Update blogcontent.md

blogcontent.md

@@ -4,16 +4,12 @@
 In February, Reddit announced a [new content partnership with Google](https://www.cnet.com/tech/services-and-software/reddits-60-million-deal-with-google-will-feed-generative-ai/) where they would provide data that would power the new Generative AI based search engine using Retrieval Augmented Generation (RAG). [That attempt did not go as planned](https://www.technologyreview.com/2024/05/31/1093019/why-are-googles-ai-overviews-results-so-bad), and soon, people were seeing recommendations like adding [glue to pizza](https://www.theverge.com/2024/6/11/24176490/mm-delicious-glue):
 
 
+<p align="center">
+  <img src="glueonpizza.png" />
+</p>
 
-<p id="gdcalert1" ><span style="color: red; font-weight: bold">>>>>>  gd2md-html alert: inline image link here (to images/image1.png). Store image on your image server and adjust path/filename/extension if necessary. </span><br>(<a href="#">Back to top</a>)(<a href="#gdcalert2">Next alert</a>)<br><span style="color: red; font-weight: bold">>>>>> </span></p>
+In the age of artificial intelligence, [massive amounts of data](https://arxiv.org/abs/2401.00676) fuel the growth and sophistication of machine learning models. But not all data is created equal; AI systems [require](https://dl.acm.org/doi/abs/10.1145/3394486.3406477) [high-quality](https://arxiv.org/abs/2212.05129) [data](https://proceedings.neurips.cc/paper/1994/hash/1e056d2b0ebd5c878c550da6ac5d3724-Abstract.html) to produce [high-quality](https://dl.acm.org/doi/abs/10.1145/3447548.3470817) [outputs](https://arxiv.org/abs/1707.02968).
 
-
-![alt_text](images/image1.png "image_tooltip")
- \
- \
- \
-In the age of artificial intelligence, [massive amounts of data](https://arxiv.org/abs/2401.00676) fuel the growth and sophistication of machine learning models. But not all data is created equal; AI systems [require](https://dl.acm.org/doi/abs/10.1145/3394486.3406477) [high-quality](https://arxiv.org/abs/2212.05129) [data](https://proceedings.neurips.cc/paper/1994/hash/1e056d2b0ebd5c878c550da6ac5d3724-Abstract.html) to produce [high-quality](https://dl.acm.org/doi/abs/10.1145/3447548.3470817) [outputs](https://arxiv.org/abs/1707.02968).  \
- \
 So, what makes data "high-quality," and why is it crucial to prioritize data quality from the outset? Achieving data quality is not just a matter of accuracy or quantity; it requires a [holistic, responsible approach](https://huggingface.co/blog/ethics-soc-3) woven throughout the entire AI development lifecycle. As data quality has garnered [renewed ](https://twitter.com/Senseye_Winning/status/1791007128578322722)attention, we explore what constitutes "high quality" data, why prioritizing data quality from the outset is crucial, and how organizations can utilize AI for beneficial initiatives while mitigating risks to privacy, fairness, safety, and sustainability.
 
 In this article, we will first provide a high-level overview of the relevant concepts, followed by a more detailed discussion.