EP 254: Freestyle Friday - Ask Me Anything (about AI)

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life. This show is for you. There's no topic, it's anything goes. You asked for it. So we're here. Our first ever. Who knows, maybe this will become a thing, but our first ever Freestyle Friday. Ask me anything about AI. What's going on, everyone? My name is Jordan Wilson and this is for you. Everyday AI is your guide to AI. It's for us all to learn how to leverage generative AI to grow our companies and to grow our careers. So if that sounds like you, then you should be going to your everydayai.com and signing up for our freely free day free daily newsletter. Because the learning doesn't stop here with, with the live stream or the podcast. The newsletter is how you put that all into action. So make sure if you haven't already, go sign up. And hey, like I said, when I say this is for you, it's literally for you. Because we asked in our newsletter yesterday, hey, what should tomorrow's show be? And just narrow, just narrowly, you all wanted to hear Freestyle Friday, so whatever that is, we're going to find out. So hey, if, if you're, if you're on the podcast, this one's probably going to go all over the place depending on what our live stream audience wants to hear about today. So hey, if you are a normal podcast listener, if you're not doing anything at 7:30am Central Standard Time, it's, it's a great time here on, you know, LinkedIn and YouTube. There's actually some of, some of our greatest guests that we've had, you know, over the last year, you know, now show up to the LinkedIn live stream. So it's a great place to network, meet other people who are helping push their organizations forward with AI. So hey, live audience. So whether it's Frank who already has a question, Chrissy, our friends here on YouTube, Tara, Doug, everyone else, let me know what is your biggest AI question? Right, if you don't ask it, it's going to be, it's going to be a show just full of random rambling and rants. So get your questions in now. Before we do though, let's start it off as we do every single day with the AI news. So Olympic organizers just released their plan to use AI in Olympic sports. So the International Olympic Committee has just announced plans to use artificial intelligence in sports with a focus on identifying promising athletes, personalizing training and improving judging. Yeah, that'll be a good one. They aim to do this in a responsible way and also use AI to protect athletes from online harassment and enhance the viewing experience for spectators. So AI will play a significant role in the upcoming Paris Olympics with plans to use it for athlete identification, training, judging and security. The ioc, so that's the International Olympic Committee, is determined to take advantage of AI while being responsible and ensuring the uniqueness and relevance of the Olympic Games. So, yeah, we've seen AI used in a lot of different ways. You know, I think just the masters had AI commentary. We've seen it used, you know, in tennis. You know, the NBA just kind of announced their AI plans a couple of months ago. So it should be interesting how the Olympics and this is, is this going to be kind of a fresh take that sticks around or is this going to be one of those things like, you know, how they used to have the, the hockey puck light up, you know, 20 years ago. So we'll see. All right, our next piece of AI news. The US military just conducted in a human versus AI dog fight in the air. So an AI controlled F16 fighter jet has successfully engaged in a dog fight against a human piloted jet. So pretty interesting there making a significant milestone in the use of machine learning in piloting military aircraft. So the first ever AI versus human dogfight occurred apparently in September at Edwards Air Force Base in California. So this is just coming out now. The autonomous aircraft was called the X62A Vista and it has undergone so far, 21 test flights and has a modified F16 fitted with an AI program. I have no clue what any of those, you know, letters and numbers mean. By the way, I don't follow fighter jets, but maybe if you do, that means something. But the use of machine learning and piloting military aircraft has been historically prohibited due to safety concerns. But the success of this dogfight program represents progress in the area. All right, last, but I would say definitely not least, meta has released Llama 3. It's somewhat open source model. So Meta's new large language model and you know, I guess medium language model. LLAMA 3 is actually now live. It is already released on cloud providers, model libraries and on Meta AI. And so far it is, the company is, is boasting its improved performance and outperforming other similarly sized models and benchmark tests and human evaluations. So the Llama 3 collection of models aims to be multilingual and multimodal, have longer context and improve overall performance. So right now there's just two released varieties. So there's the smaller 8 billion parameter model which is benchmarking above the smaller models from Google's Gemma in Mistral. There's also the medium model 70, the 70 billion parameter model which is already benchmarking above, you know, Google's mid Tier Gemini Pro 1.5 and Claude 3 Sonnet. And the model that I think most people are going to be looking at that is not yet released is the 400 billion parameter model which is expected to be the biggest model, but it is still in training. Meta CEO Mark Zuckerberg did release a video yesterday talking about some of the, some of these human evaluation scores. Right. So we talk about this on the show a lot, the MMLU kind of benchmark, which is one of the most, I would say the most, I don't know if the famous is the right word, but it's one of the most widely regarded benchmarking tests. So that's the massive Multitask language understanding test. And it's essentially a benchmark to say like, hey, can, can this model think, understand and reason similarly to a human? So most AI experts look at the MMLU scores to see how capable a model is in terms of can it perform tasks like a human across a wide spectrum. So so far, you know, Meta's smaller 8. 8 billion parameter model and 70 billion parameter model code. So if you think about as small and medium, already out benchmarking the leading models out there right now, which is interesting. So we'll see if the 400B is going to out benchmark, you know, Claude 3 opus, which is kind of the leader now by like 0.1 over GPT4. So there's going to be a lot of model talk and a lot of Meta talk and you know even what that means, right? It is Meta is not completely open source, but it is essentially open source for all intent and purposes. That's another episode for another day. But I mean that's pretty big news, Meta going this open source route with these pretty big models. All right, so let's get into it. Let's talk what you want to talk about. So let me know, what is your biggest AI question from our audience? So hey, maybe you want to know, I don't know, maybe you want to know something random about AI. Maybe you want to know something meaningful. Maybe you're still trying to implement AI in your company and you've hit a roadblock. Maybe you just want to know what's better between these two tools. Maybe you want to know about GPU chips or energy required for the future of Generative AI. Maybe you want to know where to start. It doesn't matter what your question is. Today is the day you all are running the show. You're interviewing me, our livestream audience. So let's go ahead. I'm going to go ahead. If you don't have a question in now, please get it in. Please get it in. I want this to be a fun, interactive show. Don't be shy. Our. Our audience. No one's going to laugh at you. People laugh at me, not you. So get your. Get your questions in now, and I'm going to try to answer them as I see them coming up. So we already have a couple questions, so let's just start right, running them down. And hey, if you want to put me on the spot, put me on the spot. You know, you can, you can grill me. You know, sometimes I grill our. Our audience or, sorry, our guests. So maybe this is your time to, to grill me. Doug Doug. Starting off topic, but that's fine. So, so Doug Doug here asking coffee of choice. Hey, that, that, that question counts. You can ask about AI. You can ask about not AI, I guess I don't know if anyone's interested in that, but Full Nespresso, Doug. Full Nespresso. I can't. You know, that's. That's what I have right here. That's already kind of. Kind of warm and not. Not hot anymore. You know what? I actually used to, I didn't drink coffee until I think I was maybe 23 or 24, a little older than that now. And you know, each time, you know, you try to do coffee, you're like, oh, these, these K cups, these are pretty good. And then you, you know, get your own beans or whatever, and you're like, oh, these are great. And then when you have an espresso, there's no turning back. So it's. It's Nespresso all day. But I fast, right? So I fast. So it's not like, you know, latte, it's just, you know, straight, straight black with a little bit of sugar free. Something syrup. All right, so here we go into the A.I. frank. What's up, Frank? Frank, thanks for joining us from. From the YouTube land. So Frank's asking, does chat4. So does chat GBD4 remember across threads? Now that's a great question, Frank. So as far as I know, I haven't checked since last night, but no. So there's actually two different things that you could in theory be asking about here. So there is a feature called memory that Chat GPT, I wouldn't say they, they had a broad rollout, but a fairly broad rollout. But still, not everyone has access to that yet. I'm even checking, as of last night, I usually check my Chat GPT settings for all my accounts every single day, just for when new features or beta features or features that have been, you know, teased do get publicly released that I can kind of know about it. So it looks like, at least right now I don't have access on that account. Let me go ahead and check, check my other one. So if you want to know, there should be a, in your settings, you should have a thing that says personalize. So yeah, I still don't have it. So there's two different things here, Frank, and let me try to explain this to our live stream audience. So there is just a memory feature that OpenAI. They promoted it. Right. But still not everyone has it. I'm not sure why. So this one, I think there's good and there's bad. And I, I did a kind of a five minute, you know, we do an AI in five almost every single day. I did a rundown on the different pros and cons of this. So essentially when you're having a chat within ChatGPT open or, you know, ChatGPT is going to decide, you know, what are these things it wants to commit to memory, and then those memories are going to follow you, follow you around from all chats. So maybe it might be something personal like, oh, I'm a marketing consultant and, you know, I, I, I care about, you know, edge computing as an example. So then if you're asking ChatGPT in a new thread about something, it's going to keep that in mind. However, I don't like this implementation or at least how it was previewed, but again, this hasn't been a public release yet either. So the bad thing is, is Chat GPT is picks up on a lot of those things on its own and commits it to your memory. You can obviously go in, you know, and click in your settings and delete things from your memory. But I don't like that, I don't like that Chat GPT automatically applies things to memory and then kind of takes that knowledge and applies it unilaterally to all your chats. Because what if it's something that you necessarily don't care about, right? What if I'm talking, you know, getting restaurant recommendations or, you know, things in my, in my personal life and then I'm working on something, you know, for everyday AI or for my other company, Accelerant Agency. And I don't want that information, you know, following me around and influencing the output. So I don't personally like it. So there's that aspect and then there's another thing called cross chat memory and I'd say even fewer people have access to this and this was pretty previewed like four months ago. So what that is is it's more about the context window being shared between multiple chats, which I don't see how that's going to work well in the short term until Chat GPT has a much larger memory. Right. OpenAI has said that, you know, you have 128000 tokens of memory. You do not. At least the last time I checked it was Tuesday. It's still stuck at 32 000. So if you're sharing a context window of 32,000 or even 128,000 tokens, right, which is, let's just say for 128,000, that's, let's just say a hundred thousand words. So think 100,000 words of context between all of your chats. I don't think that's great, right? If we were talking Google Gemini's 1 million token context window, perfect. Share that knowledge, that working knowledge or working context window across all chats, right? And it is different. The, the kind of cross chat memory is remembering literally everything as a context window across all of your different chats. So whereas this other feature, just called memory is more of, you know, it creates a memory bank that you can then go in there and modify. So two different things. So hopefully, hopefully, Frank, that was a good, good answer to the question. All right, so now Doug, Doug says from yesterday, who can see custom GPTs in copilot? Doug, that's a great question. You know what, you already stumped me. That's, that's so easy, right? Just because our team right now is not using Microsoft 365 copilot, you know, I think we are going to start testing out Copilot Pro a little bit more. You know, we're just kind of standard Copilot users right now. But I do believe, especially in the Pro and Enterprise version, Doug, in the same way that in chat GPT and GPTs you have sharing controls. I do believe the same thing can be said for Copilot365. You know what we, I know we have a couple people from Microsoft who tune in the show, so maybe they'll. And the live stream, so maybe they'll answer that question a Little better than I, than I have or than I can. Tara. Oh, good question here, Tara. So Tara's asking what AI apps do you have on your phone? So I might get judged for this, Tara. But just chat GPT and copilot. That's it. Here's why. And you know, anyone who knows me personally can, can attest to this. I suck at everything on the phone. I have hundreds of unread emails every day. I have hundreds of unread tests texts. So I'm, I'm everything Apple right now, right? So I'm, I'm on an Apple computer. I, I have an iPhone. So I do all of my texting for part on my computer. I very rarely text on my phone. I have fat fingers. You know, I'm 38, going on 83. I, I don't really like, I would say I don't like to be on my phone, but I don't like to use it. I guess I've always been kind of spoiled by big screens. You know, I, I, I have, you know, two 20, I don't know, 27 inch screens in front of me. So when I try to do anything on my phone, I don't like it. I don't like doing tasks on my phone, but I do have obviously chat GPT on my phone. I do have Copilot on my phone. I should probably get perplexity on my phone because I love perplexity and I use perplexity often. So I actually don't know why I don't have Perplexity on my phone. That's, that's a great question. But yeah, I don't really use apps on my phone a whole lot, which I know I'm probably in the minority, not just for my age group. Whatever someone in their upper 30s is, I don't know what that is. What genre? I don't know if that's a millennial or a gen something. I don't know. Is anyone else in their mid-30s? What are we called? So, yeah, I don't actually use apps a lot on my phone in general. All right, Chrissy. Chrissy. So, Chrissy, asking thoughts on how open source, in quotes, llama3 will impact the AI space. Do you think it can handle the PPP model? Oh, good question. Question, Chrissy. Okay, so let me, let me start just with a very, very brief overview of, of this, you know, open source and quotes. Right, I said that to start with. So essentially this, most models that we all use and you know, if, if you're a little bit of a Newbie, I'm going to generalize. So you, if you're an expert, some of this is probably a little wrong, but I'm just trying to generalize it. Right. So most models that, that we talk about are closed, they're proprietary. Right. So you know, Google, Gemini, chat GPT, you know, ironically enough from OpenAI is closed. So you know, chat GPT, Gemini, co pilot Claude, those are all closed models. Proprietary. Right. You can't go in and see how they work or, or build off of them. You, you can with, with, with Llama. Llamas. It is somewhat open source. Right. So I guess the one thing is it's not an approved open source license. So there is the open Source initiative or OSI in llama and Meta do I guess not adhere to those standards. So it's Meta is almost trying to redefine what open source means, which I guess when you are one of the biggest companies in the world and right now you have probably one of the leading open source AI. Well, yeah, I don't know any other AI product that would compete with them in the open source space. So maybe they get to redefine what open source is. But traditionalists will look and say, ah, open source, Meta, llama, Not really. Okay. But thoughts on how it will impact the AI space? Oh my gosh, this is a great question, Chrissy. So I'm actually, I'm actually going to pause and I'm gonna go back and see how many more questions we have so I know how long I can go on a small little rant on this. So hey, live stream audience, keep the questions coming. All right, it looks like we got a lot, so I'm gonna, I'm gonna try to go a little quickly on these so I can try to get to them all. And if you could keep them a little shorter too, that, that, that would be helpful. Just, just so I can kind of read them live and, and still, you know, multitask and process them here. So Chrissy, how will it impact? Huge, huge power move from Meta, if I'm being honest. Right. So again, these are meta's benchmarks that they release. So we'll wait until we see everyone else's benchmarks, but it's very capable. Right, you can go, you can go right now. Go to Meta. Meta AI. Right. So you have to sign in with a, I think a Facebook account. You know, I was playing around with it for 20, 30 minutes yesterday. It's a very capable model for being an open source model. So this changes. Right? I think this is One of those things where Meta is looking at the open AIs of the world, they're looking at the anthropics of the world. They're even looking at, you know, Google and Microsoft and, and they're saying they're in a place of dominance. Meta is right, because they're not as affected as everyone else with the potential downside, the potential financial downside of how we as humanity start to use large language models. Specifically, I'm talking about search. So I'm talking, you know, the big players in the room, you know, Google and Microsoft, right? Because they get a lot, you know, Google more so than Microsoft, but those are two big competitors. So they get so much of their revenue from search. Right. So there's been so much of this uncertainty about how, you know, all these large language models are going to monetize, how AI search is going to change, how traditional SEO is going to change, how search engine search engines are going to continue to make money. So that's something that, you know, is, is on top of mind for sure. For Microsoft, for Microsoft and Copilot and for Google and Gemini and by proxy, technically, then perplexity as well. Meta doesn't care. Right. I think this is one of those things where, you know, yes, OpenAI now is, you know, probably at the point now they're going to be crossing about a billion dollars annual revenue, which is a ton for a company that, you know, just put out its first kind of quote unquote, flagship paid product, you know, like a year and a half ago. This is one of those things where Meta, I think is just being like, hey, let's just create something better than everyone else. Not like squash the competitors, but make the competitors less meaningful to their overall strategy. Right. At least right now the AI space is a very small space for Meta and it's, it's current monetization, right, Because Meta, they obviously don't have a monopoly on social media, but they are bigger than everyone else, right. Between Facebook and Instagram, you throw in, you know, WhatsApp as well, you know, they, they have a straight up monopoly on that space where everyone else is still competing in the search space. And, you know, kind of the, the crossover between search and large language models is starting to blend as well. So I think this is a power move from Meta just going out there and saying, hey, we're going to release models that are at the same tier or punching above the most powerful models out there and we're going to create them open source. Right. I think it almost builds this, this Moat to like to speak where they don't even necessarily need a moat, which is super interesting. So, you know, I'm fascinated by that. I'll probably have a dedicated show in the future about this strategy from Meta, but I like it. Right. At least right now, you know, I'm sure how like they are rolling out AI in all of their platforms. Right. So it does make sense for them to make a heavy investment into AI, even though the AI model itself right now is not, you know, it's open source. So they don't need to necessarily monetize it, but they want to get it better. Right. That's the, that's the main advantage when you put something out there. Open source is then you have, you know, some of the world's brightest developers working essentially for, for you making your model better, poking holes in it, you know, so I, I love the move. All right, Lorena joining us from Australia. Good evening. So first time live. That's awesome. Is anyone else first time live here? So Lorena's asking, hearing about the benchmarking scoring MMLU system, why is this important? Great question. So, you know, essentially there's no one good way to measure models. Right. Even since the mmlu, which again is the multitask language understanding or the massive multi language task understanding, there's been different benchmarks. So this is, you know, to put it in the most oversimplified terms, think of this as a test that a college graduate would take. Right. That is just testing their abilities as a human across so many different areas. You know, testing a human's ability to reason. Right, Right. So if there's a test like above the ACT or SAT that we have here in the US for high school students, if there's the equivalent of that, that essentially you had to take to get out of college to say you are a very smart human being who is well educated and you understand how the world works. You can reason, you can understand and you are the smartest of the smart. So that's you could think of. It's like MMLU is like that, but for AI models, for large language models, Right. So it's the ability to reason and really understand how this knowledge in its data sets, how the training of this, all of this knowledge in the data set can apply to real world tasks and completing real world tasks at a high level across multiple mediums. Right. So that's, that's an easy way to think about it. And it's important because at least for now, the MMLU is kind of the industry standard benchmark and saying, hey, like testing it almost like against human performance. Right. Because others, you know, there's tests that, oh, this is on math and there's other tests that are on specified fields or you know, you know, kind of specialized sectors, so to speak. So MML use is kind of in across the board. How close is this model to being able to reason like a human, to be able to understand like a human, you know, trick questions, so to speak. Maybe I'll have to do a dedicated episode on MMLU in the future. Great question though, Lorena. I love, love that. So hey, if you are joining us midway through, thank you for tuning in. This is the free sale Friday. You guys voted for this in the newsletter. This is what you wanted. I know it's a little random for our podcast audience, but hopefully this is fun for our, our live stream audience. So Christopher joining from LinkedIn, thanks for tuning in. Can you say that are we on AM radio tuning in Shadow wbbm. That's, that's my favorite AM radio station here in Chicago. So Christopher says what are some good resources to learn how to create a local model or cloud that can be treated pre trained with my data? Yeah, this is a little more technical for the everyday AI show, but I'll tell you this, there has been so many new models recently that are now open source. So you know, obviously I would look at Mistral, I would look at Llama's new model, you know, and then essentially applying rag. So you know, there's different processes without getting too technical, you know that you can kind of apply your own data through rag. And there's, you know, third party systems that make that pretty easy now for you know, people who aren't even very technical. So yeah, without, without getting too far into the details there, Christopher. I would say look at some of the Mistral Llama and then look at some of the kind of popular third party ways that you can bring in your, your data, your own data via rag. Also chat with RTX from Nvidia. You know, you can tap into other models that way and it's kind of like a built in almost RAG system that you can run locally on your PC if you have a certain level of, of Nvidia RTX chip. So hopefully that helps. Brian, what's up Brian? Long time, long time kind of viewer here. So appreciate your support as always. Okay, I like this. What is your number one go to AI process? Interesting. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been Tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use gen AI. So whether you're looking for chat GPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everyday AI.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GEni. Okay, I'd say a lot of them are tied, right? And it depends on what I'm working on. But let's just say, hey, for everyday AI, right? Because that's what I spend, you know, a couple hours every single day working on. I'd say my go to process, which is weird, is chatting with my transcript because one of the things that I still do manually as a human is I write our newsletter every day, right? And our newsletter usually goes out about two to three hours after the show is done. However, what usually happens in that time is I, a lot of times have other meetings, you know, once I'm done with the show, I might be answering some, some, you know, LinkedIn comments, questions, etc. And then, you know, maybe I start writing the newsletter which, you know, every single day we recap, you know, the show. So probably today what we'll do is I'll probably pick my, you know, three to five favorite questions and you know, just do a little bullet point recap of our take on that. But so, so Brian, what happens is a lot of times I'm already forgetting. I'm already forgetting, right? Like if I interview someone, you know, we had Darren on the show this week and we were talking about AI and education and as I recall, I think I had a meeting right after and so I'm going in to, you know, type up the newsletter. Yes. Written by me, a human, and I'm forgetting things. So a tool I love. Two tools I love aside from chat GPT. So if you're saying my go to AI process, well, one of my go to AI processes is Chatting with a conversation that I had, right? And also, full disclosure, you know, sometimes when I'm interviewing someone here on the show, I'm. I'm typing down notes or I'm looking at the comments and, you know, looking for the, A good question for, for the guest. So sometimes I might miss something that a guest says, even though I'm trying my best to both tune in and to, you know, read and kind of put questions up from you all. So but to chat with the transcript, what that means is I use Cast Magic. Love Cast Magic. We had the, the co founder and CEO Blaine on the show. So Cast Magic is a, an AI power tool where as soon as I'm done, my co worker Brandon, like, he's so quick. But I can, you know, a lot of times I'm chatting with the guest right after the show and, and I can jump into castmagic. It's already there. It has a breakdown of all the key topics, which is great. But I can have a conversation, right? And I can say, oh, what did you know? I'm just using Darren as an example because he was a guest that we had on the show this week. I can say, oh, what did Darren say about how his son was using ChatGPT? Right? Because I want to write about it in the newsletter. I know he referenced it, but there was a detail I forgot. So versus reading through the transcript or hitting command F and it's like, oh, what was he talking about? I can just ask a question in castmagic of the transcript. I'll often do the same thing with Voila. So Voila is another. It's a Chrome extension that I use that I like. I technically have a paid version of it, but I think there's a free version as well. So it does something similar. So I can do the same thing. I can go on our website, you know, pages. So as an example, I spoke at a, a DePaul event here, here in Chicago. So DePaul is one of the largest, you know, private universities in the country. And I was on an AI panel this week. So, hey, shout out if you're listening. Shout out Jackie for inviting me, one of our, you know, loyal live stream listeners. But I went through with Voila. I looked at some of my old episodes and I just said, hey, you know, recap the five main points that we talked about here. Or, you know, I might remember, oh, a guest said this, or, you know, I was talking about a certain news item that day that I wanted to talk about at this, you know, event that I Was part of the guest panel for. So I like Voila. It can, you know, use the context of a web page, but you can also save kind of like different prompts, etc. That was a long one. I got to start going through these a little faster, otherwise this is going to turn into a three hour show. All right. Hey, theme, thank you for the question. What are the most. What are the most startup ideas to integrate AI with education? And do you suggest any. Okay, it's a great question. I would say this. If you're just looking for a random startup idea in AI and education, I would say go niche. Like, right, there's, there's riches in the niches. Right. So personalized tutors, I think are going to be great. There's already great versions. There's great GPTs that do this, Khan Academy, et cetera. So I would say to do one that is very specific. As an example, create a, you know, kind of a character version of a tutor that is specific for studying for the ACT or something like that, or that is specific to help, you know, college students study for the lsat. I would do something like that to make it very narrow because there's already great wide applications. And like we talked about on the show, sometimes smaller, more refined models work better than large models. So maybe that's what something I would do there. Woozy. What's up? Woozy. So Woozy says last stock in the tech space that everyone loved, tell me they were early on was Nvidia. What's the next ones talking about yet, in your opinion, Jordan? Oh, gosh. All right, well, I'll say this. I'm not, this is not financial advice. I'm not a financial advisor. Yes. I was obviously pretty early on Nvidia. Well, I mean there's obviously people earlier, but you know, nine months ago I told everyone, I said, I said Nvidia is the most important company in the AI and they're going to blow up. And they've did in that time, they've done unprecedented, like literally unprecedented growth that has never been seen before. Meta. Right? I'll say Meta. Right, like, all right. Yeah, Matt has been around for a while and Meta's stock is strong. Just yesterday, Hey, I like, I should be full disclosure right after I saw what, what Meta. What Meta did yesterday, I'm like, okay, yep, gonna, gonna put a little more Meta stock in. In my portfolio there. Right. I'm not doing that a lot, but sometimes I'm like, oh, okay. You know, if, if I see, you know, a company make a Strong move or come out with a product that I'm like, not really. I might go adjust things. Right. Like even my portfolio is obviously a little heavy on the tech. The AI side. Right. I think there's a fairly popular. What is it? It's, it's Invesco qqq, which I think is a more of a kind of tech heavy, AI heavy etf. So again, y', all, I'm, I'm not, I'm probably the worst person to talk about like, hey, what, what stocks in, in the AI space are worth looking at to tell you the truth. I, I don't follow kind of these, you know, smaller, you know, smaller public companies up and coming. Don't follow them for the most part. I'm looking at the big boys, the Fortune 500s. So yeah, I don't, I don't have any great takes there. Sorry. Woozy. Douglas, let's see. What do you think about PPP course that is Co pilot? Oh yes, Douglas, I am. You know what, I gotta reach out to my friends at Microsoft. Yeah, I need, I need more computers. I need more compute. Yeah, I need to get a Windows machine. I haven't done it yet. A couple, couple ones that I want. I'm like, oh, that one's a little expensive. But so many people are asking about Copilot Minimum. I think we're going to get on, get our team on Copilot Pro just because I've been personally underwhelmed with Gemini. Although Google did just have their big cloud next announcement and they're saying, hey, all these things are going to be public rollout so I might give Gemini one last try across the, kind of the Google workspace across the, you know, quote unquote old school G suite of products. But yeah, we might, we might become Microsoft Office, you know, Microsoft Copilot Pro team. So once we do, Douglas, I'm sure we're gonna have some, some co pilots training. And you know what? I know I've been talking about this for a while, but that is one thing that we also are going to have planned for our free community once we launch it. I know I've been talking about this since January, y'. All. It's just we've been getting these crazy opportunities that I'm super thankful for. Like when an Nvidia says, hey, come out and partner with us for our conference. You know, gotta, gotta sometimes prioritize those things. But yeah, I think we'll be doing a lot of, you know, more, more in depth trainings, recorded trainings in our in our community that is going to be free to join. So I know most of you have already hit me up but you can just, you know, hit me up, say inner circle. We'll give you early access which is like, I know, three months late. Now Florin, what is the most effective approach to utilizing AI to counter disinformation? Florin, that's great. So we've had some great guests on the show and actually what a lot of companies in cyber security are doing is using AI to detect AI deepfakes to detect AI threats in cyber security. I'm not an expert necessarily and it is a little bit different because you know, misinformation or disinformation is a little harder to detect versus deepfakes. So you know what Florin, I'm going to add that to my list of experts or if anyone listening, maybe if you are an expert in that space, let me know, maybe we'll get you on the show. So yes, disinformation, misinformation is a little harder to detect than you know, kind of like traditional deep fakes or AI, you know, AI generated media, you know, because a lot of just misinformation, disinformation is text based, right? So there's different methods and there's different in, I guess there's different watermarking systems that a lot of these companies are trying to implement a little more on the media side, you know, on the video, on the photo side there, you know, a lot of the big companies are trying to embed invisible watermarks whereas on the tech side not as much. So I think it's going to be a little more difficult to detect misinformation and disinformation that is text based versus media based, you know, photo or video. But again, hey, if you're an expert on that, hit me up. We'd love to have you on the show. Chrissy, do you get AI anxiety? It changes so rapidly. How do you balance the urgency regarding your work life balance? If I'm being honest Chrissy, I don't have great work life balance. Love my wife for that. She, she pushes me to, you know, always invest, you know, in everyday AI because we, we believe what we're doing is, is, is, you know, needed. I need more work life balance 100%. So I'd love to say I, I have a great answer for you. I don't necessarily on how do I balance it? Do I get AI anxiety? Yeah, for sure. Right. You know, I was kind of on a pseudo vacation for, for a couple of days this Past week. And even though I was still kind of working every day, you know, reading and writing the newsletter, like late at night, yeah, I felt behind, right? I'm like, man, you know, even just, you know, working half days for a couple of days, I'm like, man, like, am I losing my skill set? Right? Just working, you know, I was working two or three hours every day and I still felt like, man, I'm gonna fall behind. So, yes, I do feel that. I think it's normal. I think as humans, it's something we're hopefully going to quote, unquote, get over. You know, I, I think what it is right now is, you know, you just see thousands of new products popping up every day. I don't see that continuing to happen because I think eventually venture capital firms, private equity firms are going to stop investing in so many of these startups that I think don't have a moat and they're just going to get squashed by OpenAI by Google, Gemini by Microsoft, Copilot by Claude Anthropic. So I don't see this like, oh my gosh, there's these 50 new, you know, AI writing tools. There's these, you know, 20 new AI image tools. I actually see that slowing down because I see the VC and private equity money hopefully drying up. I think, Pete, like, if I'm being honest, a lot of these companies, hey, you can judge me for it. Venture capital, private equity. A lot of you have no clue what you're doing. Sorry. A lot of you do, right? Yes, yes, the biggest ones. But like still to this day I'm seeing these companies, right, announced because I, I follow these companies on, on LinkedIn and I'm looking at their websites and it's like, hey, you know, oh, this company just raised, you know, 2.3 million dollar seed round and I'm looking at the companies that invested in them and I'm like, that was a bad investment. Right. Obviously they, they know that the company's roadmap. But I'm also saying, hey, I know this next version of, you know, GPT 4.5 or GPT 5 is going to kill thousands of these startups. So I don't understand, sorry. I know this isn't hot. Take Tuesday VC's private equity. Reach out to me. You should be talking to me because you're making terrible investments. You're going to be losing so much money. I'm sure you don't care because you're, you know, have hundred billion dollars of in, in your portfolios and you're Just taking flyers on random companies thinking they'll get acquired. You know, that's the thing. The acquisition space is not like it, like what it used to be, right, with, with Meta and you know, social media, you know, these little startups would go raise a couple million dollars, get a bunch of users and you know, Instagram or Facebook or Twitter is just, you know, acquiring them by the dozen. It's not happening in the generative AI space. That's not how it necessarily works. Yes, there is still going to be some acquisitions by these big tech companies, but for the most part they don't need to because the, the next versions of their models. Right. Especially as it becomes more multimodal input output, as it becomes more Internet connected. They don't need your startup. That's, that's not going to be worth anything. So hey, random hot take Tuesday there. But VC companies, private equity, you're losing, you're losing a lot of your money because you don't know what you're doing. All right, I'm going to try to get to one or two more questions. Some of these are long, sorry. So I'm not sure if they're, if they're questions or just comments. I'm going to go through at the end and see if I missed anything. So I think we have two or three more here. Two or three more. So let's go ahead and tackle these and then we'll wrap up the first edition of Ask Me Anything. So Don. Wait, Don on Twitter? Wow, cool. Someone's on, on our Twitter or X or whatever it's called. What's up, Don? You're our first. So Don says, what do the AI large language models do with all the data they collect? Yeah, well, it goes into their training set. Right. So as an example, OpenAI just updated its knowledge cutoff date to December 2023. When I tested Meta last night, it looks like their knowledge cutoff is December 2023, which I love it. Now we have two different models that only have a, you know, four month gap in the knowledge cutoff. But essentially don't, you know, and this is a whole nother conversation for another day because I could talk for hours about this. More and more companies are going to block all of these. You know, the Google, you know, Google has a separate scraper for kind of SEO versus for Gemini. You know, you have the OpenAI bot that scrapes websites for data. More and more publishing companies have already blocked these, so they're finding it harder to scrape data. But essentially it goes into their models. Right. Yes. Copyrighted data. Yeah. Hey, whether, whether we want to talk about it or not, the elephant in the room, these, these large language model, these companies, they're going to be facing a lot of lawsuits, especially after we see whatever settlement, which I think is going to be a settlement from OpenAI versus New York Times is going to be, is it's going to be a cascading effect of, you know, dozens or hundreds of lawsuits for, against these companies that are, you know, scraping the open web. And I know, you know, companies like OpenAI are just even making arguments, right? About, like, hey, let's try to argue against copyright law, right? And there, you know, I know that we, we talked about in the newsletter before, you know, they kind of put out a letter to the European Union, just not saying like, hey, yeah, we're using copyrighted data. But they're kind of challenging, like, hey, this copyrighted, you know, data law here in the U.S. it's, it's, they're challenging its application and its meaning. So I think that's going to be the norm as well. But essentially all these companies are scraping everything on the open Internet, the closed Internet, works of art, copyrighted material. It goes into their data or into the training set. Humans are, you know, training the model on it. They're using a lot of different training techniques. But essentially what humans do is they look at all this data, they do question and answer pairings, and they kind of train the model, right? And I guess what the companies are saying is, hey, we don't use any one copy, you know, any one piece of copyrighted material. It's a collection, right? So it's like, okay, as an example, if the New York Times releases, you know, something, it's obviously it's copyrighted. But if the whole world is then talking about it and people are talking about it on podcasts and blogging about it, right? So then they're saying, well, it's, hey, this is public discourse, right? Yes, it was maybe originally copyrighted material, but now it's public discourse. So, you know, these models are essentially very advanced next token prediction engines, you know, are taking the, all the public discourse because once something is out there on the Internet, everyone else is talking about it, right? Social media, blogs are blogs still a thing. Did I just date myself? Other news websites, podcasts, YouTube, etc, everyone's talking about these things and that, that just becomes part of the public discourse, but all of that data becomes part of the model, right? So that's hopefully helpful. Douglas, given the acceleration of AI, any considerations for specialized live Streams in addition to the daily general example, everyday AI health, AI science, AI wearables, etc, Douglas. Great. I love that. So I've, I have all these ideas, right? I use ClickUp. I have this task in my ClickUp for just like great ideas. It's like, oh, I'd love to do this and this and this. So I've had thoughts of doing essentially a weekly series with other guests that, you know, come on, you know, once a month and having like four rotating categories, like as an example, maybe like creativity, health, finance and you know, general business, whatever. And then having like rotating guests that come on the show and we do one deep dive and it's kind of this panel, the same group and then doing that show like once a week right now. It's a capacity thing for myself, right, if, if I'm being honest, because have so many great ideas and I love these great ideas. Keep them coming, you know, because at the same time, we're also getting a lot of great opportunities, you know, to train large companies on prompt engineering, which is something that we're excited about. Great opportunities to go speak at conferences, to speak in panels. So, yeah, it's, it's balancing new initiatives with other opportunities that kind of quote, unquote, pay the bills. So, Douglas, I love the idea. It's been an idea now for, for many months. But I love, I love your suggestion here. Even the wearables. Never thought about that one. That could be a cool kind of a specialized thing there. All right, Ty from YouTube, what's up? Ty, what are your suggestions about sharing AI chats and tools with people who don't necessarily share an account? I'm thinking about co workers with separate accounts or teachers and students. It's great. It's a great way to learn, right? It's a great way to learn. Prompting. It's a great way to learn how to interact with the model. So if you have one person on your team who is great at prompt engineering, who's great at getting the most out of models, you know, they can go through that whole chat, they can share that chat with others, right? They have to have an account. So as an example, if I have a, you know, great chat in GPT4 and I'm calling on all the, all these other GPTs, I can't just share that to someone who doesn't have an account. They have to have an account. And if they want to use it in the same way that I'm using it, not only do they have to have a chat GPT plus account, right? Because otherwise they won't be able to access it. But they'd also have to first install and use those GPTs if they want to continue using them in the chat context that I share with them. So I think that's a great way to collaborate cross team, if I'm being honest. I think it's one of the most underutilized features of most large language models is the ability to share chats. You know, they're not quite collaborative yet. So it's essentially, you know, the, the equivalent of, you know, if you have a file on your desktop like a Word doc and you create a copy of it and send it to someone else, they only get what, you know, what you did up until that point. It's not like you can both collaborate on a live chat. That's not how it works. Although that's where I see. I would have thought Google would have been the leader in that didn't happen yet. But I do see that's where it's going. There's other third party systems that allow that kind of real time collaboration working with APIs. I don't necessarily think those third party tools are very good, mainly because you can't always use all the features right. You know, like you are only getting the base model that's available via the API and those third party tools that do allow that real time collaboration between teams working in unison on the same chat. But you don't get to, as an example, you know, connect with, you know, your, your Google Drive, you know, if you're talking Google Gemini or you don't get to mention GPTs right in, in chat GPT. So when you're doing that in these third party tools, you're using a much more limited version because all you're getting is essentially the model, nothing else. And I think the true capabilities of large language models, especially in real business applications, come from these quote unquote third party features integrations, you know, whatever you want to call them. You know, you have a couple plugins from, you know, Microsoft Copilot, etc. That's where I think you get the real power in business utility. Whoo. That was a lot. All right y', all, was this, was this good? I, I, I, I probably, I probably missed a couple, I probably missed a couple comments. I'll go through the, the live stream here and try to get to any questions, but let me know, yes or no. Did you like this show? Was it terrible? Should we never do it again? Or maybe should it be a thing that we do every, every once in a while, right? Maybe every once in a while. If we don't have a show planned for Friday, we'll throw out a little Freestyle Friday or whatever this thing's called. Hey, you guys. You guys voted for this, right? Not if so if it's. If it stinks, I can't take, you know, all the blame. You all wanted a little, like, ask me anything informal Q A. Hey, podcast audience. I know this one was all over the place, but hopefully you learned from some of our great questions from our live stream audience. So, hey, as a reminder, if you haven't already, please go to your everydayai.com I'm gonna pick either the favorite questions that we talked about here on the live stream or, you know, maybe I'll. I'll actually pick one or two that I didn't get to as well and answer those in the newsletter. So should be a fun kind of random newsletter today. But there was a lot of news that we're going to dive into a bit deeper as well. So thank you for tuning tuning in for our first Ask Me Anything Freestyle Friday show. Hope this was fun. Thanks for tuning in. We hope to see you back next time and every day for more Everyday AI. Thanks, y'. All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.