Learn. Share. Evolve…

0:00:05 The following is a conversation with Demis Hassabis, his second time on the podcast.
0:00:11 He is the leader of Google DeepMind and is now a Nobel Prize winner.
0:00:16 Demis is one of the most brilliant and fascinating minds in the world today,
0:00:24 working on understanding and building intelligence, and exploring the big mysteries of our universe.
0:00:28 This was truly an honor and a pleasure for me.
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0:08:23 And now, dear friends, here’s Demis Hassabis.
0:08:46 In your Nobel Prize lecture, you propose what I think is a super interesting conjecture that, quote,
0:08:54 any pattern that can be generated or found in nature can be efficiently discovered and modeled by a classical learning algorithm.
0:08:59 What kind of patterns or systems might be included in that?
0:09:04 Biology, chemistry, physics, maybe cosmology, neuroscience?
0:09:06 What are we talking about?
0:09:06 Sure.
0:09:12 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.
0:09:14 And I wanted to follow that tradition.
0:09:18 What I was talking about there is if you take a step back and you look at all the work that we’ve done,
0:09:20 especially with the Alpha X projects.
0:09:23 So I’m thinking Alpha Go, of course, Alpha Fold.
0:09:30 What they really are is we’re building models of very combinatorially high-dimensional spaces that, you know,
0:09:36 if you try to brute force a solution, find the best move and go, or find the exact shape of a protein,
0:09:41 and if you enumerated all the possibilities, there wouldn’t be enough time in the, you know, the time of the universe.
0:09:44 So you have to do something much smarter.
0:09:48 And what we did in both cases was build models of those environments,
0:09:53 and that guided the search in a smart way, and that makes it tractable.
0:09:59 So if you think about protein folding, which is obviously a natural system, you know, why should that be possible?
0:10:00 How does physics do that?
0:10:02 You know, proteins fold in milliseconds in our bodies.
0:10:08 So somehow physics solves this problem that we’ve now also solved computationally.
0:10:13 And I think the reason that’s possible is that in nature, natural systems have structure
0:10:18 because they were subject to evolutionary processes that shape them.
0:10:23 And if that’s true, then you can maybe learn what that structure is.
0:10:25 This perspective, I think, is a really interesting one.
0:10:35 You’ve hinted at it, which is almost like crudely stated, anything that can be evolved can be efficiently modeled.
0:10:36 Think there’s some truth to that?
0:10:42 Yeah, I sometimes call it survival of the stabilist or something like that because, you know, it’s, of course,
0:10:45 there’s evolution for life, living things.
0:10:50 But there’s also, you know, if you think about geological time, so the shape of mountains,
0:10:54 that’s been shaped by weathering processes, right, over thousands of years.
0:10:56 But then you can even take it cosmological.
0:11:02 The orbits of planets, the shapes of asteroids, these have all been survived kind of processes
0:11:04 that have acted on them many, many times.
0:11:10 So if that’s true, then there should be some sort of pattern that you can kind of reverse
0:11:16 learn and a kind of manifold really that helps you search to the right solution, to the right
0:11:21 shape, and actually allow you to predict things about it in an efficient way because it’s not
0:11:23 a random pattern, right?
0:11:28 So it may not be possible for man-made things or abstract things like factorizing large numbers
0:11:32 because unless there’s patterns in the number space, which there might be, but if there’s
0:11:35 not and it’s uniform, then there’s no pattern to learn.
0:11:37 There’s no model to learn that will help you search.
0:11:38 You have to do brute force.
0:11:42 So in that case, you know, you maybe need a quantum computer, something like this.
0:11:46 But in most things in nature that we’re interested in are not like that.
0:11:51 They have structure that evolved for a reason and survived over time.
0:11:54 And if that’s true, I think that’s potentially learnable by a neural network.
0:12:01 It’s like nature is doing a search process and it’s so fascinating that it’s in that search
0:12:04 process is creating systems that can be efficiently modeled.
0:12:06 Yes, right.
0:12:06 Yeah.
0:12:07 So interesting.
0:12:12 So they can be efficiently rediscovered or recovered because nature is not random, right?
0:12:16 These, everything that we see around us, including like the elements that are more
0:12:21 stable, all of those things, they’re subject to some kind of selection process, pressure.
0:12:25 Do you think, because you’re also a fan of theoretical computer science and complexity,
0:12:30 do you think we can come up with a kind of complexity class, like a complexity zoo type
0:12:38 of class where maybe it’s the set of learnable systems, the set of learnable natural systems,
0:12:38 LNS?
0:12:39 Yeah.
0:12:46 This is a demo, this new class of systems that could be actually learnable by classical systems
0:12:50 in this kind of way, natural systems that can be modeled efficiently.
0:12:57 I mean, I’ve always been fascinated by the P equals MP question and what is modelable by
0:13:02 classical systems, i.e. non-quantum systems, you know, Turing machines in effect.
0:13:07 And that’s exactly what I’m working on actually in kind of my few moments of spare time with a few
0:13:12 colleagues about is, should there be, you know, maybe a new class of problem that is solvable by
0:13:18 this type of neural network process and kind of mapped onto these natural systems. So, you know,
0:13:25 the things that exist in physics and have structure. So I think that could be a very interesting new way
0:13:29 of thinking about it. And it sort of fits with the way I think about physics in general, which is that,
0:13:33 you know, I think information is primary. Information is the most sort of fundamental unit
0:13:37 of the universe, more fundamental than energy and matter. I think they can all be converted into
0:13:41 each other. But I think of the universe as a kind of informational system.
0:13:46 So when you think of the universe as an informational system, then the P equals MP question is a,
0:13:48 is a physics question.
0:13:48 That’s right.
0:13:54 And it’s a question that can help us actually solve the entirety of this whole thing going on.
0:13:58 Yeah. I think it’s one of the most fundamental questions, actually, if you think of physics as
0:14:03 informational. And the answer to that, I think it’s going to be, you know, very enlightening.
0:14:10 More specific to the PNP question, this, again, some of the stuff we’re saying is kind of crazy
0:14:15 right now, just like the Christian Atkinson Nobel Prize speech controversial thing that he said
0:14:20 sounded crazy. And then you went and got a Nobel Prize for this with John Jumper, solved the problem.
0:14:26 So let me, let me just stick to the P equals MP. Do you think there’s something in this thing we’re
0:14:35 talking about that could be shown if you can do something like a polynomial time or constant time
0:14:42 compute ahead of time and construct this gigantic model, then you can solve some of these extremely
0:14:46 difficult problems in a theoretical computer science kind of way?
0:14:51 Yeah. I think that there are actually a huge class of problems that could be couched in this way,
0:14:56 the way we did alpha go and the way we did alpha fold, where, you know, you, you model what the
0:15:01 dynamics of the system is, the, the, the, the properties of that system, the environment that
0:15:07 you’re trying to understand. And then that makes the search for the solution or the prediction of the
0:15:15 next step efficient, basically polynomial time. So tractable by a classical system, which a neural
0:15:20 network is, it runs on normal computers, right? Classical computers, uh, Turing machines in effect.
0:15:26 And, um, I think it’s one of the most interesting questions there is, is how far can that paradigm
0:15:31 go? You know, I think we’ve proven, uh, and the AI community in general, that classical systems,
0:15:36 Turing machines can go a lot further than we previously thought, you know, they can do things
0:15:42 like model the structures of proteins and play go to better than world champion level. And, uh, you know,
0:15:47 a lot of people would have thought maybe 10, 20 years ago, that was decades away, or maybe you
0:15:52 would need some sort of quantum machines to, to quantum systems, to be able to do things like
0:15:59 protein folding. And so I think we haven’t really, uh, even sort of scratched the surface yet of what,
0:16:06 uh, classical systems so-called, uh, uh, could do. And of course, AGI being built on a, on a neural
0:16:10 network system on top of a neural network system on top of a classical computer would be the ultimate
0:16:15 expression of that. And I think the limit that, you know, the, the, what, what the bounds of that
0:16:20 kind of system, what it can do, it’s a very interesting question and, and, and directly speaks
0:16:21 to the P equals MP question.
0:16:28 What do you think, again, hypothetical might be outside of this, maybe emergent phenomena.
0:16:33 Like if you look at cellular automata, some of the, you have extremely simple systems and then
0:16:39 some complexity emerges, maybe that would be outside or even, would you guess even that might
0:16:43 be amenable to efficient modeling by a classical machine?
0:16:49 Yeah. I think those systems would be right on the boundary, right? So, um, I think most emergent
0:16:53 systems, cellular automata, things like that could be modelable by a classical system. You just sort of
0:16:58 do a forward simulation of it and it’d probably be efficient enough. Um, of course, there’s the question
0:17:04 of things like chaotic systems where the initial conditions really matter. And then you get to some,
0:17:10 you know, uncorrelated end state. Now those could be difficult to model. So I think these are kind of
0:17:15 the open questions, but I think when you step back and look at what we’ve done with the systems and the,
0:17:21 and the problems that we’ve solved, and then you look at things like VO3 on like video generation,
0:17:27 sort of rendering physics and lighting and things like that, you know, really in core fundamental
0:17:31 things in physics. Um, it’s pretty interesting. I think it’s telling us something quite fundamental
0:17:36 about how the universe is structured in my opinion. Um, so, you know, in, in a way that’s what I want
0:17:43 to build AGI for is to help, uh, us, uh, as scientists answer these questions, uh, like P equals MP.
0:17:48 Yeah. I think we might be continuously surprised about what is modelable by classical computers. I
0:17:55 mean, alpha fold three on the interaction side is surprising that you can make any kind of progress
0:18:03 on that direction. Alpha genome is surprising that you can map the genetic code to the function kind of
0:18:07 playing with the emergent kind of phenomena. You think there’s so many combinatorial options that,
0:18:10 and then here you go, you can find the kernel that is efficiently model.
0:18:15 Yes. Because there’s some structure, there’s some landscape, you know, in the energy landscape
0:18:19 or whatever it is that you can follow some gradient you can follow. And of course, what neural networks
0:18:24 are very good at is following gradients. And so if there’s one to follow an object and you can specify
0:18:30 the objective function correctly, you know, you don’t have to deal with all that complexity, which I think
0:18:36 is how we maybe have naively thought about it for decades. Those problems, if you just enumerate all
0:18:40 the possibilities, it looks totally intractable and there’s many, many problems like that. And then you
0:18:46 think, well, it’s like 10 to the 300 possible protein structures, uh, 10 to the hundred and,
0:18:51 you know, 70 possible go positions. All of these are way more than atoms in the universe. So how could
0:18:57 one possibly find the right solution or predict the next step? And, and it, but it turns out that it is
0:19:03 possible. And of course, reality in nature does do it right. Proteins do fold. So that, that gives you
0:19:09 confidence that there must be, if we understood how physics was doing that, uh, in a sense, uh, then,
0:19:15 and we could mimic that process, I model that process. Uh, it should be possible on our classical
0:19:20 systems is, is, is, is basically what the conjecture is about. And of course there’s nonlinear dynamical
0:19:26 systems, highly nonlinear dynamical systems, everything involving fluid. Yes. Right. You know,
0:19:31 I recently had a conversation with Terence Tao who mathematically, uh, contends with a very difficult
0:19:38 aspect of systems that have some singularities in them that break the mathematics. And it’s just
0:19:42 hard for us humans to make any kind of clean predictions about highly nonlinear dynamical
0:19:48 systems. But again, to your point, we might be very surprised what classical learning systems might
0:19:53 be able to do about even fluid. Yes, exactly. I mean, the fluid dynamics, Navier-Stokes equations,
0:19:58 these are traditionally thought of as very, very difficult, intractable kind of problems to do on
0:20:02 classical systems. They take enormous amounts of compute, you know, weather prediction systems,
0:20:08 you know, these kinds of things all involve fluid dynamics calculations. And, um, but again,
0:20:14 if you look at something like VO, our video generation model, it can model liquids quite well,
0:20:20 surprisingly well and materials, specular lighting. I love the ones where, you know, there’s, there’s
0:20:24 people who generate videos where there’s like clear liquids going through hydraulic presses and then
0:20:30 being squeezed out. I used to write, uh, physics engines and graphics engines and in my early days in
0:20:36 gaming. And I know it’s just so painstakingly hard to build programs that can do that. And yet somehow
0:20:43 these systems are, you know, reverse engineering from just watching YouTube videos. So presumably what’s
0:20:49 happening is it’s extracting some underlying structure around how these materials behave.
0:20:55 So perhaps there is some kind of lower dimensional manifold that can be learned if we actually fully
0:21:00 understood what’s going on under the hood. That’s maybe, you know, maybe true of most of reality.
0:21:06 Yeah. I’ve been continuously precisely by this aspect of VO3. I think a lot of people highlight
0:21:11 different aspects, including the comedic and the meat and all that kind of stuff. And then the ultra
0:21:18 realistic ability to capture humans in a really nice way that’s compelling and feels close to
0:21:23 reality. And then combine that with native audio. All of those are marvelous things about VO3, but
0:21:29 the exactly the thing you’re mentioning, which is the physics. Yeah. It’s not perfect, but it’s pretty
0:21:36 damn good. And then the really interesting scientific question is what is it understanding about our world
0:21:43 in order to be able to do that? Because of the cynical take with diffusion models, there’s no way it understands
0:21:49 anything, but it seems, I mean, I don’t think you can generate that kind of video without understanding.
0:21:54 And then our own philosophical notion of what it means to understand then is like brought to the surface.
0:21:58 To what degree do you think VO3 understands our world?
0:22:04 I think to the extent that it can predict the next frames, you know, in a coherent way,
0:22:08 that’s some, that is a form, you know, of understanding, right? Not in the anthropomorphic
0:22:13 version of, you know, it’s not some kind of deep philosophical understanding of what’s going on.
0:22:18 I don’t think these systems have that, but they, they certainly have modeled enough of the dynamics,
0:22:24 you know, put it that way that they can pretty accurately generate whatever it is, eight seconds
0:22:29 of consistent video that by eye, at least, you know, at a glance is quite hard to distinguish
0:22:34 what the issues are. And imagine that in two or three more years time, that’s the thing I’m thinking
0:22:38 about and how incredible that will, they will look, uh, given where we’ve come from, you know,
0:22:44 the early versions of that, uh, uh, one or two years ago. And so, um, the rate of progress is
0:22:50 incredible. And I think, um, I’m like you is like a lot of people love all of the, the, the,
0:22:55 the standup comedians and the, the, the actually captures a lot of human dynamics very well and,
0:22:59 and body language. But actually the thing I’m most impressed with and fascinated by is the physics
0:23:07 behavior, the lighting and materials and liquids. And it’s pretty amazing that it can do that. And I
0:23:14 think that shows it, that it has some notion of at least intuitive physics, right? Um, how things
0:23:19 are supposed to work, uh, intuitively may be the way that a human child would understand physics,
0:23:25 right? As opposed to, uh, you know, a PhD student really, uh, being able to unpack all the equations.
0:23:31 It’s more of an intuitive physics understanding. Well, that intuitive physics understanding, that’s
0:23:36 the base layer. That’s the thing people sometimes call it common sense. Again, it really understands
0:23:41 something. I think that really surprised a lot of people. It blows my mind that I just didn’t think
0:23:48 would be possible to generate that level of realism without understanding. There’s this notion that you
0:23:54 can only understand the physical world by having an embodied AI system, a robot that interacts with
0:23:59 that world. That’s the only way to construct an understanding of that world. Yeah. But VO3 is
0:24:01 directly challenging that. Right.
0:24:05 It feels like, yes. And it’s very interesting, you know, even if we, if you were to ask me five,
0:24:09 10 years ago, I would have said, even though I was a must in all of this, I would have said, well,
0:24:13 yeah, you probably need to understand intuitive physics. You know, like if I push this off the
0:24:19 table, this glass, it will maybe shatter, you know, um, and the, and the liquid will spill out.
0:24:23 Right. So we know all of these things, but I thought that, you know, and there’s a lot of theories
0:24:27 in neuroscience is called action in perception where, you know, you, you need to act in the
0:24:32 world to really truly perceive it in a deep way. And there was a lot of theories about you’d need
0:24:38 embodied intelligence or robotics or something, or maybe at least simulated action, uh, so that you
0:24:43 would understand things like intuitive physics. But it seems like, um, you can understand it through
0:24:48 passive observation, which is pretty surprising to me. And, and again, I think hints at something
0:24:54 underlying about the nature of, uh, reality in, in, in my opinion, beyond, um, just the, you know,
0:24:59 the cool videos that it generates. Um, and, and of course there’s next stages is maybe even making
0:25:05 those videos interactive. So, uh, one can actually step into them and move around them, um, which would
0:25:11 be really mind blowing, especially given my games background. So you can imagine, uh, and then,
0:25:14 and then I think, you know, you’re, we’re starting to get towards what I would call a world model,
0:25:19 a model of how the world works, the mechanics of the world, the physics of the world and the things
0:25:23 in that world. And of course, that’s what you would need for a true AGI system.
0:25:29 I have to talk to you about video games. So you, you were being a bit trolly. Uh, I think you’re,
0:25:34 you’re having more and more fun on Twitter on X, which is great to see. So guy named Jimmy Apples
0:25:41 tweeted, let me play a video game of my VO3 videos already. Uh, Google cooks so good playable world
0:25:47 models when spelled W E N question mark. Um, and then you quote tweeted that with now,
0:25:53 wouldn’t that be something? So how, how hard is it to build game worlds with AI? Maybe can you look
0:26:00 out into the future, uh, of video games five, 10 years out? What do you think that looks like?
0:26:06 Well, games were my first love really. And doing AI for games was the first thing I did professionally
0:26:13 in my teenage years and was the first, uh, major AI systems that I built. And, uh, I always want to
0:26:18 have, I want to scratch that itch one day and come back to that. So, you know, and I will do, I think.
0:26:24 And, um, I think I’d sort of dream about, you know, what would I have done back in the nineties if I’d had
0:26:28 access to the kind of AI systems we have today. And I think you could build absolutely mind blowing
0:26:33 games. Um, and I think the next stage is I always used to love making all the games I’ve made
0:26:38 are open world games. So they’re games where there’s a simulation and then there’s AI characters
0:26:44 and then the player, uh, interacts with that simulation and the simulation adapts to the way
0:26:48 the player plays. And I always thought they were the coolest games because, uh, so games like theme
0:26:53 park that I worked on where everybody’s game experience would be unique to them, right? Because
0:26:58 you’re kind of co-creating the game, right? Uh, we set up the parameters, we set up initial conditions
0:27:03 and then you as the player immersed in it. And then you are co-creating it with the, with the
0:27:08 simulation. But of course it’s very hard to program open world games. You know, you’ve got to be able
0:27:13 to create a content, whichever direction the player goes in and you want it to be compelling no matter
0:27:19 what the player chooses. Um, and so it was always quite difficult to build, uh, things like cellular
0:27:23 automata actually type of those kinds of classical systems, which created some emergent behavior.
0:27:28 Um, but they’re always a little bit fragile, a little bit limited. Now we’re maybe on the cusp in
0:27:33 the next few years, five, 10 years of having AI systems that can truly create around your imagination,
0:27:40 um, can sort of dynamically change the story and storytell the narrative around, uh, and make it
0:27:45 dramatic no matter what you end up choosing. So it’s like the ultimate choose your own adventure
0:27:50 sort of game. And, uh, you know, I think maybe we’re within reach if you think of a kind of
0:27:56 interactive version of VO, uh, and then wind that forward five to 10 years and, um, you know,
0:27:57 imagine how good it’s going to be.
0:28:03 Yeah. So you said a lot of super interesting stuff there. So one, the open world built into
0:28:08 that as a deep personalization, the way you’ve described it. So it’s not just that it’s open
0:28:13 world, but you can open any door and there’ll be something there. It’s that the choice of which
0:28:21 door you’re open in an unconstrained way defines the worlds you see. So some games try to do that.
0:28:27 They give you choice, but it’s really just an illusion of choice because the only, uh, like,
0:28:33 like Stanley Parable, it’s, it’s, it’s really, there’s a couple of doors and it really just
0:28:37 takes you down the narrative. Stanley Parable is a great video game. I recommend people play
0:28:44 that kind of, uh, in a meta way, uh, mocks the illusion of choice and there’s philosophical
0:28:50 notions of free will and so on. But, uh, I do like one of my favorite games of Elder Scrolls
0:28:58 is Daggerfall. I believe that they really played with a, like random generation of the dungeons
0:29:04 of if you can step in and they give you this feeling of an open world. And there you mentioned
0:29:09 interactivity. You don’t need to interact. That’s a first step because you don’t need to interact that
0:29:15 much. You just, when you open the door, whatever you see is randomly generated for you. And that’s
0:29:19 already an incredible experience because you might be the only person to ever see that.
0:29:25 Yeah, exactly. And, and so, but what you’d like is a little bit better than just sort of a random
0:29:31 generation, right? So you’d like, uh, and, and also better than a simple AB hard code of choice,
0:29:36 right? That’s not really, uh, open world, right? As you say, it’s just giving you the illusion of
0:29:43 choice. What you want to be able to do is potentially anything in that game environment. Um, and I think
0:29:48 the only way you can do that is to have, uh, generated systems, systems that, uh, will generate
0:29:52 that on the fly. Of course, you can’t create infinite amounts of game assets, right? It’s expensive
0:29:58 enough already how AAA games are made today. And that was obvious to, to us back in the nineties,
0:30:03 when I was working on all these games, I think maybe black and white, uh, was the game that I worked on
0:30:08 early stages of that, that had the still probably the best AI learning AI in it. It was an early
0:30:13 reinforcement learning system that you, you know, you were, you were looking after this mythical creature
0:30:18 and growing it and nurturing it. And depending how you treated it, it would treat the villagers in that
0:30:22 world in the same way. So if you were mean to it, it would be mean. If you were good, it would be
0:30:28 protective. And so it was really a reflection of the way you played it. So actually all of the, uh,
0:30:33 I’ve been working on sort of simulations and AI through the medium of games at the beginning of
0:30:38 my career. And, and really the whole of what I do today is still a follow on from, uh, those early,
0:30:43 more hard coded ways of doing the AI to now, you know, fully general learning systems that,
0:30:48 that are trying to achieve the same thing. Yeah. It’s been, uh, interesting, hilarious,
0:30:54 and, uh, fun to watch you and Elon, obviously itching to create games because you’re both gamers.
0:31:00 And one of the sad aspects of your, uh, incredible success in so many domains of science,
0:31:07 like serious adult stuff that you might not have time to really create a game. You might end up
0:31:14 creating the tooling that others would create the game and you have to watch others create the thing
0:31:19 you’ve always dreamed of. Do you think it’s possible you can somehow in your extremely busy
0:31:25 schedule, actually find time to create something like black and white, some, some, an actual video
0:31:32 game where like you could make the childhood dream. Yeah. Well, you know, there’s two things where
0:31:37 I think about that is maybe with vibe coding as it gets better and there’s a possibility that I could,
0:31:41 you know, one could do that actually in the, in your spare time. So I’m quite excited about that as a,
0:31:47 as that would be my project. If I got the time to do some vibe coding, um, I’m actually itching to do
0:31:52 that. And then the other thing is, you know, maybe it’s a sabbatical after AGI has been safely
0:31:56 stewarded into the world and delivered into the world, you know, that, and then working on my physics
0:32:02 theory, as we talked about at the beginning, those would be the two, my, my two post AGI projects,
0:32:08 let’s call it that way. I would love to see which post AGI, which you choose solving, uh, the,
0:32:14 the problem that some of the smartest people in human history contended with. So P equals MP
0:32:19 or creating a cool video. Yeah. Well, but they might, but in my world, they’d be related because
0:32:26 it would be an open world simulated game, uh, as realistic as possible. So, you know, what,
0:32:30 what is, what is the universe? That’s, that’s, that’s speaking to the same question, right?
0:32:33 MP equals MP. I think all these things are related, at least in my mind.
0:32:38 I mean, in a really serious way, it’s like video games, sometimes are looked down upon.
0:32:45 That’s just this fun side activity, but especially as, uh, AI does more and more of, um, the difficult,
0:32:54 uh, boring tasks, something that we in modern world called work, you know, video games is the thing in
0:32:59 which we may find meaning in which we may find like what to do with our time. You could create
0:33:06 incredibly rich, meaningful experiences. Like that’s what human life is. And then in video games,
0:33:16 you can create more sophisticated, more diverse ways of living. Yeah. I think so. I mean, those of us who
0:33:24 love games and I still do is, is, is, um, you know, it’s almost can let your imagination run wild, right?
0:33:30 Like I, I used to love games, um, and working on games so much because it’s the fusion, especially in the
0:33:36 nineties and two early two thousands, the sort of golden era, maybe the eighties of, of, of game of the games
0:33:40 industry. And it was all being discovered. New genres were being discovered. We weren’t just making games.
0:33:45 We felt we were, we were creating a new entertainment medium that never existed before, especially with
0:33:49 these open world games and simulation games where you were co-create you as the player were co-creating
0:33:55 the story. There’s no other media, uh, entertainment media where you do that, where you as the audience
0:34:01 actually co-create the story. And of course now with multiplayer games as well, it can be a very social
0:34:07 activity and can explore all kinds of interesting worlds in that. But on the other hand, you know,
0:34:13 it’s very important to, um, also enjoy and experience, uh, the physical world. But the
0:34:17 question is then, you know, I think we’re going to have to kind of confront the question again of what
0:34:21 is the fundamental nature of reality? Uh, what is there going to be the difference between these
0:34:28 increasingly realistic simulations and, uh, multiplayer ones and emergent, um, and what we do in the real
0:34:35 world? Yeah, there’s clearly a huge amount of value to experiencing the real world nature. There’s
0:34:40 also a huge amount of value in experiencing other humans directly in person, the way we’re sitting
0:34:47 here today. But we need to really scientifically rigorously answer the question, why? Yeah. And
0:34:53 which aspect of that can be mapped into the virtual world? Exactly. And it’s not, it’s not enough to say,
0:34:59 yeah, you should go touch grass and hang out in nature. It’s like, why exactly is that valuable?
0:35:04 Yes. And I guess that’s maybe the thing that’s been, uh, haunting me, obsessing me from the beginning of
0:35:07 my career. If you think about all the different things I’ve done, that’s, they’re all related in
0:35:13 that way. The simulation, nature of reality, and what is the bounds of, you know, what can be modeled?
0:35:18 Sorry for the ridiculous question, but so far, what is the greatest video game of all time? What’s up
0:35:23 there? Well, my favorite one of all time is civilization. I have to say that that was the,
0:35:29 the, the civilization one and civilization two, my favorite games of all time. Um, I can only assume
0:35:35 you’ve avoided the most recent one because it would probably, you would, that would be your sabbatical
0:35:40 that you would disappear. Yes, exactly. They take a lot of time, these civilization games. So,
0:35:47 uh, I’ve got to be careful with them. Fun question. You and Elon seem to be somehow solid gamers. Uh,
0:35:53 is there a connection between being great at gaming and, and, uh, being great leaders of AI companies?
0:35:59 I don’t know. I, it’s an interesting one. I mean, uh, we both love games and, uh, it’s interesting. He
0:36:04 wrote games as well to start off with. It’s probably, especially in the era I grew up in where home
0:36:09 computers were just became a thing, you know, in the late eighties and nineties, especially in the UK,
0:36:13 I had a spectrum and then a Commodore Omega 500, which is my favorite computer ever.
0:36:19 And that’s why I learned all my programming. And of course, it’s a very fun thing, uh, to program is
0:36:26 to program games. So I think it’s a great way to learn programming probably still is. And, um, and then
0:36:31 of course I immediately took it in directions of AI and simulations, which, so I may, it was able to
0:36:38 express my interest in, in games and my sort of wider scientific interests altogether. And then the
0:36:45 final thing I think that’s great about games is it fuses, um, artistic design, you know, art with the,
0:36:51 the, the most cutting edge programming. Um, so again, in the nineties, all of the most interesting,
0:36:57 uh, technical advances were happening in gaming, whether that was AI graphics, physics engines,
0:37:02 uh, hardware, even GPUs, of course, were designed for gaming originally. Um, so everything that was
0:37:09 pushing computing forward in the, in the nineties was due to gaming. So interestingly, that was where
0:37:15 the forefront of research was going on. And it was this incredible fusion with, with art, um, you know,
0:37:21 graphics, but also music and just the whole new media of storytelling. And I love that for me, it’s
0:37:26 sort of multidisciplinary kind of effort is again, something I’ve enjoyed my whole, my whole life.
0:37:32 I have to ask you, I almost forgot about one of the many, and I would say one of the most
0:37:37 incredible things recently, uh, that somehow didn’t yet get enough attention is alpha evolve.
0:37:43 We talked about evolution a little bit, but it’s the Google deep mind system that evolves algorithms.
0:37:48 Yeah. Are these kinds of evolution like techniques promising as a component of future super
0:37:52 intelligence system? So for people who don’t know, it’s kind of, um, I don’t know if it’s fair
0:38:00 to say it’s LLM guided evolution search. Yeah. So evolutionary algorithms are doing the search
0:38:06 and LLMs are telling you where. Yes, exactly. So LLMs are kind of proposing some possible solutions.
0:38:12 And then you do, you use evolutionary computing on top to, to, to find some novel part of the,
0:38:18 of the search space. So actually, I think it’s an example of very promising directions where you
0:38:24 combine LLMs or foundation models with other computational techniques. Evolutionary methods is
0:38:30 one, but you could also imagine Monte Carlo tree search, basically many types of search algorithms
0:38:37 or reasoning algorithms sort of on top of, or using the foundation models as a basis. So I actually
0:38:41 think there’s quite a lot of interesting, uh, things to be discovered probably with these sort
0:38:47 of hybrid systems, let’s call them. But not to romanticize evolution. Yeah. I’m only human,
0:38:52 but you think there’s some value in whatever that mechanism is? Cause we already talked about natural
0:38:58 systems. Do you think we’re, there’s a lot of low hanging fruit of us understanding being,
0:39:05 being able to model, uh, being able to simulate evolution and then using that, whatever we
0:39:10 understand about that nature inspired mechanism to, to then do search better and better and better.
0:39:16 Yes. So if you think about, uh, again, uh, breaking down the solar systems we’ve built, uh, to their
0:39:22 really fundamental core, you’ve got like the model of the, of the underlying dynamics of the system.
0:39:27 Uh, and then if you want to discover something new, something novel that hasn’t been seen before,
0:39:33 um, then you need some kind of search process on top to take you to a novel region of the,
0:39:39 of the, of the search space. And, um, you can do that in a number of ways. Evolutionary computing is
0:39:45 one, um, with alpha go, we just use Monte Carlo tree search, right? And that’s what found move 37,
0:39:52 the new kind of never seen before strategy in go. And so that’s how you can go beyond potentially what is
0:39:56 already known. So the model can model everything that you currently know about, right? All the data
0:40:01 that you currently have, but then how do you go beyond that? So that starts to speak about the
0:40:05 ideas of creativity. How can these systems create something new fight, discover something new?
0:40:10 Obviously this is super relevant for scientific discovery or pushing med science and medicine
0:40:16 forward, which we want to do with these systems. And you can actually bolt on some, uh, fairly
0:40:22 simple search systems on top of these models and get you into a new region of space. Of course,
0:40:27 you also have to, um, make sure that, uh, you’re not searching that space totally randomly. It was
0:40:31 to be too big. So you have to have some objective function that you’re trying to optimize and hill climb
0:40:36 towards and that guides that search. But there’s some mechanism of evolution that are interesting,
0:40:41 maybe in the space of programs, but then the space of programs that extremely important space. Cause you
0:40:48 can probably generalize the, uh, to everything, but you know, for example, mutation, this is not just
0:40:55 Monte Carlo tree search where it’s like a search. You could every once in a while combine things,
0:41:01 yeah. Combine things out there like sub like a components of a thing. Yes. So then, you know,
0:41:08 what evolution is really good at is not just the natural selection. It’s combining things and
0:41:14 building increasingly complex hierarchical systems. Yes. So that component is super interesting. Yeah.
0:41:18 Especially like with alpha evolve in the space of programs. Yeah, exactly. So there’s a,
0:41:23 you can get a bit of an extra property out of evolutionary systems, which is some new emergent
0:41:29 capability may come about, right? Of course, like happened with life. Interestingly with naive,
0:41:34 uh, sort of traditional evolutionary computing methods without LLMs and the modern AI, the problem
0:41:40 with them, they were very well studied in the nineties and early two thousands and some promising results.
0:41:45 But the problem was they could never work out how to evolve new properties, new emergent properties.
0:41:51 You always had a sort of subset of the properties that you put into the system, but maybe if we combine them
0:41:56 with these foundation models, perhaps we can overcome that limitation. Obviously, uh, natural
0:42:01 evolution clearly did because it, it did evolve new capabilities, right? So, uh, bacteria to where we
0:42:09 are now. So clearly that it must be possible with evolutionary systems to generate, uh, new patterns,
0:42:15 you know, going back to the first thing we talked about and, uh, new capabilities and emergent properties.
0:42:17 And maybe we’re on the cusp of discovering how to do that.
0:42:23 Yeah. Listen, uh, alpha wall is one of the coolest things I’ve ever seen. I’ve, I’ve, uh, uh,
0:42:27 on my desk at home, you know, most of my time is spent behind that computer is just programming.
0:42:37 And next to the three screens is a skull of a, uh, uh, Tiktalic, which is one of the early organisms that
0:42:45 crawled out of the water onto land. And I just kind of watch that little guy, it’s like you, the,
0:42:52 whatever the computation mechanism of evolution is, is quite incredible. It’s truly, truly incredible.
0:42:57 Yeah. Now, whether that’s exactly the thing we need to do to do our search, but never, never, uh,
0:43:03 dismiss the power of nature with what it did here. Yeah. And it’s amazing. Um, which is a relatively
0:43:09 simple algorithm, right? Effectively. And it can generate all of this immense complexity emerges,
0:43:15 obviously running over, you know, 4 billion years of time, but, but it’s, it’s, it’s, you know,
0:43:20 you can think about that as again, a process, a search process that ran over the physics substrate
0:43:25 of the universe for a long amount of computational time, but then it generated all this incredible,
0:43:31 uh, rich diversity. So, uh, so many questions I want to ask you. So one, you do have a dream.
0:43:38 One of the natural systems you want to try to model is, uh, is a cell. That’s a beautiful dream.
0:43:45 Uh, I could ask you about that. I also just for that purpose on the AI scientist front, just broadly.
0:43:52 So there’s a essay, uh, from Daniel Cucataglio, Scott Alexander, and others that outlines steps
0:43:59 along the way to get to ASI and has a lot of interesting ideas in it. One of which is, uh,
0:44:07 including a superhuman coder and a superhuman AI researcher. And in that there’s a term of research
0:44:12 taste. That’s really interesting. So in everything you’ve seen, do you think it’s possible for AI
0:44:21 systems to have research taste to help you in the way that AI co-scientists does to help steer human,
0:44:29 human brilliant scientists, and then potentially by itself to figure out what are the directions
0:44:34 where you want to generate truly novel ideas? Because that seems to be like a
0:44:37 really important component of how to do great science.
0:44:42 Yeah. I think that’s going to be one of the hardest things to, to, uh, mimic or model is,
0:44:47 is this, this idea of taste or, or judgment. I think that’s what separates the, you know,
0:44:52 the, the great scientists from the good scientists, like all, all professional scientists are good
0:44:56 technically, right? Otherwise it wouldn’t have been made it, uh, that far in, in academia and things
0:45:01 like that. But then do you have the taste to sort of sniff out what the right direction is,
0:45:06 what the right experiment is, what the right question is. So the crystal is picking the right question is,
0:45:12 is the hardest part of science, um, and, and making the right hypothesis. And, um, that’s what,
0:45:17 you know, today’s systems definitely, they can’t do. So, you know, I often say it’s harder to come
0:45:22 up with a conjecture, a really good conjecture than it is to solve it. So we may have systems soon
0:45:28 that can solve pretty hard conjectures. Um, you know, I, I, um, in math Olympiad problems,
0:45:32 where we, we, you know, alpha proof last year, our system got, you know, silver medal in that
0:45:37 really hard problems. Maybe eventually we’ll better solve a millennium price kind of problem,
0:45:43 but could a system have come up with a conjecture worthy of study that someone like Terence Tao would
0:45:47 have gone, you know what, that’s a really deep question about the nature of maths or the nature of
0:45:54 numbers or the nature of physics. And that is far harder type of creativity. And we don’t really know,
0:45:59 today’s systems clearly can’t do that. And we’re not quite sure what that mechanism would be. This
0:46:04 kind of leap of imagination, like, like Einstein had when he came up with, you know, special relativity
0:46:10 and then general relativity with the knowledge he had at the time. And for conjecture, the,
0:46:16 you want to come up with a thing that’s interesting. It’s amenable to proof. Yes. So like,
0:46:20 it’s easy to come up with a thing that’s extremely difficult. Yeah. It’s easy to come up with a thing
0:46:25 that’s extremely easy, but that, at that very edge, that sweet spot, right. Of, of basically advancing
0:46:30 the science and splitting the hypothesis space into two ideally, right. Whether if it’s true or not true,
0:46:37 you, you’ve learned something really useful and, um, and, and that’s hard and, and, and, and making
0:46:43 something that’s also, uh, you know, falsifiable and within sort of the technologies that you have,
0:46:49 you currently have available. So it’s a very creative process, actually highly creative process that,
0:46:54 um, I think just a kind of naive search on top of a model won’t be enough for that.
0:47:00 Okay. The idea of splitting the hypothesis space into super interesting. So, uh, I’ve heard you say
0:47:06 that there’s basically no failure in, or failure is extremely valuable if it’s done. If you construct
0:47:11 the questions, right. If you construct the experiments, right. If you design them right, that failure or
0:47:17 success are both useful. So perhaps because it splits the hypothesis basically too, it’s like a binary search.
0:47:23 That’s right. So when you do like, you know, real blue sky research, there’s no such thing as failure,
0:47:28 really, as long as you’re picking experiments and hypotheses that, that, that, that meaningfully
0:47:32 spit the hypothesis space. So, you know, and you learn something, you can learn something kind of
0:47:37 equally valuable from, uh, an experiment that doesn’t work. That should tell you if you’ve designed
0:47:42 an experiment well, and your hypotheses are interesting, it should tell you a lot about where to go next.
0:47:49 And, um, and then it’s you’re, you’re effectively doing a search process, um, and using that information
0:47:50 in, in, you know, very helpful ways.
0:47:59 So to go to your dream of modeling a cell, um, what are the big challenges that lay ahead for us to
0:48:04 make that happen? We should maybe highlight that alpha for, I mean, there’s just so many leaps.
0:48:05 Yeah.
0:48:10 So alpha fold solved, if it’s fair to say protein folding, and there’s so many incredible things we
0:48:16 could talk about there, including the open sourcing, uh, everything you’ve released alpha fold three
0:48:23 is doing protein, RNA, DNA interactions, which is super complicated and fascinating. This, uh, amenable
0:48:29 to modeling alpha genome, uh, predicts, uh, how small genetic changes. Like if we think about single
0:48:36 mutations, how they link to actual, uh, function. So, um, those are, it seems like it’s creeping along.
0:48:42 So sophisticated, to much more complicated, uh, things like a cell, but a cell has a lot of
0:48:44 really complicated components.
0:48:50 Yeah. So what I’ve tried to do throughout my career is I have these really grand dreams, and then I try
0:48:53 to, as you’ve noticed, and then I try to break, but I try to break them down at any, you know,
0:48:59 it’s easy to have a kind of, uh, a crazy ambitious dream, but the, the, the trick is how do you break it
0:49:05 down into manageable, achievable, uh, interim steps that are meaningful and useful in their own
0:49:11 right. And so virtual cell, which is what I call the project of modeling a cell, uh, I’ve had this
0:49:16 idea, you know, of wanting to do that for maybe more like 25 years. And, uh, I used to talk with
0:49:20 Paul nurse, who is a bit of a mentor of mine in biology. He runs the, the, you know, he founded the
0:49:27 Crick Institute and, and won the Noah prize in, in 2001, uh, is, is we’ve been talking about it since,
0:49:33 you know, before the, you know, in the nineties and, um, and I come used to come back to every five
0:49:37 years is like, what would you need to model of the full internals of a cell so that you could do
0:49:43 experiments on the virtual cell and what those experiment, you know, in silico and those predictions
0:49:47 would be useful for you to save you a lot of time in the wet lab, right? That would be the dream.
0:49:52 Maybe you could a hundred X speed up experiments by doing most of it in silico, the search in silico,
0:49:57 and then you do the validation step in the wet lab. That would be, that’s the, that’s the dream.
0:50:02 And so, uh, but maybe now finally, uh, so I was trying to build these components, alpha fold being
0:50:09 one that, that would allow you eventually to model the full interaction, a full simulation
0:50:14 of a cell. And I’d probably start with the yeast cell. And partly that’s what Paul nurse studied
0:50:18 because the yeast cell is like a full organism. That’s a single cell, right? So it’s the kind
0:50:24 of simplest single cell organism. And so it’s not just a cell, it’s a full organism. And, um,
0:50:31 and yeast is very well understood. And so that would be a good candidate for, uh, a kind of full simulated
0:50:37 model. Now alpha fold is the, is the solution to the kind of static picture of what does a, what does
0:50:42 a protein look 3d structure protein look like a static picture of it. But we know that biology,
0:50:46 all the interesting things happen with the dynamics, the interactions, and that’s what alpha fold three
0:50:51 is, is the first step towards is modeling those interactions. So first of all, pair wise, you know,
0:50:56 proteins with proteins, proteins with RNA and DNA, but then, um, the next step after that would be
0:51:01 modeling maybe a whole pathway, maybe like the TOR pathway that’s involved in cancer or something like
0:51:05 this. And then eventually you might be able to model, you know, a whole cell.
0:51:09 Also, there’s another complexity here that stuff in a cell happens at different timescales.
0:51:16 Is that tricky? It’s like the, you know, protein, uh, folding is, you know, super fast.
0:51:21 Yes. Um, I don’t know all the biological mechanisms, but some of them take a long time.
0:51:26 Yeah. And so is that, that’s a level. So the levels of interaction has a different temporal
0:51:27 scale that you have to be able to model.
0:51:32 So that would be hard. So you’d probably need several simulated systems that can interact at
0:51:37 these different temporal dynamics, or at least, uh, maybe it’s like a hierarchical system. So, um,
0:51:40 you can jump up and down the, the different temporal stages.
0:51:49 So can you avoid, I mean, one of the challenges here is not avoid simulating, for example, the,
0:51:53 the, the quantum mechanical aspects of any of this, right? You want to not over model.
0:52:00 You can skip ahead to just model the really high level things that get you a really good
0:52:01 estimate of what’s going to happen.
0:52:04 Yes. So you, you’ve got to make a decision when you’re modeling any natural system, what is the
0:52:09 cutoff level of the granularity that you’re going to model it to that then captures the dynamics
0:52:14 that you’re interested in. So probably for a cell, I would hope that would be the protein level,
0:52:20 uh, and that one wouldn’t have to go down to the atomic level. Um, so, you know, and of course,
0:52:26 that’s where alpha fold stock kicks in. So that would be kind of the basis. And then you’d build these,
0:52:33 um, uh, higher level simulations that, um, take those as building blocks and then you get the
0:52:34 emergent behavior.
0:52:39 Yeah. Apologize for the pothead questions ahead of time, but, uh, do you think, uh,
0:52:48 we’ll be able to simulate a model, the origin of life? So being able to simulate the first
0:52:53 from, from non-living organisms, the, the birth of a living organism.
0:52:58 I think that’s a, one of the, of course, one of the deepest and most fascinating questions. Um,
0:53:03 I love that area of biology, you know, uh, there’s people like, there’s a great book by Nick Lane,
0:53:09 one of the top, top experts in this area called the 10 great inventions of, of, of evolution. I think it’s
0:53:13 fantastic. And it also speaks to what the great filters might be, but you know, prior or are they
0:53:18 ahead of us? I think, I think they’re most likely in the past. If you read that book of how unlikely
0:53:23 to go, you know, have any life at all. And then single cell to multi-cell seems an unbelievably
0:53:28 big jump that took like a billion years, I think on earth to do. Right. So it shows you how hard it
0:53:29 was. Right.
0:53:33 Bacteria were super happy for a very long, very long time before they captured mitochondria somehow.
0:53:39 Right. I don’t see why not, why AI couldn’t help with that. Some kind of simulation again,
0:53:44 it’s again, it’s a bit of a search process through a combinatorial space. Here’s like all the,
0:53:49 you know, the chemical soup that, that you start with the primordial soup that, you know,
0:53:55 maybe it was on earth near these hot vents. Here’s some initial conditions. Can you generate
0:53:58 something that looks like a cell? So perhaps that would be a next stage after the virtual
0:54:04 cell project is, well, how, how could you actually, um, something like that emerge from the chemical
0:54:09 soup? Well, I would love it if there was a move 37 for the origin of life. I think that’s
0:54:13 one of the sorts of great mysteries. I think ultimately what we will figure out is their
0:54:17 continuum. There’s no such thing as a line between non-living and living, but if we can make that
0:54:24 rigorous, that the very thing from the big bang to today has been the same process. If we can break
0:54:30 down that wall that we’ve constructed in our minds of the actual origin of, from non-living to living,
0:54:35 and it’s not a line that it’s a continuum that connects physics and chemistry and biology.
0:54:40 Yeah. There’s no line. I mean, this is my whole reason why I worked on AI and AGI my whole life,
0:54:45 because I think it can be the ultimate tool to help us answer these kinds of questions. And
0:54:51 I don’t really understand why, um, you know, the average person doesn’t think like worry about this
0:54:56 stuff more. Like how, how, how can we not have a good definition of life and not, and not living and
0:55:02 non-living and the nature of time and let alone consciousness and gravity and all these things.
0:55:07 It’s, it’s just, and quantum mechanics, weirdness. It’s just, to me, it’s, I’ve always had this,
0:55:12 this sort of screaming at me in my face. The whole, I need that. It’s getting louder. You know,
0:55:16 it’s like how, what is going on here? You know, in, in, in, and I mean that in the deeper sense,
0:55:21 like in the, you know, the nature of reality, which has to be the ultimate question, uh, that would
0:55:25 answer all of these things. It’s sort of crazy. If you think about it, we can stare at each other
0:55:30 and all these living things all the time. We can inspect it microscopes and take it apart,
0:55:35 uh, almost down to the atomic level. And yet we still can’t answer that clearly in a simple way.
0:55:39 That question of how do you define living? It’s kind of amazing.
0:55:44 Yeah. Living. You can kind of talk your way out of thinking about, but like consciousness,
0:55:48 like we have this very obviously subjective conscious experience. Like we’re at the center
0:55:54 of our own world and it, it feels like something. And then how, how, how are you not screaming?
0:56:00 Yeah. At the mystery of it all. We haven’t, I mean, but really humans have been contending
0:56:05 with the mystery of the world around them, uh, for a long, long, there’s a lot of mysteries
0:56:12 like what’s up with the sun and, and the rain. Yeah. Like what’s that about? And then like last year,
0:56:16 we had a lot of rain and this year we don’t have rain. Like, what did we do wrong?
0:56:19 Humans have been asking that question for a long time.
0:56:23 Exactly. So we’re quite, I guess we’ve developed a lot of mechanisms to cope with this, uh,
0:56:27 these deep mysteries that we can’t fully, we can see, but we can’t fully understand. And
0:56:32 we have to have to just get on with daily life and, and, and we get, we keep ourselves busy,
0:56:34 right? In a way, do we keep ourselves distracted?
0:56:39 I mean, weather is one of the most important questions of human history. We still, that’s,
0:56:44 that’s the go-to small talk direction of the weather, especially in England.
0:56:51 And then it’s, which is, you know, famously is an extremely difficult system to model. And, uh,
0:56:56 even that system, uh, uh, Google deep mind has made progress on.
0:57:01 Yes. We’ve, we are, we’ve created the, the best weather prediction systems in the world,
0:57:06 and they’re better than traditional fluid dynamics sort of systems that usually calculated on massive
0:57:12 supercomputers takes days to calculate it. Uh, we’ve managed to model a lot of the weather dynamics
0:57:18 dynamics with neural network systems without where the next system. And again, it’s interesting that
0:57:23 those kinds of dynamics can be modeled, even though they’re very complicated, almost bordering on chaotic
0:57:28 systems. In some cases, a lot of the interesting aspects of that, um, uh, can be modeled by these
0:57:33 neural network systems, including very recently we had, you know, cyclone prediction of where,
0:57:37 you know, paths of hurricanes might go, of course, super useful, super important for the world.
0:57:42 And, and, and it’s super important to do that very timely and very quickly and as well as accurately.
0:57:47 And, uh, I think it’s very promising direction again of, you know, simulating and, uh, so that you can
0:57:51 run forward predictions and simulations of very complicated real world systems.
0:57:57 I should mention that, uh, I’ve got a chance in, uh, Texas to meet a community of folks called the
0:57:58 storm chasers.
0:57:59 Yes.
0:58:03 And what’s really incredible about them. I need to talk to them more is they’re extremely tech savvy
0:58:07 because what they have to do is they have to use models to predict where the storm is.
0:58:14 So there it’s this, it’s, it’s this beautiful mix of like crazy enough to like go into the eye of the
0:58:19 storm and like, in order to protect your life and predict where the extreme events are going to be,
0:58:23 they have to have increasingly sophisticated models of, uh, of weather.
0:58:24 Yeah.
0:58:24 Yeah.
0:58:31 It’s, it’s a beautiful balance of like being in it as living organisms and the, the cutting
0:58:35 edge of science. So they actually might be using a deep mind system. So that’s.
0:58:38 Yeah, they are. Hopefully they are. And I’d love to join them on one of those.
0:58:41 They look amazing. Right. To actually experience it one time.
0:58:46 Exactly. And then also to experience the correct prediction of where something will come
0:58:48 and how it’s going to evolve. It’s incredible.
0:58:56 Yeah. You’ve estimated that we’ll have AGI by 2030. Um, so there’s interesting questions around that.
0:59:04 How will we actually know that we got there? Uh, and, uh, what may be the move quote,
0:59:12 move 37 of AGI. My estimate is sort of 50% chance by in the next five years. So, you know, by 2030,
0:59:17 let’s say. And, uh, so I think there’s a good chance that that could happen. Part of it is what,
0:59:20 what is your definition of AGI. Of course, people are arguing about that now. And,
0:59:26 and, uh, mine’s quite a high bar and always has been of like, can we match the cognitive
0:59:31 functions that the brain has? Right. So we know our brains are pretty much general Turing machines,
0:59:38 approximate. And of course we created incredible modern civilization with our minds. So that also
0:59:45 speaks to how general the brain is. And, um, for us to know, we have a true AGI, we would have to like,
0:59:49 make sure that it has all those capabilities. It isn’t kind of a jagged intelligence where some
0:59:55 things it’s really good at like today’s systems, but other things it’s really, uh, flawed at. And,
0:59:58 and that’s what we currently have with today’s systems. They’re not consistent. So you’d want that
1:00:04 consistency of intelligence across the board. And then we have some missing, I think, capabilities,
1:00:09 like sort of, uh, the true invention capabilities and creativity that we were talking about earlier.
1:00:14 So you’d want to see those, how you test that. Um, I think you just test it. One way to do it would
1:00:20 be kind of brute force test of tens of thousands of cognitive tasks that, um, you know, we know that
1:00:27 humans can do, uh, and maybe also make the system available to, uh, a few hundred of the world’s top
1:00:33 experts, uh, Terence Tows of each, each subject area and see if they can find, you know, given,
1:00:39 give them a month or two and see if they can find an obvious flaw in the system. And if they can’t,
1:00:43 then I think you’re, you’re pretty, uh, you know, pretty, you can be pretty confident. We have a,
1:00:44 a fully general system.
1:00:48 Maybe to push back a little bit, it seems like humans are really incredible
1:00:55 as the intelligence improves across all domains to take it for granted. Uh, like you mentioned,
1:01:03 Dr. Terence Tao, uh, these brilliant experts, they might quickly in a span of weeks take for granted
1:01:08 all the incredible things it can do and then focus in while ha ha right there. You know, I, I consider
1:01:19 myself, uh, first of all, human. Yeah. Uh, I identify as human. Um, I, you know, some people listen to me
1:01:25 talk and they’re like, that guy is not good at talking, the stuttering, the, you know, so like even
1:01:31 humans have obvious across domains limits, uh, even just outside of calculus, mathematics and physics
1:01:38 and so on. It, I, I wonder if it will take something like a move 37. So on the positive
1:01:46 side versus like a barrage of 10,000 cognitive tasks where it would be one or two where it’s like,
1:01:47 yes, holy shit.
1:01:53 Exactly. So I think there’s the sort of blanket testing to just make sure you’ve got the consistency,
1:02:00 but I think there are the sort of lighthouse moments like the move 37 that I would be looking for. So
1:02:07 one would be inventing a new conjecture or a new hypothesis about physics like Einstein did. So
1:02:12 maybe you could even run the back test of that very rigorously, like have a cutoff of knowledge,
1:02:18 cutoff of 1900 and then give the system everything that was, you know, that was written up to 1900 and
1:02:22 then, and then see if it could come up with special relativity and general relativity, right? Like
1:02:28 Einstein did that, that would be an interesting test. Another one would be, can it invent a game
1:02:33 like go not just come up with move 37, a new strategy, but can it invent a game that’s as deep
1:02:39 as aesthetically beautiful, as elegant as go. And those are the sorts of things I would be looking out for,
1:02:44 uh, uh, and probably a system being able to do, uh, uh, several of those things, right. For it to be
1:02:50 very general, um, not just one domain. And so I think that would be the signs, at least that I would
1:02:56 be looking for that we’ve got a system that’s AGI level. And then maybe to fill that out, you would
1:03:00 also check their consistency, you know, make sure there’s no holes in that system either.
1:03:05 Yeah. Something like a new conjecture or scientific discovery, that would be a cool feeling.
1:03:10 Yeah. That would be amazing. So it’s not, not just helping us do that, but actually coming up with
1:03:16 something brand new. And you would be in the room for that. And so it would be like probably two or
1:03:22 three months before announcing it. And you would just be sitting there trying not to tweet.
1:03:28 something like that. Exactly. It’s like, what is this amazing new, you know, physics, uh, idea. And
1:03:34 then we would probably check it with world experts in that domain, right. And validate it and kind of
1:03:41 go through its workings. And it, I guess it would be explaining its workings to, um, yeah, be an amazing
1:03:45 moment. Do you worry that we as humans, even expert humans, like you might miss it?
1:03:51 Well, it may be pretty complicated. So it could be the analogy I give there is, I don’t think it will be,
1:03:57 um, uh, uh, totally mysterious to the, to the best human scientists, but it may be a bit like,
1:04:04 for example, in chess, if I was to talk to Gary Kasparov or Magnus Carlsen and play a game with them,
1:04:08 and they make a brilliant move, I might not be able to come up with that move, but they could explain
1:04:13 why afterwards that move made sense. And we will be able to understand it to some degree,
1:04:17 not to the level they do, but you know, if they were good at explaining, which is actually part of
1:04:22 intelligence too, is being able to explain in a simple way that what you’re thinking about. Um,
1:04:26 uh, I think that that will be very possible for the best human scientists.
1:04:31 But I wonder, maybe you can, you can educate me on the side of go. I wonder if there’s moves from
1:04:36 Magnus or Gary where they at first will dismiss it as a bad move.
1:04:41 Yeah, sure. It could be. But then afterwards they’ll figure out with their intuition that,
1:04:45 that this, why this works. And then, and then, and then empirically, the nice thing about games is
1:04:49 one of the great things about games is you can, it’s a sort of scientific test. Does it,
1:04:54 do you win the game or not win? And then, um, that tells you, okay, that move in the end was
1:04:59 good. That strategy was good. And then you can go back and analyze that and, and, and, and explain
1:05:05 even to yourself a little bit more why explore around it. And that’s how chess analysis and things
1:05:09 like that works. So perhaps that’s why my brain works like that. Cause I I’ve been doing that since
1:05:13 I was four and you’re trained, you know, it’s sort of hardcore training in that way.
1:05:17 But even, even now, like when I generate code,
1:05:24 there is this kind of nuanced, fascinating contention that’s happening where I might
1:05:30 at first identify as a set of generated code is incorrect in some interesting nuanced ways.
1:05:35 But then I’m always have to ask the question, is there a deeper insight here that
1:05:41 I’m the one who’s incorrect? And that’s going to, as the systems get more and more intelligent,
1:05:46 you’re going to have to contend with that. It’s like, what, what, what do you, is this a bug or
1:05:50 a feature where you just came up with? Yeah. And they’re going to be pretty complicated to do,
1:05:55 but of course it will be, you can imagine also AI systems that are producing that code or whatever that
1:06:00 is. And then human programmers looking at, but also not unaided with the help of AI tools,
1:06:05 tools as well. So it’s going to be kind of an interesting, you know, maybe different AI tools
1:06:09 to the ones that they’re more, you know, kind of monitoring tools to the ones that generated it.
1:06:17 So if we look at an AGI system, sorry to bring it back up, but Alpha Evolve, super cool. So Alpha
1:06:24 Evolve enables on the programming side, something like recursive self-improvement, potentially.
1:06:30 Like what, if we can imagine what that AGI system, maybe not the first version, but if
1:06:34 a few versions beyond that, what does that actually look like? Do you think it will be
1:06:39 simple? Do you think it will be something like a self-improving program and a simple one?
1:06:44 I mean, potentially that’s possible. I would say, um, I’m not sure it’s even desirable because that’s
1:06:50 a kind of like hard takeoff scenario, but, but you, you, these current systems like Alpha Evolve,
1:06:55 they have, you know, human in the loop deciding on various things. They’re separate hybrid systems
1:07:01 that interact. Uh, one could imagine eventually doing that end to end. I don’t see why that wouldn’t
1:07:06 be possible, but right now, um, you know, I think the systems are not good enough to do that in terms of
1:07:11 coming up with the architecture of the code. Um, and again, it’s a little bit reconnected to
1:07:16 this idea of coming up with a new conjectural hypothesis, how they’re good if you give them
1:07:21 very specific instructions about what you’re trying to do. Um, but if you give them a very vague,
1:07:26 high level instruction, that wouldn’t work currently. Like, uh, and I think that’s related to this idea of
1:07:31 like invent a game as good as go, right? Imagine that was the prompt that’s, that’s pretty underspecified.
1:07:36 And so the current systems wouldn’t know, I think what to do with that, how to narrow that down to
1:07:40 something tractable. And I think there’s similar, like, look, just make a better version of yourself.
1:07:45 That’s too, that’s too unconstrained, but we’ve done it in, you know, in, and as you know, with Alpha
1:07:51 Evolve, like things like faster matrix multiplication. So when you, when you hone it down to a very specific
1:07:56 thing you want, um, it’s very good at incrementally improving that. But at the moment, these are more
1:08:01 like incremental improvements, sort of small iterations. Whereas if, you know, if you wanted
1:08:07 a big leap in, uh, understanding, you’d need a, you need a much larger, uh, advance.
1:08:13 Yeah. But it could also be sort of to push back against hard takeoff scenario. It could be just
1:08:21 a sequence of, um, incremental improvements like matrix multiplication. Like it has to sit there for days
1:08:26 thinking how to incrementally improve a thing and that it does so recursively. And as you do more
1:08:32 and more improvement, it’ll slow down. So there’ll be a, like, uh, like, uh, the path to AGI won’t be
1:08:39 like a, uh, it’d be a gradual improvement over time. Yes. If it was just incremental improvements,
1:08:43 that’s how it would look. So the question is, could it come up with a new leap, like the transformers
1:08:49 architecture, right? Could it have done that back in 2017 when, you know, we did it and brain did it.
1:08:54 And it’s, it’s not clear that, that these systems, something that AlphaVolv wouldn’t be able to do,
1:08:59 make such a big leap. So for sure, these systems are good. We have systems, I think that can do
1:09:03 incremental hill climbing. And that’s a kind of bigger question about, is that all that’s needed
1:09:08 from here? Or do we actually need one or two more, um, uh, big breakthroughs?
1:09:13 And can the same kind of systems provide the breakthroughs also? So make it a bunch of
1:09:18 S curves, like incremental improvement, but also every once in a while leaps.
1:09:24 Yeah. I don’t think anyone has systems that can have shown unequivocally those big leaps that,
1:09:28 that, that, right. We have a lot of systems that do the hill climbing of the S curve that you’re
1:09:31 currently on. Yeah. And that would be the move 37.
1:09:37 Yeah. I think it would be a leap. Um, something like that. Uh, do you think the scaling laws are
1:09:43 holding strong on the pre-training, post-training test time compute? Uh, do you, uh, on the flip side
1:09:46 of that anticipate AI progress hitting a wall?
1:09:52 We certainly feel there’s a lot more room just in the scaling. So, um, actually all steps,
1:09:58 pre-training, post-training and inference time. So, uh, there’s sort of three scalings that are
1:10:06 happening concurrently. Um, and we, again, there it’s about how innovative you can be. And we, you know,
1:10:11 we pride ourselves on having the broadest and, um, deepest research bench. Uh, we have amazing,
1:10:16 you know, incredible, uh, researchers and, uh, people like Noam Shazir, who, you know,
1:10:21 came up with Transformers and, and Dave Silver, you know, who led the AlphaGo project and so on.
1:10:28 And, um, it’s, it’s, it’s that research base means that if some new, new breakthrough is required,
1:10:33 like an AlphaGo or Transformers, uh, I would back us to be the place that does that.
1:10:38 So I’m actually quite like it when the terrain gets harder, right? Because then it veers more from just
1:10:44 engineering to, to true research and, you know, research plus engineering, and that’s our sweet spot.
1:10:50 And I, I think that’s harder. It’s harder to invent things than to, than to, um, you know,
1:10:56 fast follow. And, um, so, you know, we don’t know, I would say it’s a, it’s kind of 50, 50,
1:11:02 whether new things are needed or whether the scaling, the existing stuff is going to be enough. And so
1:11:07 in true kind of empirical fashion, we’re pushing both of those as hard as possible, the new blue sky
1:11:13 ideas and, you know, maybe about half our resources on that and then, and then, uh, scaling to the max,
1:11:18 the, the current, the current capabilities. And, um, we’re still seeing some, you know,
1:11:22 fantastic progress on, uh, each different version of Gemini.
1:11:25 That’s interesting. The way you put it in terms of the deep bench,
1:11:35 that if, uh, progress towards AGI is more than just scaling compute. So the engineering side of the
1:11:41 problem and is more on the scientific side where there’s breakthroughs needed, then you feel confident
1:11:47 deep mind as well, uh, Google deep mind as well positioned to kick ass in that domain.
1:11:51 Well, I mean, if you look at the history of the last decade or 15 years, um, it’s been,
1:11:55 I mean, you know, maybe, I don’t know, 80, 90% of the breakthroughs that more that underpins
1:12:00 modern AI field today was from, you know, originally Google brain, Google research and deep mind. So,
1:12:03 yeah, I would back that to continue. Hopefully.
1:12:09 Uh, so on the data side, are you concerned about running out of high quality data, especially high
1:12:14 quality human data? I’m not very worried about that partly because I think there’s enough data,
1:12:19 data, uh, or, and it’s been proven to get the systems to be pretty good. And this goes back
1:12:24 to simulations. Again, if you do, you have enough data to make simulations or so that you can create
1:12:30 more synthetic data that are from the right distribution, obviously that’s the key. So you
1:12:35 need enough real world data in order to be able to, uh, uh, create those kinds of generator data
1:12:39 generators. And, um, I think that we’re at that step at the moment.
1:12:44 Yeah. You’ve done a lot of incredible stuff on the side of science and biology doing a lot with
1:12:48 not so much data. Yeah. I mean, it’s still a lot of data, but I guess enough.
1:12:56 Take that going. Exactly. Exactly. Uh, how crucial is the scaling of compute to building AGI? This is a
1:13:03 question. That’s an engineering question. It’s a, almost a geopolitical question because it also
1:13:09 integrated into that is the supply chains and energy, a thing that you care a lot about,
1:13:13 which is, um, potentially fusion. So innovating on the side of energy also, do you think we’re
1:13:15 going to keep scaling compute?
1:13:19 I think so for several reasons. I think compute there’s, there’s the amount of compute you have
1:13:25 for training, uh, often it needs to be co-located. So actually even like, you know, uh, bandwidth
1:13:30 constraints between data centers can affect that. So it’s, it’s, it’s, there’s additional constraints
1:13:34 even there. And that that’s important for training, obviously the largest models you can,
1:13:41 but there’s also, because now AI systems are in products and being used by billions of people around
1:13:46 the world, you need a ton of inference compute now. Um, and then on top of that, there’s the thinking
1:13:52 systems, the new paradigm, uh, of the last year that, uh, where they get smarter, the longer amount of
1:13:58 inference time you give them at test time. So all of those things need a lot of compute. And I don’t
1:14:04 really see that slowing down. Um, and as AI systems become better, they’ll become more useful and
1:14:08 there’ll be more demand for them. So both from the training side, the training side actually is,
1:14:13 is only just one part of that may even become the smaller part of, of what’s needed, um, uh,
1:14:19 in the overall compute that that’s required. Yeah. That’s one sort of almost meme-y kind
1:14:25 of thing, which is like the success and the incredible aspects of VO3. There’s, uh, people
1:14:29 kind of make fun of like the more successful it becomes the, you know, the servers are sweating.
1:14:32 Yes. Yeah, yeah, exactly. We did a little
1:14:38 video of, of, of the servers frying eggs and things. And, um, that’s right. And, and, and we’re
1:14:42 going to have to figure out how to do that. Um, there’s a lot of interesting hardware innovations
1:14:46 that we do, as you know, we have our own TPU line and we’re looking at like inference only things,
1:14:51 inference only chips, and how we can make those more efficient. We’re also very interested in building AI
1:14:57 systems. And we have done the help with energy usage. So help, um, data center energy, like for
1:15:03 the cooling systems, be efficient, um, grid optimization. Um, and then eventually things
1:15:08 like helping with, uh, plasma containment fusion reactors, we’ve done lots of work on that with
1:15:13 commonwealth fusion. And also, uh, one could imagine reactor design. Um, and then material design,
1:15:18 I think is one of the most exciting new types of solar material, solar panel material, super room
1:15:24 temperature superconductors has always been on my list of dream breakthroughs and, um, optimal batteries.
1:15:29 And I think a solution to any, you know, one of those things would be absolutely revolutionary
1:15:34 for, you know, climate and energy usage. And we’re probably close, you know, and again,
1:15:38 in the next five years to having AI systems that can materially help with those problems.
1:15:43 If you were to bet, sorry for the ridiculous question, what, what is the main source of energy
1:15:49 in like 20, 30, 40 years? Do you think it’s going to be nuclear fusion?
1:15:55 I think fusion and solar are the two that I, I would bet on. Um, solar, I mean, you know,
1:16:00 it’s the fusion reactor in the sky, of course, and I think really the problem there is, is,
1:16:04 is batteries and transmission. So, you know, as well as more efficient, more and more efficient
1:16:09 solar material, perhaps eventually, you know, in space, you know, these kinds of Dyson sphere type
1:16:17 ideas and fusion, I think is definitely doable seems, uh, if we have the right design of reactor
1:16:23 and we can control the plasma and, uh, fast enough and so on. And I think both of those things will
1:16:27 actually get solved. So we’ll probably have at least, those are probably the two primary
1:16:31 sources of renewable, clean, almost free, or perhaps free energy.
1:16:38 What a time to be alive. If I, uh, traveled into the future with you a hundred years from now,
1:16:45 how much would you be surprised if we’ve passed a type one Kardashev scale civilization?
1:16:50 I would not be that surprised if there’s a, like a hundred year timescale from here. I mean,
1:16:54 I think it’s pretty clear if we crack the energy problems in one of the ways we’ve just discussed
1:17:01 fusion or, or very efficient solar. Um, then if energy is kind of free and renewable and clean,
1:17:08 um, then that solves a whole bunch of other problems. So for example, the water access problem
1:17:13 goes away because you can just use desalination. We have the technology. It’s just too expensive.
1:17:18 So only, you know, uh, fairly wealthy countries like Singapore and Israel and so on, like actually
1:17:23 use it. But, but if it was cheap, then every, then, you know, all countries that have a coast could,
1:17:28 but also you’d have unlimited rocket fuel. You could just separate seawater out into hydrogen and oxygen
1:17:36 using energy and that’s rocket fuel. So, uh, combined with, you know, Elon’s amazing self landing rockets,
1:17:41 then it could be like, you sort of like a bus service to, to space. So that opens up, you know,
1:17:47 incredible new resources and domains, uh, asteroid mining, I think will become a thing and maximum
1:17:51 human flourishing to the stars. Like that’s what I, uh, dream about as well as like Carl Sagan’s sort
1:17:57 of idea of bringing consciousness to the universe, waking up the universe. And I think human civilization
1:18:03 will do that in the full sense of time. If we get AI right and, uh, and, and, and crack some of
1:18:07 these problems with it. Yeah. I wonder what it would look like if you’re just a tourist flying through
1:18:14 space, you would probably notice earth because if you solve the energy problem, you would see a lot of
1:18:21 space rockets probably. So it would be like traffic here in London, but in space, it’s just a lot of
1:18:27 rockets. Yes. And then you would probably see floating in space, some kind of source of energy,
1:18:34 like solar. Yeah. Potentially. So earth would just look more on the surface, more, um, technological.
1:18:40 And then, then you would use the power of that energy then to preserve the natural. Yes. Like
1:18:43 the rainforest and all that kind of stuff. Exactly. Because for the first time in, in human history,
1:18:51 we wouldn’t be, uh, resource constrained. And I think that could be amazing new era for humanity,
1:18:57 where it’s not zero sum, right? I have this land, you don’t have it. Or if we take, you know, if the
1:19:01 tigers have their forest, then the, the local villagers can’t, what are they going to use?
1:19:07 I think that this will help a lot. No, it won’t solve all problems because there’s still other human,
1:19:12 uh, foibles that will, will, will still exist, but it will at least remove one. I think one of the big
1:19:19 vectors, which is scarcity of resources, you know, including land and more materials and energy.
1:19:22 And, um, we, you know, we should be, I sometimes call it like, and others call it about this kind
1:19:27 of radical abundance era where, um, there’s plenty of resources to go around. Of course,
1:19:32 the next big question is making sure that that’s fairly, you know, shared fairly, uh,
1:19:37 and everyone in society benefits from that. So there is something about human nature where
1:19:45 I go, you know, it’s like Borat, like my neighbor, like, like you start trouble. We, we, we do start
1:19:52 conflicts and that’s why games throughout, as I’m learning actually more and more, even in ancient
1:19:59 history serve the purpose of pushing people away from war, actually a hot war. So maybe we can figure
1:20:06 out increasingly sophisticated video games that pull us, they, they give us that, uh, that scratch
1:20:13 the itch of like conflict, whatever that is about, about us, the human nature, and then avoid the actual
1:20:21 hot wars that would come with increasingly sophisticated technologies, because we’re now we’ve
1:20:26 long past the stage where the weapons we’re able to create can actually just destroy all of human
1:20:34 civilization. So it’s no longer, um, that’s no longer a great way to, uh, start shit with your
1:20:39 neighbor. It’s better to play a game of chess or football or football. Yeah. Yeah. And I think,
1:20:45 I mean, I think that’s what my modern sport is. So, and I love football watching it and, and I just feel
1:20:50 like, uh, and I used to play it a lot as well. And it’s, it’s, it’s, it’s, it’s, it’s very visceral and
1:20:55 it’s tribal. And I think it does channel a lot of those energies into a, which I think is a kind
1:21:02 of human need to belong to some, some group. And, um, but into a, into a, into a fun way,
1:21:08 a healthy way, and, and a not, and not destructive way, kind of constructive, uh, thing. And I think
1:21:12 going back to games again is I think they’re originally why they’re so great as well for kids
1:21:16 to play things like chess is they’re great little microcosm simulations of the world that they’re
1:21:20 simulations of the world too. They’re simplified versions of some real world situation, whether it’s
1:21:26 poker or, or go or chess, different aspects or diplomacy, different aspects of, of the real
1:21:32 world and allows you to practice at them too. And, and cause you know, how many times do you get to
1:21:37 practice a massive decision moment in your life? You know, what job to take, what university go to,
1:21:41 you know, you get maybe, I don’t know, a dozen or so key decisions one has to make, and you’ve got to
1:21:46 make those as best as you can. Um, and games is a kind of safe environment, repeatable environment
1:21:52 where you can get better at your decision-making process. Um, and it maybe has this additional
1:21:58 benefit of channeling some energies into, uh, into more creative and constructive pursuits.
1:22:02 Well, I think it’s also really important to practice, um, losing and winning, right?
1:22:07 Like losing is a really, you know, that’s why I love games. That’s why I love even, um, things like, uh,
1:22:13 Brazilian jiu-jitsu where you can get your ass kicked in a safe environment over and over.
1:22:18 It reminds you about the way, about physics, about the way the world works, about the,
1:22:23 sometimes you lose, sometimes you win. You can still be friends with everybody, but that, that feeling
1:22:30 of losing, I mean, it’s a weird one for us humans to like, really like make sense of like,
1:22:33 that’s just part of life. That is a fundamental part of life is losing.
1:22:37 Yeah. And I think the martial arts, as I understand it, but also in things like,
1:22:41 like chess is a lot, at least the way I took it, it’s a lot to do with self-improvement,
1:22:47 self-knowledge, you know, that, okay. So I did this thing. It’s not about really being the other
1:22:52 person. It’s about maximizing your own potential. If you do in a healthy way, you learn to use victory
1:22:57 and losses in a way, don’t get carried away with victory and, and think you’re the, just the best in
1:23:02 the world and, and, and the losses keep you humble and always knowing there’s always something more
1:23:07 to learn. There’s always a bigger expert that you can mentor you. You know, I think you learn that I’m
1:23:13 pretty sure in martial arts. And, and, and I think that’s also the way that at least I was trained in
1:23:17 chess. And so in the same way, and it can be very hardcore and very important. And of course you want
1:23:22 to win, but you also need to learn how to deal with setbacks, uh, in a, in a healthy way that,
1:23:27 and, and, and, and wire that, that feeling that you have when you lose something into a constructive
1:23:31 thing of next time I’m going to improve this, right. Or get better at this.
1:23:36 There is something that’s a source of happiness, a source of meaning that improvements that it’s not
1:23:37 about the winning or losing.
1:23:39 Yes. The mastery. Yeah.
1:23:43 There’s nothing more satisfying in a way is like, oh, wow, this thing I couldn’t do before.
1:23:48 Now I can. And, and, and again, games and physical sports and mental sports,
1:23:52 they’re ways of measuring. They’re beautiful because you can measure that, that progress.
1:23:56 Yeah. I mean, there’s something about, that is why I love role-playing games. Like the, uh,
1:24:02 number go up of like on the skill tree. Like literally that is a source of meaning for us
1:24:07 humans, whatever. Yeah. We’re quite, we’re quite addicted to this sort of, yeah, these numbers going
1:24:12 up and, uh, and, and, and, and maybe that’s why we made games like that because obviously that is
1:24:15 something where we’re, we’re, we’re hill climbing systems ourselves, right.
1:24:19 Yeah. It would be quite sad if we didn’t have any mechanism.
1:24:23 Different color belts. We do this everywhere, right. Where we just have this thing that
1:24:27 I don’t want to dismiss that. That is a source of deep meaning for us humans.
1:24:33 Um, so one of the incredible stories on the business, on the leadership side is, um, what
1:24:40 Google has done over the past year. So I, uh, I think it’s fair to say that Google was losing on
1:24:47 the LLM product side, uh, a year ago with Gemini 1.5 and now it’s winning with Gemini 2.5 and you took
1:24:52 the helm and you led this effort. What did it take to go from, let’s say, quote unquote losing to
1:24:55 quote unquote winning in the, in the span of a year?
1:25:00 Yeah. Well, firstly, it’s absolutely incredible team that we have, you know, led by Corey and Jeff
1:25:07 Dean and, and Oriol and the amazing team we have on Gemini, absolutely world-class. So you can’t do it
1:25:13 without the best talent. Um, and of course you have, you know, we have a lot of great compute as well,
1:25:19 but then it’s the research culture we’ve created, right. And basically coming together, both different
1:25:24 groups in, in Google, you know, there was Google brain world-class team and, and then the old deep
1:25:31 mind and pulling together all the best people and the best ideas and gathering around to make the
1:25:38 absolute greater system we could. And it was been hard. Um, but we’re all very competitive. Uh, and we,
1:25:44 you know, love research. This is so fun to do. Um, and we, you know, it’s great to see our trajectory.
1:25:49 It wasn’t a given, but we’re very pleased with, um, the, the, where we are in the rate of progress
1:25:54 is the most important thing. So if you look at where we’ve come to from two years ago to one year
1:25:59 ago to now, you know, I think our, we call it relentless progress along with relentless shipping
1:26:05 of that progress is, um, being very successful. And, you know, um, it’s unbelievably competitive,
1:26:11 uh, the whole space, the whole AI space with some of the greatest entrepreneurs and leaders,
1:26:16 uh, and companies in the world all competing now because everyone’s realized how important
1:26:20 AI is. Um, and it’s very, you know, been pleasing for us to see that progress.
1:26:25 You know, Google is a gigantic company. Uh, can you speak to the natural things that happen
1:26:30 in that case is the bureaucracy that emerges? Like you want to be careful, like, you know,
1:26:35 like the, the, the natural kind of there’s, uh, there’s meetings and there’s managers and that,
1:26:39 like what, what are some of the challenges from a leadership perspective of breaking through that
1:26:46 in order to, like you said, ship like the number of products. Yeah. Gemini related products has been
1:26:51 shipped over the past year. It’s just insane. Right. It is. Yeah, exactly. That’s, that’s what
1:26:57 relentlessness looks like. Um, I think it’s, it’s a question of like any big company, you know, ends up
1:27:02 having, uh, a lot of layers of management and things like that. It’s sort of the nature of how it works.
1:27:09 Um, but I still operate and I was always operating with old deep mind as a, as a startup, still large
1:27:14 one, but still as a startup. And that’s what we still act like today as with Google deep mind and
1:27:21 acting with decisiveness and the energy that you get from the best smaller organizations. And we try to
1:27:26 get the best of both worlds where we have this incredible billions of users surfaces, uh, incredible
1:27:32 products that we can power up with our AI and our, and our research. Um, and that’s amazing.
1:27:36 And you can, you know, that’s very few places in the world. You can get that do incredible world-class
1:27:41 research on the one hand, and then plug it in and improve billions of people’s lives the next day.
1:27:48 Uh, that’s a pretty amazing combination and we’re continually fighting and cutting away bureaucracy
1:27:53 to allow the research culture and the relentless shipping culture to flourish. And I think we’ve
1:27:58 got a pretty good balance whilst being responsible with it, you know, as you have to be as a large
1:28:03 company and also, uh, with a number of, you know, uh, huge products surfaces that we have.
1:28:09 Uh, so funny thing you mentioned about like the, the surface of the billion. I had a conversation
1:28:15 with a guy named, um, brilliant guy, uh, here at the British museum called Irvin Finkel. He’s a world
1:28:24 expert at Cuneiforms, which is a ancient writing on tablets. And he doesn’t know about Chad GPT or
1:28:31 Gemini. He doesn’t even know anything about AI, but this first encounter with this AI is AI mode on
1:28:36 Google. Yes. He’s like, is that what you’re talking about? This AI mode. And then, you know,
1:28:40 it’s just, it’s just a reminder that there’s a large part of the world that doesn’t know about
1:28:46 this AI thing. Yeah. I know. It’s funny. Cause if you live on, uh, X and Twitter, and I mean,
1:28:50 it’s sort of, at least my feed, it’s all AI and, and there’s certain places where, you know, in the
1:28:55 valley and certain pockets where everyone’s just, all they’re thinking about is AI, but a lot of the
1:29:01 normal world hasn’t, hasn’t come across it yet, but that’s a great responsibility to the, their first
1:29:07 interaction. Yeah. Um, the, the, the grand scale of the rural India or anywhere across the world.
1:29:11 Right. And we want it to be as good as possible. And in a lot of cases, it’s just under the hood
1:29:18 powering, making something like maps or search work better. And, um, and ideally for a lot of those
1:29:22 people should just be seamless. It’s just new technology that makes their lives more, you know,
1:29:27 productive and, and, and helps them. A bunch of folks on the Gemini product and engineering teams
1:29:32 spoken extremely highly of you on another dimension that I almost didn’t even expect. Cause I kind of
1:29:39 think of you as the like deep scientists and caring about these big research scientific questions,
1:29:44 but they also said you’re a great product guy, like how to create a thing that a lot of people would use
1:29:51 and enjoy using. So can you maybe speak to what it takes to create a AI based product that a lot of
1:29:56 people don’t enjoy using? Yeah. Well, I mean, again, that comes back from my game design days where I used
1:30:00 to design games for millions of gamers, people forget about that. I’ve had experience with
1:30:05 cutting edge technology in product that, that, that, that is how games was in the nineties.
1:30:12 And so I love actually the combination of cutting edge research and then being applied in a product
1:30:18 and to power a new experience. And so, um, I think it’s the same skill really of, of, you know,
1:30:24 imagining what it would be like to use it viscerally, um, and having good taste coming back to earlier,
1:30:29 the same thing that’s useful in science, um, I think is, is, can also be useful in,
1:30:35 in product design. And, um, I I’ve just had a very, you know, always being a sort of multidisciplinary
1:30:42 person. So I don’t see, uh, the boundaries really between, you know, arts and sciences or product and
1:30:46 research. It’s, it’s a continuum for me. I mean, I only work on, I like working on products that are
1:30:50 cutting edge. I wouldn’t be able to, you know, have cutting edge technology under the hood. I wouldn’t
1:30:55 be excited about them if they were just run-of-the-mill products. Um, so it requires this
1:30:57 invention creativity capability.
1:31:03 What are some specific things you kind of learned about when you, um, even on the LLM side, you’re
1:31:10 interacting with Gemini, you’re like, this doesn’t feel like the layout, the interface, maybe the trade
1:31:18 opportunity, the latency, like how, how to present to the user, how long to wait and how that waiting
1:31:22 is shown or the reasoning capabilities. There’s some interesting things. Cause like you said, it’s the
1:31:27 very cutting edge. We don’t know how to present it, how to present it correctly. So is there some
1:31:28 specific things you’ve, you’ve learned?
1:31:34 I mean, it’s such a fast evolving space. We’re evaluating this all the time, but where we are
1:31:39 today is that you want to continually simplify things. Um, the, whether that’s the interface
1:31:44 or the interact, what you build on top of the model, you kind of want to get out of the way of the model.
1:31:49 The model train is coming down the track and it’s improving unbelievably fast. This relentless progress
1:31:54 we talked about earlier, you know, you look at 2.5 versus 1.5 and it’s just a gigantic
1:31:59 improvement. And we expect that again for the future versions. And so the models are becoming
1:32:04 more capable. So you’ve got the interesting thing about the design space in, in, in today’s world,
1:32:09 these AI first products is you’ve got to design, not for what the thing can do today, the technology
1:32:15 can do today, but in a year’s time. So you actually have to be a very technical product person
1:32:21 because, uh, you’ve got to kind of have a good intuition for, and feel for, okay, that thing that
1:32:26 I’m dreaming about now can’t be done today, but is the research track on schedule to basically
1:32:31 intercept that in six months or a year’s time. So you kind of got to intercept where this highly
1:32:37 changing technology is going as well as the, um, uh, new capabilities are coming online all the time
1:32:42 that you didn’t realize before that can allow like deep research to work, or now we’ve got video
1:32:48 generation. What do we do with that? Um, this multimodal stuff, you know, is it one question I have is,
1:32:54 is it really going to be the current UI that we have today? These text box chats seems very unlikely
1:33:00 once you think about these super multimodal, uh, uh, systems, shouldn’t it be something more like
1:33:05 minority report where you’re, you’re sort of vibing with it in a, in a coat, in a kind of collaborative
1:33:10 way, right? It seems very restricted to that. I think we’ll look back on today’s interfaces and
1:33:15 products and systems as quite archaic in maybe in a, just a couple of years. So I think there’s a lot of
1:33:21 space actually for innovation to happen on the product side, as well as the research side.
1:33:28 And then we’re offline talking about this keyboard is the open question is how, when, and how much will
1:33:34 we move to audio as the primary way of interacting with the machines around us versus typing stuff?
1:33:39 Yeah. I mean, typing is a very low bandwidth way of doing, even if you’re very fast, you know,
1:33:44 typer. And I think we’re going to have to start utilizing other devices, whether that’s smart glasses,
1:33:52 you know, audio earbuds, um, and eventually maybe some sorts of neural devices where we can increase
1:33:57 the input and the output bandwidth to something, uh, you know, maybe a hundred X of what is today.
1:34:04 I think that, you know, under appreciated art form is the interface design, but I think you can not
1:34:09 unlock the power of the intelligence of a system. If you don’t have the right interface, their interface
1:34:15 is really the way you unlock its power. Yeah. It’s such an interesting question of how to do that.
1:34:20 Yeah. So how you would think like getting out of the way is in real art form.
1:34:24 Yes. You know, it’s the sort of thing that I guess Steve Jobs always talked about, right? It’s
1:34:29 simplicity, beauty, and elegance that we want. Right. And we’re not there. Nobody’s there yet,
1:34:34 in my opinion. And that’s what I would like us to get to. Again, it sort of speaks to like go again,
1:34:38 right. As a game, the most elegant, beautiful game. Can you, you know, that, uh, can you make an
1:34:43 interface as beautiful as that? And actually, I think we’re going to enter an era of AI generated
1:34:48 interfaces that are probably personalized to you. So it fits the way that you, your aesthetic,
1:34:54 your feel, the way that your brain works. And, um, and, and, and the AI kind of generates that
1:34:58 depending on the task, you know, that feels like that’s probably the direction we’ll end up in.
1:35:03 Yeah. Cause some people are power users and they want every single parameter on screen, everything,
1:35:08 everything based, like perhaps me with a keyboard, keyboard based navigation. I like to have shortcuts
1:35:12 for everything. And some people like the minimalism. Just hide all of that complexity. Yeah,
1:35:18 exactly. Yeah. Uh, well, I’m glad you have a Steve Jobs mode in you as well. This is great.
1:35:23 Einstein mode, Steve Jobs mode. Um, all right, let me try to trick you into answering a question.
1:35:29 When, when will Gemini three come out? This is before or after DTS six, the world waits for both.
1:35:37 And what does it take to go from two five to three Oh, because it seems like there’s been a lot of
1:35:42 releases of two five, which are already leaps in performance. So what, what does it even mean to
1:35:48 go to a new version? Is it about performance? Is this about a completely different flavor of an
1:35:55 experience? Yeah. Well, so the way it works with our different, uh, version numbers is we, you know,
1:36:01 we try to collect, so maybe it takes, you know, roughly six months or something to, to do a new
1:36:08 kind of full run and the full productization of a new version. And during that time, lots of new,
1:36:13 interesting research iterations and ideas come up and we sort of collect them all together that,
1:36:18 you know, you could imagine the last six months worth of interesting ideas on the architecture
1:36:23 front. Uh, maybe it’s on the data front. It’s like many different possible things. And we collect,
1:36:28 package that all up, test, which ones are likely to be useful for the next iteration,
1:36:34 and then bundle that all together. And then we start the new, you know, giant hero training run.
1:36:39 Right. And, and then, uh, and then of course that gets monitored. Uh, and then at the end,
1:36:42 then there’s the, of the pre-training, then there’s all the post-training, there’s many different ways
1:36:46 of doing that, different ways of patching it. So there’s a whole experiment and phase there,
1:36:50 which you can also get a lot of gains out. And that’s where you see the version numbers usually
1:36:56 referring to the base model, the pre-trained model, and then the interim versions of 2.5,
1:37:01 you know, and the different sizes and the different little additions, they’re often, uh, patches or
1:37:07 post-training ideas that can be done afterwards, uh, off the same basic architecture. And then of
1:37:12 course, on top of that, we also have different sizes, pro and flash and flashlight that are often
1:37:17 distilled from the biggest ones, you know, the flash model from the pro model. And that means we have a
1:37:23 range of different choices. If you are the developer of, do you want to prioritize performance
1:37:29 or speed, right? And cost. And we like to think of this Pareto frontier of, of, you know, on the one
1:37:35 hand, uh, the Y axis is, you know, like performance. And then the, the, the X axis is, you know, cost or
1:37:43 latency and speed, uh, basically. And we, we have models that completely define the frontier. So whatever
1:37:48 your trade-off is that you want as an individual user, or as a, as a developer, you should find one of
1:37:54 our models satisfies that constraint. So behind diversion changes, there is a big hero run.
1:38:05 Yes. And then there’s, uh, just an insane complexity of productization. Then there’s the distillation of
1:38:11 the different sizes along that Pareto front. And then as each step you take, you realize there might be a
1:38:14 cool product. There’s side quests. Yes, exactly.
1:38:18 But, and then you also don’t want to take too many side quests because then you have a million versions
1:38:22 of a million products. Yes, precisely. It’s very unclear. Yeah. But you also get super excited because
1:38:28 it’s super cool. Yeah. Like how does even you look at videos, very cool. How does it fit into the bigger
1:38:34 thing? Exactly. Exactly. And then you’re constantly, this process of converging upstream, we call it, you
1:38:40 know, ideas from the, from the product surfaces or, or, or from the post training and, and even further
1:38:45 downstream than that, you, you kind of upstream that into the, the core model training for the next
1:38:51 run. Right. So then the main model, the main Gemini track becomes more and more general and eventually,
1:38:52 you know, AGI.
1:38:55 One hero run at a time.
1:38:56 Yes, exactly. A few hero runs later.
1:39:05 Yeah. So sometimes when you release these new versions or every version really, are benchmarks, um,
1:39:09 productive or counterproductive for showing the performance of a model?
1:39:14 You need them. And, and I bet it’s important that you don’t overfit to them, right? So there
1:39:18 shouldn’t be the end with a be all and end all. So there’s, there’s LM arena or used to be called
1:39:23 a LMSS. That’s one of them that turned out sort of organically to be one of the, the main ways people
1:39:28 like to test these systems, at least the chatbots. Um, obviously there’s loads of academic benchmarks on
1:39:33 from, from, from the test, uh, mathematics and coding ability, general language ability,
1:39:38 science ability, and so on. And then we have our own internal benchmarks that we care about.
1:39:43 It’s a kind of multi-objective, you know, optimization problem, right? You want, you don’t want to be
1:39:47 good at just one thing. We’re trying to build general systems that are good across the board
1:39:54 and you try and make no regret, uh, improvements. So where you improve in like, you know, coding,
1:39:59 uh, but it doesn’t reduce your performance in other areas. Right. So that’s the hard part. Cause you,
1:40:04 you can, of course you could put more coding data in, or you could put more, um, I don’t know,
1:40:10 gaming data in, but then does it make worse your language, uh, system or, or, uh, in your
1:40:14 translation systems and other things that you care about. So it’s, you’ve got to kind of continually
1:40:21 monitor this increasingly larger and larger suite of, of benchmarks. And also there’s, uh, when you stick
1:40:27 them into products, these models, you also care about the direct usage and the direct stats and the
1:40:32 signals that you’re getting from the end users, whether they’re coders or, or, or the average
1:40:35 person using, uh, using the chat interfaces. Yeah. Because ultimately you want to measure
1:40:41 the usefulness, but it’s so hard to convert that into a number. Right. It’s, it’s really vibe based
1:40:47 benchmarks across a large number of users. And it’s hard to know. And I, it would be just terrifying to
1:40:54 me to, you know, you have a much smarter model, but it’s just something vibe based. It’s not,
1:40:59 not, not, not quite working. That’s just scary because, and everything you just said, it has to be smart
1:41:06 and useful across so many domains. So you, you get super excited because it’s all of a sudden solving
1:41:11 programming problems. It’d never been able to solve before, but now it’s crappy poetry or something.
1:41:18 And it’s just, I don’t know. That’s a stressful, that’s so difficult, um, to balance and because
1:41:23 you can’t really trust the benchmarks. You really have to trust the end users. Yeah. And then other
1:41:28 things that are even more is a terror come into play. Like, um, you know, the style of the persona
1:41:35 of the, the, the system, you know, how it, you know, is it verbose? Is it succinct? Is it humorous?
1:41:39 You know, and, and different people like different things. So, um, you know, it’s very interesting.
1:41:44 It’s almost like cutting edge part of psychology research or personal personality research. You
1:41:49 know, I used to do that in my PhD, like five factor personality. What do we actually want our
1:41:54 systems to be like? And different people will like different things as well. So these are all just sort
1:41:59 of new problems in product space that I don’t think have ever really been tackled before, but, um,
1:42:03 we’re going to sort of rapidly have to deal with now. I think it’s a super fascinating space
1:42:08 developing the character of the thing. Yeah. And in so doing, it puts a mirror to ourselves.
1:42:14 What are the kinds of things, um, that we like because prompt engineering allows you to control
1:42:22 a lot of those elements, but can the product, uh, make it easier for you to, uh, control the
1:42:26 different flavors of those experiences, the different characters that you interact with? Yeah, exactly.
1:42:31 So, so what’s the probability of Google DeepMind winning? Well, I don’t see it as sort of winning.
1:42:36 I mean, I think we need to think winning is the wrong way to look at it given how important
1:42:41 and consequential what it is we’re building. So funnily enough, I don’t, I try not to view
1:42:46 it like a game or competition, even though that’s a lot of my mindset. It’s, it’s about, in my
1:42:51 view, all of us have those of us at the leading edge, uh, have a responsibility to, um, steward
1:42:56 this unbelievable technology that could be used for incredible good, but also has risks.
1:43:02 Um, steward it safely into the world for the benefit of humanity. That’s always, um, what
1:43:07 I’ve, um, uh, I dreamed about and what we’ve always tried to do. And I hope that’s what
1:43:11 eventually the community, maybe the international community will rally around when it becomes
1:43:16 obvious that as we get closer and closer to, to AGI that, um, that’s what’s needed.
1:43:21 I agree with you. I think that’s beautifully put. You’ve said that, um, you talk to and
1:43:27 are on good terms with the leads of some of these, uh, labs as the competition heats up.
1:43:32 Um, how hard is it to maintain sort of those relationships?
1:43:38 It’s been okay. So if I tried to pride myself in being, uh, collaborative, I’m a collaborative
1:43:42 person. Research is a collaborative endeavor. Science is a collaborative endeavor, right? It’s
1:43:46 all good for humanity. In the end, if you cure incredible, you know, terrible diseases and you
1:43:52 come with an incredible cure, this is net win for humanity. And the same with energy, all of the
1:43:57 things that I’m interested in, in, in, in helping solve with AI. So I just want that technology to
1:44:02 exist in the world and be used for the right things. And, and, and the, the kind of the benefits of
1:44:08 that, the productivity benefits of that being shared for every, the benefit of everyone. So I try to
1:44:12 maintain good relations with all the leading lab people. They’ve very interesting characters, many
1:44:17 of them, as you might expect. Um, but yeah, I’m on good terms. I hope with pretty much all of them.
1:44:23 And, uh, I, I think that’s going to be important when, when things get even more serious than they
1:44:28 are now, uh, that there are those communication channels and, uh, that’s what will facilitate,
1:44:34 uh, cooperation or collaboration. If that’s what is required, especially on things like safety.
1:44:40 Yeah. I hope there’s some collaboration on stuff that’s, uh, sort of less high stakes and in so doing
1:44:45 serves as a mechanism for maintaining friendships and relationships. So for example, I think the internet
1:44:50 would love it if you and Elon somehow collaborate on creating a video game, that kind of thing that I
1:44:56 think that enables camaraderie and good terms and also you two are legit gamers. So it’s just fun to,
1:45:00 yeah, fun to, yeah, that would be awesome. And we’ve talked about that in the past and it may be a cool
1:45:05 thing that, that, you know, we can do. And I agree with you. It’d be nice to have, um, kind of side
1:45:12 projects in a way where, where one can just lean into the collaboration aspect of it. And it’s a sort of,
1:45:18 uh, win-win for both sides. And it’s, um, and it kind of builds up that, that, that, uh, collaborative
1:45:24 muscle. I see the scientific endeavor as that kind of side project for humanity. Yeah. And I think deep
1:45:30 Google deep mind has been really pushing that. Uh, I would love it if to see other labs do more
1:45:34 scientific stuff and then collaborate. Cause it just seems like easier to collaborate on the big
1:45:38 scientific questions. I agree. And I would love to see a lot of people, all of the other labs talk
1:45:42 about science, but I think we’re really the only ones using it for science and doing that.
1:45:47 And that’s why projects like AlphaFold are so important to me. And I think to our mission is to
1:45:54 show, uh, how AI can this, you know, be clearly used in a very concrete way for the benefit of
1:45:59 humanity. And, and also we spun out companies like Isomorphic off the back of AlphaFold to do drug
1:46:03 discovery. And it’s going really well and build sort of, you know, you can think of build additional
1:46:09 AlphaFold type type systems to go into chemistry space to help accelerate drug design. And the
1:46:14 examples I think we need to show, uh, and society needs to understand how well AI can bring these
1:46:20 huge benefits. Well, from the bottom of my heart, thank you for pushing the scientific efforts forward
1:46:25 with, with rigor, with fun, with humility, all of it. I just love to see it. And still talking about
1:46:32 P equals MP, I mean, it’s just incredible. So I love it. Um, there are, there’s been, uh, seemingly a war
1:46:38 for talent. Some of it is meme. I don’t know. Um, what do you think about meta buying up talent with
1:46:45 huge salaries and, and the heating up of this battle for talent? And I should say that I think a lot of
1:46:50 people see DeepMind as a really great place to do, uh, cutting edge work for the reasons that you’ve
1:46:57 outlined is like, there’s this vibrant scientific culture. Yeah. Well, look, I, of course, um, you
1:47:02 know, there’s a strategy that, that meta is taking right now. I think that, um, from my perspective,
1:47:07 at least, I think the people that are real, uh, believers in the mission of AGI and what it can
1:47:12 do and understand the real consequences, both good and bad from that. And what’s what that responsibility
1:47:18 entails. I think they’re mostly doing it to be like myself, to be on the frontier of that research.
1:47:23 So, you know, they can help influence the way that goes and steward that technology safely into the
1:47:28 world. And, you know, meta right now are not at the frontier. Maybe they’ll, they’ll manage to get
1:47:32 back on there. And, um, you know, it’s probably rational what they’re doing from their perspective
1:47:36 because they’re behind and they need to do something. But I think, um, there’s more important
1:47:40 things than, than just money. Of course, one has to pay, you know, people, their market rates and
1:47:45 all of these things, and that continues to go up. Um, but as pro and, and, and I was expecting this
1:47:50 because more and more people are finally realizing leaders of companies, what I’ve always known
1:47:56 for 30 plus years now, which is the AGI is the most important technology probably that’s
1:48:00 ever going to be invented. So in some senses it’s, it’s rational to be doing that. But I
1:48:05 also think there’s a much bigger question. I mean, people in AI these days are very well
1:48:10 paid. You know, I remember when we were starting out back in 2010, you know, I didn’t even pay
1:48:13 myself a couple of years because there wasn’t enough money. We couldn’t raise any money. And
1:48:18 these days interns are being paid, you know, the amount that we raised as our first entire seat
1:48:23 round. So it’s pretty funny. And I remember the days where we used, I used to have to, to work for
1:48:27 free and, and almost pay my own way to do an internship. Right now it’s all the other way
1:48:32 around, but that’s just how it is. It’s the new world. And, um, but I think that, you know,
1:48:36 we’ve been discussing like what happens post AGI and energy systems are solved and so on.
1:48:41 What is even money going to mean? So I think, uh, you know, in the economy and, and we’re going to
1:48:46 have much bigger issues to work through and how does the economy function in that world and companies.
1:48:52 So I think, you know, it’s a little bit of a side issue about, uh, uh, salaries and things of like
1:48:58 that today. Yeah. When you’re facing such gigantic consequences and, and gigantic, fascinating
1:49:03 scientific questions, which may be only a few years away. So, so on the practical sort of pragmatic
1:49:09 sense, uh, if we zoom in on jobs, we can look at programmers because it seems like AI systems are
1:49:15 currently doing incredibly well at programming and increasingly. So, so a lot of people that, uh,
1:49:22 program for a living, love programming are worried they will lose their jobs. How worried should they
1:49:28 be? Do you think? And what’s the right way to, uh, sort of adjust to the new reality and ensure that
1:49:31 you survive and thrive as a human in the programming world?
1:49:36 Well, it’s interesting that programming, and it’s again, counterintuitive to what we thought
1:49:41 years ago, maybe that some of the skills that we think of as harder skills are turned out maybe to be
1:49:46 the easier ones for various reasons, but you know, coding and math, because you can create a lot of
1:49:51 synthetic data and verify if that data is correct. So because of that nature of that, it’s easier to make
1:49:56 things like synthetic data to train from. Um, it’s also an area, of course, we’re all interested in
1:50:01 because as programmers, right, to help us and get faster at it and more productive. So I think the,
1:50:06 for the next era, like the next five, 10 years, I think what we’re going to find is people who are
1:50:12 kind of embrace these technologies become almost at one with them. Um, whether that’s in the creative
1:50:18 industries or the technical industries will become sort of superhumanly productive, I think. So the
1:50:22 great programmers will be even better, but there’ll be even 10x even what they are today. And because
1:50:29 there you’ll be able to use their skills to utilize the tools to the maximum, uh, exploit them to the
1:50:34 maximum. And, um, so I think that’s what we’re going to see in the next domain. Um, so that’s going to
1:50:39 cause quite a lot of change, right? And so that’s coming. A lot of people benefit from that. So I think
1:50:45 one example of that is if coding becomes easier, um, it becomes available to many more creatives
1:50:51 to do more. Uh, and, uh, but I think the top programmers will still have huge advantages as
1:50:56 terms of specifying, going back to specifying what the architecture should be. The question should be
1:51:02 how to guide these, um, uh, coding assistants in a way that’s useful, you know, check whether
1:51:09 the code they produce is good. So I think there’s plenty of, um, uh, headroom there for the foreseeable,
1:51:10 you know, next few years.
1:51:14 So I think there’s, there’s several interesting things there. One is there’s, uh, a lot of
1:51:20 imperative to just get better and better consistently of using these tools. So they are, they’re riding
1:51:27 the wave of the improvement, improving models versus like competing against them. But sadly,
1:51:33 but that’s the, the nature of, of life on earth. Um, there could be a huge amount of value to certain
1:51:40 kinds of programming at the cutting edge and less value to other kinds. For example, it could be like,
1:51:48 you know, front end web design might, uh, be more amenable to, to, to, as, as you mentioned,
1:51:55 to generation, uh, by AI systems and maybe, for example, game engine design or something like this,
1:52:01 or backend design or, or guiding systems in high performance situations, high performance programming
1:52:08 type of design decisions that might be extremely valuable, but it will shift where the humans are
1:52:11 needed most. And that’s scary for people to adjust.
1:52:15 Yeah, I can, I think that’s right. The, the, any time where there’s a lot of disruption and change,
1:52:19 you know, and we’ve had, this is not just this time. We’ve had this in many times in human history with
1:52:26 the internet, uh, mobile, but before that was the industrial revolution. Um, and it’s going to be one
1:52:30 of those eras where there will be a lot of change. I think there’ll be new jobs. We can’t even imagine
1:52:35 today, just like the internet created. And then those people with the right skillsets to
1:52:41 ride that wave will become incredibly valuable, right? Those skills, but maybe people will have
1:52:47 to relearn or adapt a bit, uh, their current skills. And it’s the, the thing that’s going to be harder
1:52:53 to deal with this time around is that I think what we’re going to see is something like probably 10 times
1:52:59 the impact the industrial revolution had and, but 10 times faster as well. Right. So instead of a hundred
1:53:03 years, it takes 10 years. And so that’s going to make it, you know, it’s like a hundred X, uh, the
1:53:09 impact and the speed combined. So that’s, what’s I think going to make it more difficult for society to,
1:53:14 to, to deal with. And it’s good. There’s a lot to think through. And I think we need to be discussing
1:53:20 that right now. And I, I, you know, encourage top economists in the world and philosophers to start
1:53:26 thinking about, um, uh, how should, is society going to be affected by this and what should we do,
1:53:32 including things like, um, you know, uh, universal basic provision or something like that, where a lot
1:53:39 of the, um, increased productivity, uh, gets shared out and distributed, uh, to society. Um, and maybe in
1:53:45 the form of surface services and other things where if you want more than that, you still go and get
1:53:51 some incredibly rare skills and things like that, um, and, and make yourself unique. Um, but, uh, but
1:53:55 there’s a basic provision that is provided. And if you think of government as technology, there’s also
1:54:01 interesting questions, not just in economics, but just politics. How do you design a system that’s
1:54:09 responding to the rapidly changing times such that you can represent the different pain that people feel
1:54:17 from the different groups and how do you reallocate resources in a way that, um, addresses that pain
1:54:23 and represents the hope and the pain and the fears of different people, uh, in a way that doesn’t lead
1:54:31 to division because politicians are often really good at sort of fueling the division and using that to
1:54:38 get elected. The other defining the other and then saying that’s bad. And so based on that,
1:54:45 I think that’s often counterproductive to leveraging a rapidly changing technology, how to help the world
1:54:52 flourish. So we almost, I need to improve our political systems as well rapidly. If you think
1:54:53 of them as a technology.
1:54:59 Definitely. And I think, I think we’ll need new governance structures, institutions probably to
1:55:04 help with this transition. So I think political philosophy and political science is going to be key,
1:55:11 uh, to that. But I think the number one thing, first of all, is to create more abundance of resources,
1:55:16 right? Then there’s the, so that’s the number one thing, increase productivity, get more resources,
1:55:22 maybe eventually get out of the zero sum situation. Then the second question is how to use those resources
1:55:27 and distribute those resources. But yeah, you can’t do that without having that abundance first.
1:55:34 Uh, you mentioned to me, uh, the book, the maniac, uh, by Benjamin, a little bit to a book on,
1:55:40 uh, first of all, about you, there’s a bio about you. Um, it’s strange. Yeah, it’s unclear. Yes,
1:55:47 sure. It’s unclear how much is fiction, how much is reality. Um, but I think the central figure that
1:55:52 that is, uh, John von Neumann, I would say it’s a haunting and beautiful exploration of madness and
1:56:00 genius. And let’s say the double-edged, uh, sword of discovery. And, you know, for, um, people who
1:56:05 don’t know, John von Neumann is a kind of legendary mind. He contributed to quantum mechanics. He was
1:56:11 on the Manhattan project. He is widely considered to be the father of, or pioneered the modern computer
1:56:18 and AI and so on. So as many people say, he is like one of the smartest humans ever. So it’s just
1:56:26 fascinating. And what’s also fascinating as a, as a person who saw nuclear science and physics become
1:56:33 the atomic bomb. So you, you got to see ideas become a thing that has a huge amount of impact on the world.
1:56:41 Um, he also foresaw the same thing for computing. Yeah. He’s he, and that’s the, a little bit again,
1:56:48 beautiful and haunting aspect of the book, uh, then taking a leap forward and looking at this at least
1:56:58 it all alpha zero alpha go alpha zero big moment that maybe John von Neumann’s thinking was brought to,
1:57:04 to, to, to, to, to reality. So I, I guess the question is, um, what do you think if you got to
1:57:09 hang out with John von Neumann now, what would, what would he say about what’s going on?
1:57:14 Well, that would be an amazing experience. You know, he’s a fantastic mind. And I also love the
1:57:19 way he spent a lot of his time at Princeton at the Institute of Advanced Studies is a very special place
1:57:26 for thinking. And, um, it’s amazing how much of a polymath he was in the spread of things he helped
1:57:30 invent, including of course, the von Neumann architecture that all the modern computers are
1:57:36 based on. And, um, he had amazing foresight. I think he would have loved where we are today.
1:57:42 And he would have, um, I think he would have really enjoyed alpha go being a game, he also did game
1:57:48 theory. I think he foresaw a lot of what would happen with learning machines systems that, that,
1:57:52 that, uh, kind of grown, I think he called it rather than programmed. I’m not sure how even,
1:57:57 maybe he wouldn’t even be that surprised. There’s the fruition of what I think he already foresaw
1:58:03 in the 1950s. I wonder what advice he would give. He got to see the building of the atomic bomb with
1:58:07 the Manhattan project. I’m sure there’s interesting stuff that maybe is not talked about enough. Maybe
1:58:13 some bureaucratic aspect, maybe the influence of politicians, maybe, maybe not enough of picking up
1:58:19 the phone and talking to people that are called enemies by the said politicians. There might be
1:58:22 some like deep wisdom that we just may have lost from that time actually.
1:58:27 Yeah, I’m sure. I’m sure there is. I mean, I’ve, you know, studied, I read a lot of books for that
1:58:33 time as well, Chronicle Time, um, and some brilliant people involved. I agree with you. I think maybe there
1:58:39 needs to be more dialogue and understanding. Um, I hope we can learn from those, those times. I think
1:58:44 the difference here is that the AI has so many, it’s a multi-use technology. Obviously we’re trying to do
1:58:52 things like that, like solve, you know, all diseases, um, uh, help with energy, uh, and scarcity. These
1:58:57 incredible things. This is why all of us and myself, you know, I worked, started on this journey 30 plus
1:59:04 years ago. And, um, but of course there are risks too. And probably von Neumann, my guess is he foresaw
1:59:10 both. And, um, and I think he sort of said, I think it is to his wife that, that, that it would be,
1:59:16 this is computers would be even more impactful in the world. And as we just discussed, you know,
1:59:20 I think that’s right. I think it’s going to be 10 times at least of the industrial revolution.
1:59:26 So I think he’s right. So I think he would have been, I imagine fascinated by, uh, uh, where we are
1:59:33 now. And I think one of the, maybe you can correct me, but one of the takeaways from the book is that
1:59:41 reason has, uh, said in the book, mad dreams of reason, it’s not enough for guiding humanity as we
1:59:46 build these super powerful technology that there’s something else. I mean, there’s also like a religious
1:59:52 component, whatever God, whatever religion gives, it pulls at something in the human spirit that
1:59:56 raw, cold reason doesn’t give us.
2:00:00 And I agree with that. I think we need to approach it with whatever you want to call it, the,
2:00:05 a spiritual dimension or humanist dimension doesn’t have to be to do with religion, right? But this
2:00:10 idea of, of a soul, what makes us human, this spark that we have, perhaps it’s to do with consciousness
2:00:16 when we finally understand that. Um, I think that has to be at the heart of the endeavor. Um, and
2:00:21 technology, I’ve always seen technology as the enabler, right? The tools that enable us to,
2:00:27 to flourish and to understand more about the world. And I, I’m sort of with Feynman on this,
2:00:33 and he used to always talk about science and art being companions, right? You can understand it from
2:00:38 both sides, the beauty of a flower, how beautiful it is, and also understand why the colors of the
2:00:42 flower evolved like that, right? That just makes it more beautiful that, that, that just the intrinsic
2:00:47 beauty of the flower. And, and I’ve always sort of seen it like that. And maybe, you know, in the
2:00:51 Renaissance times, the great discoverers then like people like Da Vinci, you know, they were,
2:00:56 I don’t think he saw any difference between science and art, uh, and perhaps religion,
2:01:01 right? They were, everything was, it’s just part of being human and, um, being inspired about the
2:01:06 world around us. And that’s what I, the philosophy I tried to take. And, um, one of my favorite
2:01:11 philosophers is Spinoza. And I think he combined that all very well, you know, this idea of trying
2:01:16 to understand the universe and understanding our place in it. And that was his kind of way of
2:01:21 understanding religion. And I think that’s quite beautiful. And for me, every, all of these things
2:01:28 are related, interrelated, the technology and, um, what it means to be human. And, uh, I think it’s
2:01:35 very important though, that we remember that as when we’re immersed in the technology and the research,
2:01:41 I think a lot of researchers that I see in, in our field are a little bit too narrow and only
2:01:47 understand the technology. And I think also that’s why it’s important for this to be debated by society
2:01:52 at large. And I’m very supportive of things like this, the AI summits that will happen and governments
2:01:57 understanding it. And I think that’s one good thing about the chatbot era and the product era of AI is
2:02:03 that everyday person can actually feel and interact with cutting edge AI and, and, and feel, feel it for
2:02:07 themselves. Yeah. Because they, they force the technologists to have the human conversation. Yeah,
2:02:13 that’s the hopeful aspect of it. Like you said, it’s a dual use technology that we’re forcefully
2:02:19 integrating the entire humanity into it by, into the discussion about AI, because ultimately AI, AGI
2:02:27 will be used for things that states use technologies for, which is a conflict and so on. And the more
2:02:35 we, uh, integrate humans into this picture by having chats with them, the more we will guide.
2:02:39 Yeah. Be able to adapt society will be able to adapt to these technologies like we’ve always done
2:02:44 in the past with, with, uh, the incredible technologies we’ve invented in the past.
2:02:53 Do you think there will be something like a Manhattan project where, um, there will be an escalation of
2:02:58 the power of this technology and states in their old way of thinking, we’ll try to use it as weapons
2:03:00 technologies. And there will be this kind of escalation.
2:03:09 I hope not. Um, I think that would be, uh, very dangerous to do. And I think also, um, you know,
2:03:13 not the right use of the technology. I hope we’ll end up with more, something more collaborative
2:03:21 if needed, like more like a, like a CERN project, you know, where, um, it’s research focused and the
2:03:28 best minds in the world come together to carefully complete the final steps and make sure it’s
2:03:34 responsibly done before, you know, like deploying it to the world. We’ll see. I mean, it’s difficult
2:03:39 with the current geopolitical climate, I think, uh, to, to see cooperation, but things can change.
2:03:44 And, um, I think at least on the scientific level, it’s important for the researchers to,
2:03:50 to, to, to keep in touch and, and, and keep close to each other on, at least on those kinds of topics.
2:03:55 Yeah. And I personally believe on the education side and, um, immigration side, it would be great if
2:04:02 both directions, uh, people from the West immigrated China and China back. I mean, there is some like
2:04:09 family human aspect of people just intermixing. Yeah. And thereby those ties grow strong. So you
2:04:15 can’t sort of divide against each other at this kind of old school way of thinking. And so, uh, multi,
2:04:20 uh, multicultural, multidisciplinary research teams working on scientific questions. That’s like the
2:04:26 hope. Don’t, don’t let the, the warm leaders that are warmongers divide us. I think science is the
2:04:31 ultimately really beautiful connector. Yeah. Science has always been, uh, I think quite a,
2:04:36 a very collaborative endeavor and, you know, scientists know that it’s, it’s a, it’s a collective
2:04:41 endeavor as well, and we can all learn from each other. So perhaps it could be a vector to get a bit
2:04:45 of cooperation. What’s your, uh, ridiculous question. What’s your P doom probability of the
2:04:52 human civilization destroys itself. Well, look, I, I don’t have a, it’s a, you know, I don’t have a
2:04:58 P doom number. The reason I don’t is because I think it’s would imply a level of precision that
2:05:03 is not there. So like, I don’t know how people are getting their P doom numbers. I think it’s a kind of
2:05:10 a little bit of a ridiculous notion because, um, what I would say is it’s definitely non-zero
2:05:17 and it’s probably non-negligible. So that in itself is pretty sobering. And my, my view is it’s just
2:05:22 hugely uncertain, right? What these technologies are going to be able to do, how fast are they going to
2:05:26 take off, how controllable they’re going to be. Some things may turn out to be, and hopefully
2:05:33 like way easier than we thought. Right. Um, but it may be, there’s some really hard, um, uh, problems
2:05:39 that are harder than we guess today. And I think, uh, we don’t know that for sure. And so in, under those
2:05:45 conditions of a lot of uncertainty, but huge stakes both ways, you know, on the one hand, we could solve
2:05:51 all diseases, energy problems, the, not the, the, the, the scarcity problem, and then travel to the stars and
2:05:55 consciousness of the stars and maximum human flourishing. On the other hand, is this sort of
2:06:00 P doom scenarios. So given the uncertainty around it and the importance of it, it’s clear to me,
2:06:06 the only rational, sensible approach is to proceed with cautious optimism. So we want the outcome. We
2:06:13 want the, um, uh, the benefits of course, uh, and, uh, all of the, the amazing things that AI can bring.
2:06:19 And actually I would be really worried for humanity. If I, if given the other challenges that we have
2:06:25 climate disease, you know, aging, uh, resources, all of that, if I didn’t know something like AI was
2:06:30 coming down the line, right. How would we solve all those other problems? I think it’s hard. Um,
2:06:35 so I think we’ve, you know, it could be amazingly transformative for good. Um, but on the other hand,
2:06:41 you know, there are these risks that we know are there, but we can’t quite quantify. So the,
2:06:47 the best thing to do is to use the scientific method to do more research, to try and, uh, more
2:06:53 precisely define those risks and of course, address them. Um, and I think that’s what we’re doing. I
2:06:59 think there probably needs to be, uh, 10 times more effort of that than there is now as we’re getting
2:07:03 closer and closer to the, to the, to the AGI line. What would be the source of worry for you more?
2:07:09 Would it be human caused or AI, AGI caused? Yeah.
2:07:13 Humans abusing that technology versus AGI itself through mechanism that you’ve spoken about,
2:07:18 which is fascinating deception or this kind of stuff getting better and better and better
2:07:22 secretly. And then I think they’re, they’re, they operate over different timescales and they’re
2:07:27 equally important to address. So there’s just the, the, the, the common garden or variety of like,
2:07:32 you know, bad actors using new technology, uh, in this case, general purpose technology and
2:07:39 repurposing it for harmful ends. And that’s a huge risk. And I think that has a lot of complications
2:07:45 because generally, you know, I’m in huge favor of open science and open source. And in fact,
2:07:49 we did it with all our science projects like alpha fold and all of those things, uh, for the benefit of,
2:07:56 of, of the scientific community. Um, but how does one restrict bad actors access to these powerful
2:08:03 systems, whether they’re individuals or even rogue states, uh, and, but enable access at the same time
2:08:08 to good actors to, to maximally build on top of, it’s pretty tricky problem that there’s, I’ve not
2:08:13 heard a clear solution to. So there’s the bad actor use case problem. And then there’s obviously,
2:08:19 uh, as the systems become more agentic and closer to AGI, um, uh, and more autonomous,
2:08:25 how do we ensure the guardrails and they stick to what we want them to do, uh, and under our control.
2:08:33 Yeah. I tend to, maybe on my mind is limited, worry more about the humans, the bad actors and there it
2:08:39 could be, uh, in part, how do you not put destructive technology in the hands of bad actors, but in
2:08:44 another part from, again, geopolitical technology perspective, how do you reduce the number of bad
2:08:50 actors in the world? That’s, that’s also interesting human problem. Yeah. It’s a hard problem. I mean,
2:08:57 look, we, we, we can, um, maybe also use the technology itself to help, um, uh, uh, early
2:09:04 warning on some of the bad actor use cases, right? Whether that’s bio or nuclear or whatever it is,
2:09:09 like AI could be potentially helpful there, as long as the AI that you’re using is itself reliable,
2:09:14 right? So it’s a sort of interlocking problem and that’s what makes it very tricky. And, and again,
2:09:20 it may require some agreement internationally, at least between China and the U and the U S of,
2:09:26 of, of, of some, uh, basic standards. Right. Uh, I have to ask you about the, uh, the book,
2:09:31 the maniac there, there’s this, the, the hand of God moment, at least it all’s move 78,
2:09:41 that perhaps the last time a human did a move of sort of pure human genius and beat AlphaGo or like
2:09:46 broke its brain. Yes. If sorry to anthropomorphize, but it’s an interesting moment because I think in
2:09:50 so many domains, it will keep happening. Yeah. It’s a special moment. And, you know,
2:09:55 it was great for Lisa doll. And, you know, I think it’s in a way they were sort of inspiring each other.
2:10:02 We as a team were inspired by Lisa doll’s brilliance and nobleness. And then maybe he got inspired by,
2:10:08 you know, what AlphaGo was doing to then conjure this incredible inspirational moment. It’s all,
2:10:13 you know, captured very well in the, in the documentary about it. And, um, I think that’ll
2:10:17 continue in many domains where there’s this, at least for the, for the, again, for the foreseeable,
2:10:24 future of like the humans bringing in their ingenuity, um, and asking the right question,
2:10:31 let’s say, uh, and then utilizing these tools, uh, in a way that, um, then cracks a problem.
2:10:37 Yeah. What is the AI becomes smarter and smarter? One of the interesting questions we can ask ourselves
2:10:44 is what makes humans special? It does feel perhaps biased that we humans are deeply special.
2:10:51 I don’t know if it’s our intelligence. It could be something else that, that other thing that’s
2:10:58 outside the mad dreams of reason. I think that’s what I’ve always imagined. Uh, when I was a kid and
2:11:04 starting on this journey of like, um, I was fascinated by things like consciousness, did, did a neuroscience
2:11:09 PhD to look at how the brain works, especially imagination and memory. I focused on the hippocampus.
2:11:12 And it’s sort of going to be interesting. I always thought the best way, of course, one can,
2:11:17 kind of philosophize about it and have thought experiments and maybe even do actual experiments
2:11:23 like you do in neuroscience on, on real brains. But in the end, I always imagined that building
2:11:28 AI, a kind of intelligent artifact, and then comparing that to the human mind and seeing what the differences
2:11:33 were, uh, would be the best way to uncover what’s special about the human mind. If indeed there is
2:11:38 anything special. And I suspect there probably is, but it’s going to be hard to, you know,
2:11:42 I think this journey we’re on will help us, uh, understand that and define that. And, you know,
2:11:48 there may be a difference between carbon based substrates that we are and silicon ones when they
2:11:53 process information. You know, one of the best definitions I like of, of, of consciousness is it’s
2:11:58 the way information feels when we process it, right? Um, it could be, I mean, it doesn’t have,
2:12:02 it’s not very helpful scientific explanation, but I think it’s kind of interesting intuition, intuitive
2:12:07 one. And, um, and so, you know, on this, this, this journey, this scientific journey we’re on will,
2:12:10 I think, um, help uncover that mystery.
2:12:15 Yeah. What I cannot create. I do not understand. That’s a, somebody you deeply admire, Richard Feynman,
2:12:23 like you mentioned, you also reach, um, for the, the Wigner’s dreams of universality that he saw in
2:12:29 constrained domains, but also broadly generally in, in mathematics and so on. So, so many aspects on
2:12:34 which you’re pushing towards not to start trouble at the end, but, uh, Roger Penrose.
2:12:35 Yes. Okay.
2:12:41 So, uh, you know, do you, do you think consciousness does this hard problem of
2:12:49 consciousness, how information feels? Um, do you think consciousness, first of all, is a computation?
2:12:55 And if it is, if it’s information processing, like you said, everything is, is it something that
2:12:57 could be modeled by a classical computer? Yeah.
2:12:59 Or is it a quantum mechanical in nature?
2:13:04 Well, look, Penrose is an amazing thinker, one of the greatest of the modern era. And he,
2:13:08 we’ve had a lot of discussions about this. Of course, we cordially disagree, which is,
2:13:12 you know, I, I feel like, um, I mean, he collaborated with a lot of good neuroscientists
2:13:18 to see if he could find mechanisms for quantum mechanics behavior in the brain. And they, to my
2:13:23 knowledge, they haven’t found anything, um, convincing yet. So my betting is there is,
2:13:29 is that it’s mostly, you know, it is just classical computing that’s going on in the brain, which suggests
2:13:35 that all the phenomena, uh, are modelable or mimicable by a classical computer. But we’ll see,
2:13:41 you know, that there may be this final mysterious things of the feeling of consciousness, the qualia,
2:13:47 these kinds of things that philosophers debate, where it’s unique to the substrate. We may even
2:13:52 come towards understanding that when, if we do things like neural link and, and, uh, have neural
2:13:57 interfaces to the AI systems, which I think we probably will eventually, um, maybe to keep up
2:14:02 with the AI systems, uh, we might actually be able to feel for ourselves what it’s like to compute
2:14:09 on silicon. Right. So, um, and maybe that will tell us. Uh, so I think it’s, it’s going to be
2:14:14 interesting. And I had a debate once with the late Daniel Dennett about why do we think each
2:14:19 other are conscious? Okay. So it’s for two reasons. One is you’re exhibiting the same behavior that I am.
2:14:24 So that’s one thing behaviorally, you seem like a conscious being if I am. But the second thing,
2:14:28 which is often overlooked is that we’re running on the same substrate. So if you’re behaving in the same
2:14:33 way and we’re running on the same substrate, it’s most parsimonious to assume you’re feeling the same
2:14:39 experience that I’m feeling, but with an AI, uh, that’s on silicon, we won’t be able to rely on the
2:14:43 second part, even if it exhibits the first part, that behavior looks like a behavior of a conscious
2:14:50 being. It might even claim it is. Um, but we, but, but we wouldn’t know how it actually felt. Um,
2:14:55 and it probably couldn’t know we, what we felt at least in the first stages, maybe when we get to
2:14:59 super intelligence and the technologies that builds, perhaps we’ll, we’ll be able to, um,
2:15:05 bridge that. No, I mean, that’s a huge test for radical empathy is to empathize with a different
2:15:11 substrate, right? Exactly. We never had to confront that before. Yeah. So maybe, maybe through brain
2:15:16 computer interfaces, be able to truly empathize what it feels like to be a computer.
2:15:20 Well, for information to be computed, not on a carbon system.
2:15:24 I mean, that’s deeply exciting. I mean, some people kind of think about that with plants,
2:15:30 with other life forms, which is different, similar substrate, but sufficiently far enough
2:15:36 on the, uh, evolutionary tree that it’s requires a radical empathy, but to do that with a computer.
2:15:40 I mean, no, we sort of, there are animal studies on this of like, of course, higher animals,
2:15:46 like, you know, killer whales and dolphins and dogs and, and monkeys, you know, they have some,
2:15:50 and elephants, you know, they have some aspects certainly of consciousness, right? Even though
2:15:54 they’re not, might not be that, that, that smart on an IQ sense. So, so we can already empathize with
2:15:59 that. And maybe even some of our systems one day, like we built this thing called dolphin Gemma,
2:16:04 you know, which can, one, a version of our system was trained on dolphin and whale sounds. And maybe
2:16:08 we’ll be able to build a, an interpreter or translator at some point, which would be pretty cool.
2:16:11 What gives you hope for the future of human civilization?
2:16:19 Well, what gives me hope is I think our almost limitless ingenuity. First of all, I think the
2:16:26 best of us and the best human minds are incredible. Um, and you know, I love, you know, meeting and
2:16:32 watching any human that’s the top of their game, whether that’s sport or science or art, you know,
2:16:36 it’s, it’s, it’s just nothing more wonderful than that. Seeing them in their element in flow.
2:16:42 Um, I think it’s almost limitless, you know, our brains are general systems, intelligent systems.
2:16:45 So I think it’s almost limitless what we can potentially do with them. And then the other
2:16:52 thing is our extreme adaptability. I think it’s gonna be okay in terms of, there’s gonna be a lot of
2:16:58 change, but that, but look where we are now without effectively our hunter gatherer brains. How is it we
2:17:05 can, you know, we can cope with the modern world, right? Flying on planes, doing podcasts,
2:17:09 you know, playing computer games and virtual simulations. I mean, it’s already mind blowing,
2:17:14 given that our mind was, was developed for, you know, hunting buffaloes on the, on the tundra.
2:17:20 And, and so I think this is just the next step. And, and, and it’s actually kind of interesting to see how
2:17:25 society is already adapted to this mind blowing AI technology we have today already. It’s sort of like,
2:17:28 Oh, I talk to chat bots totally fine.
2:17:33 And it’s very possible that this very podcast activity, which I’m here for will be completely
2:17:36 replaced by AI. I’m very replaceable and I’m waiting for it.
2:17:38 Not to the level that you can do it, Lex, I don’t think.
2:17:41 All right. Thank you. That’s, that’s what we humans do to each other. We compliment.
2:17:47 All right. And, uh, I’m, uh, deeply grateful for us humans to have this, uh, infinite capacity for
2:17:52 curiosity, adaptability, like you said, and also compassion and ability to love.
2:17:54 Exactly. All of those, all the things that are deeply human.
2:18:00 Well, this is a huge honor, Demis. You’re one of the truly special humans in the world. Uh,
2:18:03 thank you so much for doing what you do and for talking today.
2:18:04 Well, thank you very much, Lex.
2:18:10 Thanks for listening to this conversation with Demis Kasabas. To support this podcast,
2:18:15 please check out our sponsors in the description and consider subscribing to this channel.
2:18:21 And now let me answer some questions and try to articulate some things I’ve been thinking about.
2:18:28 If you’d like to submit questions, including in audio and video form, go to lexfreeman.com/ama.
2:18:34 I got a lot of amazing questions, thoughts, and requests from folks. I’ll keep trying to pick
2:18:39 some, uh, randomly and comment on it at the end of every episode.
2:18:47 I got a note on May 21st this year that said, “Hi Lex, 20 years ago today, David Foster Wallace
2:18:54 delivered his famous, this is water speech at Kenyan College. What do you think of this speech?”
2:19:03 Well, first, I think this is probably one of the greatest and most unique commencement speeches
2:19:08 ever given. But of course I have many favorites, including the one by Steve Jobs.
2:19:14 And David Foster Wallace is one of my favorite writers and one of my favorite humans.
2:19:23 There’s a tragic honesty to his work and it always felt as if he was engaging in a constant battle with
2:19:31 his own mind. And the writing, his writing, were kind of his notes from the front lines of that battle.
2:19:39 Now onto the speech, let me quote some parts. There’s of course the parable of the fish and the water
2:19:46 that goes, “There are these two young fish swimming along and they happen to meet an older fish swimming
2:19:55 the other way, who nods at them and says, ‘Morning boys, how’s the water?’ And the two young fish swim on for
2:20:01 a bit, and then eventually one of them looks over at the other and goes, ‘What the hell is water?’
2:20:09 In the speech, David Foster Wallace goes on to say, “The point of the fish story is merely that the
2:20:16 most obvious, important realities are often the ones that are hardest to see and talk about.” Stated as an
2:20:22 English sentence, of course, this is just a banal platitude. But the fact is that in the day-to-day
2:20:28 trenches of adult existence, banal platitudes can have a life or death importance, or so I wish to
2:20:35 suggest to you in this dry and lovely morning. I have several takeaways from this parable and the
2:20:41 speech that follows. First, I think we must question everything, and in particular, the most basic
2:20:49 assumptions about our reality, our life, and the very nature of existence. And that this project is a
2:20:56 deeply personal one. In some fundamental sense, nobody can really help you in this process of discovery.
2:21:05 The call to action here, I think, from David Foster Wallace, as he puts it, is to “to be just a little
2:21:13 less arrogant, to have just a little more critical awareness about myself and my certainties.” Because a
2:21:19 huge percentage of the stuff that I tend to be automatically certain of is, it turns out, totally
2:21:28 wrong, and deluded. All right, back to me, Lex speaking. The second takeaway is that the central
2:21:36 spiritual battles of our life are not fought on a mountaintop somewhere at a meditation retreat,
2:21:45 but it is fought in the mundane moments of daily life. Third takeaway is that we too easily give
2:21:53 away our time and attention to the multitude of distractions that the world feeds us, the insatiable
2:22:02 black holes of attention. David Foster Wallace’s call to action in this case is to be deeply aware of the
2:22:10 the beauty in each moment and to find meaning in the mundane. I often quote David Foster Wallace in his
2:22:18 advice that the key to life is to be unboreable. And I think this is exactly right. Every moment,
2:22:26 every object, every experience, when looked at closely enough, contains within it infinite richness to
2:22:34 explore. And since Demis Khasabas of this very podcast episode and I are such fans of Richard Feynman,
2:22:42 allow me to also quote Mr. Feynman on this topic as well. Quote, “I have a friend who’s an artist
2:22:49 and has sometimes taken a view which I don’t agree with very well. He’ll hold up a flower and say,
2:22:58 look how beautiful it is. And I’ll agree.” Then he says, “I as an artist can see how beautiful this is,
2:23:06 but you as a scientist take this all apart and it becomes a dull thing.” And I think that’s kind of nutty.
2:23:15 First of all, the beauty that he sees is available to other people and to me too, I believe. Although I
2:23:22 may not be quite as refined aesthetically as he is, I can appreciate the beauty of a flower. At the same
2:23:29 time, I see much more about the flower than he sees. I can imagine the cells in there, the complicated
2:23:36 actions inside which also have beauty. I mean, it’s not just beauty at this dimension, at one centimeter.
2:23:42 There’s also beauty at the smaller dimensions, the inner structure, also the processes. The fact that
2:23:49 the colors in the flower evolved in order to attract insects to pollinate it is interesting. It means that
2:23:56 the insects can see the color. It adds a question: does this aesthetic sense also exist in lower forms?
2:24:02 Why is it aesthetic? All kinds of interesting questions which the science knowledge only adds
2:24:08 to the excitement, the mystery, and the awe of a flower. It only adds.
2:24:18 All right, back to David Foster Wallace’s speech. He has a great story in there that I particularly enjoy.
2:24:25 It goes, “There are these two guys sitting together in a bar in the remote Alaskan wilderness.
2:24:31 One of the guys is religious. The other is an atheist. And the two are arguing about
2:24:36 the existence of God with that special intensity that comes after about the fourth beer. And the
2:24:42 atheist says, “Look, it’s not like I don’t have actual reasons for not believing in God. It’s not
2:24:50 like I haven’t ever experimented with the whole God and prayer thing. Just last month, I got caught away
2:24:57 from the camp in that terrible blizzard. And I was totally lost. And I couldn’t see a thing. And it was
2:25:04 50 below. And so I tried it. I fell to my knees in the snow and cried out, “Oh God, if there is a God,
2:25:10 I’m lost in this blizzard. And I’m going to die if you don’t help me.” And now back in the bar, the religious
2:25:17 guy looks at the atheist all puzzled. “Well, then you must believe now,” he says. “After all,
2:25:24 there you are, alive.” The atheist just rolls his eyes. “No, man. All that happened was a couple of
2:25:29 Eskimos happened to be wandering by and show me the way back to the camp.”
2:25:37 All this, I think, teaches us that everything is a matter of perspective. And that wisdom may arrive
2:25:43 if we have the humility to keep shifting and expanding our perspective on the world.
2:25:49 Thank you for allowing me to talk a bit about David Foster Wallace. He’s one of my favorite
2:25:56 writers and he’s a beautiful soul. If I may, one more thing I wanted to briefly comment on.
2:26:04 I find myself to be in this strange position of getting attacked online often from all sides,
2:26:10 including being lied about sometimes through selective misrepresentation, but often through
2:26:17 downright lies. I don’t know how else to put it. This all breaks my heart, frankly. But I’ve come to
2:26:23 understand that it’s the way of the internet and the cost of the path I’ve chosen. There’s been days when it’s
2:26:31 been rough on me mentally. It’s not fun being lied about, especially when it’s about things that are
2:26:38 usually, for a long time, have been a source of happiness and joy for me. But again, that’s life.
2:26:45 I’ll continue exploring the world of people and ideas with empathy and rigor, wearing my heart on my sleeve,
2:26:54 as much as I can. For me, that’s the only way to live. Anyway, a common attack on me is about my time
2:27:02 at MIT and Drexel, two great universities I love and have tremendous respect for. Since a bunch of lies
2:27:09 have accumulated online about me on these topics, to a sad and at times hilarious degree, I thought I
2:27:14 would once more state the obvious facts about my bio for the small number of you who may care.
2:27:23 TLGR, two things. First, as I say often, including in a recent podcast episode that somehow was listened
2:27:30 to by many millions of people, I proudly went to Drexel University for my bachelor’s, master’s, and
2:27:39 doctorate degrees. Second, I am a research scientist at MIT and have been there in a paid research position
2:27:46 for the last 10 years. Allow me to elaborate a bit more on these two things now, but please skip
2:27:53 if this is not at all interesting. So like I said, a common attack on me is that I have no real affiliation
2:28:02 with MIT. The accusation, I guess, is that I’m falsely claiming an MIT affiliation because I taught a lecture
2:28:12 there once. Nope, that accusation against me is a complete lie. I have been at MIT for over 10 years
2:28:23 in a paid research position from 2015 to today. To be extra clear, I’m a research scientist at MIT working
2:28:31 in LIDS, the Laboratory for Information and Decision Systems in the College of Computing. For now, since
2:28:41 I’m still at MIT, you can see me in the directory and on the various lab pages. I have indeed given many
2:28:48 lectures at MIT over the years, a small fraction of which I posted online. Teaching for me always has
2:28:55 been just for fun and not part of my research work. I personally think I suck at it, but I have always
2:29:02 learned and grown from the experience. It’s like Feynman spoke about, if you want to understand something
2:29:10 deeply, it’s good to try to teach it. But like I said, my main focus has always been on research. I published
2:29:18 many peer-reviewed papers that you can see in my Google Scholar profile. For my first four years at MIT,
2:29:26 I worked extremely intensively. Most weeks were 80 to 100 hour work weeks. After that, in 2019,
2:29:32 I still kept my research scientist position, but I split my time taking a leap to pursue projects in AI
2:29:39 and robotics outside MIT, and to dedicate a lot of focus to the podcast. As I’ve said, I’ve been continuously
2:29:45 surprised just how many hours preparing for an episode takes. There are many episodes of the podcast for
2:29:52 which I have to read, write, and think for 100, 200 or more hours across multiple weeks and months.
2:30:01 Since 2020, I have not actively published research papers. Just like the podcast, I think it’s something
2:30:08 that’s a serious full-time effort. But not publishing and doing full-time research has been eating at me.
2:30:15 Because I love research. And I love programming and building systems that test out interesting technical
2:30:23 ideas. Especially in the context of human AI or human robot interaction. I hope to change this in the coming
2:30:31 months and years. What I’ve come to realize about myself is, if I don’t publish or if I don’t launch
2:30:37 systems that people use, I definitely feel like a piece of me is missing. It legitimately is a source
2:30:46 of happiness for me. Anyway, I’m proud of my time at MIT. I was and am constantly surrounded by people much
2:30:54 smarter than me, many of whom have become lifelong colleagues and friends. MIT is a place I go to
2:30:59 escape the world, to focus on exploring fascinating questions at the cutting edge of science and
2:31:08 engineering. This, again, makes me truly happy. And it does hit pretty hard on a psychological level when
2:31:16 I’m getting attacked over this. Perhaps I’m doing something wrong. If I am, I will try to do better.
2:31:24 In all this discussion of academic work, I hope you know that I don’t ever mean to say that I’m an
2:31:32 expert at anything. In the podcast and in my private life, I don’t claim to be smart. In fact, I often call
2:31:39 myself an idiot and mean it. I try to make fun of myself as much as possible and, in general, to
2:31:49 celebrate others instead. Now, to talk about Drexel University, which I also love, am proud of, and am
2:31:55 deeply grateful for my time there. As I said, I went to Drexel for my bachelor’s, master’s, and doctorate
2:32:01 degrees in computer science and electrical engineering. I’ve talked about Drexel many
2:32:08 times, including, as I mentioned, at the end of a recent podcast, the Donald Trump episode, funny
2:32:14 enough, that was listened to by many millions of people, where I answered a question about graduate
2:32:21 school and explained my own journey at Drexel and how grateful I am for it. If it’s at all interesting to
2:32:28 you, please go listen to the end of that episode or watch the related clip. At Drexel, I met and worked
2:32:34 with many brilliant researchers and mentors from whom I’ve learned a lot about engineering, science,
2:32:40 and life. There are many valuable things I gained from my time at Drexel. First, I took a large number
2:32:46 of very difficult math and theoretical computer science courses. They taught me how to think deeply and
2:32:52 rigorously, and also how to work hard and not give up, even if it feels like I’m too dumb to find a
2:33:00 solution to a technical problem. Second, I programmed a lot during that time, mostly C, C++. I programmed
2:33:06 robots, optimization algorithms, computer vision systems, wireless network protocols, multimodal
2:33:15 machine learning systems, and all kinds of simulations of physical systems. This is where I really develop a love
2:33:21 for programming, including, yes, Emacs, and the Kinesis keyboard.
2:33:29 I also during that time, read a lot. I played a lot of guitar, wrote a lot of crappy poetry,
2:33:39 and trained a lot in judo and jiu-jitsu, which I cannot sing enough praises to. Jiu-jitsu humbled me
2:33:45 on a daily basis throughout my 20s, and it still does, to this very day, whenever I get a chance to train.
2:33:52 Anyway, I hope that the folks who occasionally get swept up in the chanting online crowds that want to tear
2:34:00 down others don’t lose themselves in it too much. In the end, I still think there’s more good than bad
2:34:11 in people. But we’re all, each of us, a mixed bag. I know I am very much flawed. I speak awkwardly. I
2:34:17 sometimes say stupid shit. I can get irrationally emotional. I can be too much of a dick when I
2:34:23 should be kind. I can lose myself in a biased rabbit hole before I wake up to the bigger,
2:34:33 more accurate picture of reality. I’m human. And so are you. For better or for worse. And I do still
2:34:39 believe we’re in this whole beautiful mess together. I love you all.
2:34:55 *music*