The AI for Physicians Show: Webinar Series. Session 1: The Can and Can’ts of AI: What is AI and Generative AI and What AI Models are Available to Use and the Benefits of Each?

Session 1: AI Marketing and Operations for Physicians. Topic: The Can and Can’ts of AI: What is AI and Generative AI and What AI Models are Available to Use and the Benefits of Each? (August 30th, 2024)
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[00:00:00] Speaker: Oh, that looks weird. Doesn’t it

[00:00:27] Speaker 2: get started here in just about a minute group.

[00:00:33] Speaker 2: All right. Sorry about that. I am traveling. If I had known better, I would have actually planned appropriately, but campaign strategy is pretty important. And I, I missed the mark on the timing. So I hope everyone is doing outstanding today. I actually have just a quick, a quick little set of notes here to kind of go by, if you just give me just a moment.

[00:01:00] Speaker 2: The check into a hotel price link kind of made a mess of the reservation a little bit here, so. Let’s see, I am pulling up my notes. Maybe, maybe, maybe. Alright, here we go. All right, so here’s what do today. I’m going to go over the show and what this is all about. That’s the first thing we’re going to be doing.

[00:01:21] Speaker 2: Why you need to listen to me? Like, who am I? What do I do? Like, my goodness, why should I give you the time? We’re going to discuss the today’s topic and what we hope to learn from that and how to apply it. Why that’s important. And then we’re going to get into some Q and A and things like that.

[00:01:37] Speaker 2: So we have a very, actually it’s a pretty exciting, I think it’s pretty exciting, I hope you feel the same way. Pretty exciting situation here. So I’m actually going to kind of pull up the, I’m gonna go to our website actually, you can see so many different tools going on that I use on a regular basis I’m going to go over what we’re actually doing here first.

[00:02:00] Speaker 2: So. This, this is the AI show, right? So we’re actually focusing on physicians and we want to make sure that as physicians, you guys and gals are busy running your jobs, your surgery center, your practices, and you’ve heard of this AI thing, right? You’ve gone through and you’ve heard of chat GPT and how it’s changing everything you read about NVIDIA and you’re like, Oh my goodness, why is this company making so much money and why do people believe in it?

[00:02:25] Speaker 2: Yeah. The goal of this is actually to really, you know, we’ve done all the hard work, right? So we understand how to use these technologies. We use them every single day within this 15 minute time span. What I’m hoping to do, and we have the topics in here on the screen here. We’re going to go through these over the next couple of months, kind of step by step, starting with some of the basic foundational things, and then we’re going to be building that up to some, you know, to things that are much greater and bigger, right?

[00:02:57] Speaker 2: We’re going to teach you soup to nuts, how to use this stuff practically. There might be tools that we recommend that are paid. There might be ones that, you know, that you can use for free. I’m going to look at opportunities to kind of help take some of those things that we know that work. And what I know about your businesses, right.

[00:03:15] Speaker 2: In terms of what you’re doing every single day to basically give you the tactical pieces necessary to either do that yourselves. Hire somebody to do it inside, you know, use Upwork or one of the other ones to go do it, or maybe you want to outsource to an agency that’s your caliber of situation. But at the end of the day, it’s about your growth in that.

[00:03:36] Speaker 2: So my name is Chris Bernard. I’m the lead strategist at Content Engagement Lab. I have been right out of school, actually. Actually, I went to school for advertising and marketing at Kent State University. I was, I interned at a, I call it slave labor, but interned at an agency in Cleveland and that became an agency opportunity in Chicago.

[00:03:59] Speaker 2: I’ve had the privilege and honor of working with some really amazing people throughout my tenure. So General Motors, Sherwin Williams National City Bank. Edward Jones Investments Whirlpool, KitchenAid, Midas, you name it, usually I’ve touched it in some way, shape, or form, right? Airlines, different types of Delta Airlines so I’ve had to work with some really neat clients.

[00:04:21] Speaker 2: And one of the neat things I’ve had the ability to do is to see what works at the grassroots level, like at General Motors was one of my accounts. So seeing at the very, you know, the actual, the dealership level to the LMG or the local marketing group level and up to corporate when GM acquired Saab and working with the executive team to be able to do that what I loved about that is it gave a rich.

[00:04:45] Speaker 2: Understanding of the tools available and then the privilege of being able to switch to small and medium sized businesses where you actually get a chance to see those analytics and things like that actually working at a tactical level to where you’re actually making decisions literally on the fly, testing things in a week later, kind of meeting up and then making a whole new set of changes.

[00:05:03] Speaker 2: So that’s one of the reasons why I love small and medium sized businesses to be, to be quite frankly, because that, that, that factor being able to evolve. And, you know keep moving is really neat. So really, really enjoy that. Today’s topic, which we will discuss in here. Oh, so actually just bring it back.

[00:05:24] Speaker 2: So we have the honor of working with a lot of, a lot of small, medium size and a few large companies that want to be efficient and things like that. So I want to kind of bring that up to you. What are we discussing today is the can and can’ts of AI. That’s this one right here. And what is AI? What’s general generative AI?

[00:05:42] Speaker 2: What are AI models that we can, we can look at? Now, if you, if you do a search for heck, let’s just do it for fun, right? Let’s go, let’s go, let’s go look at Google and we’ll say AI tools, 2024. Let’s just see what comes up. If we go ahead and do that, look at all these wonderful tools in here. Look at all these really great.

[00:06:06] Speaker 2: I mean, these are the only top 50. There are hundreds. Here’s some more. These are all AI tools that, you know, you can go and play with, right? We got to take a step back. We got to actually go back and look at some basic fundamental things in here. So what I’m going to bring up to you here is that we’re going to, we’re going to start real broad stroke and I will make sure we have time for Q and A and things like that.

[00:06:30] Speaker 2: So, you know, you can ask questions in the chat later on here. In fact, I’ll try to keep it up and visible. Maybe, maybe not, don’t know. Let’s see, I’m not sure how to, yeah, I’ve kind of given that up. I guess I’m just going to kind of watch a little bit here as we go through it. So let’s just focus on the material first and we’ll get through the Q& A.

[00:06:54] Speaker 2: If there’s chats and stuff like that, or some of you have my contact information, you can obviously. Text it to me near the end of this so that I can answer some of the questions. I can do either one. In fact, I can probably, all of you have my chat number, I mean, my text number. But 941 730 4677.

[00:07:14] Speaker 2: Text your questions in now. We’ll be able to take them real time and live at the end of this. So in order for us to understand like all these incredible tools today, we have to understand that there are differences in the, in AI models. So AI stands for artificial intelligence. What that is for us is that there’s different.

[00:07:37] Speaker 2: There’s different there’s different kind of types or categories of it, right? So, it’s important to understand the foundational pieces between basic AI systems, machine learning models, and generative models. And the reason why is because of what I like to call risk, okay? Knowing the limitations of these systems.

[00:07:57] Speaker 2: Allows you to better watch out for things that may or may not be true in the situation or potentially putting your company at risk, especially if you integrate something that, let’s say, gets compromised legally, but it’s part of your workflow in, let’s say, eight months or nine months. That’s going to impact your business.

[00:08:16] Speaker 2: So don’t, don’t put your ladder against that specific wall. Right. So that’s why we’re kind of going through this is to start out with some of these basics. Okay. So when we talk about AI models we’re going to go through and you might hear the term LLM, right? Large language models. This is where like chat GPT and everything kind of started, right?

[00:08:37] Speaker 2: So it’s using words. It’s using our normal vocabulary and what’s able to find online. And it’s using those things to interpret and create its understanding of the world based on that data set now that might data set might be in chat. It was limited to a certain number of years and up to a certain point.

[00:08:56] Speaker 2: And then, you know, that’s that data sets kind of abilities, right? You may find that. Okay. As right now, these LLMs are being introduced to just the web in general, and they’re able to look at actual live websites and things like that, and it’s using that information. In some cases, these LLMs are actually taking in video and magazine and publications material, and they’re, again, taking that information and translating it into real world kind of situations, and then inferring things, or even creating new things.

[00:09:29] Speaker 2: It’s important to understand the basic language that we’re talking about. LLMs are large language models, require a lot of processing. But the three basic areas are this, the basic AI systems, machine learning tools, and generative AI or generative models. Okay. Basic AI systems are where we’ll start because it’s foundational and it’s going to make sense to you, especially if you’re a manager or business owner of some kind, or you’re in charge of a department, you’re going to get it.

[00:09:58] Speaker 2: You understand already some of the mechanics of this, what they can do. They can follow simple rules to automate tasks, and these would be simply things like chatbot, or answering FAQs. Like, the benefit is they’re very reliable for specific, repetitive tasks. Now, I’m going to give you an example, right? So, I often use AI models like ChatGPT, Claude Google’s Gemini, Let’s see.

[00:10:27] Speaker 2: There’s there’s multiple out there. We use PO allows you look at different ones automatically. It’s a great one to go check out po. com. You can see different ones. You can even build your own chat bot right there, but they’re great for specific things. I often write prompts that basically give the information.

[00:10:47] Speaker 2: Who let’s just take and use chat keeping key. For example, I want to give it the information it needs to go ahead and create article content. I’m not going to let it pontificate. I’m not going to get. I’m not going to let it go. Search other things. I’m giving it the information that’s required to go ahead and output some piece of information that this is what basic AI chat is AI systems are about, right?

[00:11:13] Speaker 2: You kind of hedge it in. It’s kind of, you know, kind of. I give you some stuff and you basically write stuff from that stuff that I gave you, okay? You’re not looking at anything else. So this would be essentially like The, the college professor giving the student, you know, five different resources, right, whether those are videos and books or workbooks and say, okay, go through this material, and I want you to go ahead and create X document on it.

[00:11:41] Speaker 2: Right. So I’m hedging it in. So that’s, that’s the first and most basic way of using AI tools, that very little in terms of what they call hallucinations, because you’re, you’re giving it the facts and you’re asking them to kind of basically kind of regurgitate those in a new kind of just, just organize it in a, in a way that makes sense that, you know, it’s a blog post of a thousand words and it sounds like, you know, college level piece of material, right?

[00:12:08] Speaker 2: You’re kind of hedging it in. The machine learning models are going to be something like a little bit more advanced. You’re giving it the ability to start inferring things. So what they do, they learn from data and make predictions or decisions. I like recommending products like on Amazon, right? So it’s given a bucket of things to think about.

[00:12:31] Speaker 2: And it’s starting to kind of put those things in place a little bit for us, right? Now, the benefit is it’ll improve over time with more and more data, which is what you constantly see with whether it’s NVIDIA chips or, you know, GROK or Elon Musk you know, with, with needing more and more data processing or Facebook’s meta, right?

[00:12:50] Speaker 2: With llama, right? All these require more date, more, more power to go ahead and do a great example. This is something like for a webinar. Let’s just take a webinar or a meeting. I had a conference. I was speaking at or about 100 or so responses. I believe from the specific engagement. So the. The information, the request for information came out.

[00:13:13] Speaker 2: I wanted to see how large these businesses were, what their sales was, what their industry specific, you know, kind of context was where they were at with some of the marketing. So I basically created, and I think it was like. I don’t know, nine or 10 questions, maybe some, most of which were either dropdowns, there were some that were kind of open ended, I think there were like one or two that were a little bit open ended, but the rest were like dropdowns and radio buttons and stuff like that to kind of hedge it a little bit.

[00:13:37] Speaker 2: So basically I got this data back from, let’s say, let’s say it was a hundred responses, got this data back. And in that I had from the survey. I had the email address as well as the phone number. Well, I removed that and I put in the order, right? So before I removed it, I had 1, 2, 3, 4, 5, 6, 7. So I could name like the respondent number, right?

[00:13:58] Speaker 2: So I could tie that information back to the individual on my side. It’s here. Listen closely. Listen closely. This is very, very important. If I had just put that data out there with the email address and the phone number, which would have been data that now, now in this case, ChatGPT now has, it could, it’s time information responses back to a specific email address or phone number, right?

[00:14:23] Speaker 2: I don’t want that, because that lives on the web forever. You guys do not put private information, copyrighted material that you don’t want republished out on chat GPT or any of these other AI models, unless it’s in your own that you will get into a little bit more specifically, but it’s locked away, it doesn’t get reported back.

[00:14:43] Speaker 2: Do not trust the little turn off little radio button and chat GPT. You’ll notice that that constantly gets updated. Just because you think it’s private today, just because you’ve said it does not mean anything. If it’s marketing materials and things like that,

[00:14:57] Speaker: fine. Not a big deal,

[00:14:59] Speaker 2: right? Because you want that stuff to get out anyway, right?

[00:15:01] Speaker 2: But if it’s private information, if it’s, you know, especially in healthcare, right? Do not be sharing. Patient information, anything you need to, you need to get rid of that before you start looking at this stuff from a research standpoint or whatever. So you have to anonymize the data just like I did. So you’ve got, you know, respondent number.

[00:15:23] Speaker 2: I’ve got all my responses and basically I had to evaluate the data. I said, you know, give me, give me a, tell me about, tell me, tell me about the highlights, create an executive summary, really from this set of data. It did an amazing job. Okay, now I gave it data and it started to infer stuff. So do you understand the difference there between the basic AI system and the machine learning system?

[00:15:46] Speaker 2: Okay, those are the differences there. It’s a great example. This was, I hedged it in, I only gave it the right information. I’m saying don’t, this is what I want you to kind of rejigger in a way that it makes sense. This, I want you to interpret some of this. This is where you can start getting a little bit of hallucinations.

[00:16:02] Speaker 2: In terms of like math and things like that, I haven’t had it really screw up. I did double check the stuff to make sure it looked like it was going to be good. You know you know, you run through several of the calculations. Yep. That’s the right average, or that does make sense when you talk about it being a two X, that makes sense, whatever it was.

[00:16:19] Speaker 2: Right. So we’ve got the difference between those. This generative AI is more advanced. And what I mean by that is this is where it can, it takes, let’s say, for example, Well, let’s just read what they can do. They can create new content by learning patterns from existing data, like generating realistic images or writing in writing texts.

[00:16:42] Speaker 2: The benefits of this is that it’s useful for creative tasks and content generation. I’d even go further in terms of visual, like videos and animations and even just new pictures. So a great example of this, in my opinion, is. Let’s say you wanted to do, you wanted to do like, you know, everyone knows Star Wars, right?

[00:17:01] Speaker 2: So you take Star Wars, you feed a whole bunch of Star Wars videos of stormtroopers, right? And they’re always in the white, sometimes in the red, depends on their, you know, their, what the decor requires, right? For the scenes that were there, right? But let’s say it’s a regular stormtrooper, right? But you don’t want just the white guard with the, you know, the gun, right?

[00:17:20] Speaker 2: The, the laser, right? Whatever it was. You know, you, you can actually say, okay, I want a storm trooper. I want the theme in a country like scene with it in front of a pickup truck and, and just, just go with a country scene, right? So the, the storm troopers no longer going to be in the white garb. He’s going to have like the six shooter kind of thing.

[00:17:41] Speaker 2: And it’s silver. He might have, you know, like the wheat hanging out of his mouth, right? These are all things that’s kind of inferring and then just creating literally on the fly. Okay. So that’s where generative models video. Advanced stuff, even music, music there’s a great site called Oudio.

[00:17:57] Speaker 2: I UDIO, I think it’s UDIO, there’s so many of these guys, which, go ahead and check it out. I, I think they’re getting sued right now because you can actually make any genre of music. So you can say like, you know, write me a birthday song for my wife for her 40th birthday. And I want it in the sound of counting crows or the Beatles and it’ll do it.

[00:18:18] Speaker 2: Like it is amazing stuff. Right. So you can see why some of these companies are getting sued because their likeness is being, you know, like the production companies are spending huge money with some of these stars. And yeah, so that’s, those are the things that you’re reading on a regular basis. But that’s where it’s going, you guys.

[00:18:34] Speaker 2: So, that, that, with that understanding, you go, okay, well I understand now basic AI systems, I understand machine learning, I understand generative models, but what, how does it impact me? Like, why do I need to know this? And the big thing I’m having with this is that it’s, it’s the hallucinations And knowing the risk.

[00:18:53] Speaker 2: So, hallucinations occur when, basically, you either get the AI guesses, or just make stuff up, right? So, you can see under this system, with the basic AI, I’ve fed it all the stuff, and basically it’s kind of creating it, right, from the box that I put it in. This, I’m, the machine learns, so, Hallucinations are rare, if at all.

[00:19:14] Speaker 2: Okay. Like it gets it right. Cause you put the box in, you’ve given it all the materials that it needs to consider. The machine learning models are where they start kind of starting to infer different things. You do want to start checking that, right? And then the generative models, knowing that they’re creating something completely new because you’ve asked it to, or you’ve been so broad in your prompt.

[00:19:35] Speaker 2: That’s the question, the thing that you put in there, the prompt. Right that it can just it can just go create stuff, right? That’s where you get these risks going on from very low risk to medium risk to potentially high risk big picture guys You’re responsible for the content in your organization.

[00:19:53] Speaker 2: You’re responsible for what it pushes out. You’re likely not gonna get sued All of these companies are getting sued right now. And the question I get on a regular basis, do I have to worry about that? And it’s like, no, even the big AI companies aren’t worried. They’re just basically setting aside cash to pay out either licensing fees or attorney’s fees.

[00:20:12] Speaker 2: So they’re going to take the brunt of that. And everyone seems to be okay with. Getting these content publishers, their content ripped off. Like even with Google. Even with Google, for example, with YouTube, you, you can’t just download a, a copyrighted video. In fact, one of the things that you do when you, when you upload a video on YouTube is you click the little thing and saying, no, it’s not copyrighted, or it is copyrighted, right?

[00:20:35] Speaker 2: YouTube has been training their models off of that stuff. They violate their own terms and conditions. On a regular basis. So it’s not anything new for them to do that. But what I am saying is they’re just going to simply paste it, right? You know New York times has their articles ripped in a chat GPT.

[00:20:53] Speaker 2: There’s just going to be a licensing deal and it’s, it’s going to be water under the bridge. So that’s going to be going away. Like it is no, no big deal with that. Let’s see, do I have, do I have some questions going on? Let me see if I’ve got questions, you know, load them in chat or

[00:21:18] Speaker 2: let’s say,

[00:21:22] Speaker 2: and load them in chat if you guys want. That’s funny. Okay, cool. All right. I’ve got a couple going on in here. Shoot. I just, I just lost it. I get too many of these here. Okay, here we go. So one of the questions, right? How are these things getting trained? Well, we kind of covered some of that, right? So they’re getting trained by surfing the web by being fed video content, book content. Yeah. Yeah, I mean, any of the tools, like when chat GPP came out, I think it went to like 2018 or 2019.

[00:22:02] Speaker 2: And if you asked it, what’s the latest, you know, can you, can you show me this website? And if the website wasn’t around at that point in time, you, it would have said, no, my data only goes to like 2018 or whatever it is. So it’s, it’s only, it’s only as. Good as the data it has, or has been trained. So, for example, we have an there’s an ADHD app that we’re kind of, we’re, we’re, we’re creating, right?

[00:22:28] Speaker 2: We had to train that to be empathetic and smart. So we had to give it information in order to do that. We had a panel of five I’m sorry, one, two, three, four, For clinicians, you know, a panel of four clinicians that helped verify and help train that information into the system, right? In order to go ahead and be smart.

[00:22:48] Speaker 2: But we also had to train it to be empathetic because computers generally are not real empathetic. So bedside manner is not usually a thing. And I know that takes a lot of not only my clients, my clients are great physicians. Who really care and are more than comfortable. What I am saying is that it’s computers are not empathetic unless you’re trained.

[00:23:08] Speaker 2: So we needed something that was especially with ADHD, very, very, like, very empathetic to the individual, right. Encouraging and those kinds of things. So that, that should answer this question about being trained. What’s the, what’s the idea behind NVIDIA and other chip companies. Okay. So. You’ve got this is like a baby, right?

[00:23:29] Speaker 2: So baby is born, goes into the world and then starts to put everything in their mouth, smell it, touch it, eat it, whatever it is they need to do in order to kind of explore the world. They’re just curious. Computers are that way. The guy that invented clog. So Claude is a great, their, their new model son is really good.

[00:23:51] Speaker 2: Definitely an AI tool worth checking out. It’s, it’s similar to chat GPT and stuff like that. Most of these, you know, try them out. They’re, they’re really cool. But he was like, I’m not sure how it does what it does. And you’re like, dude, it’s just like a baby. They learn right. By experiencing and, you know, playing with data and information, then they create kind of maps beyond that.

[00:24:11] Speaker 2: And. So the net net is these things need more and more horsepower, right? They need more and more processing power. That’s why you see him video like doubling and their, their, their value because you’ve, you’ve got a situation where we’re talking about, you know, tens of thousands of dollars to buy like one chip, right?

[00:24:31] Speaker 2: And then you tie those multiple chips together and you’ve got kind of a network that you can tap into. And then. You’re talking about acquiring more and more of those processors in order to create more and more processing power. This is not you know, the cost per token will be driving down, but in the meantime, there needs to be systems that allow for the stuff.

[00:24:49] Speaker 2: So that’s the reason that’s really, what’s kind of driving this is that everyone wants to get to. Everyone wants to be the best in this, right? These large language models. And there is a kind of a push to go where, you know, these devices, for example, they have AI kind of baked in them. The new Google phone’s got the AI baked right on into it.

[00:25:08] Speaker 2: So, the processing isn’t done at the large language model area. It’s done actually some degree here. And then the heavy lifting can be done if it needs to elsewhere. Apple had their what is it, WWD something or other maybe two months ago, right? In, in the fall, these devices are going to be the same thing.

[00:25:25] Speaker 2: They’re, they’re going to be offshooting that AI technology into the devices themselves. And then if it has to go tap, in this case with Apple, they’re going to have a relationship with OpenAI or ChatGPT, that it’ll ask you, do you want me to go use ChatGPT? So it’s obvious that it’s going to be using that stuff.

[00:25:43] Speaker 2: But the A is going to be baked right on into this. You’re not going to have to go out separately for it. Who’s going to win? So the question there is you’re going to use the seven big players, right? Your Google, your meta, your that GPT, your Claude. Your Amazon, these, these big companies are all going to try to continue to leapfrog each other, right?

[00:26:06] Speaker 2: They’re not going away. They’re, they’re pressing firmly into it. Forget X slash Twitter. These are all companies that are going to be continuing to try and jump each other in that next process. Google, right?

[00:26:19] Speaker: They’re

[00:26:20] Speaker 2: all going to keep moving forward, right? They’re all going to keep pushing that. Can this stuff really help my business?

[00:26:25] Speaker 2: Yeah, actually, if you’re not using it right now, we’ll get into some of these other topics in here, but you definitely, yeah, I can actually show this, right. You can show that you guys see that again. You, you definitely can grow your business using these tools. We’re going to get into later on the concept of let’s see, those window, I’m gonna go back to the, the actual

[00:26:50] Speaker 2: The actual topics that we’re going to talk about right here. So, yeah, especially next time, actually on September 13th, we’re going to be talking about, we’re going to start talking about prompts and stuff like that. So, you do not want to miss that. We are going to be, we’re going to be using actual case study examples, real life ones that you guys need to be using so that you get the great output, right?

[00:27:13] Speaker 2: If you’re not using it to do content generation, if you want to speak to audiences, you want to, you want to make your opinion known, you want to make your brand known, you need to be, this, this is a tool that allows you to do that so fast compared to what you were able to do before. Right. And the process is duplicatable, like kind of going back to that original, that original AI, this, this this statement, right?

[00:27:36] Speaker 2: I lost it already. Where’d it go? Where’d it go? My graphic. I missed my graphic. It’s not there. Yeah. Sorry guys. Canceled it out there. Boom. So yeah, you’re not gonna wanna miss that research, right? Again, it’s fantastic at research all the time that you would, you’re like, oh, I don’t have time to do.

[00:28:03] Speaker 2: Likely you can do it with a chat GT or Claude or one of these other ones. Okay? So we’ll get into that a little bit later on. Will lawsuits shut down or tamper services for these tools in the future? Not no, there’s the pattern is the pattern is no because they’ve all been training off of You know content that’s been that’s that’s been private for some time And so there’s the trend is set now guys where basically they’re just gonna be paying, you know for licensing They’ll get the slap on that wrist and all of them are gonna be allowed to just move forward So it’s not gonna it’s not gonna impact you what other things do I want to tell you about with, with this?

[00:28:50] Speaker 2: I think that’s, I think that’s going to be, it’s, it’s a primer, you guys, for what you guys need moving forward to understand that, look, you don’t have to be scared of this stuff. Yes, it can be used to help your business grow. And we’re going to just kind of walk through it together with you guys.

[00:29:08] Speaker 2: So that you understand that, you know that this, this is something that can, that can really truly help your business. So I think that’s it. Oh, there’s,

[00:29:22] Speaker 2: oh, yeah, okay, well, yeah. So we’ve got, yeah, so we’ve got the answers to these, oh, copyright infringement. We got that one. Lawsuits. So yeah, I’ve got the text string going on here. Who’s going to win? Yeah. I’ve got it. How are these? Yeah, perfect. So yeah, if you guys have any questions, feel free to reach out.

[00:29:42] Speaker 2: Our next session is going to be on the 13th of September. The title is going to be good stuff in gets good stuff out getting the most out of AI, the secrets of writing great prompts. Well, they’ll see you here same time September 13th. I will be back in the office, though, hopefully by that time, I think.

[00:30:02] Speaker 2: Yes, probably, God willing. I hate doing these on the road because you never know exactly what you’re going to get. But I really appreciate your efforts and meeting here today. If you guys have any questions, feel free to reach out. My email address is Chris, C H R I S, dot Bernard, B E R N A R D, at, Dock 29.

[00:30:19] Speaker 2: That’s D as in dazzle. O C K two nine. com. Or you can do Chris at content engagement lab. com to the, that’ll work as well. So hope you have a great holiday weekend and we’ll talk to you next time. Bye bye now.