EP 167: When AI Outsmarts Humans - DeepMind's Historic Breakthrough

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People think that AI is just an autocomplete and that's kind of true, but AI is actually so much more than that, especially when we're talking about generative AI in large language models. So in just one short year and a half, we've gone from thinking of generative AI and large language models to this ChatGPT craze to now large language models making new discoveries that have plagued humans for decades. So we're going to be talking about DeepMind's historic breakthrough and talking about what does it mean now when AI starts to outsmart humans? We're going to talk about that and a lot more today on Everyday AI. Thanks for joining us. My name is Jordan Wilson. I am your host and if you're new here, to Everyday AI, welcome. It's a daily live stream, podcast and free daily newsletter helping everyday people like you and me not just learn what's going on in the world of generative AI, but how we can all actually understand it and use it. Right? That's the most important thing is how can we actually use it to, to grow our companies, to grow our careers. So that's what we're going to talk about today. But before we get into this historic breakthrough at DeepMind, let's, as we always do, go talk about what's going on in the world of AI news. So first, more AI on the campaign trail and this time it's coming from Pakistan. So artificial intelligence was used to create a virtual reality for Pakistan campaign's former Prime Minister Iman Khan or Imran Khan. So why does that matter? Well, Khan is currently in jail and is unable to campaign in person. So this rally was organized by Khan's party, pti and it garnered millions of views on social media, leveraging AI technology to replicate Khan's voice and speeches. So this is a new one, right? We've talked about at least here in the US with the the 2024 election cycle ramping up, we've talked about it being used in ads, but here's a new one, use live in a campaign rally AI to replicate his voice and likeness. So interesting. All right, number two, and this is a hot topic for today in AI news is GPT 4.5 actually out? Well, maybe. Well, let's talk about it. So we talked about this last week on the Everyday AI show and CEO Sam Altman essentially just denied it on Twitter after there were rumors and reports and leaked screenshots of OpenAI's next version of their large language model, GPT 4.5 being a thing, being a reality, right? So there was a screenshot, people were like, ah, it's not real. Uh, the CEO Sam Altman said, nope, it's not real. Uh, but there's some updates. So over the weekend a lot of users, including myself, notice that if you ask ChatGPT a certain way what model it is specifically running, it will say GPT 4.5 Turbo. However, it's important to note that asking any large language model, the version of what it's using is not always very accurate at all. But this is kind of a widespread response that a lot of people are getting. So you're going to be hearing a lot of buzz this week as okay, is are we on GPT 4 or are we on GPT 4.5? Also what's important to see is there's been some under the hood updates that no one else has talked about or reported. So I talked about on the Everyday AI show, this is probably about two weeks ago on how the plugins mode, the GPT4 version in plugins mode had actually been rolled back to January 2022. It's knowledge cutoff, which is important because now as of since chat, GPT has been responding. It's on 4.5 that has been rolled up to April 2023. So the GPT4 that's being used in plugins mode was kind of, you know, cut off at the knees for about a month and it's been updated again. So is that a sign that yes, GPT 4.5 is out? I'm not sure, but it's worth taking a look at, especially after Google released its Gemini, its new update to Bard. Kind of it's under the hood releases with Gemini Pro now powering Google Bard. So all these big companies, they're obviously going back and forth trying to one up each other and trying to make sure that their stocks continue to go up. So keep an eye on that. All right. Last but not least in the world of AI news, AI can now create more AI with what's being called a self evolving AI. Right. All right, so researchers have developed a new technology that allows artificial intelligence models to to create smaller AI systems without human intervention. All right. Showcasing the potential for self evolving AI. So this breakthrough could have applications in improving things like hearing aids, monitoring pipelines, tracking endangered species. I mean there's so many different potential use cases for this. So in Short, larger AI models, as this report shows, can now design smaller, more specific AI applications for everyday use. So this study comes from several companies in universities that include the group AI Zip Inc. As well as researchers from MIT and multiple University of California campuses. So a lot of different researches and private company being involved in this. So this is something you're going to be hearing a lot about, especially as we talk about, you know, moving into 2024, what we should be expecting or could be expecting from generative AI and what's happening in the space or what could happen. Kind of wild, right? We, we've talked about this for, for years, right? Is this the, the first sign of, you know, super intelligence when AI models can create different versions of themselves? But it looks like, hey, it's 2023 and we're already there. So before we talk about this historic breakthrough from DeepMind, let's just go over what we have coming up this week in Everyday AI. So tomorrow is going to be a good one. So we're going to be talking about AI in higher education if is it broken? And how to fix it. So make sure to tune into that one tomorrow with Jason Gula, who's the chair of the Artificial intelligence speaking of colleges in Berkeley, he's the chair of the Artificial Intelligence Council at Berkeley. Wednesday, if you haven't heard of DID it is one of the most impressive and unique generative AI tools. So we'll be talking with Rod Freeman, who is the head of content and creative marketing at did so that should be exciting, right? And hey, where else aside from everyday AI can you tune in live and talk to the actual people who are shaping generative AI and the tools that we use. So make sure to tune in on Wednesday. On Thursday, we're going to be talking with Marcus Bernhardt about AI business strategy and if you're ready for 2024. And then I'm going to follow that up the day after Friday solo show. So many hot takes, you're gonna need oven mitts. All right. To, to touch the computer. I'm coming in hot and talking about generative AI in 2024. What's coming and what it means for you. All right, so if you haven't already, please make sure to go to your everyday AI dot com. Sign up for that free daily newsletter. But let's talk about, let's talk about this, right? Let's talk about the reason for or the topic for today's show. And thank you to everyone who's joining us from all over the country and technically all over the world. So Tara's, Tara's joining us from her family road trip. That's great. Thanks for tuning in. Josh, what's going on from joining us from Dallas, Ellington, Brian, thank you everyone, for, for tuning in. Also Harvey from Texas. We have a lot of people from Texas listening. All right, but let's, let's talk about this, this new deep mind discovery. Let me first talk about what it is, and then we're going to talk about what it means. And this is pretty historic. All right, so here's what's going on. So this just came out late last week. We talked about it on the show and in the newsletter. But I thought this was worth its own kind of deep dive to talk about this. So there's this, I think, very common misconception that all AI is, is an autocomplete, right? So whether we're talking about generative AI, large language models, traditional AI, I think people assume that all AI does is. It just, it's a very advanced autocomplete. It's not creating anything new. It's not creating new intelligence. So that's wrong because, I mean, as this new breakthrough From Google's, Google's AI arm, if you don't know, is called DeepMind. But they have a language model called FunSearch and Fun Search. And the DeepMind team have successfully solved a famous problem in pure mathematics using AI to make valuable and previously unknown discoveries. So here's what happened. They used their model fund search to solve what has been an unsolved math problem for decades called capset. All right, so I'm not a mathematician and maybe someone tuning in today is and can enlighten us all. But from what I can read and what I can understand, cap set is essentially a famous math problem or a math question, right, that no one has been able to solve for decades. And it's finding the largest set of points in a specific 3D space without three points forming a straight line. So if you can visualize that, I mean, hopefully that makes sense. But essentially you've had many, many researchers, mathematicians, and, you know, even artificial intelligence with the help of artificial intelligence, people have been trying to solve this or understand this cap set problem for decades, right? And DeepMind did it by using generative AI, using their language model fund search. All right, so why is this important? Well, it is a new discovery, right? So this was not technically derived from existing data. So yes, this math problem and the data that it technically contains is the parameters. But this new discovery, this is a language model, right? Creating a New math, like mathematical discovery. All right? So to do this, it combined the large language model with other systems. And how it did, is it, it used it to reject incorrect answers and generate the correct code, okay? And this is one of those things that I think people misunderstand or maybe when you're looking at large language models or generative AI and what it can do for yourself or your company, I think people sell large language models short just because of the sheer volume in quantity that a large language model can work at, you know, much faster than any group of humans. So, you know, this, this problem, right? This cap set problem, it obviously has a solution, right? But like I said, it has perplexed humans for decades. And I think this is one of the first definitive and somewhat easy to understand instances where a language model has created a new discovery, right? It has created something that was not there before. This isn't autocomplete, right? So this, this problem, like I said, it has its own, its own rules for solving. It has its own kind of confinement that the, that the solution must work within. However, Google DeepMind and their fun search model has done this, right? So let me, let me illustrate, let me illustrate this. And I'm also wondering from our, from our live audience if you've noticed this or what are your thoughts, right? And I mean, do you agree, do you agree is this the first instance where AI is outsmarting humans? I mean, there's arguments that, you know, that has happened before, but this is one of those things where you can look at it and say, this might be one of those kind of like what Dr. Harvey Castro is mentioning here about the AlphaGo, right? The AlphaGo move from so long ago, right, when you had AI initially competing in different games, you know, like AlphaGo against the best human players in the world. So this is something I think even greater than that, right? Because in head to head human competitions, right, when AI, you know, there's the famous Watson, you know, winning on Jeopardy. Against human contestants, you know, so that was, I think, a lot of people's first interaction with artificial intelligence. Seeing it live, you know, on their, on their television sets, you know, many years ago, however, that was always AI system versus human, right? So in theory, you could point at, okay, well, this is just the AI was just a little better than the human today. Or, okay, well, the human today was a little off, right? A lot of pressure for humans, right? People, people made those argum, you know, when these, you know, kind of AI versus human competitions or, you know, events happened, you know, decades ago. But this is completely different, right? This cap set math problem and Google's, Google DeepMind's new discovery is completely different about that. Because it's, it's not pitting, you know, DeepMind against a single human. It's not doing something live. It's not, you know, setting down, you know, Google's large language model or their fun search model versus a human. This is against the history of humankind, right? Countless mathematicians, scientists, researchers have tried to solve this cap set problem for decades to no avail. And Google DeepMind did it. So I kind of, I kind of want to talk a little bit, just a little analogy here, right? And hopefully this can kind of set our expectation or reset our mind when we look at generative AI, when we look at large language models, when we look at what they, they mean and also what they're capable of. Because I think, and you know more about this Friday, so make sure you tune into that show. I think that we're going to see a completely different side of generative AI moving into 2024. I think a lot of companies, you know, enterprise and even tech giants, right, have barely put their toes into their, their collective toes into artificial intelligence, right? I think 2024 is going to be wild, but that's beside the point. So. But I want to illustrate what I think can hopefully help us better understand or, or get over what I think is a misconception that AI can, cannot create its own intelligence, right? I mean one of the news stories today says, you know, says otherwise, right? So this, this company, aizip Incorporated and the researchers from MIT and University of California campuses have shown now that large AI models can create smaller AI models to solve problems. I mean if that doesn't tell you right now that AI is, has the capability to outperform humans and that AI is more than just an autocomplete and AI can do a lot more than just work with data that it is given. If that alone doesn't tell you, right? Just the fact that researchers are now showing that large language models can create their own AI models, right? This self, self evolving AI system or self solving AI system. If that doesn't show you, let's take a little exploration together, right? So think of space exploration, right? So for about, oh yeah, it's been more than 60 years, right? Since the 50s since humans were physically exploring space, right? In manned aircrafts, unmanned aircrafts. It's been more than 60 years. So you would think, right? When there's billions of dollars of research going into this and humans have been Tackling space space exploration for decades, right? 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 ChatGPT 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 everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen AI. That's what I want to set as the baseline for thinking of generative AI. And here's what I mean. I think generative AI in large language models are space explorers, right? In this scenario, humans have much, and I know this sounds weird to even say out loud, humans are limited compared to large language models, compared to generative AI, right? You need large teams of people, you need large teams of researchers, you need large teams of funding to explore space, right? Whereas think of generative AI, think of large language models. The level at which generative AI can compute is unfathomable to the human mind, right? Because there was the, the other study that we talked about, this was a couple of weeks ago about just the, this was also Google and DeepMind. So they said that with one of their new discoveries that they unlocked 800 years worth of knowledge after discovering 2.2 new, 2.2 million new crystals, right? So 800 years of knowledge. So that is the difference, right? And if, if we take this analogy of space exploration, right? What large language models and generative AI can accomplish, even just like by using this, this same methodology that Google DeepMind used with fun Search, right? Which was essentially testing all of, you know, creating a system to quickly at scale, reject incorrect answers, even just by doing that, by, by being able to reject incorrect answers at thousands times the scale of what humans can do, that leads to new discoveries, right? So in this example that I'm trying to illustrate, where a large language Model or generative AI is a space explorer. We know those stars, right. We have pictures of the galaxy and from satellites and NASA and all these things. Right. But large language models, more quickly than humans, can explore things at the rate of hundreds of years at a time when humans cannot. That's the difference. So large language models in generative AI, by using existing data, can actually create new intelligence. It can create new discoveries that can benefit humans that humans may otherwise never be able to come to on their own because it would take too long. Long, right. That is, I think, one of the most powerful things about large language models, about generative AI that people just look over. People think, oh, you know, large language models, they write, you know, they make my C email to my boss into a A minus or a B plus email. Right. They think that's what a large language model is. And yes, obviously it can do that. But when we talk about all of this new technology that we're going to be seeing in 2024, it is much more than that. I think we are going to be talking about on a weekly or maybe daily basis in a year or so about generative AI, about large language models making new discoveries non stop. Right. So yeah, over the last year we've heard about a handful of examples. I think we are literally going to be seeing this almost on a daily basis where large language models, generative AI systems are going to be outsmarting humans daily, tackling societal issues, problems, equations. Right. That have plagued humans for decades. I think this is going to become the new reality. Yes, Douglas, it's the, the elephant in the room. Yes. Are we, are we moving toward this, this Terminator type world? I mean, kind of. Right, Kind of. You know, you even have the, the company in Asia that is actually called Skynet and creating this technology. Right. So yeah, that's, that's kind of where we're getting where large AIs are creating on their own, smaller AIs, when we have artificial intelligence and large language models making new discoveries on their own. And then when they're, let's, let's think about that. What happens when, you know, you combine these two kind of instances, right, where, oh, AI is now making discoveries on its own. But then what happens when AI can create an AI system or an AI model to do something with? It's this, this new intelligence that it discovered, right? Yeah, it's, it's hard for me to wrap my brain around. Yes. I talk about AI every single day, but that's hard for me to even fathom. But that is the future of where we're heading. Right. When AI is outsmarting humans pretty easily. Right. Cecilia, I love this comment here and thanks for joining. And hey, if you listen on the podcast, check your show notes. You know, we do this live every day. You can come, you can ask questions when we have guests. But Cecilia, I love what Cecilia is saying here. I'm wondering whether sometimes discoveries against the history of humankind is complete history. Is it possible that someone or somebody who is or are not part of the circle of those trying to solve a problem may have solved it and their interactions on it? Yeah. That's great. Right? Because. Yeah, I think you're. We're also going to see a lot of accidental discoveries or new intelligence that is creative, that is created when maybe that was not the outcome. Right. So many of the greatest products that we use services were not created ultimately for what we're using them for. So, Cecilia, Absolutely. I think that we are going to see researchers and generative AI and large language models solving, you know, not just problems that we didn't know existed, but also creating new intelligence in areas where it was not even intended to create intelligence on. Absolutely. So I hope today's episode is helpful. I'm not going to continue to talk for 45 minutes, but let's just quickly. Let's just quickly recap what we're talking about here with Google DeepMind's historic breakthrough. All right, so by using their language model fund search, they successfully solved this mathematical equation that has been eluding humans for decades by solving this math problem. Capset and why this is significant. And, you know, I think this news came out Thursday or Friday, and I haven't seen a lot of people talking about this, which is interesting. But this is significant because this is one of the first times where definitively we can say that an AI has outsmarted humans and has created a form of new intelligence. Right. And this fun search and what Google DeepMind is doing is indicative of something so much more than just, yes, they crossed off quicker or more quickly, crossed off all of the incorrect answers to this math problem to more quickly get the correct answer. Yes, that is in theory, how they went about this process, but it signals something so much greater than that. It signals the enormous potential for large language models and for generative AI moving forward. And I hope that this also opens up your eyes to seeing that large language models are so much more than just writing some content. There's so much more than, you know, creating a marketing plan, large language models and generative AI as we head into 2024 are creating their own intelligence. I don't know what it means, but we're obviously going to find out together because we do this every day. And as more and more of these breakthroughs come, you better believe we're not only going to be talking about them, we're going to be bringing experts on for for you all to ask questions and to understand. But I will tell you this. If you have been on the fence, right, about the powers or capabilities of generative AI, if you think that generative AI and large language models are nothing more than an autocomplete working off of a fixed data set, I think you have to open up your your mind to the possibility that generative AI and large language models can be so much more than that. Right? Yes, in theory they are trained off of a data set, but they're creating new discoveries. They are creating new intelligence. And I think that's what's important to know. What's also important is for you to go to your everydayai.com Sign up for the free daily newsletter. And like I said, we have some great shows, some great shows coming up the rest of the week that I hope you'll join us. So as a reminder, tomorrow we're going to be talking about AI and higher education. This is going to be good. I can't wait for this show. Wednesday we're going to be talking with Ron Friedman, the head of content marketing from did, one of the largest generative AI tools out there, bar none. Then we're going to be talking about AI business strategy in 2024 and my hot takes for 2024 as well. So thank you for joining us. Appreciate you. I hope to see you back tomorrow and every day with more everyday AI. Thanks y'. All.

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