about deciding the tasks to be performed by the artificial intelligence system and the system's functioning under domain-independent conditions. We require domain description, task specification, and goal description for any planning system. A plan is considered a sequence of actions, and each action has its preconditions that must be satisfied before it can act and some effects that can be positive or negative. So, we have Forward State Space Planning (FSSP) and Backward State Space Planning (BSSP) at the basic level. FSSP behaves in the same way as forwarding state-space search. It says that given an initial state S in any domain, we perform some necessary actions and obtain a new state S' (which also contains some new terms), called a progression. It continues until we reach the target position. Action should be taken in this matter. BSSP behaves similarly to backward state-space search. In this, we move from the target state g to the sub-goal g, tracing the previous action to achieve that goal. This process is called regression (going back to the previous goal or sub-goal). These sub-goals should also be checked for consistency. The action should be relevant in this case. So for an efficient planning system, we need to combine the features of FSSP and BSSP, which gives rise to target stack planning which will be discussed in the next article. Planning in artificial intelligence is about decision-making actions performed by robots or computer programs to achieve a specific goal. Execution of the plan is about choosing a sequence of tasks with a high probability of accomplishing a specific task. The start position and target position are shown in the following diagram. Components of the planning system The plan includes the following important steps: The important steps of the algorithm are mentioned below: iii. If the stack top is an action, pop it off the stack, execute it and replace the knowledge base with the action's effect. If the stack top is a satisfactory target, pop it off the stack. This Planning is used to set a goal stack and is included in the search space of all possible sub-goal orderings. It handles the goal interactions by the interleaving method. Advantages of non-Linear Planning Non-linear Planning may be an optimal solution concerning planning length (depending on the search strategy used). Disadvantages of Nonlinear Planning It takes a larger search space since all possible goal orderings are considered. Complex algorithm to understand. Algorithm We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence as a Service | AI OFF THE SHELF | AIaaS - Javatpoint

Artificial Intelligence as a Service - AI OFF THE SHELF Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Involvement of AI Levels of AI Make the Right Choice for the AI Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler In recent years, technology giants like Amazon, Google, Microsoft, and IBM (along with a hosting company) have all started to provide Artificial Intelligence as a service (AIaaS). These services, in essence, offer a broad range of AI algorithms accessible to the public. Some examples are algorithms used to classify, regression, or Deep Learning - a modern learning system based upon Artificial Deep Neural Networks. With increasing frequency, businesses are beginning to adopt AlaaS and other cloud-based services. Having a clear understanding of the best way to use it implemented into your business can be the difference between an enormous cost-saving opportunity and a huge headache. Companies used to spend many hours creating their own AI programs and doing this at a high cost. Since creating an AI infrastructure and the development of AI algorithms oneself is not easy, AIaaS delivers a working solution quickly and efficiently, saving time and cash. AI off the shelf, as it's often known, is able to achieve this through already set up infrastructure and pre-trained algorithms that reduce the time needed to develop and also the number of resources needed all over the world to accomplish difficult tasks. Cloud hosting providers have been offering IaaS (Infrastructure as a Service) as well as SaaS (Software as a Service) for a long time. AIaaS is also built on the previous offerings. The concept has been utilized in AI. Apart from reducing time to develop and expenses. Additionally, it reduces the risk of investment and improves flexibility in strategic planning. However, companies must also take into account the drawbacks of AlaaS. They are dependent on a service provider as well as the ability to connect to data at a significant speed. There is also the possibility of a lower level of data security and standardization, which puts limitations on the development of new technologies. Alongside the benefits in comparison to disadvantages essential to recognize two different levels in AI: high and low-level AI. The high-level AI solves difficult but ultimately standardized problems. One example of high-level AI is software for face recognition. Because the user interface is straightforward - put an image into the program and then wait for an answer. Even non-AI experts can utilize advanced AI without difficulty. Low-level AI, however, it is designed to handle various tasks with different needs. Examples include logistic regression, which can be utilized for churn prediction or to detect fraud. The proper use of low-level AI requires expertise in modelling training and data processing and optimizing parameters and their evaluation. The lengthy processing pipeline implies an increased chance of making a mistake during the various problem-solving phases. Consequently, it isn't easy to put low-level AI into use without AI experts. With the cost of AI falling and the growing capabilities of AI allowing a more diverse range of companies (many of them not necessarily tech-oriented) to use these two forms of AIaaS, understanding the essentials to keep them running is crucial. In the beginning, it is crucial to select the appropriate solution for our company. This isn't easy due to the fact that AlaaS providers don't reveal their algorithms' implementations. The only thing known is the API for an algorithm in most cases. A purchase that is not informed is not a guarantee with regard to AI. As with all software, companies are better off testing the product thoroughly before purchasing it. In low-level AI, the majority of clients are stuck in creating the right processing pipeline. There are numerous intricate processes involved in this process implemented in different ways by the different service providers. This is why it is advised that companies compare the service to self-coded implementation before accepting anything. Test. Compare. Repeat. This is vital, as AI algorithms are ultimately the only software that could be unstable. One way to avoid this is to insert our code, which some service provider's permit. This is a good option, but only if the firm has skilled teams aware of what they want to achieve by making changes to specific code. When utilized correctly, Alaas is an amazing device that allows nearly all businesses to dramatically increase their capabilities by using AI to use, with an affordable cost in terms of time, equipment, and personnel. The variety of services offered can be as valuable as a hindrance. Research is essential for obtaining the correct AI service and using it to serve the correct goals. Consulting with various service providers is essential, particularly for the latest technology, where there are a few glitches, always something to prepare for. When we do that, an entirely new world of possibilities will open. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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AI in Banking - Javatpoint

AI in Banking Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI in banking and finance sectors Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Cybersecurity and fraud detection Chatbots Loan and credit decisions Tracking market trends Data collection and analysis Customer experience Contact info Follow us Tutorials Interview Questions Online Compiler Almost every industry, including banking and finance, has been significantly disrupted by artificial intelligence. This industry is now more customer-centric and technologically relevant thanks to the use of AI inside banking applications and services. By enhancing efficiency as well as making a judgement based upon information that is incomprehensible to a particular operator, AI-based technologies can assist bankers reduce expenses. Additionally, clever algorithms may quickly detect incorrect facts. The environment we live in now includes artificial intelligence, therefore banks have also already begun incorporating this technology into their products and services. Here are several significant AI applications in the banking sector that will allow you to take advantage of something like the technology's many advantages. Let's therefore get started. Large numbers of online payments happen every day when consumers utilise applications with account information to pay bills, withdraw money, deposit checks, and do much more. As a result, every financial system must increase its operations towards cybersecurity and fraud detection. At this point financial artificial intelligence enters the picture. Artificial intelligence can help banks with eliminating hazards, tracking system flaws, and enhancing the security of online financial transactions. AI and machine learning can quickly spot potential fraud and notify both consumers as well as banks. Unquestionably, chatbots represent some of the best instances of how artificial intelligence is used in banking. They may work any time they want once deployed, in contrast to people who've already set operating time. They also expect to study more concerning a specific customer's usage statistics. It aids in their effective comprehension of user expectations. The banks may guarantee that they remain accessible to their consumers 24 hours a day by introducing bots within existing banking apps. Additionally, chatbots can provide focused on customer attention and make appropriate financial service and product recommendations through comprehending consumer behaviour. In order to make better, safer, and more profitable loan and credit choices, banks are trying implementing AI-based solutions. Presently, most banks still only consider a person's or business's dependability based on their credit history, credit scores, and consumer recommendations. Somebody can ignore the fact that these credit reporting systems frequently contain inaccuracies, exclude after all histories, and incorrectly identify creditors. Consumers with little payment history can use an AI-based loan and credit system to analyse existing behavioral patterns to assess its trustworthiness. Additionally, this technology notifies banks from certain actions that can raise the likelihood of bankruptcy. In short, these innovations were significantly altering the way that customer borrowing will be conducted in the future. Bankers can analyse huge amounts of data as well as forecast the most recent economic movements, commodities, and equities thanks to artificial intelligence in financial institutions. Modern machine learning methods offer financial suggestions and assist in evaluating market sentiments. AI for banking additionally recommends when and how to buy equities and issues alerts when there is a potential consequence. This cutting-edge technology additionally aids in the speed of decision-making and makes trading convenient for both banks and their clients because of its powerful data computational power. Everyday, financial and banking institutions record millions of transactions. Due to the vast amount of knowledge gained, it becomes challenging for staff must acquire and register it. This became difficult to structure and collect one such large amount of data without making any mistakes. AI-based alternative approaches can aid in effective data collection and analysis in these kind of circumstances. Thus, the whole user development is achieved. Additionally, the data may be utilised to identify theft or make credit decisions. Customers are always looking to have a more convenient environment. For instance, ATMs were successful since they allowed clients to access necessary services like money withdrawal and deposit even when banks were closed. More development has merely been spurred by this degree of convenience. Consumers are able to use their smartphones to open bank accounts from the convenience of their own homes. Artificial intelligence integration will improve comfort conditions as well as the customer experience in banking and finance operations. AI technology speeds up the recording of Know Your Customer (KYC) data and removes mistakes. Furthermore, timely releases of new goods and monetary incentives are possible. 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AI Tools - Javatpoint

AI Tools Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Scikit Learn 2. Tensorflow 3. Theano 4. Keras 5. Caffe 6. MxNet 7. Google ML Kit Contact info Follow us Tutorials Interview Questions Online Compiler Machine learning (ML) and artificial intelligence (AI) are employed across all fields and industries. This enables the interpretation of huge amounts of information, enabling professionals to even use the research more effectively. Even with development of AI and ML, programmers as well as researchers already have a diverse range of platforms as well as AI tools. The most well-liked artificial intelligence technologies as well as platforms on the marketplace are mentioned here. The list of the most commonly used tools and frameworks for artificial intelligence is provided here. These artificial intelligence tools are available from which you can select among based around what best suits their needs. The unsupervised and administered calculations are supported by Scikit train, another of the widely used ML libraries techniques. The antecedent may include clumping, choosing tree, planned and spontaneous depressive episodes, etc. SciPy, Python, and NumPy libraries are added towards the application. There are numerous computations required for data mining and everyday AI tasks. Extremely complex tasks like addressing the challenges related, shifting data, including characteristic identification can be completed in a matter of lines. Scikit learn is the perfect tool that are using if you would like to implement one for young users. Although you might undertake computations since they can be meaningful, are they consistently necessary? I guess sometimes not. Would these be computations, even when implemented right, worthwhile? The response is a resounding YES. With Tensorflow, users could create a Python application which can be run and organised on either the GPU or the CPU. Therefore, you do not need to write the programme at the CDA or C levels when we want to execute something on GPUs. Tensor uses multi-layered cores which enable quick setup, development, & transmission of fake neural networks together with sizable datasets. This is what enables Google to recognise inquiries that were already presented in a visual format. Additionally, it enables Google to understand audibly spoken words in the application for voice recognition. Over through the Keras is wrapped river Theano. With Tensorflow or Theano, Keras seems to be a Python package that enables for deep learning. Theano were developed to develop simple, rapid models of profound learning that could be applied to creative activity. It is Python-based and therefore can operate from both GPUs as well as CPUs. Your PC's GPU can be used by Theano. This enables it to increase elevated communication quantities significantly over when it is retained to function solely upon that CPU. It is incredibly lucrative to perform most complex calculations thanks of Theano's speed. Keras is suitable if you prefer the Python method about doing business. It is a high-level neural network library that uses Majority of the respondents agree or TensorFlow just like its backend. Logistical problems typically look somewhat like: "Caffe" is a sophisticated cognitive system that places a high value on pronunciation, quickness, and measurable excellence. This was made through networking contributors as well as the Berkeley Vision and Learning Center (BVLC). Caffe Framework is required by Google's DeepDream. This structure is a C++ library with something like a Python interface that is BSD-approved. For recurrent nets on very long passages, the ability to trade calculation time and data via "scatterbrained features are important" might be quite helpful. Google's machine learning beta SDK for mobile developers, known as Google ML Kit, aims to make it possible to user to build customised functionality for Android and iOS phones. Through android APIs operating on the device or in the cloud, the kit enables developers to implement machine learning technology. This would include functions like picture identification, face and text recognition, barcode scanning, and much more. In circumstances in which the constructed APIs could not be appropriate, programmers can also create their own TensorFlow Lite models. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Cognitive AI - Javatpoint

Cognitive AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Cognitive Computing: What is it? How Would Cognitive Computing Work properly? Important Characteristics Cognitive Computing vs AI Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Cognitive Computing 2. AI Contact info Follow us Tutorials Interview Questions Online Compiler A cognitive computer or system communicates among people inside a conventional manner, learns on something like a global level, and uses intentional reasoning. These systems learn and reason via connections between individuals and what they've experienced in their surroundings, as opposed to becoming deliberately coded. Artificial intelligence as well as cognitive computing have some overlapping, as well as the technology used to power cognitive applications are comparable. Using computerised systems to imitate human mental processes in complicated problems in which the solutions could be ambiguous and uncertain is known as cognitive computing. The expression is intimately linked to Watson, IBM's intelligent software application. Although machines become quicker then people for thinking as well as performing calculations, machines are still yet adept at certain activities, including comprehending spoken speech or identifying items in an image. The goal of cognitive computing is to ensure that machines function similarly towards the nervous system. The term "cognitive computing" describes specialised technology that carry out particular functions to support cognitive abilities. All of those are essentially the intelligent decision support systems that we have been developing since the start of the tech bubble. Those infrastructures are now using improved algorithmic techniques and information as a result of new advances in technology to analyse massive quantities of information more effectively. Additionally, we could call cognitive computing: Generating smarter choices on the basis of cognitive computing technologies during workplace. Speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection are a few uses using cognitive computing. Technologies of cognitive computing combine a variety of information sources, balancing contextual as well as naturalistic explanations, to recommend appropriate solutions. Cognitive systems use identity techniques that use data mining, pattern recognition, and natural language processing (NLP) to comprehend how and why the individual brain processes in order to accomplish that. It takes a lot of structured and unstructured data to address problems that should be handled by human utilizing technological tools. Cognitive systems gain the ability to anticipate new issues as well as simulate alternative solutions as machines continue to improve their patterns recognition and data processing skills through practice. Cognitive computing systems need to possess a few essential qualities in order to attain such functionalities. Artificial intelligence is a subfield within cognitive computing. These 2 have a lot of things in common and distinctions. These technology underlying cognitive computing and artificial intelligence are related. They consist of NLP, neural networks, machine learning, deep learning, and more. These do, however, also come in a multitude of ways. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Introduction of Seaborn - Javatpoint

Introduction of Seaborn Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Data Visualization ? Why the value of Data visualization is important What is Seaborn? Information Loading for Create Seaborn Plot points Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Setting up Seaborn Pip Installer usage Utilizing Anaconda Contact info Follow us Tutorials Interview Questions Online Compiler This graphical presentation of information is known as information visualisation. Due to the excellent community of Packages focused on information, it is crucial in research methodology. Through summarising effectively displaying a large quantity of data in a straightforward and including how, it also assists people grasp that information, no regardless of how complicated it may be, as well as the quality of the data. It also aids inside the efficient and transparent communication of information. The graphical display of information and data is known as data visualisation. Data visualisation tools offer an easy approach to observe and analyse trends, outliers, & patterns in information by utilising visual elements like charts, graphs, and mapping. Furthermore, this offers a great tool for staff members or business owners to clearly distribute information towards non-technical consumers. To analyse enormous amounts of information & generate information judgments, data visualisation tools and technologies are crucial in the world of big data. Data visualisation is significant because it makes data easier to see, interact with, and comprehend. No of their degree of skill, the correct visualisation can put everyone on the same page, regardless of whether it's straightforward or complicated. It's challenging to imagine a profession sector that is unlikely to gain from better data comprehension. Knowing information is beneficial for all STEM areas, as well as for those in the public sector, business, marketing, history, consumer goods, services, athletics, and others. Python's Seaborn package allows you to create statistics visuals. It incorporates tightly with Pandas data structures and has been built upon Matplotlib. You may examine and comprehend their information with Seaborn. Its charting units operate with dataframes including arrays that include entire datasets, and they automatically carry out the semantic mapping and statistical aggregation required to make useful graphs. You can concentrate on understanding what the various components of their plots represent instead of the specifics of how to draw them thanks towards its data source, straightforward API. Seaborn seems to be a fantastic Python data visualisation package that plotting statistically visuals. It offers lovely default styles and colour schemes to enhance the appeal of statistics charts. It is constructed on top of the Matplotlib toolkit and is usually combined with the Pandas data structures. Throughout this lesson, we'll learn how to use Seaborn to create a range of plotting as well as how pair everything with Matplotlib to enhance their visual appeal. Line graph - One of the most fundamental plots inside the Seaborn Library seems to be the linear representation. This graphic is primarily utilized to depict uninterrupted information in the manner of something like a response variable. Installing Seaborn onto the PC is necessary before doing so, then I'll demonstrate several installation methods in this article. The de facto new standard for installing and managing Python development tools are called pip. or This set containing numerous free software packages are available in the package management, environment manager, and Python distribution known as Anaconda. After installing Anaconda, you can use conda or the management console in Anaconda to load any extra programs that might require. And use this script with in command line, users may also install the development version of Seaborn directly from GitHub. The built-in datasets from Seaborn that are acquired immediately during setup will be discussed and summarized. Because you may import anything information utilizing Pandas, utilizing built-in datasets can be especially helpful during using Seaborn. Here's how you can obtain a list of all Seaborn's constructed information. Code - Output: Let's integrate one of those datasets immediately, and during the parts that follow, let us just visualise that information. Code - Output: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Natural Language ToolKit (NLTK) - Javatpoint

Natural Language ToolKit (NLTK) Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials NLP: What is it? NLTK: What is it ? Features of NLP How to use NLTK in Python Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Morphological Processing 2. Syntax analysis 3. Semantic analysis 4. Pragmatic analysis 5. Morphological Processing: 6. Syntax analysis: 7. Semantic analysis: 8. Pragmatic analysis: Contact info Follow us Tutorials Interview Questions Online Compiler Using a program or, indeed, a computer that can manipulate or comprehend speech through text is known as natural language processing (NLP). Comparison examples are human interaction, understanding one another's viewpoints, and responding properly. In NLP, computers can perform that communication, comprehension, and response instead of humans. The Natural Language Toolkit (NLTK) is a Python programming environment for creating applications for statistical natural language processing (NLP). It includes language processing libraries for tokenization, parsing, classification, stemming, labeling, and semantic reasoning. It also comes with a curriculum and even a book describing the usually presented language processing jobs NLTK offers, together with visual demos, including experimental data repositories. A collection of libraries and applications for statistics language comprehension can be found in the NLTK (Natural Language Toolkit) Library. One of the most potent NLP libraries, it includes tools that allow computers to comprehend natural language and respond appropriately whenever it is used. NLTK supports a wide range of languages, not just English. It provides tokenization, stemming, and morphological analysis tools for languages such as Arabic, Chinese, Dutch, French, German, Hindi, Italian, Japanese, Portuguese, Russian, Spanish, and more. In addition to the standard NLP tasks, such as tokenization and parsing, NLTK includes tools for sentiment analysis. This enables the toolkit to determine the sentiment of a given piece of text, which can be useful for applications such as social media monitoring or product review analysis. While NLTK is a powerful toolkit in its own right, it can also be used in conjunction with other machine learning libraries such as sci-kit-learn and TensorFlow. This allows for even more sophisticated NLP applications, such as deep learning-based language modeling. NLTK has a large and active community of users and contributors, which means a wealth of resources available for learning and troubleshooting. In addition to the NLTK book and curriculum mentioned in the article, online forums, tutorials, and example codes are available. NLP's initial element is morphology analysis. It involves splitting up large linguistic input blocks smaller groups of tokens that represent phrases, sections, as well as phrases. Any term like "daily," for instance, can indeed be split down into two sub-word tokens as "ever other." One of the most crucial parts of NLP is the second element, syntax analysis. The following are indeed the goals of just this element: The third component of NLP, semantics evaluation, is utilised to assess the biblical text meaning. It involves extrapolating the biblical text specific meaning, or determining what the dictionaries would claim is its interpretation. E.g. The semantics analysis will ignore phrases like "It was a heated dessert." In NLP, pragmatic advice comes in at number four. It involves tying item connections discovered by the earlier element, or sentiment analysis, to the actual objects or events that occur within every scenario. E.g. Put the fruits in the basket on the table. Because this statement can now have two different semantic readings, the pragmatist analysis may select either of the following options. Besides splitting the input into smaller groups of tokens, morphological processing also involves identifying the base form of words, lemmatization, and the different inflected forms of words, known as stemming. These techniques help NLP systems understand the relationships between different forms of words and can improve the accuracy of downstream tasks such as sentiment analysis. Syntax analysis involves determining if a sentence is properly constructed and understanding the relationships between different parts of a sentence. This includes identifying subjects, objects, verbs, and other parts of speech, as well as understanding the different grammatical structures of a language. This knowledge is critical for tasks such as machine translation, where understanding the syntax of the source and target languages is essential. Semantic analysis involves extracting meaning from text and understanding the relationships between words and concepts. This includes identifying synonyms and antonyms, understanding word sense disambiguation, and recognizing the relationships between different entities in a sentence. These techniques are essential for tasks such as question-answering systems or chatbots that require a deep understanding of natural language. The pragmatic analysis involves understanding the context in which language is used and identifying the intended meaning behind a sentence. This includes understanding sarcasm, irony, or humor and recognizing when a sentence has multiple interpretations. The pragmatic analysis is particularly important for applications such as sentiment analysis, where understanding a text's underlying tone and context can greatly improve the accuracy of the analysis. Installing the Natural Language Toolkit (NLTK) is the first step toward using it with Python. Using pip, a Python package manager, you may install NLTK. Open a terminal or command prompt and type the following command: You may start utilizing NLTK in your Python code after installing it. Here are some fundamental actions to use NLTK: 1. NLTK library import: Import the NLTK library first in your Python script: 2. Download the necessary resources: The command nltk.download('all') can be used in the Python prompt or a script. The command downloads all the NLTK resources, including the corpora, models, and other information that NLTK needs to carry out different NLP operations. Here is an example of how to use the Python scripting language's nltk.download command: You may also use the command from the Python terminal or command line. Enter Python at the terminal or command prompt to launch a Python console, and then type the instructions that follow to do this: As a result, all the materials will begin to download, and you can check the status in the console or terminal. You may quit the console when the download is finished or begin utilizing NLTK resources in your code. 3. Tokenization: Text may be divided into tokens or words using a variety of tokenizers provided by NLTK. The word tokenizer can be used, for instance, to break a statement up into words: Output: 4. Part-of-speech (POS) tagging: NLTK offers a variety of tools for part-of-speech tagging, which entails determining the sentence's grammatical structure and designating each word's part of speech. The pos_tag function, for instance, may be used to identify the various parts of speech in a phrase. Output: 5. Other features: Besides stemming, lemmatization, sentiment analysis, and many more, NLTK offers many other features. To find out more about these features and how to utilize them, you may go through the NLTK documentation. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Best books for ML - Javatpoint

Best books for ML Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition) 2. Machine Learning (in Python and R) For Dummies (1st Edition) 3. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition) 4. Pattern Recognition and Machine Learning (1st Edition) 5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data (1st Edition) Contact info Follow us Tutorials Interview Questions Online Compiler What we desire is a machine that can learn from experience, said Alan Turing in 1947 and machine learning has made this idea become a reality today! Broadly generally, machine learning seems to be the research on predictive methods & automated systems for something like a particular task that uses pattern through inferences rather than know how to keep because there is no denying that machine learning is a ridiculously well-liked job option right now. Considering such, there might be numerous publications upon that marketplace that you may choose from if you're looking to understand about machine learning (for programmers at all stages of learning). For both technological whizz children & rank amateurs, we have selected the top books for machine learning in this article. It is up to you to decide which of these books, which are all very well-liked, best suits particular types of learners. Let us just examine everything now even without ado! You want to learn machine learning but don't understand where. However, while beginning any incredible journey towards learning algorithms, you must be responsible for a number very important conceptual but instead mathematical concepts. The book that follows fills that need. The course provides complete beginners with a high-level, realistic orientation into machine learning. Understand well how simply download information, including how to use the tools and machine learning frameworks you'll require, in Machine Learning For Complete Beginners. Regression analysis, clustering, the fundamentals of neural networks, bias/variance, decision trees, and other topics are however discussed. Machine learning can be a complex idea to the average person. But for those of us who are informed, it is priceless! The management of problems like internet search outcomes, actual internet advertising, automation, or even spam detection (Yeah!) is difficult without machine learning (ML). The above book provides a clear foundation to the mysterious world of machine learning experience. By teaching you how to "understand" languages like Python and R, Machine Learning For Dummies will enable you to educate computers to perform pattern recognition as well as analysis of data. Additionally, you'll learn how to code in Python with Anaconda and R using R Studio. If you're a programmer who is now interested in data analysis, this book is ideal for us! (Let's start by making clear that perhaps the term "Hacker" in the title refers to an excellent programmers rather than a covert computer cracker!) So instead of the usual dry, arithmetic lectures, this book will guide you through the basics of machine learning using a tonne of actual case examples. Every book of Machine Learning for Hackers focuses on a particular issue, such as recommendation, prediction, optimization, and categorization. Additionally, students would study how to use R programming to create basic algorithms for machine learning and evaluate several example information. Whenever we would like to dive thoroughly through into mysterious area of pattern recognition and machine learning, definitely must have this book. This book actually the initial to cover pattern recognition from a Bayesian viewpoint. Consequently, even though the book tackles challenging subjects that call for at least a basic understanding in multidimensional calculus, fundamental linear algebra, and data science, it is also the ideal resource for drilling Pattern Recognition onto your mind. In Pattern Recognition and Machine Learning, there are high numbers of sophistication in the chapters on probability and machine learning based on patterns in information. In order to communicate its argument, each book begins with a general overview of pattern recognition. This book seems to be the route should go unless you want a "back to the basics" approach to machine learning and are working at an intermediate or expert level. Despite compromising the integrity of its key principles, it gives full respect to the astounding complexity and richness of machine learning (But that's an accomplishment!). In Machine Learning: The Art and Science of Algorithms, a huge spectrum includes logical, geometrical, as well as statistics approaches are presented, alongside challenging although relatively new concepts like principal component analysis as well as ROC analysis. This same book contains numerous research studies with different levels of complexity and plenty of instances but also visual representations (to make damn sure it isn't dull!). 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AI companies of India will lead in 2022 - Javatpoint

AI companies of India will lead in 2022 Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Tata Elxsi 2. Bosch 3. Kellton Tech 4. Persistent Systems 5. Oracle Financials 6. SaksoftSaksoft 7. Zensar Technologies Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler AI is undoubtedly actively participating throughout every industrial segment as a result of the shifting corporate world. It exhibits living thing characteristics and thus can imitate interpersonal contact. Top-ranked Indian AI companies today provide cutting-edge AI algorithms at quick speeds and even more dependable services for affordable prices. Businesses from all over the world are using AI's advantages help streamline procedures and streamline productivity. Tata Elxsi has been promoting technological improvements for the past 25 years. It includes a broad range of innovations made possible by AI and analytics, from self-driving cars to tools for advanced analytics. The Artificial Intelligence Centre of Excellence (AI CoE) at Tata Elxsi responds towards the growing need for expert machines. Utilizing its cloud-based integrated data analytics frameworks, which include software innovations, clients could adjust quickly as well as adjust their environment in order to obtain useful insights and superior outcomes. This company generated a three-year profit of 174.89 percent while the Nifty IT delivered 106.55 % to shareholders. Less than 1% of cash inflow and 56.1 percent of labour costs were devoted to finance charges inside the industry's fiscal year which ended on March 31, 2021. The Bosch Center for Artificial Intelligence (BCAI) was launched in 2017 with the goal of integrating reducing Artificial intelligence into all Bosch product offerings to produce workable ideas. Bosch laid the technological foundation for AI to provide an effect on the actual environment. Including an emphasis on fundamental AI technologies, our productive relationship differentiation in six areas leveraging information from every one of Bosch's specialties. Inside the previous 16 years, only 1.08 percent of trading sessions experienced instantaneous declines including more over 5%. Above a currently recognized, this company had a negative return of -15.94 percent as opposed to that same Nifty 100 stock's 44.16 percent. An information technology and outsourcing firm situated in Hyderabad called Kellton Tech Solutions also has offices in the US and Europe. The group employs around 1400 individuals and made net profits of 7.39 billion rupees. With expertise in deep computing and machine learning, Kellton Tech develops attempting to cut, targeted AI solutions for problems that already have previously required a significant deal of human intelligence. The company generated a three-year performance of 40.86% while the Nifty IT provided 106.55% towards shareholders. With perhaps a market capitalization of Rs. 712.75 crore, Kellton Tech Solutions Ltd., a winding up of the company in the IT computer industry, was established in 1993. Constant Structures Through products that enable Machine learning and artificial intelligence throughout every phase of production, Persistent makes the dream of AI and machine learning become actuality. Our technologies guarantee help customers make the most out of business expenditures in AI and ML thanks to a strategy which assists including use application prioritisation, platforms design documentation, scaling simulation models, and company-wide operationalization of algorithms. Sales increased at 16.16 percent annually, above the company's three-year CAGR of 10.75 percent. The company generated 208.41% over a three-year period, as opposed towards the Nifty IT's 106.55 %. Financial information Oracle can assist you in incorporating AI into both business and IT operations. Companies may accelerate productivity, obtain better results, but instead gain in - depth actionable information using Oracle cloud applications and platform, as well as Oracle autonomous database, all of which are running on Oracle's Generation 2 infrastructure. With in previous 16 years, just 2.35 percent of trading sessions experienced instantaneous increases of greater exceeding 5%. In comparison towards the Nifty 100's three-year return of 44.16 %, the stock returned -11.82 %. By utilising the crucial synergy between artificial intelligence and machine learning, Saksoft allows clients achieve transformational transformations through intelligent judgments, higher productivity improvements, excellent service to customers, and quality enhancement. Through fusing automated with cutting-edge innovations like RPA, machine learning, IoT, and Machine Intelligence, Saksoft speeds digitalization and uses intelligent automation to solve real - world problems. The Saksoft stocks had a three-year performance on 118.06 percent, while the Nifty IT provided 106.55 % towards shareholders. Zensar Technologies are placing its bets on AI (AI). This business is currently shifting its focus away from digital and toward disruptive AI. The company's R&D division, Zensar AIRLabs, has applied over 100 patents recently and has been currently exclusively based upon AI. In order to assist clients in generating business, Zensar the week before unveiled the original batch integrated tools across seven crucial sectors, including sales, marketing, IT, talent supply chain, HR, collaboration, projects, and programmes. In comparison to Nifty IT, which gave investors a return of 106.55 percent over a three-year period, Nifty IT Stock returned 15.63 percent. Instead of just providing innovative marketing strategies, Cyient helps businesses achieve their goals. AI has the ability to recognise modifications with in actual world as well as condition for effective mapping enabling self-driving automobiles. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Constraint Satisfaction Problems in Artificial Intelligence - Javatpoint

Constraint Satisfaction Problems in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Domain Categories within CSP Types of Constraints in CSP Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: The preferences restriction is a unique restriction that operates in the actual world. Contact info Follow us Tutorials Interview Questions Online Compiler We have encountered a wide variety of methods, including adversarial search and instant search, to address various issues. Every method for issue has a single purpose in mind: to locate a remedy that will enable that achievement of the objective. However there were no restrictions just on bots' capability to resolve issues as well as arrive at responses in adversarial search and local search, respectively. These section examines the constraint optimization methodology, another form or real concern method. By its name, constraints fulfilment implies that such an issue must be solved while adhering to a set of restrictions or guidelines. Whenever a problem is actually variables comply with stringent conditions of principles, it is said to have been addressed using the solving multi - objective method. Wow what a method results in a study sought to achieve of the intricacy and organization of both the issue. Three factors affect restriction compliance, particularly regarding: In constraint satisfaction, domains are the areas wherein parameters were located after the restrictions that are particular to the task. Those three components make up a constraint satisfaction technique in its entirety. The pair "scope, rel" makes up the number of something like the requirement. The scope is a tuple of variables that contribute to the restriction, as well as rel is indeed a relationship that contains a list of possible solutions for the parameters should assume in order to meet the restrictions of something like the issue. Issues with Contains A certain amount Solved For a constraint satisfaction problem (CSP), the following conditions must be met: The definition of a state in phase space involves giving values to any or all of the parameters, like as X1 = v1, X2 = v2, etc. There are 3 methods to economically beneficial to something like a parameter: The parameters utilize one of the two types of domains listed below: Basically, there are three different categories of limitations in regard towards the parameters: The main kinds of restrictions are resolved using certain kinds of resolution methodologies: Think of a Sudoku puzzle where some of the squares have initial fills of certain integers. You must complete the empty squares with numbers between 1 and 9, making sure that no rows, columns, or blocks contains a recurring integer of any kind. This solving multi - objective issue is pretty elementary. A problem must be solved while taking certain limitations into consideration. The integer range (1-9) that really can occupy the other spaces is referred to as a domain, while the empty spaces themselves were referred as variables. The values of the variables are drawn first from realm. Constraints are the rules that determine how a variable will select the scope. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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How artificial intelligence will change the future - Javatpoint

How artificial intelligence will change the future Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Future Rise of AI Which industries will AI impact? Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler In almost each sector, artificial intelligence is influencing how people will behave in the future. This already acts as the primary force behind developing technologies like big data, robotics, as well as the Internet of Things, and it's going to continuing to do so throughout the near future. The styrofoam container device uses machine learning and computer vision to detect and categorise different "safety occurrences." It cannot see everything, but somehow it sees a lot. Such as which direction his drivers is gazing while he drives, how quickly he's going, where he'll be going, where the people are around him and how other forklift drivers are controlling their trucks. IFM's technology instantly alerts warehousing supervisors to safety infractions, such as mobile telephone usage, so they may take appropriate action. The major objectives are to reduce fatalities and boost productivity. Gyongyosi asserts that perhaps the mere awareness that another one of IFM's surveillance systems is in place has had "a big influence." When considering a camera, he said, "It really is the richest sensor we have currently in an extremely attractive price bracket." The fundamental building block of machine learning, AI is significant. In a fraction of a second it would take people, computers using artificial intelligence (AI) are capable of processing enormous volumes of data and utilise their acquired knowledge effectively achieve the best outcomes as well as conclusions. IFM is merely one of several AI pioneers in a sector that is constantly expanding. For instance, 2,300 of the 9,130 patents granted to IBM inventors in 2021 were there with artificial intelligence. Elon Musk, the founder of Tesla and a giant of the IT industry, contributed $10 million to support research being done at OpenAI, a non-profit research organisation. If his $1 billion co-pledge from 2015 is any indicator, this donation is indeed a a blip in the ocean. After such an evolutionary phase that started with "knowledge representation" and lasted over many generations characterized by periodic inactivity, technology advanced to model- and algorithm-based machine learning and increasingly centred on observation, thinking, including generalisation. Now, AI has reclaimed centre stage in a way that has never before been possible, and there are no plans to give it up anytime soon. Specifically, "narrow AI," which executes optimization techniques utilizing information algorithms and frequently falls into the categories of deep learning or machine learning, have already had an impact on practically every significant business. The proliferation of connected devices, strong IoT connections, and the ever computational capabilities have all contributed to a significant increase in collecting data as well as analytics during in the last few years. While some industries are just beginning their AI journey, others are seasoned travellers. They both still have a ways to go. Whatever the case, it's difficult to overlook the impact AI is having on our daily lives. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem Solving Techniques in AI - Javatpoint

Problem Solving Techniques in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Cases involving Artificial Intelligence Issues A Reflex Agent: But What's It? Approaches for Resolving Problems Heuristics Searching Algorithms Computing Evolutionary Genetic Algorithms Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler The process of problem-solving is frequently used to achieve objectives or resolve particular situations. In computer science, the term "problem-solving" refers to artificial intelligence methods, which may include formulating ensuring appropriate, using algorithms, and conducting root-cause analyses that identify reasonable solutions. Artificial intelligence (AI) problem-solving often involves investigating potential solutions to problems through reasoning techniques, making use of polynomial and differential equations, and carrying them out and use modelling frameworks. A same issue has a number of solutions, that are all accomplished using an unique algorithm. Additionally, certain issues have original remedies. Everything depends on how the particular situation is framed. Artificial intelligence is being used by programmers all around the world to automate systems for effective both resource and time management. Games and puzzles can pose some of the most frequent issues in daily life. The use of ai algorithms may effectively tackle this. Various problem-solving methods are implemented to create solutions for a variety complex puzzles, includes mathematics challenges such crypto-arithmetic and magic squares, logical puzzles including Boolean formulae as well as N-Queens, and quite well games like Sudoku and Chess. Therefore, these below represent some of the most common issues that artificial intelligence has remedied: Depending on their ability for recognising intelligence, these five main artificial intelligence agents were deployed today. The below would these be agencies: This mapping of states and actions is made easier through these agencies. These agents frequently make mistakes when moving onto the subsequent phase of a complicated issue; hence, problem-solving standardized criteria such cases. Those agents employ artificial intelligence can tackle issues utilising methods like B-tree and heuristic algorithms. The effective approaches of artificial intelligence make it useful for resolving complicated issues. All fundamental problem-solving methods used throughout AI were listed below. In accordance with the criteria set, students may learn information regarding different problem-solving methods. The heuristic approach focuses solely upon experimentation as well as test procedures to comprehend a problem and create a solution. These heuristics don't always offer better ideal answer to something like a particular issue, though. Such, however, unquestionably provide effective means of achieving short-term objectives. Consequently, if conventional techniques are unable to solve the issue effectively, developers turn to them. Heuristics are employed in conjunction with optimization algorithms to increase the efficiency because they merely offer moment alternatives while compromising precision. Several of the fundamental ways that AI solves every challenge is through searching. These searching algorithms are used by rational agents or problem-solving agents for select the most appropriate answers. Intelligent entities use molecular representations and seem to be frequently main objective when finding solutions. Depending upon that calibre of the solutions they produce, most searching algorithms also have attributes of completeness, optimality, time complexity, and high computational. This approach to issue makes use of the well-established evolutionary idea. The idea of "survival of the fittest underlies the evolutionary theory. According to this, when a creature successfully reproduces in a tough or changing environment, these coping mechanisms are eventually passed down to the later generations, leading to something like a variety of new young species. By combining several traits that go along with that severe environment, these mutated animals aren't just clones of something like the old ones. The much more notable example as to how development is changed and expanded is humanity, which have done so as a consequence of the accumulation of advantageous mutations over countless generations. Genetic algorithms have been proposed upon that evolutionary theory. These programs employ a technique called direct random search. In order to combine the two healthiest possibilities and produce a desirable offspring, the developers calculate the fit factor. Overall health of each individual is determined by first gathering demographic information and afterwards assessing each individual. According on how well each member matches that intended need, a calculation is made. Next, its creators employ a variety of methodologies to retain their finest participants. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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AI in Manufacturing Industry - Javatpoint

AI in Manufacturing Industry Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Applications of AI in Manufacturing Industry Limitations of Applications of AI in Manufacturing Industry Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence (AI) is transforming the manufacturing industry, enabling companies to optimize production, improve quality, and reduce costs. One way AI is being used in manufacturing is through the use of robots. These robots can be programmed to perform tasks with a high degree of accuracy and consistency, and they can work around the clock without needing breaks. This can help manufacturers increase productivity and reduce the risk of errors. Another way AI is being used in manufacturing is through the analysis of data. By collecting data from sensors on production equipment, AI algorithms can identify patterns and predict when equipment is likely to fail. This can help manufacturers improve the reliability of their equipment and reduce downtime. AI is also being used to optimize production processes. For example, AI algorithms can analyze data from different stages of production and identify bottlenecks or inefficiencies. This can help manufacturers improve the flow of materials and reduce waste. AI is also being used to improve the quality of products. By analyzing data from past production runs, AI algorithms can identify patterns that are indicative of defects. This can help manufacturers proactively identify problems and prevent them from occurring in the future. In addition to these benefits, AI is also helping manufacturers reduce costs. By automating tasks, manufacturers can reduce labor costs and improve efficiency. In addition, AI can help manufacturers reduce energy costs by optimizing equipment usage and identifying opportunities for energy conservation. Overall, AI is having a significant impact on the manufacturing industry. By enabling companies to optimize production, improve quality, and reduce costs, AI is helping manufacturers stay competitive in a rapidly changing global marketplace. There are several applications of artificial intelligence (AI) in the manufacturing industry, including: There are several limitations to the applications of artificial intelligence (AI) in the manufacturing industry: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Automotive Industry - Javatpoint

Artificial Intelligence in Automotive Industry Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What effects is AI having on the automobile sector? How AI can increase earnings for automakers Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence (AI) has the potential to revolutionize the automotive industry in a number of ways. From improving the efficiency and performance of vehicles, to transforming the way we design and manufacture cars, AI is poised to play a major role in the future of the automotive industry. One area where AI has already made significant progress is in the development of autonomous vehicles. These vehicles use sensors and AI algorithms to navigate and drive themselves, without the need for human intervention. This technology has the potential to greatly improve road safety, by reducing the number of accidents caused by human error. It could also have a major impact on transportation, by making it possible for people to travel without the need for a driver. AI is also being used to improve the performance of conventional vehicles. For example, AI algorithms can be used to optimize the fuel efficiency of a car, by analyzing data from the engine and other systems and making adjustments to improve efficiency. AI can also be used to improve the handling and stability of a car, by analyzing data from sensors and making adjustments to the suspension and other systems. In the manufacturing process, AI has the potential to improve efficiency and reduce costs. For example, AI algorithms can be used to analyze data from the production line and identify bottlenecks or inefficiencies. AI can also be used to optimize the design of a car, by analyzing data and identifying ways to reduce weight or improve aerodynamics. AI is also being used to improve customer service in the automotive industry. For example, AI algorithms can be used to analyze data from customer interactions and identify patterns and trends that can help improve the customer experience. AI can also be used to personalize the customer experience, by analyzing data and making recommendations based on the individual customer's preferences and needs. Overall, it is clear that AI has the potential to transform the automotive industry in a number of ways. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. Artificial intelligence (AI) is having a significant impact on the automotive industry in a number of ways. Some of the most notable ways in which AI is impacting the industry include: Overall, it is clear that AI has the potential to transform the automotive industry in a number of ways. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. There are several ways in which artificial intelligence (AI) can help boost the profits of a car company. Some of the most notable ways in which AI can contribute to the bottom line include: Overall, it is clear that AI has the potential to play a major role in boosting the profits of a car company. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Civil Engineering - Javatpoint

Artificial Intelligence in Civil Engineering Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI Specializations in Civil Engineering Uses of AI techniques in Civil Engineering Major Civil Engineering Branches Civil Engineering Applications of AI which have already changed their field Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. AI drives Smarter Construction Methods 2. Getting Rid of Cost/Schedule Overruns 3. Risk Identification and Mitigation 4. Through Intelligent Development to Hasten Project Implementation 5. AI to improve operation and maintenance efficiency 6. AI Implementation in Worked To establish and develop Contact info Follow us Tutorials Interview Questions Online Compiler The branch of computer science called artificial intelligence deals with the study, creation, and use of machine intelligence. Artificial intelligence-based technologies may frequently offer useful options in effectively addressing challenges in civil engineering, as traditional approaches for modelling as well as optimising building and engineering networks need immense quantities of computational power. Artificial intelligence (AI), also referred to as enhanced intelligence (AI), seems to be a transformative method that uses machines to carry out tasks intelligently, effectively, and efficiently. This is regarded as being one of the methods which combines human strengths in what seems like a way that enables the project to be completed neither robots neither people by itself can perform. By taking into account AI ideas, whatever knowledge may be made standardized and easily accessible towards consumers, enabling them to make the best decision possible while taking into account both facts at hand as well as verifiable evidence. Deep learning technology have indeed been effectively used in numerous industries, including construction management, since many generations. In reality, increasing rise of complicated systems like skyscrapers distant past thrust techniques for machine learning into the spotlight inside the sector. More than anyone, you are witnessing its advancement and implementation of AI in the construction sector, such as the application of smart algorithms, big data, and deep learning machines that have revolutionised efficiency in terms of production. AI has been implemented by working contractors, network operators, including civil engineers help address a variety of issues. As an illustration, artificial intelligence in civil engineering has advanced, including its efficiency actually impacting the building project. AI is additionally employed at the beginning from many undertakings to optimise designs, manage risks, as well as boost output. It is crucial to understand that businesses engaged in development that have currently begun applying AI practises are 50% more profitable. More significantly, there are many uses for machine learning in civil engineering as a whole. Engineers can make better decisions and accomplish their tasks greater successfully in a time when robots could think in addition to doing. Following are a few examples of how AI has transformed your civil engineering field just case you're not still persuaded. Machine learning, deep learning, fuzzy logics, pattern recognition, decision trees, swarm optimization, and evolutionary computations are some of the different branches of artificial intelligence that can be utilized in building area of civil engineering. Several of these fields of artificial intelligence have applications in different branches of civil engineering. However, among the technologies mentioned, Pattern Recognition, Deep Learning, Fuzzy Logic, and Neural Networks are particularly important for resolving difficult civil engineering challenges. The field of AI known is PR, or pattern recognition, divides objects into numerous groups, classes, or categories. Photographs, signals, talks, as well as other application areas and classifications constitute the basis with this categorization. Probabilistic Decision Analysis and PR are complementary because the latter's findings are used to provide a clear division throughout various patterns in response. Deep learning is a subcategory of machine learning that consists primarily of network that use unorganized and unlabeled information. These principles have already incorporated into the DNN (Deep Neural Networks) guiding principles. Convolutional neural networks (CNN) as well as recurrent neural networks (RNN) make up various DNN architectures. The structural engineering and construction industries make extensive use of CNN's architectural design. Augmented intelligence (AI) has become increasingly essential across the various comment thread of civil engineering as a result of technological and scientific advances together with the concepts of industry 5.0 and construction 5.0. We everyone can concur that perhaps the limitations of design and engineering have been exceeded because architectural features among all different types litter the skyscrapers of important cities all over the world. All of this is possible because of the industry in terms biggest game-changer, artificial intelligence in 3D building information modelling (BIM). Before starting project, BIM tools assist civil engineers in facilitating the creation and design of more precise 3D representations. Engineers may nowadays use information collected through simulations, modeling, as well as previous initiatives to better development thanks towards the integration in AI-based design discovery. Construction professionals may develop construction architectural drawings, schematics, and other documents by incorporating machine learning into the Bim execution plan. Those who are indeed able to adjust every aspect with the highest level of precision possible. Massive construction projects frequently go over budget and are prone to errors because they have been planned under pressure and with very little knowledge of something like the program's full extent. Applying AI in building enables an engineer to gain a visual overview of estimated costs as well as outcomes from prior projects to come up with better planning as well as more precise budgeting, even while cost overruns cannot be avoided. Civil engineers can forecast budget shortfalls and imagine reasonable timetables for work progress thanks using algorithms who use traits of finished projects. Additionally, AI enables engineers can incorporate regular training materials to boost teamwork overall abilities as well as allows distant accessibility. Doxel, an AI business, is an outstanding demonstration. At employment agencies, they employs deep learning algorithms, LIDAR, and Camera equipped drones can identify things, examine the quality of building, as well as calculate the amount of resources utilised. Both actual expenditures as well as effort spent relative to the initial budget and timetable are then compared using the same data to provide real-time feedback to all participants. Continuous data collection aids in reducing cost and timeline escalations and enhancing overall employment main motivator. There are dangers associated with building which might cause injuries. For order to assist civil engineers with identifying potential problems throughout the building project, AI offers the option for even more precise collecting data using result in substantial simulations. Supporting its development and application of pertinent technology in the construction industry enables engineers to adopt practical risk management strategies since AI could understand a variety of data from a construction area to produce insightful results. Additionally, AI-enabled cameras and networks can continuously monitor all construction-related operations, enabling designers to evaluate how well their tools are being used, measure their progress, but instead decision making behavior in genuine, assisting inside the earlier identification of possible key risks. The success of Indus.ai is a nice demonstration of how such technology is being used. This San Francisco technology company installed AI-enabled cameras all over building sites to capture actual video whereas gathering as well as analysing information purpose of providing construction companies with insights on things like the movement of materials as well as the distribution of labour at different locations on the site. Additionally, this interactive higher probability civil engineers can foresee potential dangers but also take smarter choices concerning the safety of their workforce. For precise, less expensive, and less disruptive construction activities, civil engineers can apply AI models. Technology likewise incorporates infrastructure off-site run using intelligent machines which put together crucial parts of a construction project, which are subsequently put it together with entry level workers upon that worksite. In accordance with a McKinsey analysis from June, those web and disconnected constructors provide its building sector a significant productivity boost with a speedier response than being on development. Trained employees may concentrate on other more difficult activities like constructing electrical and HVAC equipment as well as sewage by using Intelligence machines to construct readymade constructions like wall and building panels more quickly than humans. Designers may receive advice via AI-powered database management systems on the most effective on-site manufacturing techniques depending on the already collected data including schematics as well as designs of construction experiences. AI might also be utilised in management positions, such as enabling staff to book vacation and sick weeks, monitoring basic materials shipments, and highlighting inefficiencies. Given the enormous amount of information recorded, AI may be used to change the construction project in question as necessary as well as identify unknown underfunded development locations that may require additional workers. The use of artificial intelligence in building may end up being practically endless as time goes on. Undoubtedly, the introduction of ai technology helps solve many issues experienced in design optimization, parameters estimation and identification, and damage detection in a profession that seems to be severely inadequately, with both the civil engineering having among the biggest consumer foundations as well as valued billions of dollars. annually. They are confident that the ongoing use of artificial intelligence in civil engineering will result in a major change in how things are done throughout the building industry. 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Artificial Intelligence in Gaming Industry - Javatpoint

Artificial Intelligence in Gaming Industry Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Applications of AI in Gaming Industry Limitations of Artificial Intelligence in Gaming Industry Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence (AI) has had a significant impact on the gaming industry in recent years, with many games now incorporating AI to enhance gameplay and make it more immersive for players. One common use of AI in gaming is in the control of non-player characters (NPCs). These characters can interact with players in a more realistic and dynamic way, adding to the immersion of the game. For example, NPC characters might have their own goals and motivations that they pursue, or they might react differently to different player actions. This can make the game feel more alive and believable, as players feel like they are interacting with real characters rather than just programmed entities. AI is also being used in game design to create more dynamic and interesting levels and content. This can help developers create more diverse and engaging games with less effort. For example, AI might be used to design game levels that are procedurally generated, meaning that they are created on the fly as the player progresses through the game. This can help keep the game fresh and interesting for players, as they are not simply playing through the same levels over and over again. AI can also be used to enhance gameplay itself by providing intelligent opponents for players to face off against. This can make games more challenging and rewarding for players, as they feel like they are really competing against a worthy opponent. In some cases, AI might even be used to adapt to a player's playstyle and provide a more personalized gameplay experience. In addition to these uses, AI can also be used to provide players with virtual assistants that can help them during gameplay. These assistants might use natural language processing (NLP) to understand and respond to player requests, or they might provide information or guidance to help players progress through the game. Overall, AI is helping to improve the quality and variety of games available, as well as making them more immersive and engaging for players. As AI technology continues to advance, it is likely that we will see even more innovative uses of AI in the gaming industry in the future. There are several ways in which artificial intelligence (AI) is being used in the gaming industry: Overall, AI is helping to improve the quality and variety of games available, as well as making them more immersive and engaging for players. There are a few limitations to the use of artificial intelligence (AI) in the gaming industry: Overall, while AI has the potential to greatly enhance the gaming industry, there are still limitations to its use that developers must consider. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in HR - Javatpoint

Artificial Intelligence in HR Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is the Role of AI in Human Resource Management What is the Impact of AI in Human Resource Management Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence (AI) is having a significant impact on the field of human resources (HR). From recruitment and hiring, to employee development and training, AI is transforming the way that HR professionals work and the services they provide. One area where AI is having a major impact is in the recruitment and hiring process. AI algorithms can be used to analyze job descriptions, resumes, and other data to identify the most promising candidates for a position. This can help to save time and reduce the risk of bias in the hiring process. AI can also be used to optimize the scheduling and conducting of interviews, by analyzing data and making recommendations based on the individual needs of the company and the candidates. AI is also being used to support employee development and training. AI algorithms can be used to analyze data on employee performance and skills, and to make recommendations for training and development programs that are tailored to the individual needs of each employee. This can help to ensure that employees are receiving the support they need to succeed in their roles, and can help to improve the overall performance of the organization. In the area of performance management, AI algorithms can be used to analyze data on employee performance and identify patterns and trends that can help improve the overall performance of the organization. AI can also be used to optimize the setting of goals and the tracking of progress, by analyzing data and making recommendations based on the individual needs of the company and the employees. Finally, AI is being used to support the management of compensation and benefits. AI algorithms can be used to analyze data on employee performance and skills, and to make recommendations for appropriate levels of compensation and benefits. AI can also be used to optimize the design of benefit plans, by analyzing data and identifying the most effective options for the organization. Overall, it is clear that AI has the potential to transform the field of HR in a number of ways. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. The role of artificial intelligence (AI) in human resource management (HRM) is to support and optimize various HR functions and processes. Some of the ways in which AI is currently being used in HRM include: Overall, the role of AI in HRM is to support and optimize various HR functions and processes, in order to improve the efficiency and effectiveness of the organization. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. Artificial intelligence (AI) is having a significant impact on the field of human resources (HR). Some of the ways in which AI is being used in HR include: Overall, it is clear that AI has the potential to transform the field of HR in a number of ways. As the technology continues to advance, it is likely that we will see even more exciting developments in the coming years. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Medicine - Javatpoint

Artificial Intelligence in Medicine Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI Application in Medicine Benefits of AI in Medicine Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial intelligence (AI) has the potential to revolutionize the field of medicine, providing new and innovative ways to diagnose, treat, and prevent diseases. From automating mundane tasks to improving diagnostic accuracy, AI has the potential to improve healthcare outcomes and increase efficiency within the healthcare system. One of the most promising applications of AI in medicine is in the area of diagnostic imaging. By using machine learning algorithms, AI can analyze medical images such as X-rays, CT scans, and MRIs to identify abnormalities and suggest a diagnosis. This can be particularly useful in detecting subtle signs of diseases that may be missed by human eyes, such as early stage cancer. AI can also assist radiologists in identifying and prioritizing cases that require immediate attention, allowing them to focus on more complex cases. Despite the potential benefits of AI in medicine, there are also ethical concerns that must be considered. One issue is the potential for biased algorithms, which may perpetuate existing inequalities in healthcare. For example, if an AI system is trained on a dataset that is predominantly made up of white patients, it may not accurately diagnose or treat patients from other racial or ethnic groups. Ensuring that AI systems are trained on diverse datasets and regularly tested for bias is crucial in order to avoid perpetuating existing inequalities in healthcare. AI can also be used to analyze electronic medical records (EMR) to identify patterns and trends that may indicate a particular medical condition. For example, machine learning algorithms can analyze a patient's EMR to identify early warning signs of diseases such as diabetes or heart disease. This can allow doctors to intervene earlier, potentially improving patient outcomes. AI can also be used to predict patient outcomes and identify those who are at risk for certain conditions, allowing for earlier and more targeted prevention efforts. Another area where AI has the potential to make a significant impact is in drug development. By analyzing large amounts of data, AI can identify patterns and trends that may not be apparent to human researchers. This can help speed up the drug development process and increase the chances of success. AI can also be used to identify new uses for existing drugs, potentially expanding their effectiveness and reducing the need for new drug development. In addition to these applications, AI can also be used to automate mundane and time-consuming tasks, freeing up healthcare professionals to focus on more complex and important tasks. For example, AI can be used to transcribe medical records, freeing up doctors and nurses to spend more time with patients. AI can also be used to assist in scheduling appointments, ordering tests, and managing patient records, increasing efficiency within the healthcare system. There are also several potential ethical considerations when it comes to the use of AI in medicine. One concern is the potential for AI to replace human healthcare professionals, leading to job loss and financial insecurity. It is important for healthcare organizations to consider the potential impact on their workforce when implementing AI systems and to ensure that proper training and support are provided to those who may be affected. Another ethical consideration is the potential for bias in AI systems. If the data used to train an AI system is biased, the system may produce biased results. This can have serious consequences in the healthcare setting, where decisions based on biased data could lead to unequal treatment and poorer outcomes for certain patient groups. It is important for AI developers to consider the potential for bias and to take steps to mitigate it. Despite these concerns, the potential benefits of AI in medicine are vast and could lead to significant improvements in healthcare outcomes and efficiency. As with any new technology, it is important for healthcare organizations to carefully consider the potential risks and benefits before implementing AI systems and to ensure that they are used ethically and responsibly. Overall, AI has the potential to greatly improve the field of medicine by automating tasks, improving diagnostic accuracy, and enabling personalized treatment plans. However, it is important to consider the ethical implications of using AI in healthcare and to ensure that any potential negative impacts are minimized. There are a number of ways that artificial intelligence (AI) can be applied in the field of medicine, including: Artificial intelligence (AI) has the potential to revolutionize the field of medicine, providing new and innovative ways to diagnose, treat, and prevent diseases. Some of the potential benefits of AI in medicine include: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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PhD in Artificial Intelligence - Javatpoint

PhD in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials How Does an Artificial Intelligence Doctorate Entail? Acceptance Criteria for just a PhD in Artificial Intelligence Programme Eligibility for a PhD in Artificial Intelligence Admission Statistics for PhD Programs on Artificial Intelligence: What Difficult Would It is to get in? Top Artificial Intelligence Doctorates: A Brief Description A list of the top institutions for AI PhDs: Resources for Artificial Intelligence Doctorates 1. Arizona State University 2. Capitol Technology University 3. Cornell University Is a PhD in artificial intelligence available on the internet? AI PhD programmes available at the top universities What Is the Time Frame for an Artificial Intelligence Doctorate? Is just an artificial intelligence Doctorate challenging? What Is the Price of an Artificial Intelligence Doctorate? Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Computer science doctorate Artificial intelligence PhD Computer science doctorate Contact info Follow us Tutorials Interview Questions Online Compiler The increasing use of cutting-edge technologies in our everyday lives has expanded the market for experts in the field of artificial intelligence. Tech professionals have a chance to help satisfy this increasing demands and secure the highest-paying artificial intelligence employment by pursuing the greatest PhDs inside the field. The best artificial intelligence PhD programme for you can be found by carefully examining certain key aspects for a PhD in artificial intelligence, such as government spent, duration, and geography. The wage for a PhD in artificial intelligence is also covered inside this article, together with some of the greatest AI careers. A doctoral programme with an emphasis on research in artificial intelligence is called a Doctorate in AI. Original study in several branches of applied artificial intelligence is a requirement for trainees. Machine learning, artificial neural networks, language understanding, and detection might indeed fall under this category. PhD students are given a designated academic advisor who will assist them with your research. A Bachelor's doctorate or a closely related subject, such as computer engineering, data science, or mathematics, is the minimal qualification for admission to an artificial intelligence PhD programme. Students will also need proficiency in numerous computer languages, a solid foundation in coding, including system analysis. Requirements for courses in disciplines like copywriting, machine - learning, neuro capabilities and organizational, & English may indeed exist. Transcribed from your undergraduate or graduate studies along with the results of standardised examinations like the GRE could also be required. Additionally, students might well be asked to provide a statement of purpose that outlines your intended scientific fields of your Doctoral dissertation and offers a concept or an early concept. a master's or a bachelor's degree Admission to a PhD programme with machine learning might be challenging. Acceptance is not always solely contingent upon ability because certain programmes are quite competitive. Nevertheless, assuming your complete all of the admissions standards, admittance to the several Doctoral programs is rather straightforward. Arizona State University, Syracuse University, and Drexel University are really the top institutions in earning a PhD in artificial intelligence. They have some of the top AI research facilities, an increasing adoption ratio, as well as the appropriate social connections. These top universities for earning a PhD in artificial intelligence are listed more depth here. On March 12, 1885, Arizona State University was established. It's a public university that provides many graduate schools, such as those in business administration, economics, and computer science. The graduate schools of Arizona State University are famous for both their structural performance and esteemed faculty. Arizona State University's PhD programme calls for 84 credits, a prospectus, a research, an orally final test, as well as a writing complete examination. Students can do work in an artificial intelligence lab just at institution. Machine learning, big data, data techniques, cloud computing, social computing, data mining, and machine learning are some of the academic subjects. PhD in Computer Science Overview PhD in Computer Science Admission Requirements On June 1, 1927, Capitol Technology University was established as a private research organisation. It is most well-known for its demonstrable academic brilliance and knowledgeable supervision of Ph.d. dissertation. Advanced degrees are available from Capitol Technology University in subjects like computer science, cyber security, business analytics, data science, and aviation scientific knowledge. With Capitol Technology University, a PhD in artificial intelligence requires approximately 60 hours of training. This programme places a strong emphasis on the fundamentals of autonomous systems and goes into great detail about just how technology works in order to mimic people's behavior when taking decisions and resolving issues. The course is accessible including both online and in person. PhD in Artificial Intelligence Overview PhD in Artificial Intelligence Admission Requirements Ezra Cornell and Andrew Dickson White established the downtown headquarters of Cornell University in Ithaca in 1865. Seven undergraduate campuses and seven graduate divisions make up the organisation. According to US News & World Report, its computer science PhD programme has been among the best of the best nationwide, and it conducts rigorous scientific studies. This curriculum is aimed for students with a particular interest in the general components of computing operations. Artificial intelligence, machine learning, data structures, robotics, natural language processing, quantitative analysis, programming languages and methods, robotic systems, including theory of computation are a few of the academic domains in which learners have the option to specialise. PhD in Computer Science Overview PhD in Computer Science Admission Requirements Yes, anyone may obtain a internet PhD in artificial intelligence. Online doctoral degrees in machine learning are offered by certain American universities, like Capitol Technology University. Depending on the university, the online artificial intelligence programme takes approximately the same length of time to finish compared to the on-campus programme. An artificial intelligence PhD can be earned within 3 to 5 years. If a person has unusually extensive or complicated investigation, or if they are pursuing their PhD component, the timescale might well be extended. The institution's standards, including quantity of study necessary before such a dissertation can indeed be presented, a student's cooperation with their academic supervisor, or the project's structure, that really is, whether it's either full-time or part-time, might all be contributing factors towards the prolonged timeframe. No, getting a PhD in AI is not that difficult. Although obtaining a PhD in a machine topic can sometimes be challenging, a PhD in artificial intelligence is comparatively simpler than other academic fields of computer science. PhD programmes put a strong emphasis on study. Its concentration on empirical evaluation over effectiveness means getting a PhD in artificial intelligence easier. In other words, you must demonstrate how the approach makes intuitive sense instead of demonstrating that it actually works. One could even demonstrate the efficacy of a known technique inside a novel context for your AI PhD. The National Center for Education Statistics estimates that a PhD in artificial intelligence costs roughly $19,792 annually. Depending on the type of university engaged, the percentage fluctuates. The average annual price at public bodies is roughly $12,410, while private universities charge about $26,597. Only in-state students can qualify for all these prices. It is significant to keep in mind that other costs and expenses, including such departmental charges and processing fees, may also be necessary. The majority of Doctoral programs do, nevertheless, offer sufficient funding, although it frequently falls short of covering the total cost of the degree. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Activation Functions in Neural Networks - Javatpoint

Activation Functions in Neural Networks Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Neural Network Components Activation Function Need of Non-linear Activation Functions Activation Function Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Input Layer Hidden Layer Output Layer Definition Linear Activation Function Non-linear Activation Function Contact info Follow us Tutorials Interview Questions Online Compiler A paradigm for information processing that draws inspiration from the brain is called an artificial neural network (ANN). ANNs learn via imitation just like people do. Through a learning process, an ANN is tailored for a particular purpose, including such pattern classification or data classification. The synapses interconnections that exist between both the neurons change because of learning. What input layer to employ with in hidden layer and at the input level of the network is one of the decisions you get to make while creating a neural network. This article discusses a few of the alternatives. The nerve impulse in neurology serves as a model for activation functions within computer science. A chain reaction permits a neuron to "fire" and send a signal to nearby neurons if the induced voltage between its interior and exterior exceeds a threshold value known as the action potential. The next series of activations, known as a "spike train," enables motor neurons to transfer commands from of the brain to the limbs and sensory neurons too transmit sensation from the digits to the brain. Layers are the vertically stacked parts that make up a neural network. The image's dotted lines each signify a layer. A NN has three different types of layers. The input layer is first. The data will be accepted by this layer and forwarded to the remainder of the network. This layer allows feature input. It feeds the network with data from the outside world; no calculation is done here; instead, nodes simply transmit the information (features) to the hidden units. Since they are a component of the abstraction that any neural network provides, the nodes in this layer are not visible to the outside world. Any features entered through to the input layer are processed by the hidden layer in any way, with the results being sent to the output layer. The concealed layer is the name given to the second kind of layer. For a neural network, either there are one or many hidden layers. The number inside the example above is 1. In reality, hidden layers are what give neural networks their exceptional performance and intricacy. They carry out several tasks concurrently, including data transformation and automatic feature generation. This layer raises the knowledge that the network has acquired to the outside world. The output layer is the final kind of layer The output layer contains the answer to the issue. We receive output from the output layer after passing raw photos to the input layer. Data science makes extensive use of the rectified unit (ReLU) functional or the category of sigmoid processes, which also includes the logistic regression model, logistic hyperbolic tangent, and arctangent function. In artificial neural networks, an activation function is one that outputs a smaller value for tiny inputs and a higher value if its inputs are greater than a threshold. An activation function "fires" if the inputs are big enough; otherwise, nothing happens. An activation function, then, is a gate that verifies how an incoming value is higher than a threshold value. Because they introduce non-linearities in neural networks and enable the neural networks can learn powerful operations, activation functions are helpful. A feedforward neural network might be refactored into a straightforward linear function or matrix transformation on to its input if indeed the activation functions were taken out. By generating a weighted total and then including bias with it, the activation function determines whether a neuron should be turned on. The activation function seeks to boost a neuron's output's nonlinearity. Explanation: As we are aware, neurons in neural networks operate in accordance with weight, bias, and their corresponding activation functions. Based on the mistake, the values of the neurons inside a neural network would be modified. This process is known as back-propagation. Back-propagation is made possible by activation functions since they provide the gradients and error required to change the biases and weights. An interconnected regression model without an activation function is all that a neural network is. Input is transformed nonlinearly by the activation function, allowing the system to learn and perform more challenging tasks. It is merely a thing procedure that is used to obtain a node's output. It also goes by the name Transfer Function. The mixture of two linear functions yields a linear function, so no matter how several hidden layers we add to a neural network, they all will behave in the same way. The neuron cannot learn if all it has is a linear model. It will be able to learn based on the difference with respect to error with a non-linear activation function. The mixture of two linear functions yields a linear function in itself, so no matter how several hidden layers we add to a neural network, they all will behave in the same way. The neuron cannot learn if all it has is a linear model. The two main categories of activation functions are: As can be observed, the functional is linear or linear. Therefore, no region will be employed to restrict the functions' output. The normal data input to neural networks is unaffected by the complexity or other factors. The normal data input to neural networks is unaffected by the complexity or other factors. Equation: A linear function's equation, which is y = x, is similar to the eqn of a single direction. The ultimate activation function of the last layer is nothing more than a linear function of input from the first layer, regardless of how many levels we have if they are all linear in nature. -inf to +inf is the range. Uses: The output layer is the only location where the activation function's function is applied. If we separate a linear function to add non-linearity, the outcome will no longer depend on the input "x," the function will become fixed, and our algorithm won't exhibit any novel behaviour. A good example of a regression problem is determining the cost of a house. We can use linear activation at the output layer since the price of a house may have any huge or little value. The neural network's hidden layers must perform some sort of non-linear function even in this circumstance. It is a functional that is graphed in a "S" shape. A is equal to 1/(1 + e-x). Non-linear in nature. Observe that while Y values are fairly steep, X values range from -2 to 2. To put it another way, small changes in x also would cause significant shifts in the value of Y. spans from 0 to 1. Uses: Sigmoid function is typically employed in the output nodes of a classi?cation, where the result may only be either 0 or 1. Since the value for the sigmoid function only ranges from 0 to 1, the result can be easily anticipated to be 1 if the value is more than 0.5 and 0 if it is not. The activation that consistently outperforms sigmoid function is known as tangent hyperbolic function. It's actually a sigmoid function that has been mathematically adjusted. Both are comparable to and derivable from one another. Range of values: -1 to +1. non-linear nature Uses: - Since its values typically range from -1 to 1, the mean again for hidden layer of a neural network will be 0 or very near to it. This helps to centre the data by getting the mean close to 0. This greatly facilitates learning for the following layer. Equation: max A(x) (0, x). If x is positive, it outputs x; if not, it outputs 0. Value Interval: [0, inf] Nature: non-linear, which allows us to simply backpropagate the mistakes and have the ReLU function activate many layers of neurons. Uses: Because ReLu includes simpler mathematical processes than tanh and sigmoid, it requires less computer time to run. The system is sparse and efficient for computation since only a limited number of neurons are activated at any given time. Simply said, RELU picks up information considerably more quickly than sigmoid and Tanh functions. Currently, the ReLU is the activation function that is employed the most globally. Since practically all convolutional neural networks and deep learning systems employ it. The derivative and the function are both monotonic. However, the problem is that all negative values instantly become zero, which reduces the model's capacity to effectively fit or learn from the data. This means that any negative input to a ReLU activation function immediately becomes zero in the graph, which has an impact on the final graph by improperly mapping the negative values. Although it is a subclass of the sigmoid function, the softmax function comes in handy when dealing with multiclass classification issues. Used frequently when managing several classes. In the output nodes of image classification issues, the softmax was typically present. The softmax function would split by the sum of the outputs and squeeze all outputs for each category between 0 and 1. The output unit of the classifier, where we are actually attempting to obtain the probabilities to determine the class of each input, is where the softmax function is best applied. The usual rule of thumb is to utilise RELU, which is a usual perceptron in hidden layers and is employed in the majority of cases these days, if we really are unsure of what encoder to apply. A very logical choice for the output layer is the sigmoid function if your input is for binary classification. If our output involves multiple classes, Softmax can be quite helpful in predicting the odds for each class. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Boston Housing Kaggle Challenge with Linear Regression - Javatpoint

Boston Housing Kaggle Challenge with Linear Regression Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Boston Housing Data Contact info Follow us Tutorials Interview Questions Online Compiler The database is kept by Carnegie Mellon University and was obtained from the StatLib library. The housing costs in Boston are the subject of this dataset. There are 506 instances and 13 features in the supplied dataset. The following table shows the summary of the dataset, which was derived from the citation below. Our goal is to develop a model with this data utilizing linear regression to forecast the price of homes. The following columns are present in the data: P.S. I am still learning how and where to interpret the graphs; this is my first analysis. Code: Output: Input: Output: Input: Output: Data conversion to nd-array to info frame and feature names addition Input: Output: Input: Output: Input: Output: Obtaining input and output data, then dividing the data into training and testing datasets. Output: utilising the dataset and a linear regression model to anticipate prices. Plotting a scatter graph to display the 'y true' value vs 'y pred' value will show the prediction results. Output: Mean Squared Error & Mean Absolute Error are the results of linear regression. Output: As a result, the accuracy of our model is just 66.55%. The prepared model is therefore not particularly effective in forecasting home prices. Using a wide range of additional machine learning methods and approaches, one can enhance the prediction outcomes. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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What are OpenAI and ChatGPT - Javatpoint

What are OpenAI and ChatGPT Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews OpenAI ChatGPT Using ChatGPT: Contact info Follow us Tutorials Interview Questions Online Compiler OpenAI and ChatGPT are the two buzz words trending in the tech world these days. This tutorial explains what these words represent? OpenAI is an AI research lab found on December 11, 2015 by Sam Altman, llya Sutskever, Greg Brockman, Wojciech Zaremba, Elon Musk and John Schulman. It consists of OpenAI LP, the for-profit organization and its parent company OpenAI Inc. which is a non-profit organization. The motive of the company was to develop a human friendly AI that can benefit the humanity. In just four years, the lab was a success; it became one of the leading AI research labs in the world. AI is taking over all the industries already like Electricity did 100 years ago. A lot of scientists like Stephen Hawking and Elon Musk predicted that in the future AI could dominate the world and take over the humans. OpenAI was found to be the first to create AGI (Artificial General Intelligence). AGI is like the friendly version of AI. A robot or a machine developed with AGI will have learning and reasoning powers of a human mind. The organization was found in San Francisco and the research done and the patents acquired are open to the public. It is also open to collaborate with other institutions and researchers. The ultimate goal of the organization is to eliminate the AI threats and develop it in a safe way that is solely beneficial to the humanity as a whole. The organization started as a group of nine leading AI researchers who are the best in their fields. Elon Musk's idea was to make every single person in the world aware of AI. He acknowledged that if everyone has some AI, then there would be no set of individuals with AI super power because everyone already has it. But, the idea was controversial. In Nick Bostrom's words, "If you have a button that could do bad things to the world, you don't want to give it to everyone". Amid the controversies, in 2016, the organization stated that not all the source code is going to be released and the goal of OpenAI was set to doing the best thing there is to do. ChatGPT stands for "Chat Generative Pre-Trained Transformer". It is a chatbot developed by the team of OpenAI in November 2022. After the release of ChatGPT, the value of OpenAI went to 29 billion dollars. It was built on top of the GPT-3 family. The goal was to build a language model that can respond like a human dialogue to dialogue. It is a language model developed to interact with humans conversationally dialogue to dialogue. From a content writer to a coder, ChatGPT helps us all. It can answer our questions from simple guidance to bugs in programs. It can even give content about a specific topic. ChatGPT has been a very helpful tool to people from different industries. It made learning a lot easier with all the answers right at the doorstep but there are a lot of complaints like students using ChatGPT to complete the assignments without putting in any self effort. There are also complaints saying it gives erroneous codes when asked. It is banned in different countries like the Chinese banned the website in fear of it providing uncensored answers to politically sensitive questions. New York City, the department of Education has also banned the application on January 4, 2023. If you are a student struggling with a code, a content creator struggling with creating some sentences, ChatGPT can give the best references rather than browsing the whole internet for searching across platforms. Here is an example: Suppose, I am coding and am struck at some point and the output isn't what I need, I can send the part of the code which I am struck in and provide some context about what I am expecting and what I've done to get the suggestion from ChatGPT. Using the website is very easy. But, the sessions expire fast and you'll need to refresh it from time to time to keep interacting continuously. For starters, to use the application, you'll need an OpenAI account. You can either use your Google account or Microsoft account to become a user. The left navigation bar is the list of all the Normal user drawbacks: The amount of people using ChatGPT across the world at a given time is vast. Hence, at times, it might not be able to respond due to the tightness of the traffic. Also, if you have used the platform for an hour or so, sometimes, it might give a notification, "Too many requests in an hour", you'll need to wait for some time before accessing again. 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Chatbot vs. Conversational AI - Javatpoint

Chatbot vs. Conversational AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction Describe Chatbot. Conversational AI: What is it? Conversational AI vs. Chatbot Which one is More Appropriate for Business? Conversational AI's Advantage over Traditional Chatbot How do you begin with Conversational AI? Advantages of Conversational AI over Chatbot: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler A chatbot is a computer program created to mimic communication with real visitors, particularly online. On the other hand, conversational AI is a more sophisticated chatbot that uses machine learning and natural language processing to enable more intelligent, human-like dialogues. A chatbot is a computer program designed to mimic conversations with actual users, especially online. Chatbots are frequently utilized in customer service, commerce, and other industries where they can organically and intuitively communicate with people using text, voice, or even video. Artificial intelligence (AI) technology known as "conversational AI" enables computers to interact with people organically and expressively, sometimes through chatbots or virtual co-workers. These technologies comprehend and interpret user input to quickly design appropriate solutions using advanced programming and machine learning techniques. Companies can automate customer care and help tasks, boost marketing campaigns, and improve the customer experience with conversational AI. An online chatbot is a computer programme that simulates chats with actual visitors. In order to respond to inquiries and help customers troubleshoot problems, chatbots are frequently utilised in customer support. Additionally, they can be employed in various contexts, such as entertainment, where they can be programmed to deliver jokes or disseminate knowledge about a specific subject. The ability of chatbots to provide users with instant assistance is one of their key features. In addition, a chatbot can manage numerous interactions at once and is accessible 24/7, unlike a human customer support person. They are, therefore, a practical and affordable option for businesses. The ability of chatbots to comprehend and adapt over time is another advantage. They may hone their responses and grow more effective at helping consumers as they engage with more people. Rule-based and AI-powered chatbots are the two main categories. Rule-based chatbots respond to user inputs following established rules, whereas AI-powered chatbots utilize machine learning algorithms to get better at responding over time. AI-powered chatbots are typically more sophisticated and can offer users more specialized support. They are typically used in customer service to react to frequently asked questions, aid clients in resolving problems, and can be programmed for other objectives. Chatbots are an effective and affordable alternative for organizations because they are available 24/7 and can manage several interactions simultaneously. Additionally, they might develop their responses over time by gaining knowledge from user interactions. There is probably a chatbot idea that can help your business, regardless of whether you manage a tiny retail store or a major corporation. Artificial intelligence (AI) is used in conversational AI to provide computers the ability to have conversations with clients that are natural and human-like. It is an area of AI that focuses on creating machines that can understand, interpret, and communicate in a manner identical to that of humans. One of the primary uses of conversational AI is the rise of chatbots. These software programs are frequently created to mimic conversations with real users through the Internet. Chatbots, for instance, can be used in customer support to address common questions and aid clients in resolving problems. They can be programmed to serve other objectives, such as entertainment. Creating virtual assistants like Apple's Siri or Amazon's Alexa is another use for conversational AI. These assistants can carry out various duties, including setting alarms, placing calls, and providing information, and they can comprehend and react to verbal orders. Advances in natural language processing (NLP), a branch of artificial intelligence that thrives in connecting computers and people through everyday language, have made conversational AI conceivable. To analyze and understand human speech, NLP algorithms are used. These algorithms can be used to produce responses that are appropriate and contextually relevant. The goal of the subfield of conversational AI is to make it possible for computers to converse with users in a natural, human-like manner. It uses natural language processing algorithms to comprehend and respond to human language while creating chatbots and virtual assistants. Conversational AI and chatbot are frequently used interchangeably. However, they are not the same. Computer programs called chatbots were created to mimic conversations with human users. Using artificial intelligence (AI) to make computers capable of having natural and human-like conversations is known as conversational AI. One of their key distinctions is the degree of intelligence and autonomy between chatbots and conversational AI. Typically rule-based, chatbots respond to user input by following pre-established rules. They must therefore comprehend and interpret human language more thoroughly, which may require them to give cliched or formulaic responses. Contrarily, conversational AI uses machine learning algorithms to enhance its responses over time and give users more specialized support. As a result, it can better comprehend and interpret human language and produce suitable and pertinent responses in various contexts. The range of tasks that chatbots and conversational AI can accomplish is another distinction between the two. As a result, chatbots are frequently restricted to carrying out tasks inside a limited realm. Concurrently, conversational AI can handle various jobs and has a wider range of applications. However, conversational AI can offer more individualized assistance and manage a wider range of activities, whereas chatbots are often limited in their comprehension and interpretation of human language. Depending on the requirements and objectives of the organization, both chatbots and conversational AI can be beneficial for organizations. Chatbots are used in customer service to respond to questions and assist clients in troubleshooting issues. They are a reliable and affordable option for organizations. Because they are accessible 24/7 and can manage several interactions at once, additionally, they can be configured for activities like lead generation or sales. On the other hand, organizations that demand more sophisticated and customized support might benefit more from conversational AI. This is so that it can grasp and interpret human language more precisely while responding in a suitable and relevant way. Because it can handle a variety of activities and give users more individualized help, it is highly suited for applications like virtual assistants. The decision between conversational AI and chatbots will ultimately depend on the specific needs and goals of the company. Both can be useful tools for enhancing customer service and automating specific jobs, but conversational AI is typically seen as more sophisticated and capable of offering individualized support. improved comprehension of spoken language. Conversational AI can better grasp and interpret human language than typical chatbots. This enables it to give users more customized and contextually suitable responses. There are a few steps you may take to get started with conversational AI if you're interested: You can successfully create a conversational AI system that satisfies your demands and assists you in achieving your goals by adhering to these procedures. Conversational AIs and chatbots are useful technologies for facilitating user interaction and automating communication. However, conversational AIs can comprehend and react to complex and contextually relevant questions and constitute a more sophisticated technology. Although they can handle direct interactions, chatbots might require a different sophistication and intelligence than conversational AI. As a result, conversational AIs may be better suited for use cases that are more demanding and complex, such as virtual assistants or customer service. To introduce AI-powered automation to sophisticated customer-facing and internal employee engagements, conversational AI solutions, which are more advanced chatbot solutions, integrate natural language understanding (NLU), machine learning (ML), and other enterprise technologies. The interchangeability of the two terms has exacerbated confusion. Here is a comparison of some of the more typical features of a conversational AI application and a simple conversational bot to help you better grasp the differences between the two. Many businesses resort to a conversational AI platform to assist them in implementing conversational AI applications because they are difficult to create and manage. 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Iterative Deepening A* Algorithm (IDA*) - Javatpoint

Iterative Deepening A* Algorithm (IDA*) Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction to Heuristic Search Algorithms A* Search Algorithm Iterative Deepening A* Algorithm Steps for Iterative Deepening A* Algorithm (IDA*) Example Implementation Advantages Disadvantages Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Continually Deepening The depth-first search and A* search's greatest qualities are combined in the heuristic search algorithm known as the A* algorithm (IDA*). The shortest route between the start state and the objective state in a network or tree is found using an optimum search method. The IDA* algorithm uses less memory than the A* algorithm because it simply keeps track of the present node and its associated cost, rather than the full search space that has been examined. This article will examine the IDA* algorithm's operation, as well as its benefits and drawbacks, and practical applications. In order to identify the best solution to a problem, heuristic search algorithms explore the search area in a methodical manner. They are employed in a number of fields, including robotics, video games, and natural language processing. A heuristic search algorithm uses a heuristic function to evaluate the distance between the current state and the goal state in order to identify the shortest route from the start state to the goal state. There are various heuristic search algorithms, including A* search, Uniform Cost Search (UCS), Depth-First Search (DFS), and Breadth-First Search (BFS). A* search algorithm is a well-known heuristic search method that calculates the distance between the current state and the objective state using a heuristic function. The A* search method adds the actual cost from the start node to the current node and the predicted cost from the current node to the target node to determine the cost of each node. A heuristic function that estimates the distance between the current node and the desired node is used to determine the estimated cost. The algorithm then chooses the node with the lowest cost, grows it, and keeps doing this until it reaches the destination node. As long as the heuristic function is acceptable and consistent, the A* search algorithm assures finding the shortest path to the destination node. This makes it an ideal search method. A heuristic function is considered acceptable if it never overestimates the destination node's distance. According to the triangle inequality, a consistent heuristic function is one in which the estimated cost from the current node to the target node is less than or equal to the actual cost plus the estimated cost from the next node to the goal node. In terms of memory utilisation, the IDA* algorithm outperforms the A* search algorithm. The whole examined search space is kept in memory by the A* search method, which can be memory-intensive for large search spaces. Contrarily, the IDA* method just saves the current node and its associated cost, not the whole searched area. In order to explore the search space, the IDA* method employs depth-first search. Starting with a threshold value equal to the heuristic function's anticipated cost from the start node to the destination node. After that, it expands nodes with an overall price less than or equivalent to the threshold value via a depth-first search starting at the start node. The method ends with the best answer if the goal node is located. The algorithm raises the threshold value to the minimal cost of the nodes that were not extended if the threshold value is surpassed. Once the objective node has been located, the algorithm then repeats the procedure. The IDA* method is full and optimum in the sense that it always finds the best solution if one exists and stops searching if none is discovered. The technique uses less memory since it just saves the current node and its associated cost, not the full search space that has been investigated. Routing, scheduling, and gaming are a few examples of real-world applications where the IDA* method is often employed. The IDA* algorithm includes the following steps: The algorithm begins with an initial cost limit, which is usually set to the heuristic cost estimate of the optimal path to the goal node. The algorithm performs a DFS search from the starting node until it reaches a node with a cost that exceeds the current cost limit. If the goal node is found during the DFS search, the algorithm returns the optimal path to the goal. If the goal node is not found during the DFS search, the algorithm updates the cost limit to the minimum cost of any node that was expanded during the search. The algorithm repeats the process, increasing the cost limit each time until the goal node is found. Let's look at a graph example to see how the Iterative Deepening A* (IDA*) technique functions. Assume we have the graph below, where the figures in parenthesis represent the expense of travelling between the nodes: We want to find the optimal path from node A to node F using the IDA* algorithm. The first step is to set an initial cost limit. Let's use the heuristic estimate of the optimal path, which is 7 (the sum of the costs from A to C to F). We're done since the ideal route was discovered within the initial pricing range. We would have adjusted the cost limit to the lowest cost of any node that was enlarged throughout the search and then repeated the procedure until the goal node was located if the best path could not be discovered within the cost limit. A strong and adaptable search algorithm, the IDA* method may be used to identify the best course of action in a variety of situations. It effectively searches huge state spaces and, if there is an optimal solution, finds it by combining the benefits of DFS and A* search. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Iterative Deepening Search (IDS) or Iterative Deepening Depth First Search (IDDFS) - Javatpoint

Iterative Deepening Search (IDS) or Iterative Deepening Depth First Search (IDDFS) Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is IDS? Advantages Disadvantages Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews How does IDS work? Contact info Follow us Tutorials Interview Questions Online Compiler An integral component of computer science and artificial intelligence are search algorithms. They are used to solve a variety of issues, from playing games like chess and checkers to locating the shortest route on a map. The Depth First Search (DFS) method, one of the most popular search algorithms, searches a network or tree by travelling as far as possible along each branch before turning around. However, DFS has a critical drawback: if the graph contains cycles, it could become trapped in an endless loop. Utilizing Iterative Deepening Search (IDS) or Iterative Deepening Depth First Search is one technique to solve this issue (IDDFS). A search algorithm known as IDS combines the benefits of DFS with Breadth First Search (BFS). The graph is explored using DFS, but the depth limit steadily increased until the target is located. In other words, IDS continually runs DFS, raising the depth limit each time, until the desired result is obtained. Iterative deepening is a method that makes sure the search is thorough (i.e., it discovers a solution if one exists) and efficient (i.e., it finds the shortest path to the goal). The pseudocode for IDS is straightforward: Code The iterativeDeepeningSearch function performs iterative deepening search on the graph using a root node and a goal node as inputs until the goal is attained or the search space is used up. This is accomplished by regularly using the depthLimitedSearch function, which applies a depth restriction to DFS. The search ends and returns the goal node if the goal is located at any depth. The search yields None if the search space is used up (all nodes up to the depth limit have been investigated). The depthLimitedSearch function conducts DFS on the graph with the specified depth limit by taking as inputs a node, a destination node, and a depth limit. The search returns FOUND if the desired node is located at the current depth. The search returns NOT FOUND if the depth limit is reached but the goal node cannot be located. If neither criterion is true, the search iteratively moves on to the node's offspring. Program: Code Output We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Genetic Algorithm in Soft Computing - Javatpoint

Genetic Algorithm in Soft Computing Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Methodology Variants History Industrial goods Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Issues with optimization Initialization Selection Genetic modifiers Heuristics Termination Limitations Depiction of the chromosomes Elitism Concurrent applications Adjustable Gas Problematic areas Contact info Follow us Tutorials Interview Questions Online Compiler A genetic algorithm (GA), which is a subset of the larger class of evolutionary algorithms (EA), is a metaheuristic used in computer science and operations research that draws inspiration from the process of natural selection. Genetic algorithms frequently employ biologically inspired operators, including mutation, crossover, and selection, to produce high-quality solutions to optimization and search problems. Optimization of decision trees for improved performance, resolving sudoku puzzles, hyperparameter optimization, causal inference, etc., are a few examples of GA applications. In a genetic algorithm, a population of potential solutions to an optimization issue (people, creatures, organisms, or phenotypes) evolves toward superior solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, although other encodings are also feasible. Each candidate solution has a set of properties (its chromosomes or genotype) that can be changed and modified. A generation is a term used to describe the population in each iteration of the evolution, which typically begins with a population of randomly generated individuals. Every member of the population has their fitness assessed once every generation; the fitness is typically the value of the objective function in the optimization issue being addressed. A new generation is created by stochastically selecting the fittest people from the current population, recombining their genomes, and introducing random mutations. The following algorithm iteration uses the fresh generation of candidate solutions. The algorithm typically ends when the population has reached a desirable fitness level or the maximum number of generations has been produced. A conventional genetic algorithm must represent the solution domain genetically and be evaluated using a fitness function. Each potential answer is typically represented as a collection of bits, also known as a bit set or string. The use of arrays of various types and structures is fundamentally the same. The fundamental benefit of these genetic representations is that their fixed size makes their pieces simple to align and allows for straightforward crossover procedures. It is also possible to employ variable-length representations, but crossover implementation is more difficult in this situation. In gene expression programming, a combination of both linear chromosomes and trees is examined. Genetic programming explores representations in the form of trees, while evolutionary programming explores representations in graphs. A GA initiates a population of solutions after defining the genetic representation and the fitness function and then improves it by repeatedly using the mutation, crossover, inversion, and selection operations. Depending on the nature of the issue, the population can range from a few hundred to thousands of potential solutions. The initial population is frequently produced randomly, allowing for the complete set of potential answers (the search space). Occasionally, the answers may be "seeded" in regions where the best answers are most likely to be found. A percentage of the current population is chosen to reproduce for a new generation during each succeeding generation. A fitness-based approach is used to choose individual solutions, with fitter solutions (as determined by a fitness function) often having a higher chance of being chosen. Some selection methods evaluate each solution's fitness and prefer the best ones. Other techniques merely rate a representative population sample because the initial procedure could take an extended period. The fitness function, defined over the genetic representation, assesses the effectiveness of the represented solution. The fitness function always depends on the problem. In the knapsack issue, for instance, the goal is to maximize the overall worth of the items that can fit within a rucksack with a specific capacity. An array of bits could represent a solution, with each bit representing a separate object and its value (0 or 1), indicating whether or not the object is in the knapsack. Such representations are only sometimes accurate since items' sizes sometimes surpass the knapsack's storage capacity. When it is difficult or even impossible to define the fitness expression for a problem, a simulation or even interactive genetic algorithms may be used to estimate the fitness function value of a phenotype (for example, computational fluid dynamics is used to estimate the air resistance of a vehicle whose shape is encoded as the phenotype). By combining the genetic operator's crossover (also known as recombination) and mutation, the next step is to produce a second-generation population of solutions from the initially chosen ones. A pair of "parent" solutions are chosen from the pool previously chosen to breed to produce each new solution. By employing the aforementioned crossover and mutation techniques to construct a "child" solution, a new solution is created that often shares many traits with its "parents." For every new child, new parents are chosen, and this process is repeated until a fresh population of solutions is produced that is of the correct size. Some study reveals that more than two "parents" produce superior quality chromosomes, even though reproduction techniques based on the utilization of two parents are more "biology inspired." These activities ultimately lead to a population of chromosomes in the subsequent generation that differs from the first generation. Since only the best creatures from the first generation are chosen for breeding, along with a small percentage of less fit solutions, the population's average fitness should have grown due to this operation. These less effective approaches guarantee genetic variation within the parental genetic pool and, consequently, guarantee the genetic diversity of the following generation of children. The relative role of crossover and mutation is controversial. The importance of mutation-based search is supported by numerous references in Fogel (2006). Although the two most common genetic operators are crossover and mutation, genetic algorithms can also make use of regrouping, colonization-extinction, or migration. To discover appropriate settings for the issue class under consideration, tweaking variables like the mutation probability, crossover probability, and population size is worthwhile. The non-ergodic phenomenon of genetic drift may result from a relatively low mutation rate. The genetic algorithm may not fully converge if the recombination rate is too high. If elitist selection is not used, a high mutation rate could result in the loss of good solutions. An appropriate population size ensures enough genetic variety for the issue at hand. Still, if set to a value greater than necessary, it can waste computational resources. Other heuristics may be used in addition to the significant operators mentioned above to speed up or strengthen the calculation. The speciation heuristic discourages population homogeneity and delays convergence to a less ideal solution by penalizing crossover between candidate solutions that are too similar. Up until a termination condition is met, this generational process is repeated. Typical termination circumstances include: The theory of the building blocks Although it is easy to create genetic algorithms, it is challenging to comprehend their behavior. It is particularly challenging to comprehend why these algorithms frequently produce highly fit answers when used to solve real-world issues. The components of the building block hypothesis (BBH) are: According to Goldberg, short, low-order, and highly fit schemata are sampled, recombined [crossed over], and resampled to generate strings of possibly higher fitness. In a sense, the complexity of our problem has been decreased by using these specific schemata [the building blocks]; rather than creating high-performance strings by testing every possible combination, we create better and better strings using the best partial answers from earlier samplings. We have already given them a particular name: building blocks. This is due to the significant role that highly fit schemata with short defining lengths and low order play in the operation of genetic algorithms. A genetic algorithm seeks near-optimal performance by juxtaposing short, low-order, high-performance schemata or building blocks, just as a toddler builds fantastic fortresses from basic wooden blocks. The building-block hypothesis has been repeatedly assessed and utilized as a reference throughout the years, despite the need for more agreement regarding its validity. For instance, numerous estimations of distribution procedures have been put out to create a setting where the hypothesis would hold. Although promising outcomes for some classes of issues have been published, doubts about the generality and viability of the building-block hypothesis as a justification for the effectiveness of GAs persist. Much research has been done to understand its limitations from the standpoint of distribution algorithm estimation. When compared to other optimization algorithms, using a genetic algorithm has some drawbacks: Each chromosome is represented as a bit string by the most basic approach. Although it is possible to employ floating point representations, integers may often be used to represent numeric parameters. Both evolutionary programming and evolution techniques are logical fits for the floating point representation. Real-valued genetic algorithms have been suggested, although this is a misnomer because it needs to accurately reflect John Henry Holland's building block theory from the 1970s. However, based on theoretical and experimental findings (see below), some evidence supports this notion. At the bit level, the fundamental algorithm executes crossover and mutation. Other variations view the chromosome as a collection of integers representing hashes, objects, nodes in a linked list, indices into instruction tables, or any other type of data structure. Data element boundaries are respected when performing crossover and mutation. Specific variation operators may be created for the majority of data types. For many specialized issue categories, various chromosomal data types appear to perform better or worse. Grey coding is frequently utilized when bit-string representations of numbers are used. This allows for easy modification of the integer through mutations or crossings. It has been shown that doing this can prevent premature convergence at so-called Hamming barriers, where too many simultaneous mutations (or crossover events) must occur to alter the chromosome to a more advantageous state. Other methods portray chromosomes as arrays of real-valued integers instead of bit strings. The idea of schemata suggests that, in general, the smaller the alphabet, the better the performance; yet, the fact that real-valued chromosomes produced good results at first surprised researchers. When selection and recombination are dominant, it was stated that the set of fundamental values in a finite population of chromosomes forms a virtual alphabet with a substantially smaller cardinality than would be predicted from a floating point representation. By concatenating many types of heterogeneously encoded genes into one chromosome, it is possible to expand the area where the Genetic Algorithm is accessible. This specific method enables the solution of optimization issues when the defining domains for the problem parameters are diverse. For example, in cascaded controller tuning issues, the external loop might implement a linguistic controller (like a fuzzy system) with an essentially distinct description. In contrast, the internal loop controller structure could correspond to a standard regulator of three parameters. The study and modeling of complex adaptive systems, notably evolution processes, benefit from this specific type of encoding, which necessitates a specialized crossover mechanism that recombines the chromosome by section. The best organism(s) from the current generation can be passed down to the following generation unchanged as a practical variation of the basic process of creating a new population. This tactic, referred to as elitist selection, ensures that the quality of the solutions the GA obtains won't deteriorate from one generation to the next. There are two types of parallel genetic algorithm implementations. Parallel evolutionary algorithms that are coarse-grained presume that there is a population on each computer node and that people move across the nodes. The assumption in fine-grained parallel genetic algorithms is that each processor node has an individual that interacts with its neighbors for selection and reproduction. Other variations include time dependence or noise into the fitness function, such as genetic algorithms for online optimization issues. Another noteworthy and exciting variation of genetic algorithms is those with adaptable parameters (adaptive genetic algorithms, or AGAs). Depending on the crossover (pc) and mutation (pm) probability, genetic algorithms can achieve varying degrees of solution accuracy and convergence speed. Researchers have done an analytical analysis of GA convergence. In order to preserve the population variability as well as the capability for convergence, AGAs use the population information from each generation rather than employing preset values for pc and pm. The adaptation of pc and pm in an AGA (adaptive genetic algorithm) depends on the fitness values of the solutions. Additional illustrations of AGA versions include: A simple, early example of enhancing convergence is the successive zooming approach. The adjustment of pc and pm in CAGA (clustering-based adaptive genetic algorithm) depends on the population's optimization stages, which are assessed via clustering analysis. Abstract variables are used to determine pc and pm in more recent methods. Examples include the dominance and co-dominance principles and LIGA (levelized interpolative genetic algorithm), which addresses search space anisotropy by combining a flexible GA with a modified A* search. Combining GA with other optimization techniques has the potential to be very successful. A GA is highly effective at locating generally sound global solutions but ineffective at locating the final few mutations necessary to locate the precise optimum. Other methods (such as straightforward hill climbing) are highly effective in locating the absolute optimal in a constrained area. Combining hill climbing with GA can increase the resilience of hill climbing while enhancing the effectiveness of GA. This implies that in the natural scenario, the principles of genetic variation may signify something different. For instance, crossing over may sum up several maternal DNA steps, add several paternal DNA steps, and so on, assuming the steps are kept in consecutive sequences. This is comparable to adding vectors to the phenotypic landscape that are more likely to follow a ridge. As a result, the process efficiency may be improved by many orders of magnitude. The inversion operator also has the option to arrange the stages in any other sensible sequence that may increase their chances of survival or increase their efficiency. Gene pool recombination is a variation in which the population evolves instead of its individuals. Several modifications have been created to enhance the performance of GAs on problems with a high degree of fitness epistasis-that is, issues where a solution's fitness is determined by interacting subsets of its variables. These algorithms seek to understand (before using) these advantageous phenotypic connections. As a result, they support the Building Block Hypothesis by adaptively minimizing disruptive recombination. A few notable instances of this strategy include the mGA, GEMGA, and LLGA. Timetabling and scheduling issues are among the issues that genetic algorithms are particularly well suited to solve, and many scheduling software solutions are GA-based. Engineers have also used GAs in their work. Global optimization challenges are frequently solved using genetic algorithms. Problem domains with a complicated fitness landscape may benefit from genetic algorithms because mixing, or mutation combined with crossover, is intended to shift the population away from local optima that a standard hill climbing algorithm could become trapped in. Keep in mind that crossover operators, which are frequently utilized, cannot alter any uniform population. Ergodicity of the entire genetic algorithm process (viewed as a Markov chain) may be achieved just through mutation. Examples of issues that evolutionary algorithms have resolved include the best design of aerodynamic bodies in complex flowfields, walking procedures for computer figures, and antennas intended to take up radio transmissions in space. Skiena cautions against using evolutionary algorithms for any purpose in his Algorithm Design Manual: Modeling applications using genetic operators like mutation and crossover on bit strings is extremely strange. Pseudobiology adds a further layer of intricacy that separates you and your issue. Second, applying genetic algorithms to complex problems takes a very long time. The evolutionary analogy is a good one because it takes millions of years for meaningful development to occur. Genetic algorithms have never been the best approach to a problem when I have met it. In addition, I have never encountered any computational outcomes of genetic algorithms that have positively pleased me. For all of your heuristic search magic requirements, stick to simulated annealing. Alan Turing suggested a "learning machine" that would mimic the processes of evolution in 1950. Nils Aall Barricelli, who was utilizing the computer at the Institute for Advanced Study in Princeton, New Jersey, pioneered the use of computers to simulate evolution in 1954. His 1954 publication received little attention. A series of publications by Australian quantitative geneticist Alex Fraser on simulating the artificial selection of animals with many loci affecting quantifiable traits began to appear in 1957. From these beginnings, computer modeling of evolution by biologists increased in the early 1960s. Fraser and Burnell (1970) and Crosby (1973) published books that outlined the techniques. The simulations of Fraser comprised all the fundamental components of contemporary genetic algorithms. Hans-Joachim Bremermann also used a population of solutions to optimization issues in several publications he wrote in the 1960s, which underwent recombination, mutation, and selection. Modern genetic algorithms were also a part of Bremermann's study. Richard Friedberg, George Friedman, and Michael Conrad are notable early pioneers. Fogel (1998) has reproduced a lot of early publications. Although Barricelli had simulated the evolution of skill in a straightforward game in work published in 1963, artificial evolution became a commonly used optimization technique in the 1960s and early 1970s, thanks to the work of Ingo Rechenberg and Hans-Paul Schwefel. Rechenberg's team used evolutionary techniques to find solutions to challenging technical issues. Another strategy was Lawrence J. Fogel's evolutionary programming method, which was suggested for creating artificial intelligence. Initially, finite state machines were employed in evolutionary programming to anticipate environments, and variation and selection were used to improve the predictive logic. Through the work of John Holland in the early 1970s, notably his book Adaptation in Natural and Artificial Systems (1975), genetic algorithms, in particular, gained popularity. His research began with cellular automata experiments by Holland and his University of Michigan pupils. Holland's Schema Theorem, which he established, is a formalized framework for projecting the quality of the following generation. Up to The First International Conference on Genetic Algorithms, which took place in Pittsburgh, Pennsylvania, in the middle of the 1980s, research in GAs remained primarily theoretical. The world's first genetic algorithm product-a mainframe-based toolset made for industrial processes-began to be sold by General Electric in the late 1980s. Evolver, the world's first commercial GA solution for desktop computers, was made available by Axcelis, Inc. in 1989. Evolver was the only interactive commercial genetic algorithm up to 1995, as the New York Times's John Markoff reported in 1990. The sixth version of Evolver, sold to Palisade in 1997 and translated into various languages, is now available. Since the 1990s, MATLAB has included two direct search algorithms (simple search and pattern search) as well as three derivative-free optimization heuristic techniques (simulated annealing, particle swarm optimization, and genetic algorithm). We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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AI and data privacy - Javatpoint

AI and data privacy Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Need of data privacy Privacy laws and responsible AI Is AI harmful for data privacy? How AI can help in data privacy ? Recent instances Future of data privacy with AI Contact info Follow us Tutorials Interview Questions Online Compiler AI stands for Artificial Intelligence. It has existed in some form or another in our societies since the time when the Ancient Greeks through its mythology, all the way up to Frankenstein and Asimov. This lengthy and colorful past cannot diminish the reality that AI is currently at the forefront of our world. When we look back at the development of AI, there's a reoccurring motif. Consequences for privacy and rights of humanity. Using AI incorrectly or without appropriate caution might result in a significant escalation of issues on several fronts. Data privacy is frequently associated with AI models built on consumer data. Users rightly have reservations about automated systems that collect and exploit their data, especially may contain sensitive information. Because AI models rely on high-quality data to produce meaningful results, their survival is dependent on safeguards for privacy being included in their design. Good confidentiality and information management procedures have a lot of to do with the company's basic organizational principles, company procedures, and security management, and are more than just a technique to assuage consumers' anxieties and concerns. Privacy concerns have been extensively researched and publicized, and information from our privacy awareness study reveals that consumer privacy is a critical concern. Addressing these issues contextually is critical, and for organizations working with consumer-facing AI, here are many strategies and tactics available to assist in resolving privacy concerns frequently associated with artificial intelligence. In today's digital era, artificial intelligence (AI) has revolutionized various industries, reshaping our lives in profound ways. However, the progress of AI raises concerns about data privacy, necessitating a careful equilibrium between the potential of AI and the protection of personal information. Data privacy is incredibly important for several compelling reasons. It plays a vital role in protecting individuals' personal information, such as their names, addresses, and financial details, from falling into the wrong hands or being misused. Maintaining data privacy ensures that individuals retain control over their own information, giving them the power to decide how and when it is collected, used, and shared. This sense of control is crucial for people to feel empowered and respected in the digital world. In addition to individual empowerment, data privacy also fosters trust and confidence between people and the organizations or service providers they interact with. When individuals trust that their personal information is being handled with care and kept secure, they are more likely to engage in online activities, share their data, and take advantage of digital services without fear of privacy breaches. Another important aspect of data privacy is its role in preventing identity theft and fraud. By safeguarding personal information, data privacy measures act as a shield against malicious actors who seek to exploit vulnerable data for nefarious purposes. When our personal information is properly protected, it becomes significantly more difficult for these individuals to carry out their harmful activities, keeping us safe from the detrimental consequences of identity theft and fraud. In summary, data privacy is essential for safeguarding our privacy rights and ensuring a secure and trusted digital ecosystem. It empowers individuals, builds trust, prevents identity theft and fraud, combats discrimination, and promotes ethical practices. By valuing and protecting data privacy, we can create a digital world where privacy, security, and individual rights are upheld and respected. The Importance of Data: Empowering AI Advancements AI's transformative power stems from its reliance on data-a crucial element that fuels its algorithms, enabling machines to learn, reason, and predict. To deliver accurate outcomes, AI systems heavily depend on vast amounts of data, including personal information such as preferences and behaviors. Respecting Privacy: Safeguarding User Information While data propels AI progress, it is crucial to prioritize and safeguard individuals' privacy. Organizations and developers must adhere to ethical practices that respect privacy when individuals share their personal information with AI systems. Ethical principles, including informed consent, transparency, and accountability, should guide AI development to ensure responsible data use. Informed Consent: Empowering Individuals with Knowledge In preserving data privacy, informed consent plays a pivotal role. Individuals must have a clear understanding of how their data will be collected, used, and protected. Developers and organizations should provide easily understandable information about data practices, enabling individuals to make informed decisions about their personal information's usage. Transparency: Shedding Light on Data Handling Transparency builds trust between AI systems and users. Organizations should adopt transparent data practices, providing individuals with insights into how their data is processed. By offering clear explanations regarding data usage purposes, scope, and potential risks, users can trust AI systems while maintaining control over their personal information. Accountability: Ethical Responsibility Accountability is essential in AI and data privacy. Developers and organizations bear ethical responsibility for the data they collect and process. Robust security measures should be implemented to protect against unauthorized access. Techniques like anonymization and pseudonymization should be employed to mitigate privacy risks while ensuring data is stored securely. Addressing Privacy Risks: Anonymization and Pseudonymization Anonymization and pseudonymization techniques play a crucial role in mitigating privacy risks in AI systems. Anonymization involves removing personally identifiable information, making data anonymous. Pseudonymization replaces identifying information with pseudonyms, allowing data analysis while protecting individual identities. These techniques strike a balance between data usability for AI systems and safeguarding personal privacy. Moving Forward: Collaborative Solutions The convergence of AI and data privacy requires collaboration among stakeholders. Governments, industry regulators, developers, and individuals must work together to establish clear guidelines, regulations, and standards that protect data privacy while fostering AI innovation. A collective commitment to ethical AI practices and privacy-conscious policies will enable technological advancement while safeguarding personal information. Broader advancements in AI governance Several good governance principles for trustworthy AI have been released in recent years. The majority of these AI governance frameworks define basic principles that overlap, such as privacy and data management, responsibility and audibility, robustness as well as security, openness, explainability, fairness and nondiscrimination oversight by humans, and promotion of human values. Some notable examples of accountable artificial intelligence frameworks developed by public organisations include the recommendation from UNESCO on the Ethics of AI, China's ethical guidelines for the implementation of AI, the Council of Europe's Report "Towards Regulation of AI Systems," the OECD AI Rules, and the Ethics Instructions for Trustworthy AI developed by the European Commission's High-Level Expert Group on AI. One of the concepts of responsible AI that is frequently highlighted is "privacy." This is similar to the requirement to apply generic privacy standards, which are the foundation of data security and privacy across the world, to AI/ML systems that handle personal data. This involves ensuring that collection is limited, data quality is high, the objective is specified, usage is limited, accountability is maintained, and individual engagement is encouraged. Transparent and explainability, fair and non-discrimination, human supervision, robustness, and security of processing information are all principles of trustworthy AI that may be linked to particular person rights and stipulations of appropriate privacy laws. AI itself doesn't necessarily harm data privacy. The real issue lies in how AI systems are designed and used. When AI is developed irresponsibly or implemented poorly, it can put data privacy at risk. However, if we handle AI systems correctly, we can protect privacy while still benefiting from AI technology. Here are a few ways AI can potentially affect data privacy: Data Collection: AI systems need a lot of data to learn and make accurate predictions. But if personal data is collected without consent or in excessive amounts, it can violate privacy. Data Breaches: AI systems handle large amounts of sensitive data. If these systems aren't properly secured, they can become targets for hackers or malicious actors, leading to privacy breaches. Biased Algorithms: AI algorithms can unintentionally perpetuate biases present in the data they learn from. If sensitive attributes like race or gender are used in training, AI systems may discriminate, compromising privacy and fairness. Profiling and Surveillance: AI can enable extensive profiling and surveillance, especially when combined with technologies like facial recognition or location tracking. This can invade personal privacy and raise concerns about mass surveillance. To address these risks, it's crucial to incorporate privacy protections into the development and use of AI. This involves anonymizing and encrypting personal data, implementing robust security measures, obtaining informed consent, conducting regular checks, and complying with privacy regulations and guidelines. Ultimately, it's the responsibility of organizations, policymakers, and developers to ensure that AI is developed and used in a way that respects data privacy and safeguards individuals' rights. By taking these steps, we can strike a balance between utilizing AI's potential and protecting privacy. AI can be a valuable ally in protecting data privacy when used appropriately. It offers several ways to enhance privacy: Anonymization and Encryption: AI techniques can help disguise and secure sensitive data by removing personal identifiers and using encryption. This safeguards privacy while still allowing data to be used for analysis and research purposes. Automated Privacy Controls: AI can assist in automating privacy safeguards, ensuring compliance with data protection rules. By monitoring data access, detecting potential privacy breaches, and enforcing privacy policies, AI helps keep personal information safe. Privacy-Preserving Machine Learning: AI techniques like federated learning and differential privacy enable the training of machine learning models without exposing individual data. This allows organizations to learn from decentralized data sources while preserving privacy. Risk Assessment and Mitigation: AI can identify potential privacy risks by analyzing data handling processes, pinpointing vulnerabilities, and alerting to possible breaches. This helps organizations take proactive steps to mitigate risks and strengthen privacy protections. Privacy-Preserving Analytics: AI enables analyzing sensitive data without directly exposing it. Techniques like secure multi-party computation or homomorphic encryption allow insights to be derived while maintaining privacy. Personalized Privacy Settings: AI-powered systems can provide individuals with personalized privacy settings and recommendations. By considering user preferences, behaviors, and context, AI helps users tailor their privacy controls and make informed choices about data sharing. It's important to approach AI's role in data privacy with ethics and responsibility. Finding the right balance between privacy and utility requires following ethical guidelines, complying with laws, and involving all stakeholders to ensure AI systems are privacy-conscious. By doing so, we can leverage AI to protect privacy while benefiting from its capabilities. The Office of the Australian Information Commissioner deemed Clearview Artificial Intelligence in violation of the business Australian Privacy Act for collecting photos and biometric data without authorization at the end of last year. Shortly after, the UK ICO revealed its intention to levy a possible fine of over seventeen million Gb for the same reason, based on an alliance with Australia's OAIC. Furthermore, three Canadian privacy regulators, as well as France's CNIL, ordered Clearview AI to cease processing and erase the data acquired. In 2021, European data protection regulators investigated many further examples of privacy infringement by AI/ML systems. In the month of December 2021, the Dutch Privacy Authority imposed a punishment of 2.75 million euros on the Dutch Tax & Customs Service for a GDPR infringement involving the discriminatory processing of applicants' nationality by an ML algorithm. The algorithm had recognized multiple citizenship as a high-risk situation, making claims by such people more likely to be false. In another historic decision from August 2021, Italy's DPA, the Garante, penalized food delivery businesses Foodinho and Deliveroo about $3 million each for GDPR violations owing to a lack of openness, impartiality, and correct information regarding algorithms employed to manage its riders. The regulator also determined that the firms' data minimization, safety, and privacy by default and default measures were inadequate, as was their data protection impact assessment. Recent FTC decisions in the United States made it plain that the costs are significant for failing to maintain privacy laws in the construction of models or programmes. In the future, when it comes to data privacy and AI, we can expect exciting developments. Privacy-preserving technologies like federated learning and secure computation will become more advanced, allowing AI to learn from data without compromising individual privacy. Governments and organizations will introduce stricter rules and guidelines to safeguard personal information. AI systems will also become more transparent and understandable, so people can have a clear understanding of how their data is being used. Moreover, individuals will have more control over their personal information and be empowered to make decisions about its usage. Overall, the future holds promise for improved data privacy as AI continues to evolve. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Future of Devops - Javatpoint

Future of Devops Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Devops? Is DevOps in High Demand? Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews The magic of serverless computing Platform engineering vs Devops Trends that will impact the future of dev ops Does all organization calls for devops engineer ? Contact info Follow us Tutorials Interview Questions Online Compiler Devops is like the perfect blend of software development and it operations working together to make sure delivering software is smooth efficient and puts customers first and when we think about the future of dev ops automation becomes a superstar imagine this with automation . Software development and IT operations teams work together dynamically and cooperatively through DevOps. They combine to produce an excellent software delivery method that is streamlined and effective. It's all about encouraging collaboration and eradicating any obstacles that can stand in the way of advancement. Imagine a team of enthusiastic workers who are operations specialists and developers that collaborate and share information. They work together to make the software development process magical by being upfront with one another, planning carefully, and communicating clearly. One of the main goals of DevOps is workflow optimization, making sure that repetitive operations are automated and freeing up time for more innovative and important work. Imagine a group of committed superheroes managing the grunt work while the developers are free to concentrate on their primary responsibilities. DevOps, however, encompasses more than just automation and communication. It also involves producing high-quality software quickly. Developers proactively detect and fix any bugs early in the process by running tests and regularly integrating their code. This guarantees a seamless and error-free deployment once the program is prepared for its target market. Infrastructure management is another essential aspect of DevOps. By using sophisticated tools, they streamline the setup and maintenance of servers, databases, and other technical components. This streamlined approach enables them to scale applications effortlessly and keep everything running efficiently. Ultimately, the heart of DevOps lies in delivering exceptional software that customers love, all while working cohesively as a team, eliminating silos, and embracing smart tools and technologies. The DevOps revolution has indeed transformed the software industry, empowering organizations to innovate and remain competitive in the ever-evolving tech landscape. Yes, DevOps is in high demand and continues to gain popularity. Organizations in various industries are realizing the value of implementing DevOps practices. The need for DevOps professionals is increasing as more companies want to improve their software development and delivery processes. DevOps brings several advantages, like faster software delivery, better teamwork between teams, improved quality assurance, and increased efficiency. By combining development and operations, organizations can release software more quickly and achieve better business outcomes. The demand for DevOps experts is present across small startups, medium-sized companies, and large enterprises. As businesses adopt digital transformation and cloud technologies, the demand for professionals with DevOps skills has risen. DevOps skills are highly sought after due to the growing reliance on automation, continuous integration and continuous delivery (CI/CD) pipelines, and cloud infrastructure. These trends have increased the need for professionals who can effectively implement and manage DevOps practices and tools. Imagine being able to write code without getting tangled up in server management or worrying about scaling your infrastructure that s the beauty of serverless computing also known as function as a service faa s with serverless developers can focus on what they do best writing code while the underlying infrastructure takes care of the rest functions are executed in response to triggers or events and resources are automatically allocated based on demand this pay as you go model eliminates the need for upfront infrastructure investments and ensures resources are used efficiently. "Platform engineering and DevOps are two approaches that are gaining attention in response to the increasing complexity of infrastructure. According to Gartner, it is predicted that by 2026, 80% of software engineering organizations will have established platform teams to bridge the gap between software developers and IT operations. Some argue that with the emergence of platform engineering, DevOps is no longer relevant. However, it is important to note that these two approaches can complement each other and benefit organizations. Rather than one replacing the other, they can work together to enhance organizational capabilities. It should be seen as a progression and expansion rather than a competition or replacement, as pointed out by Nashawaty. Platform engineering is essentially an evolution of DevOps. It shares the same objectives and can contribute to the effectiveness of DevOps practices. Both approaches foster collaboration and emphasize the creation of a robust platform rather than solely focusing on the final product. By combining the strengths of both approaches, DevOps teams can accelerate code development while operating within the guidelines established by platform engineers. However, it is important to recognize that transitioning to platform engineering will require time and effort. It necessitates a distinct skill set and mindset. The individuals currently responsible for DevOps may not necessarily be the same individuals who will become platform engineers in the future, as highlighted by Nashawaty. Adoption of microservices architecture : the use of microservices allows for a more agile flexible and scalable development and deployment process by breaking down applications into smaller independent components organizations can respond quickly to market changes add new features and scale operations without impacting the entire application. Embracing cloud native technology : cloud native technology which leverages microservices containers and immutable infrastructure is becoming increasingly popular in software design and deployment this approach offers several advantages for dev ops professionals it enables faster iteration by reducing dependencies on single applications or services it also facilitates seamless deployment of changes without disrupting production services through the use of immutable infrastructure . focus on observability and monitoring: as dev ops continues to evolve the importance of observability and monitoring becomes paramount organizations are investing in tools and practices that provide comprehensive insights into system performance application behavior and user experiences this allows for proactive identification and resolution of issues ensuring higher quality code and better overall customer satisfaction. Shift towards everything as code: the concept of everything as code emphasizes the use of version control automation and configuration management tools to treat infrastructure deployments and operational tasks as code artifacts this approach enables consistent and repeatable processes reducing manual errors and enhancing collaboration between development and operations teams Dev-sec-ops integration: the integration of security practices into the dev ops lifecycle known as dev sec ops is gaining momentum this trend emphasizes the importance of incorporating security measures and considerations from the initial stages of software development by integrating security into the dev ops pipeline organizations can enhance code security reduce vulnerabilities and ensure compliance with industry standards and regulations continued focus on culture and collaboration : dev ops is not just about technology it is a cultural shift that requires strong collaboration and communication across teams the future of dev ops will continue to prioritize fostering a collaborative culture breaking down silos and encouraging cross functional teamwork this includes promoting shared responsibilities fostering learning and knowledge sharing and establishing feedback loops to drive continuous improvement overall the future of dev ops lies in embracing new technologies improving collaboration and focusing on delivering high quality software efficiently and securely. GitOps: GitOps is an rising technique that leverages version manage structures, which include Git, as a unmarried supply of reality for defining and coping with infrastructure and alertness deployments. It allows declarative configuration control and automated deployment, selling consistency, transparency, and reproducibility within the DevOps manner. Value Stream Management (VSM): VSM makes a speciality of quit-to-cease visibility and optimization of the software transport process. It includes analyzing and measuring the waft of price from concept to deployment, figuring out bottlenecks, and constantly improving the shipping cycle. VSM gives insights into the efficiency, quality, and commercial enterprise impact of software improvement, helping corporations make data-pushed decisions. NoOps: NoOps, an evolution of DevOps, envisions a future wherein operations are completely automated, and developers take whole obligation for handling and maintaining applications in production. This idea is based heavily on automation, self-restoration structures, and cloud-local architectures, enabling developers to attention on building and deploying programs without direct involvement in operations. Serverless Computing: Serverless architectures, in which cloud vendors handle infrastructure control and scaling mechanically, will affect the future of DevOps. DevOps practices will want to evolve to the precise demanding situations and possibilities provided through serverless, inclusive of coping with function deployments, monitoring performance, and optimizing expenses in a serverless environment. Continuous Everything: The idea of "non-stop" will continue to expand beyond continuous integration and continuous shipping (CI/CD). Practices like continuous trying out, non-stop security, non-stop deployment, and continuous tracking becomes extra prominent, ensuring that software program is continuously evaluated, improved, and secured throughout its lifecycle. Low-Code/No-Code: The upward push of low-code and no-code platforms will impact DevOps practices. These structures allow business customers to create packages without massive coding, requiring a closer collaboration between developers and business groups. DevOps will need to evolve to assist the integration of low-code development into the software delivery pipeline. These are only a few potential directions for the destiny of DevOps. As technology advances and new methodologies and tools emerge, the DevOps panorama will hold to conform, pushed through the want for quicker, more reliable software delivery and operations. Not all agencies always require a dedicated DevOps engineer or group, because the adoption of DevOps practices can range depending on the character and scale of the organization's software improvement and operations. However, many groups are recognizing the benefits of implementing DevOps principles and are increasingly hiring DevOps experts or upskilling their present groups in DevOps practices. Here are some factors that could affect whether a business enterprise requires a DevOps engineer: Software Development and Operations Complexity: If a business enterprise's software program development and operations contain more than one teams, common deployments, and complex infrastructure, having a devoted DevOps engineer or group can be incredibly beneficial. DevOps professionals can assist streamline approaches, improve collaboration, and automate tasks to ensure efficient software transport. Continuous Integration and Deployment: Organizations that prioritize continuous integration and deployment, wherein code changes are often integrated, examined, and deployed, frequently find fee in having DevOps understanding. DevOps engineers can set up and maintain automated build and deployment pipelines, permitting quicker and extra reliable releases. Cloud Adoption and Infrastructure Automation: Companies leveraging cloud infrastructure and pursuing infrastructure-as-code tactics can gain from DevOps understanding. DevOps experts can layout and manage cloud environments, leverage configuration management tools, and automate infrastructure provisioning, leading to scalability, flexibility, and operational efficiency. Focus on Collaboration and Communication: DevOps emphasizes collaboration among development, operations, and different groups worried in software transport. If a corporation acknowledges the significance of breaking down silos and promoting a culture of shared duty, having DevOps professionals can help facilitate this collaboration and drive cultural alternate. Scalability and Growth: Companies experiencing speedy growth or making plans to scale their software program systems might also require DevOps know-how to ensure easy operations and efficient scaling. DevOps engineers can design scalable architectures, enforce tracking and alerting systems, and optimize infrastructure to handle extended demand. Ultimately, the selection to hire DevOps engineers or set up a devoted DevOps crew depends at the business enterprise's particular desires, desires, and the complexity in their software improvement and operations. Some agencies can also select to have a hybrid method where existing groups incorporate DevOps standards and practices into their workflows, even as others may additionally opt for specialized DevOps roles to force and control the transformation. DevOps becomes supercharged. It becomes even faster and more efficient, like having a team of superheroes working tirelessly behind the scenes. This means we can deliver software in a flash, giving customers a seamless experience. Not only that, but automation also helps us create top-notch products that are reliable and of the highest quality. But wait, there's more! Automation also opens up new horizons for businesses. It allows them to expand their services globally, reaching people from all around the world. It's like breaking down barriers and connecting with even more folks. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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How Machine Learning is Used on Social Media Platforms in 2023 - Javatpoint

How Machine Learning is Used on Social Media Platforms in 2023? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction Real life intergration of ML Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Can machine learning (ML) and artificial intelligence (AI) threaten our privacy? Contact info Follow us Tutorials Interview Questions Online Compiler Welcome to the captivating realm of social media, where connections flourish, stories unfold, and the magic of Machine Learning (ML) quietly transforms our experiences. Have you ever marveled at how social media platforms seem to anticipate your preferences? Prepare to be enchanted as we delve into the secrets behind ML algorithms, the mystical forces that tailor content, keep us safe, and add a touch of wonder to our online adventures. Join us on this journey as we uncover the magical ways ML is revolutionizing social media, making it uniquely yours and protecting you along the way. Personalized Content Recommendations: Imagine stepping into a realm where every scroll reveals a world curated just for you. ML algorithms possess a secret gift: they observe your actions, delve into your interests, and deliver a stream of captivating posts, articles, and videos that align perfectly with your preferences. It's like having a loyal guide who understands your tastes, ensuring that each moment spent on social media is an enchanting experience tailored exclusively for you. Sentiment Analysis and Content Moderation: Within this mystical realm of social media, there are guardians watching out for your safety. ML algorithms master the art of sentiment analysis, sifting through the digital winds to identify harmful or inappropriate content. Their watchful eyes ensure that negativity and harm are banished, maintaining a safe and uplifting online haven where your well-being is protected, and positive interactions thrive. Image and Video Analysis: Prepare to be awestruck as ML's magical touch brings images and videos to life. These enchanting algorithms possess the power to understand the visual tales you share. They can recognize familiar faces, decipher the objects that populate your imagery, and even add relevant tags to enhance the storytelling. ML transforms your captured moments into a vivid tapestry, painting a richer picture of your social media narrative. Automated Language Processing: Words hold immense power, and ML's linguistic prowess allows it to understand the whispers of language in the social media realm. It deciphers the nuances of your text, unraveling the meaning, detecting sentiment, and ensuring that your interactions are free from unwanted intrusions. The power of ML keeps conversations filled with positivity, respect, and genuine connections, creating a space where your voice is truly heard. Ad Targeting and Personalization: Behold the marvel of ads that seem to anticipate your deepest desires! ML algorithms act as benevolent genies, analyzing your preferences and behaviors to bring forth advertisements tailored to your interests. These mystical algorithms conjure up a magical blend of products and services, aligning seamlessly with your wishes, and making your social media encounters all the more delightful. It's like having your own personal shopper, presenting you with the offerings that truly speak to you. User Behavior Analysis and User Experience Optimization: In the enchanted realm of social media, your desires shape the landscape. ML models observe your every move, learning your engagement patterns and preferences. With this knowledge, they create a personalized experience that molds itself to your needs, making each interaction a seamless and enchanting journey catered just for you. The user experience becomes intuitive, tailored to your preferences, and designed to make your social media journey truly magical. Spam and Fake Account Detection: Within the magical kingdom of social media, guardian algorithms stand tall against the dark forces of spam and fake accounts. With their mystical insight, ML algorithms detect and ward off suspicious activities, preserving the authenticity of your digital realm. They ensure that your social media encounters are filled with genuine connections and shield you from the shadows that seek to deceive. You can explore the social media kingdom with confidence, knowing that your interactions are authentic and meaningful. Real-time Trend Analysis: In the ever-changing landscape of social media, staying in tune with the latest trends is paramount. ML's mystical powers empower you to surf the waves of real-time trends. By analyzing vast amounts of data, these enchanting algorithms reveal the hottest topics, viral content, and trending hashtags. They allow you to be at the forefront of conversations, connecting you to the vibrant pulse of the digital realm. You become a trendsetter, engaging with the zeitgeist and sparking conversations that captivate others. Let's dive into it. ML is the magic that helps virtual assistants like Siri and Alexa understand and respond to our voices. It's a cool technology that businesses use to achieve their goals, but sometimes, ethical considerations take a backseat to corporate objectives.One big concern is bias. ML algorithms can unintentionally discriminate based on race or other factors. It's like having blind spots in the data, leading to inaccurate and potentially harmful assumptions. Imagine if these biases affected important areas like healthcare - that could be really risky. ML and AI also give companies the power to analyze vast amounts of data. They can draw conclusions about us from seemingly random information. It's impressive, but it raises questions about re-identifying personal data and the need for regulations to prevent intrusive surveillance. Who's responsible when systems make decisions that negatively impact people's lives? That's another question. We worry about biased profiling, like gender discrimination during hiring processes. Transparency is essential. We want to know how companies use our data and for how long they keep it. AI-powered technologies like deepfakes add to the concerns. They can create fake videos that trick us, which is a whole new level of privacy invasion and trust issues. In a nutshell, while ML and AI bring amazing possibilities, we must address the privacy concerns. It's crucial to have regulations, transparency, and responsible data practices in place. We want to enjoy the benefits of these technologies while feeling confident that our privacy is respected and protected. Personalized Recommendations: You've probably noticed how platforms like Netflix or Spotify suggest movies or music you might enjoy. Well, that's thanks to machine learning! These platforms learn from your past choices and behaviors to recommend content that matches your tastes. It's like having a friend who knows your preferences and suggests things you'll love. Fraud Protection: Banks and financial institutions use machine learning to keep your money safe. ML algorithms analyze your transactions and detect any unusual patterns that might indicate fraud. They act as digital guardians, working behind the scenes to protect your accounts and notify you if something seems fishy. Medical Diagnostics: Machine learning is helping doctors and medical professionals in diagnosing diseases and predicting patient outcomes. By analyzing vast amounts of patient data, ML models can identify patterns, detect anomalies, and assist healthcare professionals in making accurate diagnoses and treatment plans. It's like having an extra set of expert eyes to ensure you receive the best care possible. Smart Home Assistance: Ever wished your home could anticipate your needs? With machine learning, it can! Smart home systems learn your preferences and behaviors to automate tasks and adjust settings accordingly. They can adjust the temperature, lighting, and even play your favorite music as you walk through the door. It's like having a thoughtful assistant who understands your daily routines. Social Media Safety: Machine learning algorithms help keep social media platforms safe and friendly. They work tirelessly to identify and filter out harmful or inappropriate content, ensuring a positive online environment. These algorithms learn to recognize patterns and offensive language, acting as your digital bodyguards in the virtual world. Autonomous Vehicles: Imagine cars that can drive themselves! Machine learning makes it possible. Autonomous vehicles rely on ML algorithms to analyze their surroundings, recognize objects, and make split-second decisions to ensure a safe journey. It's like having a co-pilot who watches the road and helps navigate challenging situations. These real-life integrations of machine learning bring convenience, safety, and personalization into our lives. They're like invisible helpers, making our daily experiences smoother, more secure, and tailored to our unique preferences. As technology continues to advance, we can look forward to even more exciting and human-centric applications of machine learning. The magic of Machine Learning has transformed social media into a realm where your presence is treasured and protected. With personalized content recommendations, sentiment analysis, image and video analysis, and other mystical capabilities, ML algorithms craft an online haven tailored to your interests, all while safeguarding your well-being. As we embrace this magical technology, it is essential to remember the importance of ethical and responsible practices. we can ensure that social media remains a place of wonder, connection, and positive experiences for all. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Machine learning and climate change - Javatpoint

Machine learning and climate change Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Prevention Innovation Conclusion: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Improve our forecasts of how much energy we will use Learn about new materials Improve the way freight is routed Overcoming hurdles in electric vehicle adoption: Enhancing building energy efficiency: Improving energy consumption estimates: Optimizing supply chains: Enabling large-scale precision agriculture: Enhancing deforestation monitoring: Influencing consumer behavior: Contact info Follow us Tutorials Interview Questions Online Compiler The application of machine learning could prove crucial in the ongoing fight against climate change.Dealing with emissions efficiently is a high-potential environmental mitigation method. When we can't totally remove emissions, decreasing their environmental impact is generally the best option. Researchers have utilised artificial intelligence to discover the best strategies for converting gases into methane, for example. They enabled the technology to explore characteristics such as the size and chemical composition of the catalyst small particles which govern the process. Methane, a renewable energy source, is a convenient option for storage and transfer. Scientists are using machine learning to analyze the unique patterns produced by catalysts during chemical processes. Early investigations have shown that specific catalysts can either lower the temperature or enhance the efficiency of the carbon dioxide-methane process. While many people worldwide perceive climate change as a distant concern, African farmers are already experiencing its unfortunate effects. More than 95% of them rely entirely on rainfall for irrigation, and the resulting droughts caused by climate change are leading to disastrous agricultural losses. Research indicates that the majority of global greenhouse gas emissions are attributed to energy generation and consumption. Recognizing this, numerous international leaders are taking action and committing to meaningful improvements within specified timeframes. Mexico stands out as a country that has recently made significant progress in this regard. However, achieving such goals necessitates collaboration on an international scale. Implementing widespread changes in energy production and consumption to promote environmental well-being requires careful planning and time. However, individuals can also contribute by taking steps in the right direction. Various technology companies offer machine learning tools that help reduce energy usage at the building level. One particular company claims to have developed systems capable of collecting data samples at an astounding rate of 8,000 instances per second, resulting in potential energy savings of up to 50%. Users can readily access concrete data supporting the success of these solutions through user-friendly interfaces that display consumption trends and other vital statistics. Furthermore, customers can gain fresh insights, such as identifying peak energy usage hours or determining which specific areas significantly contribute to the overall energy consumption. Scientists have discovered that plants have the ability to naturally absorb carbon dioxide, which can help reduce pollution. However, they have not fully understood the long-term effects of this process and how it can impact plant life, such as promoting growth. To delve deeper into this phenomenon, a team of experts from different fields conducted a comprehensive study using machine learning, statistical methods, and satellite data. Their goal was to better understand how soil nutrients and climate factors affect a plant's capacity to absorb carbon dioxide. The results revealed that tropical forests, like those in the Amazon and Congo, have the highest potential for both carbon dioxide absorption and regeneration of plant life. Another significant finding from the study was that by the year 2100, trees could potentially remove an amount of carbon dioxide equivalent to six years of emissions. However, this optimistic outcome could only be achieved if deforestation efforts were completely halted. Machine learning could play a role in helping climate advocates persuade policymakers about the importance of trees in combating global warming. Undoubtedly, climate change is a critical and substantial issue. These examples highlight how machine learning can contribute to addressing this challenge. While innovative technology cannot replace human knowledge and decision-making, it can complement and support real systemic changes in meaningful ways. o successfully integrate more renewable energy sources, utility companies need to improve their techniques for accurately estimating current and future power demands. While there are already algorithms for forecasting energy demand, they can be further improved by incorporating more detailed local climate and weather trends, as well as taking into account individual household behaviors. Additionally, efforts to enhance the transparency and comprehensibility of these algorithms can assist utility operators in interpreting the findings and making informed decisions about when to integrate renewable energy into the grid. Scientists are faced with the challenge of finding materials that can store, gather, and utilize energy more efficiently. However, the traditional process of discovering new materials is often time-consuming and unreliable. Machine learning offers a promising solution by speeding up the identification, development, and evaluation of novel chemical compounds that possess the desired properties. This advancement could have significant implications, such as the development of solar fuels that can capture and store energy from sunlight. Additionally, machine learning can help identify more effective absorbents for carbon dioxide or alternative building materials that require much lower carbon emissions compared to conventional options like steel and cement. Given that steel and cement production contributes nearly 10% of global greenhouse gas emissions, these innovative materials have the potential to play a crucial role in reducing environmental impact. The process of shipping products worldwide is a complex and often inefficient operation, involving the coordination of different cargo sizes, modes of transportation, and a dynamic network of origins and destinations. Machine learning can potentially assist in finding ways to optimize cargo grouping, aiming to minimize the total number of trips required. By efficiently grouping cargoes, this approach can lead to reduced transportation needs and, consequently, lower costs. Additionally, such a system would be less vulnerable to transit delays and disruptions, resulting in improved reliability and customer satisfaction. Overcoming the hurdles in electric vehicle adoption requires concerted efforts, and machine learning can play a vital role in this transformation. By leveraging advanced algorithms, machine learning can optimize battery energy management, extending the mileage per charge and alleviating concerns associated with "range anxiety." These algorithms can intelligently model and forecast aggregate charging behavior, empowering grid operators to efficiently manage and meet the dynamic load demands of electric vehicles. Moreover, machine learning algorithms can revolutionize building energy efficiency by implementing intelligent control systems. These systems leverage real-time data, including weather forecasts, occupancy patterns, and environmental factors, to dynamically adjust heating, cooling, ventilation, and lighting requirements. By adapting to these factors, energy consumption is optimized, resulting in significant reductions in carbon emissions. Furthermore, these intelligent control systems can interact with the electrical infrastructure, proactively managing energy usage during periods of low-carbon availability, thereby ensuring sustainable energy practices. In regions where energy consumption data is limited, machine learning algorithms can extract valuable insights from satellite images, leveraging computer vision techniques to identify and analyze building footprints and attributes. These algorithms can then estimate and predict city-level energy usage, aiding policymakers and urban planners in formulating efficient energy strategies and identifying areas for targeted energy-saving initiatives. The optimization of supply chains is another area where machine learning can have a substantial impact. By leveraging data-driven algorithms, machine learning can enable accurate supply and demand forecasting, minimizing manufacturing and transportation waste in sectors such as food, fashion, and consumer goods. The intelligent allocation of resources and the provision of targeted recommendations for low-carbon products can drive sustainable consumption habits, thereby reducing the overall carbon footprint. In agriculture, machine learning-powered robots hold immense potential for enabling large-scale precision farming. These robots, guided by sophisticated algorithms, can optimize crop management, taking into account factors such as soil health, weather patterns, and historical data. By ensuring the optimal mix of crops and minimizing reliance on nitrogen-based fertilizers, the overall environmental impact is reduced, leading to enhanced soil health and decreased greenhouse gas emissions. Deforestation, a major contributor to global greenhouse gas emissions, necessitates efficient monitoring and mitigation efforts. Machine learning, coupled with satellite imagery, can automate the analysis of forest cover loss, enabling the detection of illegal deforestation activities on a larger scale. Additionally, ground-based detectors and advanced algorithms can identify chainsaw noises, empowering local authorities to intervene promptly and effectively. Finally, machine learning can influence consumer behavior by utilizing techniques deployed by advertisers to target customers effectively. Through personalized interventions and tailored recommendations, consumers can be encouraged to adopt energy-saving practices, participate in sustainable initiatives, and make eco-friendly choices, ultimately contributing to a more sustainable future. In conclusion, the integration of machine learning in addressing the hurdles of electric vehicle adoption, improving building energy efficiency, estimating energy consumption, optimizing supply chains, enabling precision agriculture, enhancing deforestation monitoring, and influencing consumer behavior offers immense potential for mitigating climate change. By collaborating across industries, investing in research and development, and embracing the power of machine learning, we can collectively drive meaningful change and accelerate the transition towards a sustainable and low-carbon future. 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The Green Tech Revolution - Javatpoint

The Green Tech Revolution Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials How green tech revolution will impact future? Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Renewable Energy: Powering the Future Smart Cities: Where Tech Meets Sustainability Eco-Friendly Manufacturing: Making Stuff with a Green Touch Blockchain for Sustainability: Enhancing Transparency and Accountability Precision Agriculture: Growing Green from Farm to Table Waste Management Innovation: Turning Trash into Treasure AI for Conservation: Tech to Save the Planet Empowering Consumers: Your Green-Tech Superpowers Investing in the Future: The Green Tech Boom Contact info Follow us Tutorials Interview Questions Online Compiler The world is at a critical juncture in addressing environmental challenges and the tech industry is at the forefront of driving positive change . the green tech revolution is a growing movement that aims to harness innovative technologies to create a sustainable future from renewable energy solutions to eco friendly manufacturing practices and green transportation. this revolution is transforming industries and pushing humanity toward a more environmentally conscious and resilient world. Let us investigate the miracles of renewable energy technology that have weaved their way into our lives step by step. Behold the beautiful dance of solar panels, carefully collecting the sun's rays and changing them into an infinite source of electricity. Witness the gigantic wind turbines, delicately taming the breezes that flow across the countryside, as though orchestrated by nature herself. And pay great attention as the Earth's soothing whispers lead us to the hidden gem of geothermal systems, where the very heart of our world warms our spirits. The fascination of these green energy solutions is overwhelming, and it lies not only in their elegance but also in their actual promise. As we embrace this trip, we discover that the obstacles of cost and accessibility are disappearing like morning dew under the sun's caress. The route to saying goodbye to the age of fossil fuels grows clearer, and the possibility of peaceful living with our planet draws ever closer. In this epic narrative, we become storytellers and pioneers, weaving a tale of development, knowledge, and oneness with nature. Each step we take toward embracing renewable energy advances not just our technology but also our conscience. It's a shift that goes beyond individual efforts, reflecting the collective heartbeat of mankind as we remake ourselves. Step into the fascinating world of smart cities, where the convergence of technology and sustainability creates a vision that entices city people. The future is come, and it is one in which cities thrive thanks to innovative tech-driven solutions that pave the path for greener, more rewarding lifestyles. Imagine a bustling metropolitan landscape coexisting peacefully with the environment, and you'll be immersed in the spirit of smart cities. The intelligent infrastructure - a tapestry of linked technologies that interact seamlessly to maximize efficiency and resource management - is at the heart of this shift. Every facet of urban life is being redesigned to reduce our ecological footprint, from energy use to garbage reduction. The streets are alive with energy management systems, orchestrating a power symphony to ensure that we utilize renewable energy sources to power our cities in a sustainable manner. The persistent pursuit of eco-friendliness is a vital aspect of smart cities. Initiatives to reduce waste are at the forefront, reflecting a common commitment to preserve the world for future generations. With each inventive step, these cities move closer to a future in which waste and pollution are relics of the past, ushering in a new era of mindful living and responsible stewardship. A revolution in public transportation is pulsing through the core of smart cities. Commuting becomes an awe-inspiring trip thanks to technological advancements. Innovative developments improve the ease of public transportation by weaving together linked networks that give residents and tourists with smooth, efficient travel alternatives. The experience is no longer simply about getting somewhere; it's a seamless combination of excitement and relaxation. Smart cities embrace innovation, creativity, and diversity as we begin on this path. It's a shared story in which city planners, technology enthusiasts, environmentalists, and residents collaborate to weave a dynamic tapestry of urban existence. These cities become a tribute to human potential and the fundamental yearning to live in peace with nature, beyond the dazzling lights and towering towers. Enter the world of eco-friendly manufacturing, where a symbiotic combination of technology and environmental awareness is altering the way we manufacture things. Prepare to be astounded, for a revolution is taking place that is changing the face of industries and reinventing the very meaning of purchasing. The days of wasteful activities that burdened our earth are over; a new era of responsible manufacturing has begun. The circular economy idea takes center stage, with each step in the production process geared to minimize waste and enhance recycling. My friends, it's a hopeful trend toward sustainability, one that promises a brighter, greener future. Enter the realm of new materials, where creativity collides with environmental awareness. Industries are looking for new ways to make goods out of renewable and environmentally acceptable materials, saying goodbye to toxic toxins and non-biodegradable ingredients. We are witnessing a symphony of innovative solutions that nourish rather than deplete our fragile ecosystems, ranging from bioplastics that gently return to nature to materials generated from recycled sources. But that's not all; the transition also affects manufacturing procedures. Industries are embracing green tech techniques to reduce their carbon footprints, with an unbroken focus on energy efficiency. They're using sustainable energy sources like solar and wind to power their industrial operations. The machines buzz with environmental awareness, understanding that each revolution contributes to a better, greener planet. In terms of sustainability, the convergence of blockchain technology with responsible practices is paving the way for a more transparent and accountable society. The critical notion of supply chain transparency, where ethical sourcing and sustainable practices become a shared duty, is at the core of this transformational synergy. With its immutable and transparent ledger, blockchain is an amazing technology that has transformed data administration. We go on a mission to trace and verify sustainable behaviors throughout complex supply chains by leveraging the potential of blockchain. The potential is enormous, and the impact will be tremendous. We reveal a new level of openness and responsibility through this creative method, which connects with both consumers and businesses. Consider a scenario in which every stage of the supply chain is traceable, authenticated, and untampered with. Because blockchain guarantees that data is securely preserved, it is nearly difficult for any company to conceal immoral acts or distort information. The notion of establishing trust is at the heart of this innovative usage of blockchain. Consumers crave authenticity and want to know that the things they buy correspond with their ideals. Blockchain enables us to trace the origin of items, revealing light on the full route from conception to delivery. It establishes a stronger link between the end user and the source, so empowering individuals. Real-world examples of blockchain's impact on sustainability astound us. Fair trade practices, a cornerstone of ethical commerce, become verifiable and enforceable through blockchain's transparent ledger. We can now validate that farmers and workers receive fair compensation for their efforts, bridging the gap between producers and consumers in a way that was once elusive. Green farming has strong roots in the enthralling realm of precision agriculture, forging a symbiotic link between technology and the Earth. Welcome to the world of stewardship and innovation, where the art of growing food becomes a dance of intellect and environmental consciousness. Consider this: high-tech sensors are strategically placed over lush fields, carefully sensing the demands of crops and soil alike. These sophisticated sentinels collect crucial data, giving farmers with a detailed picture of their land's health. Farmers use this information to make data-driven decisions, adjusting their operations to maximize efficiency and decrease waste. Precision agriculture has replaced the plow and plowman with smart gadgets and intelligent farmers. We nurture the Earth while protecting her resources by embracing cutting-edge technology. The days of uncontrolled water use and excessive chemical application are over. Instead, every drop counts, and every nutrient is delivered with surgical precision. It's a delicate symphony of balance in which productivity coexists with environmental awareness. The terrain changes before our eyes as we begin on our adventure. We are no longer witnessing the thriving of no-till and reduced-till agricultural techniques, which are maintaining the soil's vitality for future generations. The soil, a living ecosystem, becomes a treasured friend on this green walk, playing an important part in the environment. Precision agriculture displays its conservation prowess with each new season. Water resources are valued because of perfectly focused irrigation. Nutrient management is transformed into an art form because nutrients are given precisely to the root zone, leaving no space for waste or runoff. The environment is happy because pollution is decreasing and biodiversity is reviving. Precision agriculture extends beyond the fields into the domain of food traceability, which is a boon for customers who want to know where their food comes from. The farm-to-table movement is gaining traction, and when we eat fresh and healthful fruit, we feel a deep connection to the Earth that grew it. Welcome to the realm of waste management innovation, where a transformative alchemy turns trash into treasure. In this enchanting journey, we witness the unfolding of a magical narrative, where creativity, technology, and environmental consciousness unite to revolutionize the way we handle waste. Picture a world where landfills are not the final resting place for discarded materials, but instead, a canvas for ingenious solutions. Waste becomes a resource, and the possibilities are as limitless as the human imagination. We embark on a quest to unlock the hidden value in every discarded item, breathing new life into what was once deemed useless. At the heart of this magical transformation lies recycling, a powerful art that rejuvenates materials to their former glory. Plastics are reborn into products, glass is melted into art, and paper is reinvented into creations of wonder. The discarded finds new purpose, and the environment rejoices in its revival. But that's not all - the enchantment extends beyond traditional recycling. Emerging technologies like waste-to-energy systems harness the latent power within our trash. Energy from waste becomes a reality, fueling our homes and communities, while reducing our reliance on fossil fuels. As we turn trash into energy, we kindle a spark of hope for a greener, sustainable future. Amidst this magic, composting emerges as a humble yet transformative act. Organic waste transforms into nutrient-rich compost, nurturing the Earth and breathing vitality into gardens and fields. It's a dance of decomposition and renewal, celebrating the cyclical harmony of nature. The enchantment of waste management innovation reaches its zenith with upcycling, a form of recycling that transcends boundaries. Artists and visionaries weave together discarded materials into art, fashion, and functional creations that mesmerize the world. The obsolete gains newfound value, resonating with beauty and purpose. Welcome to the awe-inspiring realm of AI for conservation, where cutting-edge technology becomes a formidable ally in our mission to save the planet. In this captivating journey, we witness the convergence of artificial intelligence and environmental stewardship, unleashing a powerful force for positive change. Imagine a world where AI acts as a guardian, tirelessly monitoring and protecting endangered species. Smart cameras, equipped with AI algorithms, survey vast landscapes, and remote regions, detecting elusive creatures and capturing vital data. Through this digital vigilance, we gain invaluable insights into animal behavior, population dynamics, and habitat health, empowering us to make informed conservation decisions. But the magic doesn't stop there. AI's analytical prowess comes to the forefront in ecological modeling. It processes a vast array of data, from climate patterns to wildlife movements, creating intricate simulations that unveil the intricate tapestry of Earth's ecosystems. This digital atlas enables scientists and conservationists to predict the impacts of climate change, habitat loss, and human activity, guiding us towards proactive solutions. In the race against illegal activities, AI serves as a tireless sentinel. It analyzes real-time data, flagging suspicious patterns in wildlife trafficking and illegal logging. Through this virtual watch, we disrupt criminal networks, safeguarding precious flora and fauna from the clutches of exploitation. Yet, the most enchanting aspect of AI for conservation is its ability to foster human connections with nature. Virtual reality and augmented reality experiences transport us to the heart of remote rainforests, coral reefs, and Arctic landscapes. This immersive journey sparks a newfound appreciation for the wonders of our planet, igniting a collective desire to protect and preserve. Welcome to the fascinating world of green-tech superpowers! As you embark on this incredible journey, you'll discover a gift that's as enchanting as it is empowering: knowledge. Picture yourself with a trusty companion, guiding you to make eco-friendly choices that will leave a positive impact on our precious planet. You see, green technology is like a collection of friendly superheroes, coming together to save the day and protect our environment. From energy-efficient gadgets that save you money and energy to eco-friendly appliances that make your home a cozy sanctuary, these green-tech wonders add a touch of magic to your everyday life. But there's more! As you navigate the world of shopping, you'll become a savvy consumer with a keen eye for eco-friendly products. You'll find magical symbols like eco-labels that help you identify products that are kind to nature. Supporting fair trade practices and sustainable sourcing becomes a natural choice, as you embrace the power of responsible consumption. As your green-tech superpowers grow, you'll tap into the magic of renewable energy. By harnessing the sun's energy with rooftop solar panels, you'll become a true champion of clean power. And with the extra boost of battery storage, you can save that energy for cloudy days, making sure you always have a little green magic to brighten your day. Welcoming you to the exciting world of making investments in the future, where the green technology revolution is creating waves and profoundly altering our planet. It's a thrilling adventure where innovation and sustainability coexist, and your choices could help to create a future that is both more prosperous and environmentally responsible. Green technology comes as a ray of hope in this rapid-fire age of technological wonders. You have the chance to lead this green revolution as an investor, where there are countless opportunities for expansion and making a positive difference. Consider the numerous chances available to invest in cutting-edge technologies that are revolutionizing the way we produce, store, and use energy. The spotlight is on renewable energy sources like solar and wind since they provide safe and sustainable substitutes for conventional fossil fuels. It's like entering a realm where the elements of nature serve as our partners and provide our lives boundless possibility. But green technology doesn't end there; it also explores energy efficiency. Businesses are creating cutting-edge solutions to optimize energy use and cut waste, such as smart grids and energy-efficient appliances. Technology and environmental responsibility work together in perfect harmony in this lovely symphony. As you travel through this dynamic terrain, electric cars stand out as the transportation of the future. Investing in businesses that promote electric mobility is similar to joining a movement where cities are filled with green cars and cleaner air is a reality. Beyond energy, green technology also encompasses waste minimization, sustainable agriculture, and resource management. Innovative solutions like waste-to-energy systems, precision agriculture, and water-saving technology are emerging, helping to create a future where resources are valued and waste is a thing of the past. When you invest in the sustainable technology boom, you help to accelerate progress. Your decisions have the potential to create a world that prospers in accordance with nature as well as financial rewards. The green technology revolution will make our future appear incredibly promising! We get to play the role of eco-friendly superheroes, rescuing the world one green technology advancement at a time. It's like this thrilling adventure. Imagine a world where we harness the sun's and the wind's energy without contaminating the atmosphere. We will use renewable energy sources to power our homes and communities, with green technology setting the standard. It's like a magic spell that instantly makes clean, sustainable energy available to us. And our cities will resemble something from a sci-fi novel! Buildings will be completely redesigned to be extremely energy-efficient, resulting in a warm and environmentally friendly space. Let's go on this adventure together, holding hands and hearts together. Accept green technology, and let's change the world! We have the abilities and the will to create a better future where nature flourishes and future generations may take pleasure in the delights of our magnificent planet. We can bring about significant change with each tiny step and our combined efforts. We all have a responsibility to fulfill in this regard. So let's act as the story's protagonists by cooperating to build a more enlightened and sustainable planet. Together, we can leave behind a legacy of optimism, development, and environmental responsibility. So let's take advantage of this chance to fully embrace the Green Tech Revolution. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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GoogleNet in AI - Javatpoint

GoogleNet in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Brief Overview of Convolutional Neural Networks (CNNs) How GoogleNet Works Features of GoogleNet Beyond Image Classification: GoogleNet's Impact Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Birth of GoogleNet: Need for Deeper Networks Inception Module: A Multi-Scale Feature Extractor Dimensionality Reduction and Bottleneck Layers Auxiliary Classifiers Spatial Pooling and Fully Connected Layers Overall Architecture Advantages and Impact Contact info Follow us Tutorials Interview Questions Online Compiler In artificial intelligence (AI) and machine learning (ML), there is one constant truth: innovation drives progress. Over the years, researchers and engineers have consistently pushed the boundaries of what AI can achieve, with each breakthrough paving the way for new and exciting possibilities. One such groundbreaking development is GoogleNet, a deep convolutional neural network (CNN) architecture that has left an indelible mark on computer vision and beyond. Before delving into GoogleNet, it's essential to understand the foundational concept of convolutional neural networks (CNNs). CNNs are a class of deep neural networks that are designed specifically for processing structured grid-like data, such as images or videos. They mimic the visual processing in the human brain, enabling computers to understand and interpret visual information. CNNs excel at tasks like image classification, object detection, and image generation. GoogleNet, formally known as Inception-v1, emerged from a critical realization in CNNs. As researchers aimed to improve the accuracy of image recognition models, they faced a significant challenge. While increasing the depth of a neural network generally led to better performance, it also intensified the vanishing gradient problem-a phenomenon where gradients used to update network weights become extremely small, causing the network's training to slow down or even stagnate. In 2014, GoogleNet's creators introduced an innovative solution to this problem. They designed an architecture that was not only deep but also computationally efficient. The idea was to create a network with depth and width without significant computational cost. This led to the development of the "Inception" module, which became the cornerstone of the GoogleNet architecture. GoogleNet, officially called Inception-v1, is a deep convolutional neural network (CNN) architecture designed for image classification tasks. It gained prominence for its innovative "Inception" module, which enables efficient and accurate feature extraction across multiple scales. Let's dive into how GoogleNet works and its key components: The heart of GoogleNet is the Inception module, which was developed to address the challenge of capturing features at different scales while keeping computational complexity manageable. The module employs multiple convolutional filters of varying sizes (1x1, 3x3, and 5x5) within the same layer. This parallel structure allows the network to capture fine details with the smaller filters and more global patterns with the larger ones. Additionally, 1x1 convolutions are used for dimensionality reduction, reducing the number of input channels and, thus, computational load. The outputs of different filters are then concatenated along the depth dimension, effectively combining information from various scales. This parallel approach ensures that the network can learn features at both local and global levels, leading to enhanced representation power. One of the challenges in designing deep neural networks is managing computational complexity. GoogleNet addresses this by incorporating 1x1 convolutions, which serve two primary purposes: Dimensionality Reduction: Using 1x1 convolutions, GoogleNet reduces the number of input channels before applying more computationally intensive operations like 3x3 or 5x5 convolutions. This reduces the overall number of parameters and computational costs. Bottleneck Layers: In addition to dimensionality reduction, the 1x1 convolutions act as bottleneck layers, forcing the network to learn a compressed input representation. This encourages the network to focus on the most relevant features. GoogleNet introduces auxiliary classifiers at intermediate layers during training. These auxiliary classifiers inject additional gradient information back into the network. While these classifiers are absent during inference, they help combat the vanishing gradient problem and encourage the network to learn more robust features. This approach aids in training deeper networks effectively. After multiple Inception modules, the feature maps are spatially pooled using average pooling. This reduces the spatial dimensions while retaining essential features. The pooled features are fed into fully connected layers, producing the class probabilities for image classification. The GoogleNet architecture comprises multiple stacked Inception modules, each followed by average pooling and fully connected layers. The architecture allows for efficient computation by leveraging the benefits of parallel convolutions and dimensionality reduction. GoogleNet's design innovations, such as the Inception module and dimensionality reduction, significantly improved image classification accuracy and computational efficiency. Its success inspired subsequent versions of the "Inception" architecture, each building upon the principles introduced by GoogleNet. Furthermore, GoogleNet's ideas have influenced the development of other CNN architectures, and the principles of multi-scale feature extraction, dimensionality reduction, and parallel processing continue to be essential components in modern deep learning models. GoogleNet, also known as Inception-v1, introduced several innovative features that set it apart from previous convolutional neural network (CNN) architectures. These features addressed challenges such as vanishing gradients, computational efficiency, and multi-scale feature extraction. Let's explore the critical features of GoogleNet: 1. Inception Module: The hallmark of GoogleNet is its Inception module, which uses multiple filter sizes (1x1, 3x3, and 5x5) parallel within the same layer. This allows the network to capture features at different scales, from fine details to more global patterns. The outputs of these filters are then concatenated along the depth dimension, enabling the network to learn diverse and comprehensive features. 2. Dimensionality Reduction: GoogleNet employs 1x1 convolutions to reduce the number of input channels before applying more computationally intensive operations. This serves as a form of dimensionality reduction, reducing the overall complexity of the network. 3. Bottleneck Layers: The 1x1 convolutions also act as bottleneck layers, forcing the network to learn a compact input data representation. This encourages the network to focus on the most important features while reducing the risk of overfitting. 4. Auxiliary Classifiers: During training, GoogleNet uses auxiliary classifiers at intermediate layers. These auxiliary classifiers help combat the vanishing gradient problem by providing additional gradient information to guide the learning process. Although these classifiers are not present during inference, they assist in training deeper networks more effectively. 5. Spatial Pooling: After multiple Inception modules, GoogleNet applies spatial pooling, usually average pooling, to reduce the spatial dimensions of the feature maps while retaining essential information. This prepares the data for further processing in fully connected layers. 6. Global Average Pooling: Instead of traditional fully connected layers with many parameters, GoogleNet employs global average pooling. This approach computes the average value of each feature map and uses these values as input to the final classification layer. Global average pooling reduces overfitting and the risk of high model complexity. 7. Stacking Multiple Inception Modules: GoogleNet stacks multiple Inception modules to create a deep architecture. This deep structure allows the network to learn increasingly complex and abstract features from the input data. 8. Computational Efficiency: By utilizing dimensionality reduction, parallel processing, and efficient use of 1x1 convolutions, GoogleNet achieves a good trade-off between model accuracy and computational efficiency. This efficiency was critical during its development when training deep networks was more computationally demanding. 9. Impact and Legacy: GoogleNet's features and design principles have had a lasting impact on deep learning. Using multiple filter sizes in parallel, dimensionality reduction, and efficient convolutions have influenced the development of subsequent neural network architectures, contributing to the advancement of image recognition, object detection, and other computer vision tasks. In summary, GoogleNet's innovative features, particularly its Inception module and efficient design principles, paved the way for improved accuracy and computational efficiency in deep learning models. Its legacy continues to influence the design of neural networks, driving progress in artificial intelligence. While GoogleNet's initial claim to fame was its remarkable performance in image classification tasks, its impact didn't stop there. The principles introduced by GoogleNet influenced the design of subsequent neural network architectures and catalyzed further advancements in deep learning. One notable extension of GoogleNet's ideas was the development of subsequent "Inception" models, each building upon the foundation laid by the original architecture. These models continued to push the boundaries of performance and efficiency, highlighting the enduring influence of GoogleNet's design principles. GoogleNet stands as a testament to the power of innovation in artificial intelligence. Its pioneering Inception module introduced a novel approach to designing convolutional neural networks, enabling more profound and efficient models that outperformed their predecessors. Through its groundbreaking ideas, GoogleNet revolutionized image classification and inspired subsequent generations of neural network architectures. As AI and ML continue to evolve, the legacy of GoogleNet serves as a reminder that pursuing innovative solutions to complex problems is critical to unlocking the full potential of artificial intelligence. This architectural marvel will forever be remembered as a milestone in creating intelligent machines that can understand and interpret the world around us. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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AlexNet in Artificial Intelligence - Javatpoint

AlexNet in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Birth of AlexNet Major Innovations of AlexNet: Impact on Deep Learning and AI Architecture of AlexNet Applications of AlexNet Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Fully Connected and Dropout Layers Contact info Follow us Tutorials Interview Questions Online Compiler In the landscape of synthetic intelligence and deep getting to know, the name "AlexNet" stands as a pivotal milestone that has shaped the trajectory of modern-day systems gaining knowledge of studies. Developed by means of Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet marked a turning factor in the subject, pushing the boundaries of image class, paving the manner for the resurgence of neural networks, and inspiring numerous next advancements within the area of convolutional neural networks (CNNs). The emergence of AlexNet can be traced again to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. This annual competition aimed to evaluate algorithms for object detection and photo type on a huge dataset containing hundreds of thousands of classified snap shots. The mission changed into discovering objects within 1,000 special classes. Before AlexNet, deep mastering became no longer as extensively everyday or utilized due to computational limitations and vanishing gradient troubles. Convolutional neural networks were around for a while, however they had no longer shown their full capability until AlexNet got here into the image. One element to observe here, given that Alexnet is a deep structure, the authors added padding to save you the dimensions of the feature maps from lowering notably. The entry to this version is the images of length 227X227X3. Output = 13 * 13 * 256 AlexNet, as one of the pioneering deep getting to know fashions, has had a profound effect on various fields, leading to its application in a number of domain names beyond its initial success in photo class. Here are a few notable applications of AlexNet: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Basics of LiDAR - Light Detection and Ranging - Javatpoint

Basics of LiDAR - Light Detection and Ranging Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Understanding LiDAR: Core Principles Applications of LiDAR Technology What can LiDAR generate? Future Developments regarding to LiDAR Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler LiDAR stands for "Light Detection and Ranging." It's a far flung sensing generation that uses laser light to degree distances and create designated 3-D maps of items and environments. The simple precept of LiDAR involves emitting rapid pulses of laser mild and measuring the time it takes for those pulses to tour to gadgets or surfaces inside the surroundings and lower back to the sensor. By calculating the time-of-flight for the mild, LiDAR systems can decide the gap to items with high accuracy. LiDAR generation is broadly used in diverse fields along with self reliant cars, environmental tracking, urban planning, archaeology, forestry, and greater. It affords a way to capture accurate and high-resolution 3-D facts, allowing packages that require precise spatial statistics approximately the surroundings. At its essence, LiDAR is a far flung sensing technology that makes use of laser light to degree distances and create distinct 3-D maps of gadgets and environments. It operates on the ideas of time-of-flight dimension and the reflection of light. The fundamental components of a LiDAR device encompass a laser supply, a scanner, a receiver, and a data processing unit. LiDAR generation has observed vast applications throughout numerous domains because of its capacity to capture accurate and high-resolution 3D statistics. Some first rate packages consist of: LiDAR technology generates numerous varieties of records and outputs that offer valuable statistics about the surroundings being scanned. Here are some of the important thing outputs that LiDAR can generate: The subject of LiDAR technology is evolving hastily, and numerous future traits are predicted to form its capabilities and programs similarly. Here are some of the important thing regions of advancement in LiDAR technology: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Explainable AI (XAI) - Javatpoint

Explainable AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction Explainable AI Methods: Benefits of explainable AI Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Why Explainable AI matters? How Explainable AI Works? Comparing AI with XAI: Explainability versus interpretability in AI How does explainable AI relate to responsible AI? Five considerations for explainable AI Contact info Follow us Tutorials Interview Questions Online Compiler Explainable artificial intelligence (XAI) is a bunch of cycles and strategies that permits human clients to grasp and believe the outcomes and result made by AI calculations. Explainable AI describes an AI model's projected impact and probable biases. It contributes to model correctness, fairness, transparency, and results in AI-powered decisions being made. Explainable AI is critical for an organisation to establish confidence as well as trust when bringing artificial intelligence (AI) models into production. AI explainability also enables an organisation to take an accountable approach to the creation of AI, as artificial intelligence turns out to be further developed, people are tested to understand and follow how the calculation came to an outcome. The entire computational process is converted into what is commonly called a difficult-to-understand "black box." These black box models are directly constructed from the data. Moreover, neither the specifics of what is happening inside them nor the way the artificial intelligence computation arrived at a certain result can be understood or made sense of by the experts or information researchers who do the computation. There are many benefits to understanding how an artificial intelligence empowered framework has prompted a particular result. Reasonableness can assist designers with guaranteeing that the framework is functioning true to form, it very well may be important to satisfy administrative guidelines, or it very well may be significant in permitting those impacted by a choice to challenge or change that result. It is urgent for an association to have a full comprehension of the artificial intelligence dynamic cycles with model checking and responsibility of artificial intelligence and not to indiscriminately trust them. Reasonable artificial intelligence can help people comprehend and make sense of AI and Machine learning calculations, profound learning and brain organizations. ML models are frequently spoken of as black boxes that cannot be interpreted. Deep learning neural networks can be complex for humans to comprehend. Bias, frequently related to race, gender, age, or region, has long been a concern when training AI models. Furthermore, AI model performance might drift or decline due to differences between production and training data. This makes it critical for a company to regularly monitor and maintain models in order to increase AI explainability while also analysing the financial effect of utilising such algorithms. Explainable AI also promotes end-user confidence, model auditability, and productive usage of AI. It also reduces the compliance, legal, security, and reputational concerns of manufacturing AI. Explainable AI is a major criterion for adopting responsible AI, an approach for large-scale AI deployment in real-world organisations that prioritises justice, model explainability, and accountability. To promote responsible AI adoption, organisations must include ethical concepts into AI applications and procedures by developing AI systems based on trust and transparency. Comprehending machine learning and explainable AI allow companies to get insight into the AI technology's underlying decision-making process and implement enhancements accordingly. Explainable AI has the potential to improve user satisfaction by improving the end user's trust in the AI's decision-making abilities. When will AI systems be able to make decisions with enough confidence for you to be able to rely on them, and how will they correct mistakes when they do? As AI advances, ML procedures must continue to be understood and regulated to ensure correct AI model outcomes. Let's look at the differences between AI and XAI, the methodologies and strategies used to convert AI to XAI, and the distinction between interpreting and explaining AI processes. What is the distinction between "regular" AI and explainable AI? Explainable artificial intelligence XAI employs specialised methodologies and approaches to ensure that every choice made throughout the ML procedure can be identified and explained. AI, on the other hand, frequently uses an ML algorithm to get a result, but the designers of artificial intelligence do not fully grasp how the algorithm arrived at that conclusion. This makes it difficult to check for correctness and results in a loss of control, accountability, and auditability (the ability of an auditor to get accurate results when they exam a company's financial reports). The structure of Explainable ai (XAI) approaches comprises of three major strategies. Predictive, accuracy and accountability address technological requirements, whereas decision understanding addresses human demands. Explainable AI, particularly explainable machine learning, will be critical for future war fighters to comprehend, trust, and successfully handle an emerging generation of highly intelligent machine companions. In artificial intelligence, explainability ai is the capacity for clarifying a model's decision-making process. Interpretability is more comprehensive and includes knowing the internal workings of the model. Explainability concentrates on results, but interpretability explores the structure and operations of the model to provide more profound understanding of how it works. The step to which an observer can comprehend the inspiration behind a decision is known as its interpretability. It is the achievement rate that people can anticipate for the consequence of an artificial intelligence yield, while logic goes above and beyond and takes a gander at how the simulated intelligence showed up at the outcome. Explainable AI intelligence and mindful artificial intelligence have comparable goals, yet various methodologies. The main differences between responsible AI and explainable AI are as follows: In conclusion, giving explainable AI top priority promotes openness and confidence by providing an explanation for ai model choices. Achieving complete interpretability is still a difficult task, though. In order to deploy AI in an ethical and practical manner, balance between model performance and transparency is essential. This will ensure accountability and help AI technologies be accepted by society more broadly. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Synthetic Image Generation - Javatpoint

Synthetic Image Generation Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Understanding Synthetic Image Generation: Generative Adversarial Networks (GANs): Pros and Cons of Synthetic Image Generation Techniques for Generating Synthetic Data What Are Some Challenges of Synthetic Image Generation? Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Pros: Cons: Contact info Follow us Tutorials Interview Questions Online Compiler In an age pushed by means of digital innovation, the field of synthetic intelligence (AI) continues to push the boundaries of what is possible. One high-quality aspect of AI's evolution is artificial photograph technology, a generation that holds huge ability for remodeling industries ranging from entertainment and advertising to medicinal drugs and robotics. By harnessing the power of deep studying, neural networks, and advanced algorithms, synthetic image technology gives a tantalizing glimpse right into a future in which computer-generated visuals seamlessly combo with truth. Synthetic photograph era involves the introduction of practical photos via computer systems and the use of algorithms and neural networks. These pictures are not captured by way of cameras but are generated totally from scratch primarily based on styles, patterns, and datasets supplied to the AI version for the duration of schooling. The technology has its roots within the broader field of generative adverse networks (GANs) and has superior considerably with the arrival of fashions like DALL-E and StyleGAN. At the coronary heart of the artificial picture era lies the concept of GANs. A GAN includes two major components: a generator and a discriminator. The generator creates pix, at the same time as the discriminator evaluates whether these images are real or generated. Through a system of iteration, the generator aims to produce pixels which are increasingly convincing, fooling the discriminator into believing they may be actual. The lower back-and-forth competition between the generator and discriminator results inside the refinement of both factors. This technique leads to the production of pix that possesses a terrific degree of realism, frequently indistinguishable from photographs taken by using traditional cameras. Synthetic photograph era, driven by the advancements in artificial intelligence and deep studying, gives a plethora of advantages throughout diverse industries. However, like several generations, it also comes with its set of demanding situations and disadvantages. Let's explore the pros and cons of synthetic image technology: For simple tabular information, you can create an artificial dataset without starting from real records. The system begins from an awesome previous know-how of the distribution of the real dataset and the specific characteristics of the required facts. The higher your information of the information shape, the more sensible the artificial records might be. For simple tabular statistics where an actual dataset is to be had, you may create artificial facts by means of figuring out an excellent-match distribution for the to be had dataset. Then, primarily based on the distribution parameters, it's far more viable to generate synthetic records factors (as described within the preceding segment). You can estimate a exceptional-in shape distribution by: The Monte Carlo approach-this approach uses repeated random sampling and statistical evaluation of the effects. It may be used to create versions on a preliminary dataset which can be sufficiently random to be realistic. The Monte Carlo method uses an easy mathematical structure and is computationally less expensive. However, it's far considered inaccurate as compared to different synthetic statistics era techniques. Neural networks are a greater advanced approach for producing artificial statistics. They can cope with richer distributions of records than conventional algorithms including choice timber, and also can synthesize unstructured information like photos and video. Here are 3 neural techniques usually used to generate synthetic records: While there are many benefits to synthetic information, it affords a few demanding situations: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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What is Deepfake in Artificial Intelligence - Javatpoint

What is Deepfake in Artificial Intelligence? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Defining Deepfakes Ethical and Societal Concerns Mitigating the Threat How to Combat Deepfakes with Technology? How can we spot Deepfake AI? What is the Use of a Deepfake? Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Developing Process Compatible Applications: Contact info Follow us Tutorials Interview Questions Online Compiler In a generation in which technological advancements are hastily reshaping our lives, synthetic intelligence (AI) has emerged as an effective device able to both excellent innovation and capability misuse. Among the interesting yet regarding applications of AI is the advent of deepfakes - a time period that has won significant attention in recent years. Deepfakes represent a blend of deep learning algorithms and digital manipulation strategies, capable of developing hyper-practical fake media content material that could misinform and manage audiences. This article pursuits to delve into the sector of deepfakes, exploring their definition, creation system, potential packages, and the moral demanding situations they pose. A deepfake is a artificial media creation that employs artificial intelligence to superimpose or manage existing media content, generally related to pictures, movies, or audio, with startling accuracy. The time period "deepfake" is a portmanteau of "deep studying" - a subset of AI - and "faux." Deep getting to know algorithms, in particular generative hostile networks (GANs) and autoencoders, are frequently used to generate and control those fraudulent media materials. Deepfakes are created through a multistep method that typically involves: While the introduction of deepfakes increases concerns about incorrect information and deceit, they also have ability applications in various fields, including amusement, schooling, and creative arts: Despite their potential benefits, deepfakes gift numerous ethical and societal challenges: Addressing the demanding situations posed via deepfakes calls for a multifaceted method: Several corporations have come together to make certain that AI is used for correct and deepfakes do not spoil lives. Here they're: Spotting deepfake AI has emerged as more and more challenging as the generation at the back of deepfakes continues to enhance. However, there are several strategies and techniques that could assist in figuring out capacity deepfake content material: Deepfakes, powered by superior AI technology, serve various functions throughout one of a kind sectors: However, it is important to acknowledge the Possible Negative usage: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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What is Generative AI: Introduction - Javatpoint

What is Generative AI: Introduction Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Types of Generative Models: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Generative AI is a kind of artificial intelligence technology that can produce numerous styles of content, together with text, imagery, audio and synthetic statistics. The recent buzz round generative AI has been driven with the aid of the simplicity of the latest user interfaces for creating high-quality text, pics, and movies in a matter of seconds. The technology, it needs to be referred to, is not new. Generative AI turned into added within the 1960's in chatbots. But it changed into now not till 2014, with the introduction of generative adverse networks, or GANs -- a sort of device getting to know algorithm -- that generative AI ought to create convincingly proper snap shots, videos and audio of actual human beings. On the one hand, this newfound capability has opened opportunities that encompass higher film dubbing and rich educational content material. It also unlocked concerns about deep fakes -- digitally solid photos or motion pictures -- and harmful cybersecurity assaults on groups, which includes nefarious requests that realistically mimic a worker's boss. Two additional recent advances with the intention to be mentioned in greater detail under have performed a crucial part in generative AI going mainstream: transformers and the step forward language fashions they enabled. Transformers are a kind of device getting to know that made it possible for researchers to educate ever-larger models while not having to label all of the facts in advance. New models may want to study on billions of pages of text, resulting in answers with greater intensity. In addition, transformers unlocked a new perception known as attention that enabled fashions to sing the connections among phrases across pages, chapters and books in preference to simply in individual sentences. And not just words: Transformers may also use their ability to music connections to investigate code, proteins, chemical substances, and DNA. The rapid advances in so-known as large language models (LLMs) -- i.e., models with billions or even trillions of parameters -- have opened a new technology in which generative AI fashions can write enticing text, paint photorealistic pictures or even create quite entertaining sitcoms at the fly. Moreover, innovations in multimodal AI allow groups to generate content throughout a couple of varieties of media, which includes textual content, pix, and video. This is the basis for gear like Dall-E that mechanically creates pics from a textual content description or generate textual content captions from snap shots. These breakthroughs however, we're still in the early days of the use of generative AI to create readable text and photo realistic stylized photos. Early implementations have had problems with accuracy and bias, in addition to being liable to hallucinations and spitting again bizarre solutions. Still, progress to this point indicates that the inherent skills of this type of AI may want to fundamentally exchange enterprise. Going ahead, this generation could assist in writing code, layout new tablets, broaden merchandise, redecorate business methods and rework delivery chains. Several varieties of generative models have emerged over the years, each with its specific technique to generating content material: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Power System Operation and Optimization - Javatpoint

Artificial Intelligence in Power System Operation and Optimization Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Power System Challenge Artificial Neural Networks (ANN) Applications of Artificial Intelligence in Power Systems Challenges and Considerations Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews How AI Transforms Power System Operation Fuzzy Logic Contact info Follow us Tutorials Interview Questions Online Compiler The global power landscape is undergoing a profound transformation, with a growing emphasis on sustainability, performance, and reliability. Power structures, the spine of cutting-edge society, are at the forefront of this modification. Artificial Intelligence (AI) is playing a pivotal role in revolutionizing energy system operation and optimization. This article explores the combination of AI technologies in energy structures, highlighting their blessings and capacity challenges. Power structures are complex networks that must continuously use stability technology and intake to make certain a solid supply of electricity. Traditional strength systems rely upon human operators to control this sensitive equilibrium, making actual-time choices primarily based on ancient statistics and rules-based algorithms. However, these systems face numerous demanding situations: Power machine analysis with the aid of conventional strategies becomes tougher because of: (i) Complex, versatile and significant quantities of facts, that's employed in calculation, analysis and mastering. (ii) Increase within the computational period of time and accuracy thanks to extensive and full-size gadget information dealing with. The modern-day strength grid operates on the brink of the limits thanks to the ever-increasing strength consumption and consequently the extension of currently present electrical transmission networks and capabilities. This instance calls for a less conservative electricity grid operation and manipulation function, that is possible simplest by constantly checking the gadget states during a much greater designated way than it were necessary. Sophisticated computer gear is actually the primary gear in solving the tough issues that arise in the regions of energy grid planning, operation, prognosis and style. Among those computer gear, AI has grown predominantly in recent years and has been applied to various areas of energy structures. Artificial Neural Networks are biologically inspired systems which convert a group of inputs into a group of outputs by a network of neurons, where each neuron produces one output as a function of inputs. A fundamental neuron is often considered as a processor which makes an easy non linear operation of its inputs producing a single output. They are classified by their architecture: number of layers and topology: connectivity pattern, feedforward or recurrent. Advantages: Disadvantages: Fuzzy logic or Fuzzy structures are logical systems for standardization and formalization of approximate reasoning. It is similar to human selection making with a potential to provide precise and correct answers from certain or maybe approximate records and records. The reasoning in fuzzy good judgment is much like human reasoning. Fuzzy common sense is the way in which the human mind works, and we can use this technology in machines in order that they can perform truly like human beings. Fuzzification offers advanced expressive power, better generality and an improved capability to model complex problems at low or slight answer fee. Fuzzy common sense allows a particular degree of ambiguity in the course of an analysis. Because this ambiguity can specify available facts and minimize problem complexity, fuzzy logic is beneficial in lots of applications. For strength structures, fuzzy common sense is appropriate for programs in many areas wherein the to be had facts includes uncertainty. For example, a hassle may involve logical reasoning, but may be carried out numerically, aside from symbolic inputs and outputs. Fuzzy good judgment provide the conversions from numerical to symbolic inputs, and again once more for the outputs Benefits of Fuzzy logic Artificial Intelligence (AI) is locating a wide range of applications in power structures, revolutionizing the way we generate, distribute, and devour electricity. These packages beautify efficiency, reliability, and sustainability in the energy enterprise. Here are a few key applications of AI in strength systems: 1. Predictive Maintenance: 2. Cybersecurity: 3. Energy Storage Optimization: 4. Fault Detection and Response: 5. Grid Management and Optimization: While Artificial Intelligence gives several benefits to electricity machine operation and optimization, numerous challenges ought to be addressed: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Customer Segmentation with LLM - Javatpoint

Customer Segmentation with LLM Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Customer Segmentation? Traditional Approach of Customer Segmentation Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews The LLM Advantage Challenges and Considerations Contact info Follow us Tutorials Interview Questions Online Compiler Welcome to the world of client segmentation in the future! We're going to begin on a trip that combines the age-old practise of understanding your clients with cutting-edge technology in this post. Customer segmentation, a pillar of successful business strategy, is receiving a facelift, owing to Large Language Models (LLMs). These digital wonders, such as the one you're currently engaging with, add a whole new level to how we discover and respond to client preferences. Buckle up as we investigate how LLMs are revolutionising client segmentation, allowing for levels of personalisation and insight that were previously only possible in science fiction. Customer segmentation is like the art of creating custom-made suits. It's the process of taking your diverse customer base and carefully tailoring your approach to fit each group's unique shape and style. Imagine you have a clothing store, and your customers come in all shapes, sizes, and fashion tastes. Customer segmentation is your tool to categorize them into different groups based on similarities. These shared traits can range from their age, gender, and location (the basics) to more complex aspects like as buying behaviours, brand loyalty, and even preferred colours. Businesses acquire a better knowledge of what makes each group tick by recognising these shared features. What is the significance of this? Think of it this way: You wouldn't suggest a stylish tuxedo to someone who enjoys casual jeans and t-shirts, would you? The same is true for customer segmentation. It assists organisations in developing marketing messages, product suggestions, and experiences that are relevant to each demographic. It's like tailoring the right suit exactly for them, enhancing the likelihood of a sale and developing client loyalty. So, in essence, customer segmentation is all about knowing your customers inside and out, putting them into convenient groups, and then delivering a shopping experience that feels like it was made just for them. It's a bit like being a matchmaker for businesses and their customers, ensuring everyone finds their perfect fit. In the traditional approach to customer segmentation, businesses relied on basic information like age, gender, income, and location to group their customers. While this method offered a broad view of their audience, it often missed the nuances of individual preferences and behavior. It was like having a rough sketch of your customers, useful to some extent but lacking in depth. However, individual customer behavior and preferences are diverse and intricate. People with similar demographics can have vastly different tastes and needs. To truly connect with customers and tailor products and services effectively, modern businesses are turning to advanced technologies like Large Language Models. These tools can dive deep into unstructured data, uncovering insights that go beyond demographics, helping businesses understand why customers make choices and how to meet their unique needs effectively. It's a shift from a broad-strokes approach to a finely tuned understanding of individual customer desires. Enter Large Language Models (LLMs) like GPT-3.5, the very technology behind this article. LLMs have the incredible ability to process vast amounts of text data, enabling them to understand and generate human-like text. So, how can LLMs enhance customer segmentation? While LLMs offer immense potential, there are challenges to consider. Privacy and data security are paramount when dealing with customer information. Businesses must also be cautious about over-automating customer interactions, as the human touch remains crucial. Finally, client segmentation using LLMs is a major changer for organisations. Businesses can engage with their consumers in unprecedented ways by using the potential of natural language understanding, personalisation, real-time information, and enhanced customer service. Those who embrace LLM-powered consumer segmentation will be at the forefront of innovation and customer pleasure as the business landscape evolves. Don't miss out on this game-changing approach-it's time to investigate the potential of LLM in client segmentation. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Liquid Neural Networks in Artificial Intelligence - Javatpoint

Liquid Neural Networks in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What are Liquid Neural Networks? Features of Liquid Neural Network Uses of Liquid Neural Networks Visualization of the Liquid Neural Networks Limitations and Difficulties Faced by Liquid Neural Network Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler A Neural Network is a part of Artificial Intelligence that trains the computer, allowing it to recognize patterns like a human brain. It comprises various layers, including input, hidden, and output. The activation of any node depends on the threshold value. If the node's output is above the threshold, then the node is activated. Otherwise, no data will be passed to the next network layer. The interconnected neuron network can do various complex tasks like Facial recognition, handwriting recognition, etc. Neural Networks need labeled training data for processing the model. There are different types of neural networks: Another type of neural network is discovered, namely Liquid Neural Network, that learns all over the model process with the jobs, not only at the time of the training. Liquid Neural Networks are a type of Recurrent Neural Network. It stores the memory after processing the data sequentially. It changes itself according to the new inputs. It increases its performance by working with inputs of different lengths. Liquid Neural Networks, as the name suggests, it works similarly to liquid behavior. It makes dynamic patterns that help the information flow in a fluid matter, just like a fluid. Traditional Neural Networks work with fixed weights, whereas liquid neural networks work on dynamic connections. Liquid Neural Network (LNN) works efficiently on the time series and continuous data. LNNs can adjust the number of neurons and connections in every layer according to the new input. According to studies and research, the liquid neural network can build various complex dynamics besides having 302 neurons. For instance, Sentiment analysis is used to analyze human emotions using their texts and statements. The capacity of LNNs to gain knowledge from real-time data allows them to understand changing language and new phrases, resulting in more precise sentiment analysis. Machine translation can also be done using these qualities of liquid neural networks. We can visualize the dynamic behavior of the liquid neural networks using two different visualizations: Liquid Neural Networks are much more flexible and dynamic and work more efficiently than artificial neural networks, but there are also some limitations in liquid neural networks. Liquid Neural Networks are more adaptive, dynamic, and efficient than traditional neural networks. As Artificial intelligence is evolving rapidly and giving rise to a new and advanced future, solving complex tasks and problems easily, we will learn about many new techniques contributing to the challenges more efficiently as Liquid Neural Network does. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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