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Demis Hassabis

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123
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2025-07-23
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2025-07-23
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  1. “I think so. I mean, those of us who love games and I still do is, you know, it almost can let your imagination run wild, right? I used to love games and working on games so much because it's the fusion, especially in the 90s and early 2000s, the sort of golden era, maybe the 80s of the games industry. And it was all being discovered. New genres were being discovered. We weren't just making games. We felt we were creating a new entertainment medium that never existed before, especially with these open world games and simulation games where you would co-create you as the player were co-creating the story. There's no other media, entertainment media where you do that, where you as the audience actually co-create the story. And of course now with multiplayer games as well, it can be very social activity and can explore all kinds of interesting worlds in that. But on the other hand, you know, it's very important to”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “But in my world, they'd be related because it would be an open world simulated game as realistic as possible. So, you know, what is the universe? That's speaking to the same question, right? And P equals MP, I think all these things are related, at least in my mind.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “Well, you know, there's two things to think about that is maybe with vibe coding as it gets better and there's a possibility that I could, you know, one could do that actually in your spare time. So I'm quite excited about that as that would be my project if I got the time to do some vibe coding. I'm actually itching to do that. And then the other thing is, you know, maybe it's a sabbatical after AGI has been safely stewarded into the world and delivered into the world. And then working on my physics theory, as we talked about the beginning, those would be the two post-AGI projects, let's call it that way.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “And depending how you treated it, it would treat the villagers in that world in the same way. So if you were mean to it, it would be mean. If you were good, it would be protective. And so it was really a reflection of the way you played it. So actually all of the, I've been working on sort of simulations and AI.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Yeah, exactly. And so, but what you'd like is a little bit better than sort of a random generation, right? So you'd like and also better than a simple AB hard coder choice, right? That's not really open world, right? As you say, it's just giving you the illusion of choice. What you want to be able to do is potentially anything in that game environment. And I think the only way you can do that is to have generated systems, systems that will generate that on the fly. Of course, you can't create infinite amounts of game assets, right? It's expensive enough already how AAA games are made today. And that was obvious to us back in the 90s when I was working on all these games. I think maybe black and white was the game that I worked on early stages of that that had still probably the best AI learning AI in it. It was an early reinforcement learning system that you were looking after this mythical creature and growing it and nurturing it.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “We set up the parameters, we set up initial conditions, and then you as the player immersed in it. And then you are co-creating it with the simulation. But of course, it's very hard to program open world games. You know, you've got to be able to create content, whichever direction the player goes in and you want it to be compelling no matter what the player chooses. And so it was always quite difficult to build things like cellular autometer, actually, type of those kind of classical systems, which created some emergent behavior. But they're always a little bit fragile, a little bit limited. Now we're maybe on the cusp in the next few years, five, ten years of having AI systems that can truly create around your imagination sort of dynamically change the story and storytell the narrative around and make it dramatic no matter what you end up choosing. So it's like the ultimate choose your own adventure sort of game. And, you know, I think maybe we're within reach if you think of a kind of interactive version.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “Well, games were my first love, really, and doing AI for games was the first thing I did professionally in my teenage years and was the first major AI systems that I built. And I always want to, I want to scratch that itch one day and come back to that. So, you know, and I will do, I think. And I think I sort of dream about, you know, what would I have done back in the 90s if I'd had access to the kind of AI systems we have today? And I think you could build absolutely mind-blowing games. And I think the next stage is I always used to love making all the games I've made are open world games. So they're games where there's a simulation and then there's AI characters and then the player interacts with that simulation and the simulation adapts to the way the player plays. And I always thought they were the coolest games because games like theme park that I worked on where everybody's game experience would be unique to them. Because you're kind of co-creating the game.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “And of course, the next stages is maybe even making those videos interactive. So one can actually step into them and move around them, which will be really mind-blowing, especially given my games background. So you can imagine. And then I think, you know, we're starting to get towards what I would call a world model, a model of how the world works, the mechanics of the world, the physics of the world, and the things in that world. And of course, that's what you would need for a true AGI system.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “It feels like. Yes. And this is very interesting, you know, even if you were to ask me five, ten years ago, I would have said, even though I was a must in all of this, I would have said, well, yeah, you probably need to understand intuitive physics. You know, like if I push this off the table, this glass, it will maybe shatter, you know, and the liquid will spill out, right? So we know all of these things. But I thought that, you know, there's a lot of theories in neuroscience. It's called action in perception, where you need to act in the world to really, truly perceive it in a deep way. And there was a lot of theories about you'd need embodied intelligence or robotics or something, or maybe at least simulated action so that you would understand things like intuitive physics. But it seems like you can understand it through passive observation, which is pretty surprising to me. And again, I think hints at something underlying about the nature of reality, in my opinion, beyond just the cool videos that it generates.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Think to the extent that it can predict the next frames in a coherent way, that is a form of understanding, right? Not in the anthropomorphic version of it's not some kind of deep philosophical understanding of what's going on. I don't think these systems have that. But they certainly have modeled enough of the dynamics, put it that way, that they can pretty accurately generate whatever it is, eight seconds of consistent video that by eye, at least at a glance, is quite hard to distinguish what the issues are. And imagine that in two or three more years time. That's the thing I'm thinking about and how incredible they will look given where we've come from, you know, the early versions of that one or two years ago. And so the rate of progress is incredible. And I think I'm like you is like a lot of people love all of the stand-up comedians and that actually captures a lot of human dynamics.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “Around how these materials behave. So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully understood what's going on under the hood. That's maybe true of most of reality.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “Yes, exactly. I mean, fluid dynamics, Navier-Stokes equations, these are traditionally thought as very, very difficult, intractable kind of problems to do on classical systems. They take enormous amounts of compute, whether prediction systems, these kind of things all involve fluid dynamics calculations. But again, if you look at something like VO, our video generation model, it can model liquids quite well, surprisingly well. And materials, specular lighting, I love the ones where there's people who generated videos where there's like clear liquids going through hydraulic presses and then being squeezed out. I used to write physics engines and graphics engines in my early days in gaming. And I know it's just so painstakingly hard to build programs that can do that. And yet somehow these systems are, you know, reverse engineering from just watching YouTube videos. So presumably what's happening is it's extracting some underlying structure.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “Physics was doing that, in a sense, and we could mimic that process, i.e. model that process, it should be possible on our classical systems is basically what the conjecture is about.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  14. “Yes, because there's some structure, there's some landscape in the energy landscape or whatever it is that you can follow, some gradient you can follow. And of course, what neural networks are very good at is following gradients. And so if there's one to follow and you can specify the objective function correctly, you know, you don't have to deal with all that complexity, which I think is how we maybe have naively thought about it for decades, those problems. If you just enumerate all the possibilities, it looks totally intractable. And there's many, many problems like that. And then you think, well, it's like 10 to the 300 possible protein structures. It's 10 to the 170 possible Go positions. All of these are way more than atoms in the universe. So how could one possibly find the right solution or predict the next step? But it turns out that it is possible. And of course, reality in nature does do it. Protein to do fold. So that gives you confidence that there must be, if we understood how...”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “Yeah, I think those systems would be right on the boundary, right? So I think most emergent systems, cellular automata, things like that could be modelable by a classical system. You just sort of do a forward simulation of it and it'd probably be efficient enough. Of course, there's the question of things like chaotic systems where the initial conditions really matter. And then you get to some uncorrelated end state. Now, those could be difficult to model. So I think these are kind of the open questions. But I think when you step back and look at what we've done with the systems and the problems that we've solved, and then you look at things like VO3 on video generation, sort of rendering physics and lighting and things like that, really core fundamental things in physics, it's pretty interesting. I think it's telling us something quite fundamental about how the universe is structured, in my opinion. So, you know, in a way, that's what I want to build AGI for is to help.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “Yeah, I think that there are actually a huge class of problems that could be couched in this way, the way we did Alpha Go and the way we did alpha fold, where you model what the dynamics of the system is, the properties of that system, the environment that you're trying to understand. And then that makes the search for the solution or the prediction of the next step efficient, basically polynomial time. So tractable by a classical system, which a neural network is. It runs on normal computers, right? Classical computers, Turing machines in effect. And I think it's one of the most interesting questions there is, is how far can that paradigm go? I think we've proven and the AI community.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  17. “Yeah, I think it's one of the most fundamental questions, actually, if you think of physics as informational. And the answer to that, I think, is going to be very enlightening.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “Yeah. I mean, I've always been fascinated by the equals MP question and what is modellable by classical systems, i.e. non-quantum systems, you know, Turing machines in effect. And that's exactly what I'm working on actually in kind of my few moments of spare time with a few colleagues about should there be maybe a new class of problem that is solvable by this type of neural network process and kind of mapped on to these natural systems. So, you know, the things that exist in physics and have structure. So I think that could be a very interesting new way of thinking about it. And it sort of fits with the way I think about physics in general, which is that, you know, I think information is primary. Information is the most sort of fundamental unit of the universe, more fundamental than energy and matter. I think they can all be converted into each other. But I think of the universe as a kind of informational system.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “So they can be efficiently rediscovered or recovered because nature is not random, right? Everything that we see around us, including the elements that are more stable, all of those things, they're subject to some kind of selection process, pressure.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “But if there's not, and it's uniform, then there's no pattern to learn. There's no model to learn that will help you search. So you have to do brute force. So in that case, you maybe need a quantum computer, something like this. But in most things in nature that we're interested in are not like that. They have structure that evolved for a reason and survived over time. And if that's true, I think that's potentially learnable by a neural network.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “Yeah, I sometimes call it survival of the stabilist or something like that because it's, of course, there's evolution for life, living things, but there's also, you know, if you think about geological time, so the shape of mountains, that's been shaped by weathering processes, right, over thousands of years. But then you can even take it cosmological. The orbits are planets, the shapes of asteroids. These have all been survived kind of processes that have acted on them many, many times. So if that's true, then there should be some sort of pattern that you can kind of reverse learn and a kind of manifold really that helps you search to the right solution, to the right shape, and actually allow you to predict things about it in an efficient way because it's not a random pattern, right? So it may not be possible for man-made things or abstract things like factorizing large numbers because unless there's patterns in the numbers, which there might be,”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “Proteins fold in milliseconds in our bodies, so somehow physics solves this problem that we've now also solved computationally. And I think the reason that's possible is that in nature, natural systems have structure because they were subject to evolutionary processes that shape them. And if that's true, then you can maybe learn what that structure is.”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “Sure. Well, look, I felt that it's sort of a tradition, I think, of Nobel Prize lectures that you're supposed to be a little bit provocative. And I wanted to follow that tradition. What I was talking about there is if you take a step back and you look at all the work that we've done, especially with the Alpha X projects. So I'm thinking Alpha Go, of course, Alpha Fold. What they really are is we're building models of very combinatorily high dimensional spaces that if you try to brute force a solution, find the best moving go or find the exact shape of a protein. And if you enumerated all the possibilities, there wouldn't be enough time in the time of the universe. So you have to do something much smarter. And what we did in both cases was build models of those environments. And that guided the search in a smart way. And that makes it tractable. So if you think about protein folding, which is obviously a natural system, you know, why should that be possible? How does physics do that?”

    2025-07-23 · Lex Fridman Podcast · #475 – Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games · IDENTIFIED FROM THE TRANSCRIPT · source