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What we do is we make AI approachable and actionable for marketers and business leaders in non-market. So our mandate is growing are larger than just marketing. So in my day to do a role, you know, again, starting out as a marketer when I first got started with AI was in the same spot every marketer who hasn't does know a lot about this stuff today. It's like I didn't know anything about AI. It was only through working with Paul that I learned how to start thinking about this stuff in my job in my work and how to actually use it to start transforming productivity performance in our business.

And from there, it was kind of like never look back at the ground running. And over the years, you know, learned how to start applying a lot of these tools and technologies to our business or marketing today. I'm the chief content officer. So, you know, we're a small company. So I wear a lot of marketing and sales in April.

But my primary kind of remit is to grow our traffic leads and ultimately sales with our digital marketing presence. I do that. And any given day using, you know, tons of different AI tools for a bunch of different use cases and constantly exploring how do we do more of what we do without having to, you know, go higher 200 people to do it. Instead, hopefully we can use AI to accomplish a lot of really interesting outcomes. So Mike is knee deep in AI.

And the marketing AI institute started back in 2016 with a blog and a newsletter about marketing AI. It was really just an internal initiative at a marketing agency. And started by Paul Retzor who we've actually interviewed in the past. Paul and Mike have now expanded this to obviously doing a lot of consulting. They do a weekly podcast, which is a must listen next to brushstrokes, of course.

And they've got this great event, which I actually attended called Mekon M-A-I-C-O-N. And that was an incredible lineup of speakers. And really was where we incubated the idea of doing print smart. In addition, Paul and Mike, as I mentioned, authored the book around marketing AI and have now started rubbing elbows with some of the biggest players. And so you really need to check out the marketing AI institute because they are again uniquely qualified to advise us all on our business. And with all the questions that are at the forefront of AI, I kind of wanted to flip it and ask Mike what he was actually bullish on for the future.

I'm kind of trying to attack this maybe for me and E. Cancelick. I'm very, very cognizant in both, so our business is a small business. We wear a lot of different hats. Literally, we live and die by the correct mass of the decisions we make. It's very much start up.

If we make the wrong decision or something, the focus on, we lose a day. And like, that's true of any organization, but others can recover from a little better than the company of our side. In my personal, I feel a similar urgency, not a huge amount of pressure on myself, but just trying to be really conscious and intentional of those limited times. We all have, you know, whether it's on this planet today, whatever, we all have 24 hours and how rich or poor you are. So this idea of time is really resonating a lot with me.

And this is what gets me most excited about AI is I think we all just by virtue of our crazy life and business are we waste a lot of time. We do a lot of stuff. We don't need to be doing. We burn so many hours trying to figure stuff out. It takes way longer to figure out than it should.

And I just see already in my own life, I still so early, these tools have just dramatically improved how I'm able to use my time like what kind of impact of it will be. It's not just product to be there. It's a big piece of it, but it's just approaching problems in totally new ways, getting ideas on demands like having intelligence on demand, extra brains that I can use to solve problems, which is really rewarding in general. But also, it's just like, you know, there's not like easy tricks is I'm not saying all your works take two seconds and go chill out on the beach. But being able to not spend so much time on stuff that's not rewarding stuff that doesn't interest me, stuff that is grueling mundane rigorous, but like has to be done, but I don't like doing that's extremely rewarding to someone like me and an organization like ours where it's like, Oh, okay, this is really beneficial.

You can't at the time is the only thing we can always make more money, hopefully, can always find more perspectives, whatever, but times the thing. And so that's really to me why I'm so bullish. It's like previously traditional software been really helpful, like I have no knock, I'm like, what we've used last 20 years, it's been awesome. But this is just a whole new world. They're being able to do so much more in the time.

The other variables that really, really keeps me up makes me excited and such a tangible benefit. It does a lot of do work, whatever it is. You know, when I went to the make on event last year, a gentleman by the name of Christopher Penn, who is fairly known throughout the AI world, went through about 120 different slides about AI and my head was spinning. And at the end of it, he actually said, I suppose engagement is going to be at a premium and things like direct mail and the haptic nature of print will probably come back into Vogue. I perked right up and thought, that's fantastic.

And I wanted to ask Mike a little bit more about that. How AI actually would put a premium on our ability to engage and build relationships? Here's what he had to say. In my eating and recent experience, see us a lot in like sales, especially like my new workshops with sales people. This is no knock on them.

It's like they're always interested in like, how do I personalize that scale for people? And like you can do that with some of these tools, which is interesting, but it's more about I'm like, no, no, no, no, let's actually talk about the 10 other things you have to waste a time and time on every day. What if we to free you up from those that you can? Is that about you just spending less hours a day? What if we just spent more hours on personalizing, on developing one-on-one relationship?

What how much better would your results be? More valuable would your business be perceived by your customers? So that it's like reallocation. You might end up in some cases spending more hours on certain tests as a result of AI. It's really just which one.

Right? You know, with the relentless nature of AI and technological innovation that has dominated our lives from the last decade, maybe two decades, there are clearly many of us who are a bit fearful, have a lot of questions about our relevancy. We have questions about morals, ethics, and we'll cover all of that at the Prince Mart Summit. But with everything spinning around us like this, I asked Mike simply, what's your advice? What's the first thing that you would do if you were us?

Give it a listen. If that is a great question, I think your first step has to be in whatever way it makes sense to you getting started. By that I mean we'll talk extensively at the event and further and everything we talk about all the steps to take. But really there's no substitute for simply firing up. There's no excuse.

If you can send a text message, if you could write email, you can use AI. That's what's so exciting about some of the tools out there. You can't use every single tool. You don't have it, you J.I. strategy.

You might not have these cases. That's okay. We'll get to all of that. But literally just fire up chat to you and start playing around. This is not traditional software where you have a manual.

Oopening I doesn't even have onboarding for any of the tools they create. They don't have any real instructs, which is crazy. There's no handbook, there's no rule book. You're not going to break anything. You're not going to hurt anyone by firing up today.

Right now, chat to you. I'm starting to play around with it. Even if you are the least creative person in the world, ask it. What are some things you can do? That's what's cool about this.

It's like there's literally no excuse. Just try it out. It doesn't mean you're going to get it all at once. It doesn't be hard to figure it all out at once. But really I get to see someone that plays around with it for an hour and doesn't have at least one aha moment.

So I think that's really, really important, even though it sounds really simple. That having that quick getting quickly to an aha moment, I think it will help you a lot. Yeah. Okay. This is pretty interesting.

I think it's clear you've got to come to the Prince Smart Summit May 7th, 2024 and hear Mike speak. I think the presentation is going to be fantastic. It'll probably change from now till then because that's how fast AI is changing. But he's a great resource as is his organization. So I asked him to do a little shameless plug and let us all know where to find them.

So your first stop is marketing AI institute.com. That is our main website. You can find all of our available resources there. I would also direct you. If you're on that website, click on Education.

I go to the link titled intro to AI. That is our free live class. We run every few weeks with our CEO, Paul Racer. That is exactly everything you need to know to get started with AI. About 30 or 40 minutes just build like a quick foundational knowledge.

We add probably 20,000 people take it so far. So it's really, really valuable, even though it'll say, ensure that AI is from marketers. If you're not directly related to marketing, it guarantee you still get a lot of value out of it. So start there and then also take a look. I would say at our event, Maytime, M-A-I-N-O-N-D-A-I, and also take a look at our podcast, the Marketing AI Show.

Which you can find very quickly hosted by Paul and myself, Mike Putt. Every week we cover what is going on in AI and what you need to know as business owner, leader, professional. I want to say thanks to Mike for joining us today and giving us a little teaser for the Prince Mart summit. I've said it multiple times. I think this is an event that's needed.

Nobody else has done it. So we've been able to step up and put together an unbelievable lineup. Right? With Mike headlining the show, we've got a gentleman from Google, people from IBM, we've got our friends Dave Rosendahl from Mindfire. I got a CEO from Pandata.

We've got some panel discussions. I've even got an attorney who's going to come to us and talk about the legal ramifications. Which, again, for the printing industry, in any marketing services industry, you need to understand that. This is not going to be an event for someone who is super technically sophisticated. This is for any business leader who is thinking to themselves, how am I going to compete?

How am I going to move forward? Well, you got to start somewhere. So why not come to beautiful Cleveland on May 7th and listen to this amazing lineup? Rob Elbows with the type of peers that you want to be associated with. And have a wonderful time.

I promise you, it's going to be electric. And I think it's going to be a competitive advantage for anybody that attends. My thanks again to all of you for listening. My thanks to Mike and I just can't wait to see you all the Prince Part Summit. Until then, be the Buffalo and all the best.

Transcript of 'Brush Strokes Episode 91': Hey guys, I'm Mark Potter. Welcome to another episode of Brushstrokes, a podcast powered by Canvas Magazine. Guys, today's episode is brought to us by the Prince Smart Summit 2024. Being held on May 7th at the Huntington Convention Center in Cleveland, Ohio, the Prince Smart Summit is a new event for thoughtful leaders in print. Dive deep into the world of getting smarter, explore the latest advancements in AI and its impact on all of us and network with the best in the industry.

With renowned speakers from the AI Institute, Google, IBM, Mindfire, and more, this summit promises to be a transformative experience for all attendees. Visit americusprincho.com and register for the Prince Smart Summit today. And guys, one of those speakers that I'm super, super thrilled about is a gentleman by the name of Cal Al DuBabe, CEO of PanData. Now Cal was born in Saudi Arabia and was educated in Cleveland at Case Western. And after toiling with everything from neuroscience to mathematics, he came out of there as an expert in data science.

PanData today manages and assesses risk within AI and technology for over 50 large organizations. Now Cal is also a speaker on the circuit, if you will, around AI. And I had the great privilege of hearing him before and was kind of taken with him. But I think after you hear my conversation today, you'll be equally excited because he is one that is alleviating some of our fears. He's gotten into the management of risk associated with AI and what he has come up with is that there's an opportunity to make it even more human.

And that's what made me so excited. We actually started to talk a little bit about education at the beginning of our conversation. And I was curious if in fact someone going to college might end up being behind because AI is moving so fast. And are we going to be kind of outdated? And he shared some of his thoughts.

Give it a listen. So there's an article in HBR that left an impression on me last year. And it talks about the half-life of a skill. And specifically for your average skills, the half-life is somewhere between five and seven years. And so half-life doesn't mean when it ends, but half of its usefulness is gone.

And then it might have a long tail. But in tech, that number is two to three years for the average tech skill. And what we're seeing is by the time you've invested in learning a certain skill, most of its useful lifetime has passed. And so it's a challenge for teaching, challenges for absorbing, and re-skilling. And we're still building up new wisdom that what the day-to-day is going to look like for jobs that are primarily composed of those skills that have a fast fading light.

And it could be a specific esoteric coding language, or it could be using a toolset like Power BI, or it could be prompt engineering, which was the hottest job last year. And now people are saying prompt engineering is dead. Right? There's a lot of confusion in the marketplace. Putting that all aside.

There are skills that are ever green that need to be cultivated. So decision-making is one of them. I'm a big fan of Cassie Cazercoll if he talks about decision-making as a skill that one can train. Thinking about human responsibility with respect to automated decision-making systems, conventions around what is good data, how does bias enter into data sets. These are things that one can learn, and one needs to learn at some great level of depth to be affected by.

So going back to my education, I took this very non-traditional path that seemed borderline insane to the point where in my seventh year when I call my dad to say, Dad, Dad, I know what my major is going to be. It's this new thing, and it's called data science, and it just announced the major, and I'm going to switch my major to be that. And you know, it's funny, because that's what launched my career, becoming the first data science graduate from case. But at the time, my dad said, Son, just dug your head and graduate, please. But what I was seeing at the time is, oh my gosh, this data thing is taking off.

And actually, I'm learning I was at the time my major was neuroscience, and I had studied advanced mathematics, I had studied statistics, I had studied the application statistics to population health dynamics, so these were all things that existed in other majors, and all I did was a cosmetic change, and I said, ah, these are these many, many things I've studied actually amalgamated into data science. Okay, play that for today. You're not going to have an AI major. The chances are, you're already studying components of it, that at some point are going to be assembled in a slightly different configuration, and maybe we've sought the name on it, or maybe we don't.

But that was my journey. So right off the bat, you can tell that not only has Cal spent a lot of his educational experience diving into AI and its impact, but he's now the leader of an organization that's doing it for organizations. And I wanted to find out a little bit more about what PanData did, and what the challenge is that they were facing, or that organizations that they were serving were facing. And here's what he had to say. Yeah, so I started PanData in 2016, and this was when I was wrapping up my education, and I kept hearing over and over again, we have data and don't know what to do with it.

And that was the conversation. The conversations had spun up, had due clusters, it was the realm of big data, and everyone was excited about we have all their stuff. Now what? Yeah. And so at that time, data science was still a growing profession, relatively niche, but it was the discipline of making sense of data.

And so I started PanData to address just that problem. I had been working with some health systems in the context of pilots for my first start-up, which was building machine learning models on patient data. And I discovered that they really didn't need a software solution to solve their problem. They really needed human navigators to help them understand what trends were in the data, what that meant for patient care, what that meant for operational decisions, and so that all kind of evolved into what became PanData. And so I started it to literally help organizations make better decisions with data and that evolved into machine learning, that evolved into AI.

And over the years, we, after working with more than 25 blue chip companies, about 50 altogether by now, varying depths of engagements that found a specialty in the world of high risk environments meets machine learning. So what's a high risk environment? That's health care, finance, energy, defense, situations where the cost of an error is high. There's regulation or restriction on how you can use your interact with the data. That is basically your fuel for these machine learning solutions and AI-based solutions.

And we built up a practice on helping organizations identify what's with building. How do you think about the potential risks, potential cost in the state, and then navigate a project from end to end? Idea all the way to building it and then managing it on an ongoing basis while creating value and also managing potential downsides. AI is dominating the air waves these days. But may in fact be dominating our minds even more.

And so I wanted Cal to explain a little bit more about what AI truly is and what are some concerns for humans in general. Give it a listen. So let's take a step back and define AI because it's not new. Generative AI is probably the newest to the world conceptually. And we tend to use Gen AI and AI these days almost interchangeably.

And I think that's a detriment to the field because there's conventional wisdom we've had around this for some time. AI is software that recognizes and reacts to patterns. That's it. It's looking at patterns that could be in spreadsheets, where documents or images, your video or audio, etc. And then you're doing something with that maybe it's accounting.

How much of something is present or you're labeling and categorizing or you're selecting amongst a large set of probabilities or predicting a future state or you're generating new patterns. And that small little fraction that's captured headlines is the world of generative AI. But this definition is important because when we're recognizing patterns, we're guaranteed one thing. We're going to be wrong some of night at the time. And that was true of early ML systems.

That's true of the generative AI systems that are on the market today. And so the art of using AI or machine learning these high risk environments is the art and the science of managing being wrong. Does that make sense? So I've been doing a couple of, I want to know, LinkedIn did die the last few days and a couple of headlines caught my attention. I started pontificating about them.

But a couple of my favorites that I read was a study that was done on GPT4 used in resume evaluations. And wow, this was a fascinating deepbap shocker. After simulating 800 different resumes where the only differences were the names of the individuals that came from predominantly, they used predominantly Caucasian or predominantly Hispanic or African-American names except for this experiment. But the same exact resume. And they found that for certain job positions, GPT 3.5, which is the most commonly conceived model, would select or rank the African-American names as the lowest for certain roles relative to other roles.

And the only thing that was changed was like the name. And the headline is GPT4 is biased. Well, I'm like, well, no, no, yeah, of course. We knew this. We had the same problem when Amazon attempted this solution in 2018 and uncovered that they simply could not remove bias from the data.

And the models would key in on things like, you know, somebody's resume might say activities were women's chess club or men's soccer team. And even if you were removed all the gendered words, it started to key in on active and passive forms of writing. And so there's all of these signals in data that perpetuate patterns that may have historically been true, whether or not they're desirable. And then we amplify it because again, we're seeking these patterns and we're trying to do something. I read this this experiment.

I'm like, well, duh, we knew that there would be bias in the model. And chances are, there will always be bias in some models. Now what do we do about it? That's where the thought will most come. That's where you need human evaluators coming up with, okay, what should we be looking for?

And how do we measure it? How do we detect when it's there? What do we do about it? How do we want to assess ourselves? How do we raise a flag when a pattern significantly deviates from what our expectation of good looks like?

How do we define and measure what good looks like? That's all the stuff where you still need people. With all of us a little bit nervous about what this means for the future, I wanted to take a little bit of time and share some advice. What should we be doing to get our arms around this? Clearly, I believe we should be going to the print smart summit, but seriously, what should we all be doing on a day-to-day basis?

And then the second part that I wanted to talk to him about was this idea of good versus evil. Are we going to use AI for good or evil? Here's what he had to say. So I mean, here's the bed news. AI is not necessarily, it's not self-aware, it's not coming for your job.

But people with bed intentions also have the same access to tools that we have access to, less scruples. So that's something to keep in mind. That being said, we're entering this interesting stage and tech where the biggest barrier of your five, 10 years ago was, do we even have the data? Do we even have access to data science resources to alpha-strain models? And keeping in mind that Gen AI and the use of tools like chat GPT and other official solutions, that's a small fraction of the big world of machine learning and AI.

But it's the one most folks will be interacting with. You now no longer need a data science team to download chat GPT or to set up an application that calls on the OpenAI API or any of the other Ick Roblox out there. And we've moved past the whole, we don't have data, we don't have patterns to. Now we can build things. And one of the problems with AI, specifically, generative AI, is it's not so great at saying, I'm not going to do that.

If you test it with something, it will do it. And it will fill in the blank whether or not that's a good idea. And so we're entering an era where the barrier to execution on these types of tools isn't technical. It's stopping to ask the question, should we be solving this with AI? Do we need to solve this with AI?

How are humans approaching this today? And is there another alternative? Okay, AI is the right choice. How do we want to measure the success or potential consequences of this? So we haven't had that friction.

And so we're seeing a lot of these poor implementations of AI, genying AI. And that's one of the things I've been spending a lot of time trying to educate folks on and share good healthy examples. That's part of what I'll be talking about at the conference. One of the things that I worry most about is if we're going to lose our voice, that we don't get the humanness in there, that we don't get our personality in the content that is going to be created and digested. I asked Cal Point Blank, are we simply at a point where bots are just going to be talking to bots?

LinkedIn has become somewhat depressing these days where you can almost smell the AI-generated content. But I don't know. I think we're going to get to a new equilibrium. And we've all seen some version of this meme that basically it's some executive thinking they're smart. They take a few ideas.

They throw it into chat GPT. It comes up with a whole email. And then on the other side, somebody has their AI summarizing it into to do. So we're going to get over the silliness and realize, oh, this is unnecessary. And it might force us to communicate more directly, psychously.

Why do I need a bot to make this long for another bot to make it short when I can just say, hey, I needed this. Thank you. So I think it's going to force us to question how we communicate with one another. In the meantime, there's going to be a lot of noise. And my prediction is it's going to self-select for authenticity.

We're going to start subscribing to sources of information where there is credible, trusted bot leadership. That's going to come at a premium when everyone's cost of creating goes to zero because that's what we're doing here. We're amplifying the amount of low quality content out there. Folks are going to funnel themselves into channels where they can more reliably access information that they trust and resonate with. That being said, I've started to question myself.

Like if I, you know, an AI detectors aren't really good. And I wouldn't trust my judgment 100% of the time, but you can generally smell when something is AI produced. It's got a few characteristics that I don't know how to describe it. You're just like, ah, this is definitely, this smells like chat GPT. And then I asked myself the question, why am I triggered by that?

And it's because my guard is up. And I know that the default output of these language models isn't much substance. It's very generic. And so that's not what I want to consume. But if there's high-valued information being formatted for me through one of these models, I'm okay with it.

It's really not about whether or not it's AI generated or AI was used in the synthesis. It's about the value of the underlying content. So, you know, I think we're going to get used to AI producing content. But we're going to be more, you know, the value of what we're consuming is going to be the metric that matters to those. Again, there's a lot of concern in the world.

And there's a lot of people who think that AI could go negative. Heck, Elon Musk has talked about his concerns for the world. But I wanted to hear from Kyle firsthand if he thinks that there are going to be positives that come from all of this. Here's what he had said. It starts back to something we talked about earlier.

You know, I mentioned education. And we're triggered left and right by this AI. They can raise that. Maybe we shouldn't. And it stems from like, oh, you've taken the easy route.

Well, what's wrong with the easy route? This reminds me a lot of the debates early on when I was in college about the use of scientific calculators. Are they allowed in finals? Are they, you know, do you have to actually prove them out by hand? Intiduity, that'd be silly.

I can't believe at one point. I had to prove out certain formulas and that I had memorized that in my head because from the day I've been practicing data scientists. Till today, I've not really had to prove it out. I've had to know how to slot it into a puzzle, into a recipe. Lately, I use a lot of the judgments that I've gained from that to smell a problematic setup of an AI problem, say, ah, this is where we might run into some problematic bias issues.

But I am not proving out math formulas. So, you know, we're now dealing with this on the content side of things. And I will never forget one of my most memorable classes was actually a journalism class. And I was so used to, you've got to write 12 pages on X. That was your midterm, that was your final, whenever we have like some kind of like non-stem related course.

And you're like, gosh, you've got to mash out on the keyboard 12 pages about a topic. You're like, oh, I'm on page 10. All right, let me find like two more pages to type out. This class, we were graded on how short it was. You had to get your whole story in.

And for the final, it had to be under a page and a half. Every half page beyond that, you lost a letter. It like, this professor was great. And it forced me to think about how do I say everything I need to say in less. And that was actually a harder setup than let me mash on my keyboard for two more pages.

Okay, we now have these generative AI solutions that are bringing the cost of creation to zero. That I wouldn't be worried about. Well, students can write 10 pages of content now. I'd rather raise the bar, get the students to tell the story in a half page or less. Let's see them do that.

What are they going to cut? How are they going to edit it? How do they scrutinize it? I think there's an opportunity to raise the bar on what we expect a learner to be able to do, given these new tools. People still matter, folks.

And AI is just this latest wave of technology. They don't probably never stop. But the one thing in my mind that cannot go out of style is humanity. Being a human, engaging with people. And so I asked Cal if he could drill a little bit more down on the impact that this whole thing will have on people.

And I was pleasantly surprised with what he had to say. The world has so many choices now. And cutting jobs as a knee-jerk reaction is maybe the poorest. But on the flip side, on the flip side, I was really, really impressed by Ali Miller's recent webinar. I have a whole bunch of things going on past few weeks.

And this was one of those things where I'm like, I'm going to rearrange my date and make sure I get those. And in some of the tips she shared, she actually talks about how she screens her staff. And she says, all right, part of the interviewing process is, screen share with me. Show me how you use chat GPT to accomplish a task. And this is something unlike what we would have done with hiring data scientists a few years ago.

We're going to appear a program together. We want to get a sense for how you think about problem solving. It's not like it sounds like a weird thing to somebody who's never gone through that. But we've done this for technical roles because we want to see how someone problem solves how they think. We're not necessarily looking for typos or whatnot.

But we want to see if they have the skill. And if they have the capacity to learn a certain skill. So she does this with general folks who join her team. And it's one of their KPIs. Are you effectively using gen or did AI in your day-to-day work plus to be able to do more amplify your impact?

So there's somewhere in between this is coming for all of our jobs. And we've we've got an imperative as organizations to help our communities figure out how to use this stuff to do more at higher quality while having greater fulfillment in their jobs. As we were wrapping up our conversation, I sensed that Cal had a real energy about him and seemed pretty eager to present at the Prince Smart Summit and share what he's learned. Well, don't take my word for it. Here's Cal.

I'm really excited to share some experiences that I've learned and my team has learned over the past five years of building machine learning and AI solutions in these mission critical environments. How do you establish trust? How do you think about the potential unintended consequences? And how do you think about designing systems in a way that amplify the humans? That it affects and the humans that use it?

And really to make AI work. AI shouldn't be the flashy shine object. It should be the work. It should be the results. It should be the outcome.

And so I really want everybody to walk away being able to do two things. Ask better questions of AI. And then make AI boring. I hope you will agree that Cal Aldubaib, great name, by the way, is a really, really positive influence in this world. And I could not be more excited that he is going to present at the Prince Smart Summit in May.

My thanks to Cal for sharing his time. And obviously, I'm even more grateful for the fact that he's going to come and speak at our event. Now, remember May 7th, 2024 at the Huntington Convention Center in Cleveland, Ohio, the Prince Smart Summit is a new event for leaders in print. If you're a thoughtful leader and you have aspirations of thriving going forward, then come spend some time with us. Get a little smarter, explore the latest advancements in AI and how it's going to impact us.

Come network with the best in the industry. Come listen to a renowned list of speakers from the AI Institute, Google, IBM, Mindfire and Cal and take advantage of a transformative day that could compel you to do new things and elevate your business to new heights. Go to america'sprintshow.com to register today. It's the Prince Smart Summit 2024. Again, thanks to Cal.

Thanks to all of you for listening. I can't wait to see you there. We'll continue to bring some of our speakers on this podcast. And I wish you all the best. Until I see you, be the buffalo.

Take care. Hi.