Understanding artificial intelligence. Let's start by. Getting a clearer picture of what it is. I think there's a lot of, uh, people out there talking about artificial intelligence as if it's a living thing as if we're talking about. Like the movie Terminator where machines are taking over the world and they are more intelligent than human beings and they enslave human beings in these. You know, human guard, labor garden, like in the matrix or something. So let's just first and foremost, clear up what artificial intelligence actually is. Scott. It's a newscast. Over to you Tali. So the. The thought is this, if this, this is going to be a topic that we're going to have to hit on multiple times, and part of being able to figure out what to include in your curriculum. Or not including your curriculum or the framework you give your kids, like, for example, your, your framework for politics or money or anything else. You have to study a little bit yourself to understand. So Tommy and I have started to. To go down the artificial intelligence. Rabbit hole and it's been eyeopening. And actually the slides that we're looking at now and the ones that are come up. Uh, we we've edited these, but they started with the initial kind of framework of them actually was from one of the tools that Talia was playing with. The main idea of this, this entire episode. Is why this is a critical subject to be taught. So if you're a Bitcoin homeschool or. Our opinion is. teach AI now. What level you do that too. Like you can get into, you can get into that, but. Part of this. Part of this is there's a lot of misunderstanding out there. I didn't actually read the article, but. Supposedly some things, some of the things that were coming out of the white house in terms of guidance happened after Joe Biden. Watched mission impossible. And he clearly has no clue. On anything in AI. You have tech giants. Then have a lot of political cloud because of the, the, the money that they can, they can bring. And they want to create a whole moat around this and. There's a tremendous amount of. Just a lack of understanding, whether it's just like, there's a, there's a lack of understanding of what it really is. And there's a lot of fear. So Hollywood's going to do that. It could be, there's a ton of movies we can get into for examples. And. The thing about it is let's let's first, just let's peel this thing, this peel, this onion slowly. Let's let's dig in a little bit to it. And it's just like any other tool. Right. When electricity first came out and people worried about. All everyone dying and being burned to death and electrocuted and everything else. Like it's. Could you imagine a life without electricity now? So this is something that is a technology. It's advancing quickly and let's just start with. Teaching yourselves a little bit about what AI is and then we can get, get into it. Um, more, but the premise is. Let's let's at least understand what this is. Yeah. And as you can see on the side, we're going to address not only what. AI is, but also to really view it as just another advanced tool. So for example, when cars came out, people who own horses or very upset. Uh, it was going to take over jobs like the blacksmith's job. You know, horse. Carriage. Carriage drivers. You know, and who, the people who make wagons, you know, All that type of stuff. And then what they focused on was what they're going to lose rather than to see it as just a new tool that will save them time and energy. So. The picture that you see there, even though we're talking about AI, if you look at it, it's a ruler, a pair of scissors and some thread. Well, If we look at AI, like it's anything more than a tool then of course it brings a lot of fear. But as Scott said before, When electricity first came out, people were very fearful of it because they did not understand it. And so that's what we want to. That's the way we're going to approach this presentation as well as features presentations is AI is nothing more than a tool. And the decision you have to make is are you going to teach yourself how to use the tool so that you are the master of the tool, or are you going to become a victim of this? Tool and somehow give them more power than it actually has. All right. Well, let's get into it then. AI is obviously tremendously powerful. It's it's amazing how quickly it has developed. We only started hearing about AI. In everyday conversations a year or two years ago. And suddenly it is. Absolutely everywhere. Every software that you use is now. Using AI to help make content. Better. It's changing how we interact. Almost in every single way. So. W, but this is where by first. Contention. Is, it's not actually intelligent. So as I've started to study this. Um, The, the idea that when you're using chat GPT or your. Whatever, whatever it is that like, what you're. You're talking about Tali, where you, you have something and you're interacting and there's AI behind it. So it can be anything from a Google search to. Two. Uh, who knows what everything, like you said is really touching this. It's more like a. Auto-fill it's a giant auto-fill. And it's, it's not, it's not as if the machine is actually thinking. So the first problem I have with AI is it's not, there's no, there's no intelligence that I can really, I can answer a. A. L said whatever the, whatever the test is that you take, the, the, the, the legal stats. It was the whole set, right? There was a law school. Maybe this is a wrong example. There was a thing where there, there was a, there was a test given. And judgy. GBT or some other AI was able to pass this. This graduate level exam. And that's this implies we're, we're kind of reading into this. That is intelligent. And that's not true at all. It's more like if you started typing a word. And then the rest of the word popped up. Right. I'm. I'm texting and I started writing something and then the rest of the word popped up. This is just the, it's just the probability of what the next word is and the next word, the next word. And it's, it's a whole bunch of. Probabilities. Right. It's not reading your mind. Right. It's it's deducting logically. There's no, there's no deduction. It is. It's like a whole bunch of vectors and probabilities. That you know, this. This word that like once upon a right. Once upon a, as an example. The the, the number of times the probability that the next word would be, time is higher than the next word being dog. Right. And then it says, okay. And then, and it does it with phrases and it does with other things as well. I'm not a mathematician. I certainly not sorta can program AI, but what, what I took away from my. Uh, initial. Reading and podcasts on AI is. It's more like an auto-fill. And if you think about that, it, you should relax a little bit. We are not on the verge of Ultron. Taking over the world, or if you're, if you're older, like me, how from 2001 space Odyssey, like we're. We're not to the point where this has any kind of general intelligence. This is. It's literally a probability. Tool and it. looks to us like it's really smart because it's, it's able to look at. Millions or billions or however many pieces of input. To get us out. And when people say, well, you have to be careful because AI makes up stuff. Like it would make up case study in that example. Well, Technically, it makes up a hundred percent of this stuff. It's not just making up some of this stuff. Everything you get is made up. And. Relax. We're, we're not, this is, this is literally not a thinking machine coming at you and trying to answer this, that it understands what is giving you. It's just the probability. So, um, to me that, that's the, that's the amazing thing about it. It has so much potential. And we literally only just started down this, this path, but that's what I thought of when I first started. When you, when you're talking about defining what is AI. Well, a part of me is saying, well, what is it not? It is not actually a thinking. Capable type of entity. That's going to take over the world. But anyway, that's my take. I think that was definitely one of the things that we are sure of me in the beginning when I first started hearing about AI, because my first knee-jerk reaction was also fear like, oh my gosh, You know, we have kids in college. What are they going to do? Coming out? What we have one daughter who's very interested in art. If AI is taking over art, then that means you have no job. We have another one that, that writes songs. And of course we're hearing people talk about how AI's writing songs and they're winning. Grammy's and things like that. Like, what does that. What does that leave? For the human students. And that was so that, wasn't my first fear. And when I came across this definition that it is nothing more than a probability generator. And may me relax, because if it's just a probability generator, then that means you've got to have somebody interpreting the data and that human person cannot be replaced. Right. So it's totally my thought in this. This is the most. Just rip style in terms of how we're addressing this. We should look at. This would be a good. A good episode would be just breaking down what the different levels of the intelligence are as a been defined. And where we're at. Going forward. Yeah. No, no. I mean, as a, as a future, Installment in the series. Yeah. Well, I really quick look at the compounding effect of AI and. Really the chip. It's it's really, it really comes back to the chip. Uh, development. The speed's development because AI works off of the speed of processing information. Partially. Because of its slow. Then AI just wouldn't be everywhere right now. Okay. Yes. I think you need that as a foundation, but yeah. I mean, this timeline shows you how you should read that. And 56, the term artificial intelligence is coined. So that wasn't the 1950s. And by 1997, IBM's deep blue beats world chess champion, Gary. Casper off. So I remember when that story came out, I remember that was such a big deal. And the time between the first, you know, artificial intelligence term came out. To when. Artificial intelligence, beat the world. Chess wish. I think in my mind, when you think smart people, you really think people like chess champions. And so it took about 40 years. Yeah. So, this is where again, I want. W we're going to, we're going to have to do an episode, just some what. What is AI? When you're, when you're in the world. Of chess. The, uh, computer is really good at running a lot of calculations. And keeping track of them. So in chess, there's a, it may be a log, but there is a finite number of, of options. And if player X. Chooses an option. That reduced, that changes. The the possibilities each time. Right. So the, the chest. Like a chess program, could theoretically look at all the possibilities, make it. And so this is the one that's going to go with. Whereas it's not really. Intelligent. Well, again, I think again, the intelligence thing throws me, and this is not the same. The, that that example is interesting. But it's not the same as what we're seeing with Chet GPT. It's not the same as what we're seeing with Dali. There. But that was the beginning. That was 1997 button. But what I want to say is, and I don't know if they actually did. Did this, but. When a human. Chess player is. Against artificial intelligence or I'll just call it a computer. For, for simplification and they test and you're like, oh, The computer beat, Gary. Moving on the computer is that smart, but what, what I think would have been really interesting is if you gave Gary a chance to learn how. That program. The blue works, whether or not Gary was some work could have beaten the, the AI program. I think that would have been an interesting thought. But I'm not sure that they did that. I disagree. I think the more interesting thing is. The, this is work. This is why AI is actually a tool for good. If you wanted to be a chess champion 30 years ago. And you were trying to get in your, your reps, if you will, to learn. And you were fortunate enough that your parents could afford to hire a, you know, a grand champion for you to spend a lot of time with them and study, you could get in these. Repetitions yourself to learn how to play. Today. I can download an app on my phone. And. Because we have these programs. I can actually be taught. I can actually get the repetitions in at a, and this is the Jeff Booth. As technology is deflationary at a much lower cost than it would have been. 30 years ago. So to me, the more interesting thing of that is is. The potential of AI as a tool. And what it means for learning. Yes, definitely as a tool. And I want to reference the book that we talked about in the, in last week's episode of the art of learning by Josh Watkins. When he speaks about, his experience facing off at the world, chess championships. A lot of it comes down to my games. And when you're working against the computer, it is neutral, always emotionally neutral. And you are honing your technical abilities for sure. But there's an element that can never be replaced by machine, which is that. Human reaction. Element. So he said that when the top chess players face off, they both, like, when you're talking about they're playing for first place, you know, everything they've gone through every round when they're playing for first place, was that. That champion apart from. The loser is just their ability to keep their cool or to detect minute emotional shifts in their opponent. And that's how they beat. The other person, not necessarily because they are technically more superior. So going back to what you said, AI is still just a tool and if you use it to your advantage, Then you have the power, but if you give it some, um, some like. Unreal expectation that it can somehow replace. Actual human being, you know, honing your trust skills then I think we miss the point. Yeah. So is there anything else on this? The, the milestone discussion. It went from. Yeah, so it went from 1997 to 2011. So that's what like 14 years later. And now they're talking about IBM Watson wins jeopardy against human champions. Um, so again, the AI programming is doing the data analysis and in those two cases in 1997 and 2011, they. Basically, we're used to demonstrate how powerful they are and how smart they are. But again, don't lose sight that they are still just. Data. And analyzing right. Yeah. Yeah. And then the. Oh, well, Okay. So let me jump here because the timeline also includes this stuff with where Google's getting into recognition algorithms and. And another big company, alpha is alpha go alpha. What is the one that beat the champion? I can't. Oh, so. One of the things that's on my mind, as you think about this, this history is the Reese, the intensive amount of resources necessary. To build this, right? This wasn't somebody in their garage that could build deep blue. We're talking about IDM. And then you get you fast forward to. The the. Recognition stuff that the Google was doing. Like you're talking big tech. And one of the things that I think we talk about. We might bring this up again later, but. As technology. Continues to develop at the pace it's developing. It's also getting cheaper. So not only are the chips faster. And the computers can hold more your servers or whatever you're using. But actually. There is a trend that is towards decentralization. And one of the episodes that I listened to at a it's a resource I recommend to those that want to go deep on this. Guys Swan now has a separate podcast just on AI. It's called AI on chained. And one of the very interesting points that he made. Was that AI is actually, it tends towards decentralization. And now you have programs that you could. We're the once you've done the hard work of going through a lot of data and creating whatever these to create to all that, the vectors and probabilities and whatever else. You can now get something down to 200 gigs or 250 gigs, or, you know, whatever it is, it's something that could be on a home server, a home computer, even your phone. And now you can be offline. And you would have access to the equivalent of like this. This, uh, this, this language model that can answer things. And again, we're still early in this, in this process, but what comes to mind is in this. This discussion of the milestones is it started out where you're really needed to be. Resource. Rich. In order to, to start to build this. But we're trending towards more and more de-centralized abilities. Are these things. And I actually, I was actually very relieved. To hear that otherwise. The the, um, where they call them the controller guards. You know, the Facebooks, the Amazons, the Googles of the world. Even the government. Would be able to control this because they, the massive resources, you would think like a data center or something to be. To run these things. And the reality is it's actually going the other way. And it's actually very decentralized. That actually was. Uh, pretty reassuring, but all right. I think from a. Uh, history standpoint, I'm not sure how much more we really need to, to go into that. So, all right, let's just jump into some of the other, um, some other aspects of this. Tell me, where do you want to go with. With the next, I mean, these are just a couple, a few bullet points about how AI works. Uh, they, they can extract. And analyze information for images and videos. They are obviously building robots that can perform tasks. By themselves. Um, they have models that were inspired by biological neural networks and they can recognize patterns. Very quickly. Um, other things like natural language processing, they enable computers to analyze and generate human language. I was playing around with an AI program. Where if I upload it. A. Short video of myself speaking in English, I could within minutes change my spoken language in the video lip sync to my lips. In 150. Different languages. Very. Very cool. Very cool. Yeah. So the actual way that AI works to me is beyond our, beyond the scope of what we personally know. I do think it's almost like. Do kids really know how a dishwasher works or do. Did someone really know how their microwave worked? Do you actually know how your car works or your phone? You may not need to be an expert down to the point where you could do the programming. I think this is another section that later on, I want to, I think we're gonna need to go deeper on if you're looking at a Dakota homeschool curriculum. Some portion of that needs to be able to explain, okay, here's what. If this is a computer, computer vision, and being able to analyze images and being able to analyze videos, here's kind of what's going on with that. And then say, okay, here is what language processing is like, and then say, okay, here's how a. Um, Like a mid journey is actually creating images and it's, you know, the way I heard it explained is it's, it's got all these different. Images that have been labeled and described in. In whatever language. Then you go in with your language model and you use that, it goes back and says, well, then here's all the probabilities around. What kind of colors, what pixel color is next to what picture color based on that. You throw in some randomness there, and then it starts to put these things together and it comes out looking like. There could like. The computer knows what a cat is or something. So. I think we need to put in English something that like, uh, We are, we could explain to at an elementary school level. On how AI works in the same way that you might explain to kids how the body works, how a car works, how a phone works. Right. We need. We need to do the same thing with AI, how AI works. Yeah. This episode is an introduction. I think there's so much information out there. Obviously we can't possibly cover all of it, but. This is a call to action for homeschooling parents to. To dig a little deeper and not be afraid of it and figure out how to incorporate that information into homeschooling. One of the things I, I hear from. Our kids were in college right now. Um, when they have to write papers. Somehow their professors can, can or cannot tell that. Somebody had the chat, GBT write their papers. And so there's a whole lot of rules and regulations around how you shouldn't use Chad GBT or other tools too. Perform your work. But I feel like that's a very dinosaur age way of looking at it because yes, you should know how the right paper, but then how can you use the AI tools that are available to you to become even more productive? Because the world's moving very fast and if we can up our productivity there. That also frees them up time to learn even other things more deeply or, or, uh, work on. Connecting data points versus having to spew out. Right. Regurgitating things. Yeah, I don't, I don't, if you're a professor, I think. Today, you could probably tell if the student just took. Chad GPT and, and just literally dumped it out there as a kid. That's just trying to. Get by faster could take that and edit it. And it would probably have a hard time. I, the technology though, is it increases. It's going to get harder and harder to tell. You know, with that. So the bigger thing to me is. Do I want. I want to be able to use this to, I want our kids to be able to use the tools available to them. For an hour, we're gonna get into jobs and things later, but I want them to be able to. You know how to use it for good. The question of whether you use something for good or bad, that's more of a moral teaching, right. And it's a tool. I can use my car to drive safely from, one place or another, or I can transport drugs. Right. I can. Use a gun for. Hunting or I could, you know, commit a crime or something. So there's a knife. The knife, making a gourmet meal or hurting someone. Right. She didn't say don't use a knife because you could hurt somebody with it. Well, no, you still use a knife because it's a very useful tool. The meme I liked, I want, like someone says, um, when someone's trying to attack Bitcoin, they say we need to stop Bitcoin because of terrorist use it. Well, it's. It's permissionless. Right? So if the terrorist uses a road, we're not taking away all the roads. And terrorist uses a cell phone. We're not taking away all the cell phones. You know, You just. Th this technology is here is. It is. Uh, increasing in terms of its effectiveness and abilities at a speed that we, we have a hard time with. So the question is what do you do with it? And so, all right, so next. Uh, want to go into two different types. Groups of AI, one is narrow. One is broad. An example of a narrow AI is for example, Um, a program that was trained specifically to spot something out of a group of things. So for example, if you had a picture of Waldo and you train your AI to spot Waldo, and that is the only information that you put into that algorithm, then that algorithm can spot while though faster than. People and a general, broad, um, algorithm can because that's all they know. Or if you program it to identify, you were mentioning before cancer cells. Well, if that's the only thing they know, and that's the only information they're processing through, they can do that very, very quickly. Now that doesn't take into account anything else. So the example that we were talking about before about cancer, My. My rebuttal to you about using something like this, like AI to, to identify that, or as a diagnosis. Uh, diagnosis tool or as a whatever in the, in the medical field. We, we are realizing more and more that the human body. Requires a holistic approach to, um, to treatment of different ailments. And so if you have a bot that just identifies itself, it doesn't mean that he then has the back thing. That algorithm has the. Answer to how to. Make you better, but it does make the diagnosis much faster. And then you can move on to the other holistic stuff that you can do to treat. Two different things. So the point that narrow and general or narrow and broad. Are two different ways of kind of categorizing AI tools. And I, the way I would, if I could summarize what you're saying is that. Even after you have that. The fact that AI can spot. Maybe from x-rays or. Um, scans or whatever can spot cancer. More reliably and faster than a human can. That's great, but you still, now you'd have, now, now people can move on and start working on the diagnosis. Better. Treatment the treatment. Right. So now you've. You've leveraged AI to do something better than humans can do faster. And now you can get to, to the next to the next stage. So that's an example of using AI as a tool for your benefit. Um, if we were to use a broad AI, like a chat GBT and say, Hey, look at these pictures or Gemini or whatever. Uh, Google's new AI tool coming out. If you say, Hey, look at this picture, is it a cancerous? It's going to have to process through a lot of information. A lot, most of them irrelevant. To make that determination and then may or may not be correct. And so in that sense at the narrow. Then the more narrow. Programmed AI would be a more efficient tool versus a broad, because we always think, oh, Bigger data is better, but it's not necessarily so right. Okay. So the key point is there's. There's different types of AI. There could be, they can be adjusted to very specific needs where they can be adjusted to be very broad. And they both have different. It depends on the data set that is put in there. I think it has a different ethic. Yes. How you train it? I think it has to do with what's the use case or what you're trying to use it for, so. Okay, great. Which is a great lead into. So the kind of things that you can do. With this. So, um, being able to detect fraud. That's great. The healthcare stuff we've already, we've already talked about. Um, you want to, you want to. Well, virtual assistants, AI powers, virtual assistants, like Siri and Alexa. And I must say that they are very useful. You know, But they make a lot of mistakes as well. So it's a tool self-driving cars. I know that there's lots of places testing it. Uh, what is a Tesla has self-driving mode, but only working in certain. Types of environment. You have the self parking car, like parallel parking and you just push a button and then take your hands off. The wheel type things, so they can be very, very useful. Very practical. Yeah. And they're, they're basically there. But I would put them in the narrow category. Right. If you're, if you have. I don't know how many billions of images of. Cars and trucks and bikes and roads and things. And then all around a Tesla, you have all kinds of inputs and it says, oh, based on the way. This other car is moving. Then the chance the, of them like a collision is high. Therefore apply the brakes. Right. And then you, it has every time, every person out there with their. Does this driving is adding to that database. That's training it. To become. To go and better. It's not like you just said. Drive in it. It thought of how to drive is. It's just an, it's a narrow application. And then you have facial recognition. You have. You have so many other things so that the number of applications. I know the one in the Bitcoin community is popular. It comes up is using AI to help write code. So you don't have to be an expert code writer to do some basic coding. You could use AI to help build websites, or you could be. You can use AI to check for mistakes too, in your coding, right? They do. And they do that. They're the ones that are more, some of the more advanced folks we'll have, we'll actually be able to use AI to, you know, to. To generate the code to do things and save hours and days. And be able to do what may have required someone else. Oh a week of work to do. Can now be done in. I don't know, 20 minutes or whatever it is. So in knows how to use. The application. So that's programming. That's pretty cool. I know that content creators use it. On the web. Everything from writing up your Amazon. Amazon descriptions too. Advertisements. And things like that. So it's pretty interesting. Um, lots of that. Applications. I don't think we need to really go too deep on this. I think most. People already get this part. I don't think we need to really. I don't think there's much more to add on this. On this particular part of the discussion. Well, okay. So these are examples of how AI can be interpreted to. To threaten, uh, human jobs. So, for example, for content creators, they used to have to hire somebody, a copywriter to. Right there. Copy or hire an editor to edit there. They're writing and suddenly. They don't need to do that anymore. They're using AI to help them, but I would challenge that notion. In this way, if you are a copywriter and other people are using AI to. To write their copy, then you can. Get ahead of the curve by saying, well, I use these tools instead of charging you for 10 hours of work. I only have to charge you one hour because I still have human. Um, discernment that AI doesn't necessarily have to apply to a certain situation. Okay. That makes you more productive too. Two comments. I think. The thing that I'm taking from that is. If our kids were younger and I wanted to teach them about AI, I would want to give them a framework of how to think about this. And. Uh, one of the things that is powerful is to compare like the person that was creating. You know, the buggy whips or whatever it was that you needed when there's horse and carriages and along come the cars. If the buggy whip guy went to Congress and said, you got to pass laws, that people can't make cars, because I want to go out. You're going to put people out of work. Well that you're trying to fight. Where technology's going. And this is why I know we've already talked about another other episodes, but why the price of tomorrow is just so brilliant to talk about. In simple terms about how technology is deflationary. What it means for you? If you're young. Student you're being homeschooled. It's not that you're going to be out of work. The nature of the work is just going to change. So if you want to add value, Then. If you learn how to use these tools. Better than other people. You are insanely valuable. In the next stage of where we're going with the economy and the types of jobs. In other words, Don't be the one. Worried about how to protect the, the buggy whip. Be the one who's learning how to be a mechanic on the car. Right. You. You, you know, depending on what you, I think the. I'm trying to figure out the right way of saying it to me is the framework of thinking about what the, what this means. And when the FID comes along about everybody losing their jobs. And I'm an artist or you're a copywriter or whatever it is. You still need someone who knows how to use these tools. Right. And if you do decide to at least understand what AI is, there are so many different ways that you can go into it. So many different applications, like we were just talking about. It is a world of opportunity for you. It should not be feared. It should be looked at as how do I use this tool? For myself and what I want to do, what's gonna make me. Further and make it either. Further. As opposed to trying to fight the fact that technology. Cause you're not going to stop technology from improving. Government regulators and things like that. They might be able to try to put up some artificial. Moat. Temporary temporarily. But ultimately they can't stop the advancement of where the technology is going. It's a much healthier approach to say. Well in the free market, when you have something new. Then it's going to open up new types of jobs and new types of things for people to work on that we can't even imagine today. Yeah. That's the, it's an actually really good thing. So. Oh, look at that. So the next slide actually. In our, in our notes. Talk about that. There's going to be a lot of jobs. That are going to. Really benefit from people who know how to use it. So I'll just give another example. So we had a friend who worked for Ford. And his job was, he was on the assembly line and his shoulder was constantly injured because of the repetitiveness of the job. And it was, it was causing him so much pain and. If. If that part of the job was automated by AI somehow, and he was able to do something. Different that the AI couldn't do. He would be a happy lay employee and. And it's just like AI. It kind of like, like, um, Like the cars, they, you know, when people say, oh, you're going to take away the jobs of the. The buggy drivers? Well, buggy drivers had a really tough life. They were out there in rain and snow and cold and went in and it was miserable. But if they had a car, they get to sit on the inside. Maybe they've become car drivers. And so it's just a, it's just a different way of looking at it. And also what you mentioned, the new AI related professions created. We can't even begin to imagine what those are. For example, 10, 15 years ago, when, when our boys were really interested in. Um, Minecraft and all the little boys were on YouTube, you know, recording themselves, playing. And I was like, what are you doing? This is what your brain is going to melt into butter. And it's a complete waste of your time. You need to go and study something more important and so that you can get a better job. You know, going to. When you go older and suddenly you have all these young millionaires and what were they doing? They were playing Minecraft on YouTube. Yeah. So those professions, there was no way that we could have anticipated that possibility. So this is the double-edged sword of, of the impact in terms of society. Because if you're, if you're China and you want to monitor where people are spending. Their their money. And now, like, I know I use a, a garment cause I want to March my sleep well, if they have access to your sleep data, They, they, they know when you go to bed. They know, you're your GPS. They know where you drive. They have your financial records so that they know what you eat. It's really scary. And if you tie that license plate recognition, facial recognition. If you use all of that together. These tools of AI could be used in a 1984 type of total control. Same scenario. So it's actually another reason that we really didn't even intend to get to, at least I didn't intend to get to in this thing. And that is. We actually need people. To be aware of these things so they can, they can. These bonds. Respond. Appropriately. The government saying that they're going to protect us and make sure there's no harmful speech and there's other things they. It's always going to lead to the opposite. Just like things like the Patriot act and things like now. And. We hear all these different abuses. Um, So there are risks. Of this tool being used in very bad ways, because it's just a powerful tool. And another thing that needs to be taught to our kids is this context. Uh, about this gets back to the more freedom oriented ideas of a Bitcoin. And the, the constitution, the different amendments, right? I mean, this is when we talk about the first amendment, the fourth amendment, other things like this. We just need to understand that this is a really powerful tool. It can do a lot of good. We need to be aware that in the hands of someone who. Has different motives, different incentives. It could be used bad ways. In that case, you need to be aware enough to. To protect yourself as best you can ideally speak up and stop that from happening. Like, I don't want to see the same things in us. Like the China. Um, it feels like we're, they already know a lot about us anyways. Like we're already there. So I think it's good to have some level of concern. I don't think it should paralyze you. And I don't think it should make you freak out, but I think it's good to have a little concern. Well, the thing is that as Jeff Booth mentioned, In a park, as I listened to with Preston, he said, Even if you don't participate. Uh, they still know about you and they can infer who you are. Yeah. So you not participating is only hurting yourself. All over yourself, right. I'm not being educated on it is hurting yourself and your children. Yes. So we might as well stay ahead of the curve and, and stay informed a hundred percent. A hundred percent. Can I. And speaking of that Ellis, I like to slide. The next slide for those that are listening. This is an exponential growth of AI. So. The. The. Technology is going, is advancing so fast. And, and what we mentioned earlier in our show where it's actually a de-centralizing type of technology. Is. Just mindblowing. I just can't think of any other word to describe that. And as you get into. In the Bitcoin space free and open. Um, Open source software, um, is a huge deal. If you look at the Nasr development, for example, and how fast that's going. Well, The AI development is just exploding. And you can't get that genie back in the bottle. You just can't. You can't hide from it. You can't hide from it. And the, one of the, one of the ones that I'm really excited about. That, uh, when we were adopting Bitcoin, I had the opportunity to listen to, uh, Elyx. Uh, not Alex. Um, specifying the, the guy who's developing spirit of Satoshi. This is you. You're going to have people who take the initiative and they're going to use it in really good, powerful ways. And for those of, if you're not yet familiar with spirit of Satoshi, please check it out. I think the spirit of satoshi.ai, I think is the actual. Link, but essentially what he's, what they're doing is they're building. A language model that is based on things like libertarian ideas, Austrian economic ideas, Bitcoin ideas. So that when you go and ask it a question, You're not going to get, you're not going to get an answer that talks about crypto. Currency in general, if you go to Chet GPT and ask that same general question. It's pulling from a lot of, a lot of other things that are biased. In that, so. So I would say, well, that's biased. Well, yeah. Okay, well, whatever you feed your, whatever you feed your. Your model in terms of how you train it. It gets, it's going to reflect that. I don't think that's a bad thing and I just. I am so excited. To just. That we are part of this, this history where. We're hitting this inflection point and this, this AI revolution is just getting started and holy macro, if you can, if it can pass. Law exams now and be a go champion and you can do all these things, other things. And as far as we can tell so far, We're still at the most basic level of what you would call intelligence. Right. It's not actually thinking these are just the base models. I it's. It's really, it's, it's hard to imagine. It's hard to imagine what this technology will be. A year from now or five years from now, or certainly 15 or 20 years. From now. Yeah, who was it that I heard this from? I am. I think I was on a workshop with a marketing expert. And he was talking about AI and he said, we are so early in terms of the people who are in the workshop using AI in their marketing campaigns. And he said, the thing is because AI is evolving so quickly. If you don't catch up to the movement. The gap is going to widen in. A speed that you can't even imagine. So stay ahead of the curve. It's basically our call to action today. And, uh, not just obviously for you, but for the sake of your children, you really need to take the helm in how they're exposed and not be afraid that, you know, if you were to introduce them to AI, they're not going to learn how to read and write because they're just going to speak into the computers. Well, what if they didn't read and write what is, they were able to do something that we can't even begin to fathom. So just be aware of the fear. And know that you're whatever you decide to do, you're still in control. Of this tool. Yeah. They talk. Right. I agree with that. The call to action. You should start you. You need to learn for yourself though. And learn through them. I remember I still, this was an example from about, I don't know, maybe six months ago. And it was on a different podcast where Preston was talking about using AI to help build an automatic dog feeder. With his son. Or whatever they were doing at home. And I was like, what? I just couldn't like, I just was blown away. And. And it wasn't like, you're you have like Jarvis there, but if you're, if you're at home, I'm starting to do this. Now I'm only just starting. If I have a, an issue with something, instead of going to Google. I'll go to chat GBT and I'll ask, and it could be like a home project. It could be a programming project. It could be. Um, I don't know, it just it's. It's just fascinating. And so the call to action is. As you were saying. Like your framework and open open-minded and try to learn the stuff. You don't have to go and get a degree or take formal classes or that like you just pick something and. Just go play with it. So to me, the way I interpret your call to action would be like this. There are dozens of. Of image. Creation types of like, basically like language to, to, to image. Tools out there now there are tools for videos. There are just general language models that can answer. Questions. Just pick one. Just pick one and go try it out. And actually just get some experience as fun with it. Maybe align it with somebody's interests. So if you're feeling. Right. If your kid is interested in a certain thing, If your kid is very visual, maybe that that child would be better off. With. Whichever, you know, is Dolly or mid journey or whatever. The others are now. If it's someone who really. Um, I don't know, maybe they're very technical. Maybe they're very engineering. Oriented. Well, pick a technical project, go build something like an automatic dog feeder and use AI to be your coach on. What do I need to account for, for. Uh, display, what do I need to count for and design and et cetera like that. I mean, it's that, to me. I think it's going to sound overwhelming when you say this is how much stuff is going, how fast it is. This is just an intro. No. I know. Follow up presentations. Right. I right. But you're, you're saying the call to action is you. You need to get. Started on this and what I'm trying to say. In a long-winded way. Is it doesn't have to be the whole thing. Just pick something small. And go, go play with it. There's a lot of free versions of things out there. Are we going to list them here? Or are we going to go into examples down the road? Because we're already at 50 minutes. No. Going. to tell them to go use chat GBT. Are we telling them to go use beautiful Diane? Like there are so many out there. I don't think so. I think today's purpose was. Why included. Right. I think we're done. Okay.