Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475

It's hard for us humans to make any kind It's hard for us humans to make any kind of clean predictions about highly of clean predictions about highly of clean predictions about highly nonlinear dynamical systems. But again nonlinear dynamical systems. But again nonlinear dynamical systems. But again to your point, we might be very to your point, we might be very to your point, we might be very surprised what classical learning surprised what classical learning surprised what classical learning systems might be able to do about even systems might be able to do about even systems might be able to do about even fluid. fluid. fluid. >> Yes, exactly. I mean fluid dynamics, >> Yes, exactly. I mean fluid dynamics, >> Yes, exactly. I mean fluid dynamics, Navia Stokes equations, these are Navia Stokes equations, these are Navia Stokes equations, these are traditionally thought of as very very traditionally thought of as very very traditionally thought of as very very difficult intractable problems to do on difficult intractable problems to do on difficult intractable problems to do on classical systems. They take enormous classical systems. They take enormous classical systems. They take enormous amounts of compute, you know, weather amounts of compute, you know, weather amounts of compute, you know, weather prediction systems, you know, these kind prediction systems, you know, these kind prediction systems, you know, these kind of things all involve fluid dynamics of things all involve fluid dynamics of things all involve fluid dynamics calculations. But again, if you look at calculations. But again, if you look at calculations. But again, if you look at something like VO, our video generation something like VO, our video generation something like VO, our video generation model, it can model liquids quite well, model, it can model liquids quite well, model, it can model liquids quite well, surprisingly well, and materials, surprisingly well, and materials, surprisingly well, and materials, specular lighting. I love the ones specular lighting. I love the ones specular lighting. I love the ones where, you know, there's there's people where, you know, there's there's people where, you know, there's there's people have generated videos where there's like have generated videos where there's like have generated videos where there's like clear liquids going through hydraulic clear liquids going through hydraulic clear liquids going through hydraulic presses and then being squeezed out. I I presses and then being squeezed out. I I presses and then being squeezed out. I I used to write uh physics engines and used to write uh physics engines and used to write uh physics engines and graphics engines and in my early days in graphics engines and in my early days in graphics engines and in my early days in gaming. And I know it's just so gaming. And I know it's just so gaming. And I know it's just so painstakingly hard to build programs painstakingly hard to build programs painstakingly hard to build programs that can do that. And yet somehow these that can do that. And yet somehow these that can do that. And yet somehow these systems are, you know, reverse systems are, you know, reverse systems are, you know, reverse engineering from just watching YouTube engineering from just watching YouTube engineering from just watching YouTube videos. So presumably what's happening videos. So presumably what's happening videos. So presumably what's happening is it's extracting some underlying is it's extracting some underlying is it's extracting some underlying structure around how these materials structure around how these materials structure around how these materials behave. So perhaps there is some kind of behave. So perhaps there is some kind of behave. So perhaps there is some kind of lower dimensional manifold that can be lower dimensional manifold that can be lower dimensional manifold that can be learned if we actually fully understood learned if we actually fully understood learned if we actually fully understood what's going on under the hood. That's what's going on under the hood. That's what's going on under the hood. That's maybe, you know, maybe true of most of maybe, you know, maybe true of most of maybe, you know, maybe true of most of reality.

reality. reality. The following is a conversation with The following is a conversation with The following is a conversation with Demis Hassabis, his second time on the Demis Hassabis, his second time on the Demis Hassabis, his second time on the podcast. He is the leader of Google Deep podcast. He is the leader of Google Deep podcast. He is the leader of Google Deep Mind and is now a Nobel Prize winner. Mind and is now a Nobel Prize winner. Mind and is now a Nobel Prize winner. Demis is one of the most brilliant and Demis is one of the most brilliant and Demis is one of the most brilliant and fascinating minds in the world today, fascinating minds in the world today, fascinating minds in the world today, working on understanding and building working on understanding and building working on understanding and building intelligence and exploring the big intelligence and exploring the big intelligence and exploring the big mysteries of our universe. mysteries of our universe. mysteries of our universe. This was truly an honor and a pleasure This was truly an honor and a pleasure This was truly an honor and a pleasure for me. This is the Lex Freedman for me. This is the Lex Freedman for me. This is the Lex Freedman podcast. To support it, please check out podcast. To support it, please check out podcast. To support it, please check out our sponsors in the description and our sponsors in the description and our sponsors in the description and consider subscribing to this channel. consider subscribing to this channel. consider subscribing to this channel. And now, dear friends, here's Deus And now, dear friends, here's Deus And now, dear friends, here's Deus Hassavas. Hassavas. Hassavas. In your Nobel Prize lecture, you propose In your Nobel Prize lecture, you propose In your Nobel Prize lecture, you propose what I think is a super interesting what I think is a super interesting what I think is a super interesting conjecture that quote any pattern that conjecture that quote any pattern that conjecture that quote any pattern that can be generated or found in nature can can be generated or found in nature can can be generated or found in nature can be efficiently discovered and modeled by be efficiently discovered and modeled by be efficiently discovered and modeled by a classical learning algorithm. What a classical learning algorithm. What a classical learning algorithm. What kind of patterns of systems might be kind of patterns of systems might be kind of patterns of systems might be included in that? Biology, chemistry, included in that? Biology, chemistry, included in that? Biology, chemistry, physics, maybe cosmology, physics, maybe cosmology, physics, maybe cosmology, >> neuroscience. What what are we talking >> neuroscience. What what are we talking >> neuroscience. What what are we talking about?

about? about? >> Sure. Well, look, I I felt that it's >> Sure. Well, look, I I felt that it's >> Sure. Well, look, I I felt that it's sort of a tradition, I think, of Nobel sort of a tradition, I think, of Nobel sort of a tradition, I think, of Nobel Prize lectures that you're supposed to Prize lectures that you're supposed to Prize lectures that you're supposed to be a little bit provocative and I wanted be a little bit provocative and I wanted be a little bit provocative and I wanted to follow that tradition. What I was to follow that tradition. What I was to follow that tradition. What I was talking about there is if you take a talking about there is if you take a talking about there is if you take a step back and you look at um all the step back and you look at um all the step back and you look at um all the work that we've done especially with the work that we've done especially with the work that we've done especially with the alpha x projects so I'm thinking alpho alpha x projects so I'm thinking alpho alpha x projects so I'm thinking alpho of course alpha fold what they really of course alpha fold what they really of course alpha fold what they really are is we're building models of very are is we're building models of very are is we're building models of very combinatorily highdimensional spaces combinatorily highdimensional spaces combinatorily highdimensional spaces that you know if you tried to brute that you know if you tried to brute that you know if you tried to brute force a solution find the best move and force a solution find the best move and force a solution find the best move and go or find the the exact shape of a go or find the the exact shape of a go or find the the exact shape of a protein and if you enumerated all the protein and if you enumerated all the protein and if you enumerated all the possibilities you there wouldn't be possibilities you there wouldn't be possibilities you there wouldn't be enough time in the in the you know the enough time in the in the you know the enough time in the in the you know the time of the universe. So you have to do time of the universe. So you have to do time of the universe. So you have to do something much smarter and what we did something much smarter and what we did something much smarter and what we did in both cases was build models of those in both cases was build models of those in both cases was build models of those environments. Um and that guided the environments. Um and that guided the environments. Um and that guided the search in a in a smart way and that search in a in a smart way and that search in a in a smart way and that makes it tractable. So if you think makes it tractable. So if you think makes it tractable. So if you think about protein folding which is obviously about protein folding which is obviously about protein folding which is obviously a natural system you know why should a natural system you know why should a natural system you know why should that be possible? How does physics do that be possible? How does physics do that be possible? How does physics do that? You know proteins fold in that? You know proteins fold in that? You know proteins fold in milliseconds in our bodies. So somehow milliseconds in our bodies. So somehow milliseconds in our bodies. So somehow physics solves this problem that we've physics solves this problem that we've physics solves this problem that we've now also solved computationally. And I now also solved computationally. And I now also solved computationally. And I think the reason that's possible is that think the reason that's possible is that think the reason that's possible is that in nature, natural systems have in nature, natural systems have in nature, natural systems have structure because they were subject to structure because they were subject to structure because they were subject to evolutionary processes that that shape evolutionary processes that that shape evolutionary processes that that shape them. And if that's true, then you can them. And if that's true, then you can them. And if that's true, then you can maybe learn uh uh what that structure maybe learn uh uh what that structure maybe learn uh uh what that structure is. So this perspective, I think, is is. So this perspective, I think, is is. So this perspective, I think, is really interesting one. You've hinted it really interesting one. You've hinted it really interesting one. You've hinted it at it, which is almost like uh crudely at it, which is almost like uh crudely at it, which is almost like uh crudely stated. Anything that can be evolved can stated. Anything that can be evolved can stated. Anything that can be evolved can be efficiently modeled. Think there's be efficiently modeled. Think there's be efficiently modeled. Think there's some truth to that. Yeah, I sometimes

some truth to that. Yeah, I sometimes some truth to that. Yeah, I sometimes call it survival of the stablest or call it survival of the stablest or call it survival of the stablest or something like that because you know something like that because you know something like that because you know it's it's of course there's evolution it's it's of course there's evolution it's it's of course there's evolution for life uh living things but there's for life uh living things but there's for life uh living things but there's also you know if you think about also you know if you think about also you know if you think about geological time so the shape of geological time so the shape of geological time so the shape of mountains that's been shaped by mountains that's been shaped by mountains that's been shaped by weathering processes right over weathering processes right over weathering processes right over thousands of years but then you can even thousands of years but then you can even thousands of years but then you can even take it cosmological the orbits of take it cosmological the orbits of take it cosmological the orbits of planets the um shapes of asteroids these planets the um shapes of asteroids these planets the um shapes of asteroids these have all been survived kind of processes have all been survived kind of processes have all been survived kind of processes that have acted on them many many times that have acted on them many many times that have acted on them many many times so if that's true then there should some so if that's true then there should some so if that's true then there should some sort of pattern um that you can kind of sort of pattern um that you can kind of sort of pattern um that you can kind of reverse learn and uh a kind of manifold reverse learn and uh a kind of manifold reverse learn and uh a kind of manifold really that helps you uh uh search to really that helps you uh uh search to really that helps you uh uh search to the right solution to the right shape um the right solution to the right shape um the right solution to the right shape um and actually allow you to predict things and actually allow you to predict things and actually allow you to predict things about it uh in an efficient way because about it uh in an efficient way because about it uh in an efficient way because it's not a random pattern right so um it it's not a random pattern right so um it it's not a random pattern right so um it may not be possible for for man-made may not be possible for for man-made may not be possible for for man-made things or abstract things like things or abstract things like things or abstract things like factorizing large numbers because unless factorizing large numbers because unless factorizing large numbers because unless there's patterns in the number space there's patterns in the number space there's patterns in the number space which there might be but if there's not which there might be but if there's not which there might be but if there's not and it's uniform then there's no pattern and it's uniform then there's no pattern and it's uniform then there's no pattern to learn there's no model to learn that to learn there's no model to learn that to learn there's no model to learn that will help you search. So you have to do will help you search. So you have to do will help you search. So you have to do brute force. So in that case you you brute force. So in that case you you brute force. So in that case you you know you maybe need a quantum computer know you maybe need a quantum computer know you maybe need a quantum computer something like this. But in most things something like this. But in most things something like this. But in most things in nature that we're interested in uh in nature that we're interested in uh in nature that we're interested in uh are not like that. They have structure are not like that. They have structure are not like that. They have structure um that evolved for a reason and um that evolved for a reason and um that evolved for a reason and survived over time. And if that's true I survived over time. And if that's true I survived over time. And if that's true I think that's potentially learnable by a think that's potentially learnable by a think that's potentially learnable by a neural network.

neural network. neural network. >> It's like nature is doing a search >> It's like nature is doing a search >> It's like nature is doing a search process and it's so fascinating that process and it's so fascinating that process and it's so fascinating that it's in that search process is creating it's in that search process is creating it's in that search process is creating systems that could be efficiently systems that could be efficiently systems that could be efficiently modeled. That's right. Yeah. modeled. That's right. Yeah. modeled. That's right. Yeah. >> So interesting. >> So interesting. >> So interesting. >> So they can be efficiently rediscovered >> So they can be efficiently rediscovered >> So they can be efficiently rediscovered or recovered um because nature is not or recovered um because nature is not or recovered um because nature is not random, right? These everything that we random, right? These everything that we random, right? These everything that we see around us, including like the see around us, including like the see around us, including like the elements that are more stable, all of elements that are more stable, all of elements that are more stable, all of those things, they're subject to um some those things, they're subject to um some those things, they're subject to um some kind of selection process pressure. Do kind of selection process pressure. Do kind of selection process pressure. Do you think because you're also a fan of you think because you're also a fan of you think because you're also a fan of theoretical computer science and theoretical computer science and theoretical computer science and complexity, do you think we can come up complexity, do you think we can come up complexity, do you think we can come up with a kind of complexity class like a with a kind of complexity class like a with a kind of complexity class like a complexity zoo type of class where maybe complexity zoo type of class where maybe complexity zoo type of class where maybe it's the set of learnable systems, the it's the set of learnable systems, the it's the set of learnable systems, the set of learnable natural systems, lns. set of learnable natural systems, lns. set of learnable natural systems, lns. >> Yeah, >> Yeah, >> Yeah, >> this is a deis new class of systems that >> this is a deis new class of systems that >> this is a deis new class of systems that could be actually learnable by classical could be actually learnable by classical could be actually learnable by classical systems in this kind of way. Natural systems in this kind of way. Natural systems in this kind of way. Natural systems that can be uh modeled systems that can be uh modeled systems that can be uh modeled efficiently. Yeah, I mean I' I've always efficiently. Yeah, I mean I' I've always efficiently. Yeah, I mean I' I've always been fascinated by the P= MP question been fascinated by the P= MP question been fascinated by the P= MP question and what is modelable by classical and what is modelable by classical and what is modelable by classical systems I non-quantum systems you know systems I non-quantum systems you know systems I non-quantum systems you know cheuring machines in effect and that's cheuring machines in effect and that's cheuring machines in effect and that's exactly what I'm working on actually in exactly what I'm working on actually in exactly what I'm working on actually in kind of my few moments of spare time kind of my few moments of spare time kind of my few moments of spare time with a few colleagues about is should with a few colleagues about is should with a few colleagues about is should there be you know maybe a new class of there be you know maybe a new class of there be you know maybe a new class of problem that is solvable by this type of problem that is solvable by this type of problem that is solvable by this type of neural network process and kind of neural network process and kind of neural network process and kind of mapped on to these natural systems so mapped on to these natural systems so mapped on to these natural systems so you know the things that exist in you know the things that exist in you know the things that exist in physics physics physics and have structure. So I think that and have structure. So I think that and have structure. So I think that could be a very interesting uh new way could be a very interesting uh new way could be a very interesting uh new way of thinking about it. And it sort of

of thinking about it. And it sort of of thinking about it. And it sort of fits with the way I think about physics fits with the way I think about physics fits with the way I think about physics in general which is that you know I in general which is that you know I in general which is that you know I think information is primary. think information is primary. think information is primary. Information is the most sort of Information is the most sort of Information is the most sort of fundamental unit of the universe more fundamental unit of the universe more fundamental unit of the universe more fundamental than energy and matter. I fundamental than energy and matter. I fundamental than energy and matter. I think they can all be converted into think they can all be converted into think they can all be converted into each other but I think of the universe each other but I think of the universe each other but I think of the universe as a kind of informationational system. as a kind of informationational system. as a kind of informationational system. >> So when you think of the universe as >> So when you think of the universe as >> So when you think of the universe as anformational system then the P= NP anformational system then the P= NP anformational system then the P= NP question is a is a physics question. question is a is a physics question. question is a is a physics question. >> That's right. And it's a question that >> That's right. And it's a question that >> That's right. And it's a question that can help us actually solve the entirety can help us actually solve the entirety can help us actually solve the entirety of this whole thing going on. of this whole thing going on. of this whole thing going on. >> Yeah, I think it's one of the most uh >> Yeah, I think it's one of the most uh >> Yeah, I think it's one of the most uh fundamental questions actually if you fundamental questions actually if you fundamental questions actually if you think of physics asformational think of physics asformational think of physics asformational uh and and the answer to that I think is uh and and the answer to that I think is uh and and the answer to that I think is going to be you know very enlightening. going to be you know very enlightening. going to be you know very enlightening. more specific to the PNNP question. more specific to the PNNP question. more specific to the PNNP question. This again, some of the stuff we're This again, some of the stuff we're This again, some of the stuff we're saying is kind of crazy right now. Just saying is kind of crazy right now. Just saying is kind of crazy right now. Just like the Christian Edinson Nobel Prize like the Christian Edinson Nobel Prize like the Christian Edinson Nobel Prize speech controversial thing that he said speech controversial thing that he said speech controversial thing that he said sounded crazy and then you went and got sounded crazy and then you went and got sounded crazy and then you went and got a Nobel Prize for this with John Jumper a Nobel Prize for this with John Jumper a Nobel Prize for this with John Jumper solved the problem. So, let me let me solved the problem. So, let me let me solved the problem. So, let me let me just stick to the P equals MP. Do you just stick to the P equals MP. Do you just stick to the P equals MP. Do you think there's something think there's something think there's something in this thing we're talking about that in this thing we're talking about that in this thing we're talking about that could be shown if you can do something could be shown if you can do something could be shown if you can do something like uh polomial time or constant time like uh polomial time or constant time like uh polomial time or constant time compute ahead of time and construct this compute ahead of time and construct this compute ahead of time and construct this gigantic model then you can solve some gigantic model then you can solve some gigantic model then you can solve some of these extremely difficult problems in of these extremely difficult problems in of these extremely difficult problems in a theoretical computer science kind of a theoretical computer science kind of a theoretical computer science kind of way.

way. way. >> Yeah, I think that there are actually a >> Yeah, I think that there are actually a >> Yeah, I think that there are actually a huge class of problems that could be huge class of problems that could be huge class of problems that could be couched in this way. the way we did couched in this way. the way we did couched in this way. the way we did alpha go and the way we did alpha fold alpha go and the way we did alpha fold alpha go and the way we did alpha fold where you know you you model what the where you know you you model what the where you know you you model what the dynamics of the system is the the the dynamics of the system is the the the dynamics of the system is the the the the properties of that system the the properties of that system the the properties of that system the environment that you're trying to environment that you're trying to environment that you're trying to understand and then that makes the understand and then that makes the understand and then that makes the search for the solution or the search for the solution or the search for the solution or the prediction of the next step efficient prediction of the next step efficient prediction of the next step efficient basically polomial time so tractable by basically polomial time so tractable by basically polomial time so tractable by a uh classical system uh which a neural a uh classical system uh which a neural a uh classical system uh which a neural network is it runs on normal computers network is it runs on normal computers network is it runs on normal computers right classical computers uh chewing right classical computers uh chewing right classical computers uh chewing machines in effect and um I think it's machines in effect and um I think it's machines in effect and um I think it's one of the most interesting questions one of the most interesting questions one of the most interesting questions there is is how far can that paradigm there is is how far can that paradigm there is is how far can that paradigm go? You know, I think we've proven uh go? You know, I think we've proven uh go? You know, I think we've proven uh and the AI community in general that and the AI community in general that and the AI community in general that classical systems, cheuring machines can classical systems, cheuring machines can classical systems, cheuring machines can go a lot further than we previously go a lot further than we previously go a lot further than we previously thought. You know, they can do things thought. You know, they can do things thought. You know, they can do things like model the structures of proteins like model the structures of proteins like model the structures of proteins and play go to better than world and play go to better than world and play go to better than world champion level. And uh you know a lot of champion level. And uh you know a lot of champion level. And uh you know a lot of people would have thought maybe 10 20 people would have thought maybe 10 20 people would have thought maybe 10 20 years ago that was decades away or maybe years ago that was decades away or maybe years ago that was decades away or maybe you would need some sort of quantum you would need some sort of quantum you would need some sort of quantum machines to to quantum systems to be machines to to quantum systems to be machines to to quantum systems to be able to do things like protein folding.

able to do things like protein folding. able to do things like protein folding. And so I think we haven't really uh even And so I think we haven't really uh even And so I think we haven't really uh even sort of scratched the surface yet of sort of scratched the surface yet of sort of scratched the surface yet of what uh classical systems socalled uh uh what uh classical systems socalled uh uh what uh classical systems socalled uh uh could do. And of course AGI being built could do. And of course AGI being built could do. And of course AGI being built on a on a neural network system on top on a on a neural network system on top on a on a neural network system on top of a neural network system on top of a of a neural network system on top of a of a neural network system on top of a classical computer would be the ultimate classical computer would be the ultimate classical computer would be the ultimate expression of that. And I think the expression of that. And I think the expression of that. And I think the limit you know the the what what the limit you know the the what what the limit you know the the what what the bounds of that kind of system what it bounds of that kind of system what it bounds of that kind of system what it can do it's a very interesting question can do it's a very interesting question can do it's a very interesting question and and and directly speaks to the P and and and directly speaks to the P and and and directly speaks to the P equals MP question. equals MP question. equals MP question. What do you think again hypothetical What do you think again hypothetical What do you think again hypothetical might be outside of this maybe might be outside of this maybe might be outside of this maybe emergent phenomena like if you look at emergent phenomena like if you look at emergent phenomena like if you look at cellular automa some of the you have cellular automa some of the you have cellular automa some of the you have extremely simple systems and then some extremely simple systems and then some extremely simple systems and then some complexity emerges yes complexity emerges yes complexity emerges yes >> maybe that would be outside or even >> maybe that would be outside or even >> maybe that would be outside or even would you guess even that might be would you guess even that might be would you guess even that might be amendable amendable amendable >> to efficient modeling by a classical >> to efficient modeling by a classical >> to efficient modeling by a classical machine machine machine >> yeah I think those systems would be >> yeah I think those systems would be >> yeah I think those systems would be right on the boundary right so um I right on the boundary right so um I right on the boundary right so um I think most emergent systems cellular think most emergent systems cellular think most emergent systems cellular automter things that could be modelable automter things that could be modelable automter things that could be modelable by a classical system. You just sort of by a classical system. You just sort of by a classical system. You just sort of do a forward simulation of it and it do a forward simulation of it and it do a forward simulation of it and it probably be efficient enough. Um, of probably be efficient enough. Um, of probably be efficient enough. Um, of course there's the question of things course there's the question of things course there's the question of things like chaotic systems where the initial like chaotic systems where the initial like chaotic systems where the initial conditions really matter and then you conditions really matter and then you conditions really matter and then you get to some, you know, uncorrelated end get to some, you know, uncorrelated end get to some, you know, uncorrelated end state those could be difficult to model.

state those could be difficult to model. state those could be difficult to model. So I think these are kind of the open So I think these are kind of the open So I think these are kind of the open questions. But I think when you step questions. But I think when you step questions. But I think when you step back and look at what we've done with back and look at what we've done with back and look at what we've done with the systems and the and the problems the systems and the and the problems the systems and the and the problems that we've solved and then you look at that we've solved and then you look at that we've solved and then you look at things like V3 on like video generation things like V3 on like video generation things like V3 on like video generation sort of rendering physics and lighting sort of rendering physics and lighting sort of rendering physics and lighting and things like that, you know, really and things like that, you know, really and things like that, you know, really core fundamental things in physics. Um core fundamental things in physics. Um core fundamental things in physics. Um it's pretty interesting. I think it's it's pretty interesting. I think it's it's pretty interesting. I think it's telling us something quite fundamental telling us something quite fundamental telling us something quite fundamental about how the universe is structured in about how the universe is structured in about how the universe is structured in my opinion. Um so you know in a way my opinion. Um so you know in a way my opinion. Um so you know in a way that's what I want to build AGI for is that's what I want to build AGI for is that's what I want to build AGI for is to help uh us uh as scientists answer to help uh us uh as scientists answer to help uh us uh as scientists answer these questions uh like p=mp. these questions uh like p=mp. these questions uh like p=mp. >> Yeah I think we might be continuously >> Yeah I think we might be continuously >> Yeah I think we might be continuously surprised about what is modelable by surprised about what is modelable by surprised about what is modelable by classical computers. I mean alpha fold 3 classical computers. I mean alpha fold 3 classical computers. I mean alpha fold 3 on the interaction side is surprising on the interaction side is surprising on the interaction side is surprising that you can make any kind of progress that you can make any kind of progress that you can make any kind of progress on that direction. Alpha genome is on that direction. Alpha genome is on that direction. Alpha genome is surprising that you can map the genetic surprising that you can map the genetic surprising that you can map the genetic code to the function kind of playing code to the function kind of playing code to the function kind of playing with the emergent kind of phenomena. You with the emergent kind of phenomena. You with the emergent kind of phenomena. You think there's so many combinatorial think there's so many combinatorial think there's so many combinatorial options that and then here you go. You options that and then here you go. You options that and then here you go. You can find the kernel that is efficiently can find the kernel that is efficiently can find the kernel that is efficiently modeled.

modeled. modeled. >> Yes. Because there's some structure >> Yes. Because there's some structure >> Yes. Because there's some structure there's some landscape you know in the there's some landscape you know in the there's some landscape you know in the energy landscape or whatever it is that energy landscape or whatever it is that energy landscape or whatever it is that you can follow some gradient you can you can follow some gradient you can you can follow some gradient you can follow. And of course what neural follow. And of course what neural follow. And of course what neural networks are very good at is following networks are very good at is following networks are very good at is following gradients. And so if there's one to gradients. And so if there's one to gradients. And so if there's one to follow and object and you can specify follow and object and you can specify follow and object and you can specify the objective function correctly you the objective function correctly you the objective function correctly you know you don't have to deal with all know you don't have to deal with all know you don't have to deal with all that complexity which I think is how we that complexity which I think is how we that complexity which I think is how we maybe have naively thought about it for maybe have naively thought about it for maybe have naively thought about it for decades those problems if you just decades those problems if you just decades those problems if you just enumerate all the possibilities it looks enumerate all the possibilities it looks enumerate all the possibilities it looks totally intractable and there's many totally intractable and there's many totally intractable and there's many many problems like that and then you many problems like that and then you many problems like that and then you think well it's like 10^ the 300 think well it's like 10^ the 300 think well it's like 10^ the 300 possible protein structures uh it's 10^ possible protein structures uh it's 10^ possible protein structures uh it's 10^ theund you know 70 possible go positions theund you know 70 possible go positions theund you know 70 possible go positions all of these are way more than atoms in all of these are way more than atoms in all of these are way more than atoms in the universe so how could one possibly the universe so how could one possibly the universe so how could one possibly find the the right solution or predict find the the right solution or predict find the the right solution or predict the next step and and it but it turns the next step and and it but it turns the next step and and it but it turns out that it is possible and of course out that it is possible and of course out that it is possible and of course reality nature does do it right proteins reality nature does do it right proteins reality nature does do it right proteins do fold so that that gives you do fold so that that gives you do fold so that that gives you confidence that there must be if we confidence that there must be if we confidence that there must be if we understood how physics was doing that uh understood how physics was doing that uh understood how physics was doing that uh in a sense uh then and we could mimic in a sense uh then and we could mimic in a sense uh then and we could mimic that process I model that process uh it that process I model that process uh it that process I model that process uh it should be possible on our classical should be possible on our classical should be possible on our classical systems is is is basically what the systems is is is basically what the systems is is is basically what the conjecture is about conjecture is about conjecture is about >> and of course there's nonlinear >> and of course there's nonlinear >> and of course there's nonlinear dynamical systems, highly nonlinear dynamical systems, highly nonlinear dynamical systems, highly nonlinear dynamical systems, everything involving dynamical systems, everything involving dynamical systems, everything involving fluid.

fluid. fluid. >> Yes. >> Yes. >> Yes. >> Right. >> Right. >> Right. >> You know, I recently had a conversation >> You know, I recently had a conversation >> You know, I recently had a conversation with Terrence Ta who mathematically with Terrence Ta who mathematically with Terrence Ta who mathematically uh it contends with a very difficult uh it contends with a very difficult uh it contends with a very difficult aspect of systems that have some aspect of systems that have some aspect of systems that have some singularities in them that break the singularities in them that break the singularities in them that break the mathematics and it's just hard for us mathematics and it's just hard for us mathematics and it's just hard for us humans to make any kind of clean humans to make any kind of clean humans to make any kind of clean predictions about highly nonlinear predictions about highly nonlinear predictions about highly nonlinear dynamical systems. But again to your dynamical systems. But again to your dynamical systems. But again to your point we might be very surprised what point we might be very surprised what point we might be very surprised what classical learning systems might be able classical learning systems might be able classical learning systems might be able to do about even fluid. Yes, exactly. I to do about even fluid. Yes, exactly. I to do about even fluid. Yes, exactly. I mean fluid dynamics, Navia Stokes mean fluid dynamics, Navia Stokes mean fluid dynamics, Navia Stokes equations, these are traditionally equations, these are traditionally equations, these are traditionally thought of as very very difficult thought of as very very difficult thought of as very very difficult intractable kind of problems to do on intractable kind of problems to do on intractable kind of problems to do on classical systems. They take enormous classical systems. They take enormous classical systems. They take enormous amounts of compute, you know, weather amounts of compute, you know, weather amounts of compute, you know, weather prediction systems, you know, these kind prediction systems, you know, these kind prediction systems, you know, these kind of things all involve fluid dynamics of things all involve fluid dynamics of things all involve fluid dynamics calculations. And um but again, if you calculations. And um but again, if you calculations. And um but again, if you look at something like VO, our video look at something like VO, our video look at something like VO, our video generation model, it can model liquids generation model, it can model liquids generation model, it can model liquids quite well, surprisingly well, and quite well, surprisingly well, and quite well, surprisingly well, and materials, specular lighting. I love the materials, specular lighting. I love the materials, specular lighting. I love the ones where, you know, there's there's ones where, you know, there's there's ones where, you know, there's there's people have generated videos where people have generated videos where people have generated videos where there's like clear liquids going through there's like clear liquids going through there's like clear liquids going through hydraulic presses and then being hydraulic presses and then being hydraulic presses and then being squeezed out. I I used to write uh squeezed out. I I used to write uh squeezed out. I I used to write uh physics engines and graphics engines and physics engines and graphics engines and physics engines and graphics engines and in my early days in gaming and I know in my early days in gaming and I know in my early days in gaming and I know it's just so painstakingly hard to build it's just so painstakingly hard to build it's just so painstakingly hard to build programs that can do that and yet programs that can do that and yet programs that can do that and yet somehow these systems are, you know, somehow these systems are, you know, somehow these systems are, you know, reverse engineering from just watching reverse engineering from just watching reverse engineering from just watching YouTube videos. So presumably what's YouTube videos. So presumably what's YouTube videos. So presumably what's happening is it's extracting some happening is it's extracting some happening is it's extracting some underlying structure around how these underlying structure around how these underlying structure around how these materials behave. So perhaps there is materials behave. So perhaps there is materials behave. So perhaps there is some kind of lower dimensional manifold some kind of lower dimensional manifold some kind of lower dimensional manifold that can be learned if we actually fully

that can be learned if we actually fully that can be learned if we actually fully understood what's going on under the understood what's going on under the understood what's going on under the hood. That's maybe you know maybe true hood. That's maybe you know maybe true hood. That's maybe you know maybe true of most of reality. of most of reality. of most of reality. >> Yeah. I've been continuously precisely >> Yeah. I've been continuously precisely >> Yeah. I've been continuously precisely by this aspect of V3. I think a lot of by this aspect of V3. I think a lot of by this aspect of V3. I think a lot of people highlight different aspects, people highlight different aspects, people highlight different aspects, including the comedic and the meme and including the comedic and the meme and including the comedic and the meme and all that kind of stuff. And then the all that kind of stuff. And then the all that kind of stuff. And then the ultra realistic ability to capture ultra realistic ability to capture ultra realistic ability to capture humans in a really nice way that's humans in a really nice way that's humans in a really nice way that's compelling and feels close to reality compelling and feels close to reality compelling and feels close to reality and then combine that with native audio. and then combine that with native audio. and then combine that with native audio. All of those are marvelous things about All of those are marvelous things about All of those are marvelous things about V3. But the exactly the thing you're V3. But the exactly the thing you're V3. But the exactly the thing you're mentioning, which is the physics. mentioning, which is the physics. mentioning, which is the physics. >> Yeah, >> Yeah, >> Yeah, >> it's not perfect, but it's pretty damn >> it's not perfect, but it's pretty damn >> it's not perfect, but it's pretty damn good. And then the really interesting good. And then the really interesting good. And then the really interesting scientific question is what is it scientific question is what is it scientific question is what is it understanding about our world understanding about our world understanding about our world >> in order to be able to do that because >> in order to be able to do that because >> in order to be able to do that because of the cynical take with diffusion of the cynical take with diffusion of the cynical take with diffusion models there's no way it understands models there's no way it understands models there's no way it understands anything anything anything >> but it seem I mean I don't think you can >> but it seem I mean I don't think you can >> but it seem I mean I don't think you can generate that kind of video without generate that kind of video without generate that kind of video without understanding and then our own understanding and then our own understanding and then our own philosophical notion what it means to philosophical notion what it means to philosophical notion what it means to understand then is like brought to the understand then is like brought to the understand then is like brought to the surface like do to what degree do you surface like do to what degree do you surface like do to what degree do you think V3 understands our world? I think think V3 understands our world? I think think V3 understands our world? I think to the extent that it can predict the to the extent that it can predict the to the extent that it can predict the next frames you know in a coherent way next frames you know in a coherent way next frames you know in a coherent way that's some that is a form you know of that's some that is a form you know of that's some that is a form you know of understanding right not in the understanding right not in the understanding right not in the anthropomorphic version of you know it's anthropomorphic version of you know it's anthropomorphic version of you know it's not some kind of deep philosophical not some kind of deep philosophical not some kind of deep philosophical understanding of what's going on I don't understanding of what's going on I don't understanding of what's going on I don't think these systems have that but they think these systems have that but they think these systems have that but they they certainly have uh modeled enough of they certainly have uh modeled enough of they certainly have uh modeled enough of the dynamics you know put it that way the dynamics you know put it that way the dynamics you know put it that way that they can pretty accurately generate that they can pretty accurately generate that they can pretty accurately generate whatever it is 8 seconds of consistent

whatever it is 8 seconds of consistent whatever it is 8 seconds of consistent video that by eye at least you know at a video that by eye at least you know at a video that by eye at least you know at a glance is quite hard to distinguish what glance is quite hard to distinguish what glance is quite hard to distinguish what the issues are and imagine that in two the issues are and imagine that in two the issues are and imagine that in two or three more years time. That's the or three more years time. That's the or three more years time. That's the thing I'm thinking about and how thing I'm thinking about and how thing I'm thinking about and how incredible that there will look uh given incredible that there will look uh given incredible that there will look uh given where we've come from, you know, the where we've come from, you know, the where we've come from, you know, the early versions of that uh one or two early versions of that uh one or two early versions of that uh one or two years ago. And so, um the rate of years ago. And so, um the rate of years ago. And so, um the rate of progress is incredible. And I think um progress is incredible. And I think um progress is incredible. And I think um I'm like you is like a lot of people I'm like you is like a lot of people I'm like you is like a lot of people love all of the the the the standup love all of the the the the standup love all of the the the the standup comedians and the the that actually comedians and the the that actually comedians and the the that actually captures a lot of human dynamics very captures a lot of human dynamics very captures a lot of human dynamics very well and and body language, but actually well and and body language, but actually well and and body language, but actually the thing I'm most impressed with and the thing I'm most impressed with and the thing I'm most impressed with and fascinated by is the physics behavior, fascinated by is the physics behavior, fascinated by is the physics behavior, the lighting and materials and liquids. the lighting and materials and liquids. the lighting and materials and liquids. And it's pretty amazing that it can do And it's pretty amazing that it can do And it's pretty amazing that it can do that. And I think that shows that it has that. And I think that shows that it has that. And I think that shows that it has some notion of at least intuitive some notion of at least intuitive some notion of at least intuitive physics, right? um how things are physics, right? um how things are physics, right? um how things are supposed to work uh intuitively maybe supposed to work uh intuitively maybe supposed to work uh intuitively maybe the way that uh a human child would the way that uh a human child would the way that uh a human child would understand physics right as opposed to a understand physics right as opposed to a understand physics right as opposed to a you know a PhD student really uh being you know a PhD student really uh being you know a PhD student really uh being able to unpack all the equations it's able to unpack all the equations it's able to unpack all the equations it's more of an intuitive physics more of an intuitive physics more of an intuitive physics understanding understanding understanding >> well that intuitive >> well that intuitive >> well that intuitive physics understanding that's the base physics understanding that's the base physics understanding that's the base layer that's the thing people sometimes layer that's the thing people sometimes layer that's the thing people sometimes call like common sense like it it really call like common sense like it it really call like common sense like it it really understands something I think that understands something I think that understands something I think that really surprised a lot of people it really surprised a lot of people it really surprised a lot of people it blows my mind that blows my mind that blows my mind that >> I just didn't think it would be possible >> I just didn't think it would be possible >> I just didn't think it would be possible to generate that level of realism to generate that level of realism to generate that level of realism without understanding.

without understanding. without understanding. >> You there's this notion that you can >> You there's this notion that you can >> You there's this notion that you can only understand the physical world by only understand the physical world by only understand the physical world by having an embodied AI system, a robot having an embodied AI system, a robot having an embodied AI system, a robot that interacts with that world. That's that interacts with that world. That's that interacts with that world. That's the only way to construct an the only way to construct an the only way to construct an understanding of that world. But V3 is understanding of that world. But V3 is understanding of that world. But V3 is directly challenging that it feels like directly challenging that it feels like directly challenging that it feels like >> yes and it's very interesting you know >> yes and it's very interesting you know >> yes and it's very interesting you know even if we if you were to ask me 5 10 even if we if you were to ask me 5 10 even if we if you were to ask me 5 10 years ago I would have said even though years ago I would have said even though years ago I would have said even though I was immersed in all of this I would I was immersed in all of this I would I was immersed in all of this I would have said well yeah you probably need to have said well yeah you probably need to have said well yeah you probably need to understand intuitive physics you know understand intuitive physics you know understand intuitive physics you know like if I push this off the table this like if I push this off the table this like if I push this off the table this glass it will maybe shatter you know um glass it will maybe shatter you know um glass it will maybe shatter you know um and and the liquid will spill out right and and the liquid will spill out right and and the liquid will spill out right so we know all of these things but I so we know all of these things but I so we know all of these things but I thought that you know and there's a lot thought that you know and there's a lot thought that you know and there's a lot of theories in neuroscience it's called of theories in neuroscience it's called of theories in neuroscience it's called action in perception where you know you action in perception where you know you action in perception where you know you you need to act in the world to really you need to act in the world to really you need to act in the world to really truly perceive it in a deep way. And truly perceive it in a deep way. And truly perceive it in a deep way. And there was a lot of theories about you there was a lot of theories about you there was a lot of theories about you need embodied intelligence or robotics need embodied intelligence or robotics need embodied intelligence or robotics or something or maybe at least simulated or something or maybe at least simulated or something or maybe at least simulated action uh so that you would understand action uh so that you would understand action uh so that you would understand things like intuitive physics. But it things like intuitive physics. But it things like intuitive physics. But it seems like um you can understand it seems like um you can understand it seems like um you can understand it through passive observation which is through passive observation which is through passive observation which is pretty surprising to me and and again I pretty surprising to me and and again I pretty surprising to me and and again I think hints at something underlying think hints at something underlying think hints at something underlying about the nature of uh reality in in my about the nature of uh reality in in my about the nature of uh reality in in my opinion beyond um just the you know the opinion beyond um just the you know the opinion beyond um just the you know the cool videos that it generates. Um and cool videos that it generates. Um and cool videos that it generates. Um and and of course there's next stages is and of course there's next stages is and of course there's next stages is maybe even making those videos maybe even making those videos maybe even making those videos interactive. So uh one can actually step interactive. So uh one can actually step interactive. So uh one can actually step into them and move around them. Um which into them and move around them. Um which into them and move around them. Um which would be really mind-blowing especially would be really mind-blowing especially would be really mind-blowing especially given my games background. So you can given my games background. So you can given my games background. So you can imagine and then and then I think you imagine and then and then I think you imagine and then and then I think you know you're we're starting to get know you're we're starting to get know you're we're starting to get towards what I would call a world model towards what I would call a world model towards what I would call a world model a model of how the world works the

a model of how the world works the a model of how the world works the mechanics of the world the physics of mechanics of the world the physics of mechanics of the world the physics of the world and the things in that world. the world and the things in that world. the world and the things in that world. And of course that's what you would need And of course that's what you would need And of course that's what you would need for a true AGI system. for a true AGI system. for a true AGI system. >> I have to talk to you about video games. >> I have to talk to you about video games. >> I have to talk to you about video games. So, you were being a bit trolly. I I So, you were being a bit trolly. I I So, you were being a bit trolly. I I think you're you're having more and more think you're you're having more and more think you're you're having more and more fun on Twitter on X, which is great to fun on Twitter on X, which is great to fun on Twitter on X, which is great to see. So, guy named Jimmy Apples tweeted, see. So, guy named Jimmy Apples tweeted, see. So, guy named Jimmy Apples tweeted, "Let me play a video game of my V3 "Let me play a video game of my V3 "Let me play a video game of my V3 videos already. Uh, Google cooked so videos already. Uh, Google cooked so videos already. Uh, Google cooked so good playable world models when spelled good playable world models when spelled good playable world models when spelled we n question mark." Uh, and then you we n question mark." Uh, and then you we n question mark." Uh, and then you quote tweeted that with, "Now, wouldn't quote tweeted that with, "Now, wouldn't quote tweeted that with, "Now, wouldn't that be something?" So, how how hard is that be something?" So, how how hard is that be something?" So, how how hard is it to build game worlds with AI? Maybe it to build game worlds with AI? Maybe it to build game worlds with AI? Maybe can you look out into the future can you look out into the future can you look out into the future uh of video games 5 10 years out? What uh of video games 5 10 years out? What uh of video games 5 10 years out? What do you think that looks like? do you think that looks like? do you think that looks like? >> Well, games were my first love really >> Well, games were my first love really >> Well, games were my first love really and doing AI for games was the first and doing AI for games was the first and doing AI for games was the first thing I did professionally in my teenage thing I did professionally in my teenage thing I did professionally in my teenage years and and was the first major AI years and and was the first major AI years and and was the first major AI systems that I built and uh I always systems that I built and uh I always systems that I built and uh I always want to have I want to scratch that itch want to have I want to scratch that itch want to have I want to scratch that itch one day and come back to that. though, one day and come back to that. though, one day and come back to that. though, you know, and I will do, I think, and um you know, and I will do, I think, and um you know, and I will do, I think, and um I think I'd sort of dream about, you I think I'd sort of dream about, you I think I'd sort of dream about, you know, what would I have done back in the know, what would I have done back in the know, what would I have done back in the '90s if I'd had access to the kind of AI '90s if I'd had access to the kind of AI '90s if I'd had access to the kind of AI systems we have today? And I think you systems we have today? And I think you systems we have today? And I think you could build absolutely mind-blowing could build absolutely mind-blowing could build absolutely mind-blowing games. Um, and I think the next stage is games. Um, and I think the next stage is games. Um, and I think the next stage is I always used to love making all the I always used to love making all the I always used to love making all the games I've made are openw world games.

games I've made are openw world games. games I've made are openw world games. So, they're games where there's a So, they're games where there's a So, they're games where there's a simulation and then there's AI simulation and then there's AI simulation and then there's AI characters and then the player uh characters and then the player uh characters and then the player uh interacts with that simulation and the interacts with that simulation and the interacts with that simulation and the simulation adapts to the way the player simulation adapts to the way the player simulation adapts to the way the player plays. And I always thought they were plays. And I always thought they were plays. And I always thought they were the coolest games because uh so games the coolest games because uh so games the coolest games because uh so games like theme park that I worked on where like theme park that I worked on where like theme park that I worked on where everybody's game experience would be everybody's game experience would be everybody's game experience would be unique to them, right? Because you're unique to them, right? Because you're unique to them, right? Because you're kind of co-creating the game, right? Uh kind of co-creating the game, right? Uh kind of co-creating the game, right? Uh we set up the parameters, we set up we set up the parameters, we set up we set up the parameters, we set up initial conditions, and then you as the initial conditions, and then you as the initial conditions, and then you as the player immersed in it, and then you are player immersed in it, and then you are player immersed in it, and then you are co-creating it with the with the co-creating it with the with the co-creating it with the with the simulation. But of course, it's very simulation. But of course, it's very simulation. But of course, it's very hard to program open world games. you hard to program open world games. you hard to program open world games. you know, you've got to be able to create uh know, you've got to be able to create uh know, you've got to be able to create uh content whichever direction the player content whichever direction the player content whichever direction the player goes in and you want it to be compelling goes in and you want it to be compelling goes in and you want it to be compelling no matter what the player chooses. Um, no matter what the player chooses. Um, no matter what the player chooses. Um, and so it was always quite difficult to and so it was always quite difficult to and so it was always quite difficult to build uh things like cellular automter build uh things like cellular automter build uh things like cellular automter actually type of those kind of classical actually type of those kind of classical actually type of those kind of classical systems which created some emergent systems which created some emergent systems which created some emergent behavior. Um, but they're always a behavior. Um, but they're always a behavior. Um, but they're always a little bit fragile, a little bit little bit fragile, a little bit little bit fragile, a little bit limited. Now we're maybe on the cusp in limited. Now we're maybe on the cusp in limited. Now we're maybe on the cusp in the next few years, 5 10 years of having the next few years, 5 10 years of having the next few years, 5 10 years of having AI systems that can truly create around AI systems that can truly create around AI systems that can truly create around your imagination. um can nar sort of your imagination. um can nar sort of your imagination. um can nar sort of dynamically change the story and dynamically change the story and dynamically change the story and storytell the narrative around uh and storytell the narrative around uh and storytell the narrative around uh and make it dramatic no matter what you end make it dramatic no matter what you end make it dramatic no matter what you end up choosing. So it's like the ultimate up choosing. So it's like the ultimate up choosing. So it's like the ultimate choose your own adventure sort of game.

choose your own adventure sort of game. choose your own adventure sort of game. And uh you know I think maybe we're And uh you know I think maybe we're And uh you know I think maybe we're within reach if you think of a kind of within reach if you think of a kind of within reach if you think of a kind of interactive version of VO uh and then interactive version of VO uh and then interactive version of VO uh and then wind that forward 5 to 10 years and you wind that forward 5 to 10 years and you wind that forward 5 to 10 years and you know imagine how good it's going to be. know imagine how good it's going to be. know imagine how good it's going to be. >> Yeah. So you said a lot of super >> Yeah. So you said a lot of super >> Yeah. So you said a lot of super interesting stuff there. So one the open interesting stuff there. So one the open interesting stuff there. So one the open world world world built into that is a deep built into that is a deep built into that is a deep personalization the way you've described personalization the way you've described personalization the way you've described it. it. it. >> So it's not just that it's open world >> So it's not just that it's open world >> So it's not just that it's open world like you can open any door and there'll like you can open any door and there'll like you can open any door and there'll be something there. It's that the choice be something there. It's that the choice be something there. It's that the choice of which door you open of which door you open of which door you open >> in an unconstrained way defines the >> in an unconstrained way defines the >> in an unconstrained way defines the worlds you see. So some games try to do worlds you see. So some games try to do worlds you see. So some games try to do that to give you choice. Yes. But it's that to give you choice. Yes. But it's that to give you choice. Yes. But it's really just an illusion of choice really just an illusion of choice really just an illusion of choice because because because >> the only uh like like Stanley Parable >> the only uh like like Stanley Parable >> the only uh like like Stanley Parable game I recently played. It's it's it's game I recently played. It's it's it's game I recently played. It's it's it's really there's a couple of doors and it really there's a couple of doors and it really there's a couple of doors and it really just takes you down a narrative. really just takes you down a narrative. really just takes you down a narrative. Stanley Parable is a great video game I Stanley Parable is a great video game I Stanley Parable is a great video game I recommend people play that kind of uh in recommend people play that kind of uh in recommend people play that kind of uh in a meta way uh mocks the illusion of a meta way uh mocks the illusion of a meta way uh mocks the illusion of choice and there's philosophical notions choice and there's philosophical notions choice and there's philosophical notions of free will and so on. But uh I do like of free will and so on. But uh I do like of free will and so on. But uh I do like one of my favorite games of Elder one of my favorite games of Elder one of my favorite games of Elder Scrolls is Daggerfall. I believe that Scrolls is Daggerfall. I believe that Scrolls is Daggerfall. I believe that they really played with a like random they really played with a like random they really played with a like random generation of the dungeons.

generation of the dungeons. generation of the dungeons. >> Yeah. >> Yeah. >> Yeah. >> Of you can step in and they give you >> Of you can step in and they give you >> Of you can step in and they give you this feeling of an open world and there this feeling of an open world and there this feeling of an open world and there you mentioned interactivity. You don't you mentioned interactivity. You don't you mentioned interactivity. You don't need to interact. That's a first step need to interact. That's a first step need to interact. That's a first step cuz you don't need to interact that cuz you don't need to interact that cuz you don't need to interact that much. You just when you open the door, much. You just when you open the door, much. You just when you open the door, whatever you see is randomly generated whatever you see is randomly generated whatever you see is randomly generated for you. for you. for you. >> Yeah. And that's already an incredible >> Yeah. And that's already an incredible >> Yeah. And that's already an incredible experience because you might be the only experience because you might be the only experience because you might be the only person to ever see that. person to ever see that. person to ever see that. >> Yeah. Exactly. And and so but what you'd >> Yeah. Exactly. And and so but what you'd >> Yeah. Exactly. And and so but what you'd like is a little bit better than just like is a little bit better than just like is a little bit better than just sort of a random generation, right? So sort of a random generation, right? So sort of a random generation, right? So you'd like uh and and also better than a you'd like uh and and also better than a you'd like uh and and also better than a simple AB hardcoded choice, right? simple AB hardcoded choice, right? simple AB hardcoded choice, right? That's not really uh open world, right? That's not really uh open world, right? That's not really uh open world, right? As you say, it's just giving you the As you say, it's just giving you the As you say, it's just giving you the illusion of choice. What you want to be illusion of choice. What you want to be illusion of choice. What you want to be able to do is is potentially anything in able to do is is potentially anything in able to do is is potentially anything in that game environment. Um, and I think that game environment. Um, and I think that game environment. Um, and I think the only way you can do that is to have the only way you can do that is to have the only way you can do that is to have uh generated systems, systems that uh uh generated systems, systems that uh uh generated systems, systems that uh will generate that on the fly. Of will generate that on the fly. Of will generate that on the fly. Of course, you can't create infinite course, you can't create infinite course, you can't create infinite amounts of game assets, right? It's amounts of game assets, right? It's amounts of game assets, right? It's expensive enough already how AAA games expensive enough already how AAA games expensive enough already how AAA games are made today. And that was obvious to are made today. And that was obvious to are made today. And that was obvious to to us back in the '9s when I was working to us back in the '9s when I was working to us back in the '9s when I was working on all these games. I think maybe Black on all these games. I think maybe Black on all these games. I think maybe Black and White uh was the game that I worked and White uh was the game that I worked and White uh was the game that I worked on, early stages of that that had the on, early stages of that that had the on, early stages of that that had the still probably the best AI learning AI still probably the best AI learning AI still probably the best AI learning AI in it. It was an early reinforcement in it. It was an early reinforcement in it. It was an early reinforcement learning system that you, you know, you learning system that you, you know, you learning system that you, you know, you were, you were looking after this were, you were looking after this were, you were looking after this mythical creature and growing it and mythical creature and growing it and mythical creature and growing it and nurturing it and depending how you nurturing it and depending how you nurturing it and depending how you treated it, it would treat the villagers treated it, it would treat the villagers treated it, it would treat the villagers in that world in the same way. So if you in that world in the same way. So if you in that world in the same way. So if you were mean to it, it would be mean. If were mean to it, it would be mean. If were mean to it, it would be mean. If you were good, it would be protective.

you were good, it would be protective. you were good, it would be protective. And so it was really a reflection of the And so it was really a reflection of the And so it was really a reflection of the way you played it. So actually all of way you played it. So actually all of way you played it. So actually all of the uh I've been working on sort of the uh I've been working on sort of the uh I've been working on sort of simulations and AI through the medium of simulations and AI through the medium of simulations and AI through the medium of games at the beginning of my career and games at the beginning of my career and games at the beginning of my career and and really the whole of what I do today and really the whole of what I do today and really the whole of what I do today is still a follow on from uh those early is still a follow on from uh those early is still a follow on from uh those early more hardcoded ways of doing the AI to more hardcoded ways of doing the AI to more hardcoded ways of doing the AI to now you know fully general learning now you know fully general learning now you know fully general learning systems that that are trying to achieve systems that that are trying to achieve systems that that are trying to achieve the same thing. the same thing. the same thing. >> Yeah, it's been uh interesting, >> Yeah, it's been uh interesting, >> Yeah, it's been uh interesting, hilarious, and uh fun to watch you and hilarious, and uh fun to watch you and hilarious, and uh fun to watch you and Elon obviously itching to create games Elon obviously itching to create games Elon obviously itching to create games because you're both gamers. And one of because you're both gamers. And one of because you're both gamers. And one of the sad aspects of your uh incredible the sad aspects of your uh incredible the sad aspects of your uh incredible success in so many domains of science success in so many domains of science success in so many domains of science like serious adult stuff. like serious adult stuff. like serious adult stuff. >> Yeah. >> Yeah. >> Yeah. >> That you might not have time to really >> That you might not have time to really >> That you might not have time to really create a game. You might end up creating create a game. You might end up creating create a game. You might end up creating the tooling that others would create the the tooling that others would create the the tooling that others would create the game. You have to watch game. You have to watch game. You have to watch >> other others create the thing you've >> other others create the thing you've >> other others create the thing you've always dreamed of. Do you think it's always dreamed of. Do you think it's always dreamed of. Do you think it's possible you can somehow in your possible you can somehow in your possible you can somehow in your extremely busy schedule actually find extremely busy schedule actually find extremely busy schedule actually find time to create something like black and time to create something like black and time to create something like black and white? some some an actual video game white? some some an actual video game white? some some an actual video game where like you could make the childhood where like you could make the childhood where like you could make the childhood dream come become reality.

dream come become reality. dream come become reality. >> You know, there's two things way to >> You know, there's two things way to >> You know, there's two things way to think about that is maybe with vibe think about that is maybe with vibe think about that is maybe with vibe coding as it gets better and there's a coding as it gets better and there's a coding as it gets better and there's a possibility that I could, you know, one possibility that I could, you know, one possibility that I could, you know, one could do that actually in in your spare could do that actually in in your spare could do that actually in in your spare time. So, I'm quite excited about that time. So, I'm quite excited about that time. So, I'm quite excited about that as a as that would be my project if if I as a as that would be my project if if I as a as that would be my project if if I got the time to do some vibe coding. Um got the time to do some vibe coding. Um got the time to do some vibe coding. Um I'm actually itching to do that. And I'm actually itching to do that. And I'm actually itching to do that. And then the other thing is, you know, maybe then the other thing is, you know, maybe then the other thing is, you know, maybe it's a sbatical after agi has been it's a sbatical after agi has been it's a sbatical after agi has been safely stewarded into the world and safely stewarded into the world and safely stewarded into the world and delivered into the world. You know, that delivered into the world. You know, that delivered into the world. You know, that and then working on my physics theory as and then working on my physics theory as and then working on my physics theory as we talked about at the beginning. Those we talked about at the beginning. Those we talked about at the beginning. Those would be the two my my two post AGI would be the two my my two post AGI would be the two my my two post AGI projects. Let's call it that way. projects. Let's call it that way. projects. Let's call it that way. >> I I would love to see which game post >> I I would love to see which game post >> I I would love to see which game post AGI which you choose. Solving uh the the AGI which you choose. Solving uh the the AGI which you choose. Solving uh the the problem that some of the smartest people problem that some of the smartest people problem that some of the smartest people in human history contended with, you in human history contended with, you in human history contended with, you know, P equals MP know, P equals MP know, P equals MP or creating a cool video. Yeah. Well, or creating a cool video. Yeah. Well, or creating a cool video. Yeah. Well, but they might but in my world they'd be but they might but in my world they'd be but they might but in my world they'd be related because it would be an openw related because it would be an openw related because it would be an openw world simulated game uh as realistic as world simulated game uh as realistic as world simulated game uh as realistic as possible. So, you know what what is what possible. So, you know what what is what possible. So, you know what what is what is the universe? That's that's that's is the universe? That's that's that's is the universe? That's that's that's speaking to the same question, right? speaking to the same question, right? speaking to the same question, right? NPL MP. I think all these things are NPL MP. I think all these things are NPL MP. I think all these things are related, at least in my mind. I mean in related, at least in my mind. I mean in related, at least in my mind. I mean in a really serious way like video games a really serious way like video games a really serious way like video games sometimes are looked down upon as just sometimes are looked down upon as just sometimes are looked down upon as just this fun side activity but especially as this fun side activity but especially as this fun side activity but especially as AI does more and more of the difficult AI does more and more of the difficult AI does more and more of the difficult uh boring tasks something we in in uh boring tasks something we in in uh boring tasks something we in in modern world call work.

modern world call work. modern world call work. You know video games is the thing in You know video games is the thing in You know video games is the thing in which we may find meaning in which we which we may find meaning in which we which we may find meaning in which we may find like what to do with our time. may find like what to do with our time. may find like what to do with our time. You could create incredibly rich, You could create incredibly rich, You could create incredibly rich, meaningful experiences. Like that's what meaningful experiences. Like that's what meaningful experiences. Like that's what human life is. And then in video games, human life is. And then in video games, human life is. And then in video games, you can create more sophisticated, you can create more sophisticated, you can create more sophisticated, more diverse more diverse more diverse ways of living, ways of living, ways of living, >> right? >> right? >> right? >> I think so. I mean, those of us who love >> I think so. I mean, those of us who love >> I think so. I mean, those of us who love games, and I still do, is is is games, and I still do, is is is games, and I still do, is is is um you know, it's almost can let your um you know, it's almost can let your um you know, it's almost can let your imagination run wild, right? Like I I imagination run wild, right? Like I I imagination run wild, right? Like I I used to love games um and working on used to love games um and working on used to love games um and working on games. so much because it's the fusion games. so much because it's the fusion games. so much because it's the fusion especially in the '9s and early 2000s especially in the '9s and early 2000s especially in the '9s and early 2000s the sort of golden era maybe the 80s of the sort of golden era maybe the 80s of the sort of golden era maybe the 80s of of of game of the games industry and it of of game of the games industry and it of of game of the games industry and it was all being discovered new genres were was all being discovered new genres were was all being discovered new genres were being discovered we weren't just making being discovered we weren't just making being discovered we weren't just making games we felt we were we were creating a games we felt we were we were creating a games we felt we were we were creating a new entertainment medium that never new entertainment medium that never new entertainment medium that never existed before especially with these existed before especially with these existed before especially with these open world games and simulation games open world games and simulation games open world games and simulation games where you were co-create you as the where you were co-create you as the where you were co-create you as the player were co-creating the story player were co-creating the story player were co-creating the story there's no other media uh entertainment there's no other media uh entertainment there's no other media uh entertainment media where you do that where you as the media where you do that where you as the media where you do that where you as the audience actually co-create the the audience actually co-create the the audience actually co-create the the story and of course Now with multiplayer story and of course Now with multiplayer story and of course Now with multiplayer games as well, it can be a very social games as well, it can be a very social games as well, it can be a very social activity and can explore all kinds of activity and can explore all kinds of activity and can explore all kinds of interesting worlds in that. But on the interesting worlds in that. But on the interesting worlds in that. But on the other hand, you know, it's very other hand, you know, it's very other hand, you know, it's very important to um also enjoy and important to um also enjoy and important to um also enjoy and experience uh the physical world. But experience uh the physical world. But experience uh the physical world. But the question is then, you know, I think the question is then, you know, I think the question is then, you know, I think we're going to have to kind of confront we're going to have to kind of confront we're going to have to kind of confront the question again of what is the the question again of what is the the question again of what is the fundamental nature of reality? uh what fundamental nature of reality? uh what fundamental nature of reality? uh what is the going to be the difference is the going to be the difference is the going to be the difference between these increasingly realistic

between these increasingly realistic between these increasingly realistic simulations and uh multiplayer ones and simulations and uh multiplayer ones and simulations and uh multiplayer ones and emergent um and what we do in the real emergent um and what we do in the real emergent um and what we do in the real world. world. world. >> Yeah, there's clearly a huge amount of >> Yeah, there's clearly a huge amount of >> Yeah, there's clearly a huge amount of value to experiencing the real world value to experiencing the real world value to experiencing the real world nature. There's also a huge amount of nature. There's also a huge amount of nature. There's also a huge amount of value in experiencing other humans value in experiencing other humans value in experiencing other humans directly in person the way we're sitting directly in person the way we're sitting directly in person the way we're sitting here today. here today. here today. >> But we need to really scientifically >> But we need to really scientifically >> But we need to really scientifically rigorously answer the question why. rigorously answer the question why. rigorously answer the question why. >> Yeah. And which aspect of that can be >> Yeah. And which aspect of that can be >> Yeah. And which aspect of that can be mapped into the virtual world. mapped into the virtual world. mapped into the virtual world. >> Exactly. >> Exactly. >> Exactly. >> It's not it's not enough to say, "Yeah, >> It's not it's not enough to say, "Yeah, >> It's not it's not enough to say, "Yeah, you should go touch grass and hang out you should go touch grass and hang out you should go touch grass and hang out in nature." It's like, why exactly is in nature." It's like, why exactly is in nature." It's like, why exactly is that valuable? that valuable? that valuable? >> Yes. And I guess that's maybe the thing >> Yes. And I guess that's maybe the thing >> Yes. And I guess that's maybe the thing that's been uh haunting me, obsessing me that's been uh haunting me, obsessing me that's been uh haunting me, obsessing me from the beginning of my career. If you from the beginning of my career. If you from the beginning of my career. If you think about all the different things think about all the different things think about all the different things I've done, that's they're all related in I've done, that's they're all related in I've done, that's they're all related in that way. This simulation, nature of that way. This simulation, nature of that way. This simulation, nature of reality, and what is the bounds of, you reality, and what is the bounds of, you reality, and what is the bounds of, you know, what can be modeled. Sorry for the know, what can be modeled. Sorry for the know, what can be modeled. Sorry for the ridiculous question, but so far, what is ridiculous question, but so far, what is ridiculous question, but so far, what is the greatest video game of all time? the greatest video game of all time? the greatest video game of all time? What's up there? What's up there? What's up there? >> Well, my favorite one of all time is >> Well, my favorite one of all time is >> Well, my favorite one of all time is Civilization. I have to say that that Civilization. I have to say that that Civilization. I have to say that that was the the the Civilization 1 and was the the the Civilization 1 and was the the the Civilization 1 and Civilization 2. My favorite games of all Civilization 2. My favorite games of all Civilization 2. My favorite games of all time. Um time. Um time. Um >> I can only assume you've avoided the >> I can only assume you've avoided the >> I can only assume you've avoided the most recent one because it would most recent one because it would most recent one because it would probably you would that would be your probably you would that would be your probably you would that would be your sobatical that you would disappear.

sobatical that you would disappear. sobatical that you would disappear. >> Yes, exactly. They take a lot of time >> Yes, exactly. They take a lot of time >> Yes, exactly. They take a lot of time these Civilization games. So, I got to these Civilization games. So, I got to these Civilization games. So, I got to be careful with them. be careful with them. be careful with them. >> Fun question. You and Elon seem to be >> Fun question. You and Elon seem to be >> Fun question. You and Elon seem to be somehow solid gamers. Uh is there a somehow solid gamers. Uh is there a somehow solid gamers. Uh is there a connection between being great at gaming connection between being great at gaming connection between being great at gaming and and uh being great leaders of AI and and uh being great leaders of AI and and uh being great leaders of AI companies? companies? companies? >> I don't know. I It's an interesting one. >> I don't know. I It's an interesting one. >> I don't know. I It's an interesting one. I mean uh we both love games and uh it's I mean uh we both love games and uh it's I mean uh we both love games and uh it's interesting he wrote games as well to interesting he wrote games as well to interesting he wrote games as well to start off with. It's probably especially start off with. It's probably especially start off with. It's probably especially in the era I grew up in where home in the era I grew up in where home in the era I grew up in where home computers were just became a thing, you computers were just became a thing, you computers were just became a thing, you know, in the late ' 80s and '9s, know, in the late ' 80s and '9s, know, in the late ' 80s and '9s, especially in the UK. I had a Spectrum especially in the UK. I had a Spectrum especially in the UK. I had a Spectrum and then a Commodore Omega 500 which is and then a Commodore Omega 500 which is and then a Commodore Omega 500 which is my my favorite computer ever and that's my my favorite computer ever and that's my my favorite computer ever and that's why I learned all my programming and of why I learned all my programming and of why I learned all my programming and of course it's a very fun thing uh to course it's a very fun thing uh to course it's a very fun thing uh to program is to program games. So I think program is to program games. So I think program is to program games. So I think it's a great way to learn programming it's a great way to learn programming it's a great way to learn programming probably still is and um and then of probably still is and um and then of probably still is and um and then of course I immediately took it in course I immediately took it in course I immediately took it in directions of AI and simulations which directions of AI and simulations which directions of AI and simulations which so I may was able to express my interest so I may was able to express my interest so I may was able to express my interest in in games and my sort of wider in in games and my sort of wider in in games and my sort of wider scientific interests alto together. And scientific interests alto together. And scientific interests alto together. And then the final thing I think that's then the final thing I think that's then the final thing I think that's great about games is it fuses um great about games is it fuses um great about games is it fuses um artistic design, you know, art with the artistic design, you know, art with the artistic design, you know, art with the the the most cutting edge programming.

the the most cutting edge programming. the the most cutting edge programming. Um so again, in the '90s, all of the Um so again, in the '90s, all of the Um so again, in the '90s, all of the most interesting uh technical advances most interesting uh technical advances most interesting uh technical advances were happening in gaming, whether that were happening in gaming, whether that were happening in gaming, whether that was AI, graphics, physics engines, uh was AI, graphics, physics engines, uh was AI, graphics, physics engines, uh hardware, even GPUs of course were hardware, even GPUs of course were hardware, even GPUs of course were designed for gaming originally. Um so designed for gaming originally. Um so designed for gaming originally. Um so everything that was pushing computing everything that was pushing computing everything that was pushing computing forward in the in the '9s was due to forward in the in the '9s was due to forward in the in the '9s was due to gaming. So interestingly that was where gaming. So interestingly that was where gaming. So interestingly that was where the forefront of research was going on the forefront of research was going on the forefront of research was going on and it was this incredible fusion with and it was this incredible fusion with and it was this incredible fusion with with art um you know graphics but also with art um you know graphics but also with art um you know graphics but also music and just the whole new media of music and just the whole new media of music and just the whole new media of storytelling and I love that. For me storytelling and I love that. For me storytelling and I love that. For me it's this sort of multi-disiplinary kind it's this sort of multi-disiplinary kind it's this sort of multi-disiplinary kind of effort is again something I've of effort is again something I've of effort is again something I've enjoyed my whole my whole life. I have enjoyed my whole my whole life. I have enjoyed my whole my whole life. I have to ask you, I almost forgot about one of to ask you, I almost forgot about one of to ask you, I almost forgot about one of the many and I would say one of the most the many and I would say one of the most the many and I would say one of the most incredible things recently uh that incredible things recently uh that incredible things recently uh that somehow didn't yet get enough attention somehow didn't yet get enough attention somehow didn't yet get enough attention is alpha evolve. is alpha evolve. is alpha evolve. >> We talked about evolution a little bit >> We talked about evolution a little bit >> We talked about evolution a little bit but it's the Google deep mind system but it's the Google deep mind system but it's the Google deep mind system that evolves algorithms. that evolves algorithms. that evolves algorithms. >> Yeah. >> Yeah. >> Yeah. >> Are these kinds of evolution-like >> Are these kinds of evolution-like >> Are these kinds of evolution-like techniques promising as a component of techniques promising as a component of techniques promising as a component of future super intelligence system? So for future super intelligence system? So for future super intelligence system? So for people who don't know, it's kind of um I people who don't know, it's kind of um I people who don't know, it's kind of um I don't know if it's fair to say it's LLM don't know if it's fair to say it's LLM don't know if it's fair to say it's LLM guided guided guided evolution search.

evolution search. evolution search. >> Yeah. >> Yeah. >> Yeah. >> So evolutionary algorithms are doing the >> So evolutionary algorithms are doing the >> So evolutionary algorithms are doing the search and LLMs are telling you where. search and LLMs are telling you where. search and LLMs are telling you where. >> Yes. Exactly. So LLMs are kind of >> Yes. Exactly. So LLMs are kind of >> Yes. Exactly. So LLMs are kind of proposing some possible solutions and proposing some possible solutions and proposing some possible solutions and then you do you use evolutionary then you do you use evolutionary then you do you use evolutionary computing on top to to to find some computing on top to to to find some computing on top to to to find some novel part of the of the search space. novel part of the of the search space. novel part of the of the search space. So actually I think it's an example of So actually I think it's an example of So actually I think it's an example of very promising directions where you very promising directions where you very promising directions where you combine LLMs or foundation models with combine LLMs or foundation models with combine LLMs or foundation models with other computational techniques. other computational techniques. other computational techniques. Evolutionary methods is one but you Evolutionary methods is one but you Evolutionary methods is one but you could also imagine Monte Carlo research could also imagine Monte Carlo research could also imagine Monte Carlo research basically many types of search basically many types of search basically many types of search algorithms or reasoning algorithms sort algorithms or reasoning algorithms sort algorithms or reasoning algorithms sort of on top of or using the foundation of on top of or using the foundation of on top of or using the foundation models as a basis. So, I actually think models as a basis. So, I actually think models as a basis. So, I actually think there's quite a lot of interesting uh there's quite a lot of interesting uh there's quite a lot of interesting uh things to be discovered probably with things to be discovered probably with things to be discovered probably with these sort of hybrid systems, let's call these sort of hybrid systems, let's call these sort of hybrid systems, let's call them. them. them. >> But not to romanticize evolution. Yeah, >> But not to romanticize evolution. Yeah, >> But not to romanticize evolution. Yeah, >> I'm only human. But you think there's >> I'm only human. But you think there's >> I'm only human. But you think there's some value in whatever that mechanism is some value in whatever that mechanism is some value in whatever that mechanism is because we already talked about natural because we already talked about natural because we already talked about natural systems. Do you think where there's a systems. Do you think where there's a systems. Do you think where there's a lot of lowhanging fruit of us lot of lowhanging fruit of us lot of lowhanging fruit of us understanding being being able to model understanding being being able to model understanding being being able to model uh being able to simulate evolution and uh being able to simulate evolution and uh being able to simulate evolution and then using that whatever we understand then using that whatever we understand then using that whatever we understand about that nature inspired mechanism to about that nature inspired mechanism to about that nature inspired mechanism to to then do surge better and better and to then do surge better and better and to then do surge better and better and better.

better. better. >> Yes. So if you think about uh again >> Yes. So if you think about uh again >> Yes. So if you think about uh again breaking down the sort of systems we've breaking down the sort of systems we've breaking down the sort of systems we've built uh to their really fundamental built uh to their really fundamental built uh to their really fundamental core, you've got like the model of the core, you've got like the model of the core, you've got like the model of the of the underlying dynamics of the of the underlying dynamics of the of the underlying dynamics of the system. Uh and then if you want to system. Uh and then if you want to system. Uh and then if you want to discover something new, something novel discover something new, something novel discover something new, something novel that hasn't been seen before, um then that hasn't been seen before, um then that hasn't been seen before, um then you need some kind of search process on you need some kind of search process on you need some kind of search process on top to take you to a novel region of the top to take you to a novel region of the top to take you to a novel region of the of the of the search space. And um you of the of the search space. And um you of the of the search space. And um you can do that in a number of ways. can do that in a number of ways. can do that in a number of ways. Evolutionary computing is one. um with Evolutionary computing is one. um with Evolutionary computing is one. um with Alph Go we just use Monte Carlo research Alph Go we just use Monte Carlo research Alph Go we just use Monte Carlo research right and that's what found move 37 the right and that's what found move 37 the right and that's what found move 37 the new kind of never seen before strategy new kind of never seen before strategy new kind of never seen before strategy in go and so that's how you can go in go and so that's how you can go in go and so that's how you can go beyond potentially what is already known beyond potentially what is already known beyond potentially what is already known so the model can model everything that so the model can model everything that so the model can model everything that you currently know about right all the you currently know about right all the you currently know about right all the data that you currently have but then data that you currently have but then data that you currently have but then how do you go beyond that so that starts how do you go beyond that so that starts how do you go beyond that so that starts to speak about the ideas of creativity to speak about the ideas of creativity to speak about the ideas of creativity how can these systems create something how can these systems create something how can these systems create something new discover something new obviously new discover something new obviously new discover something new obviously this is super relevant for scientific this is super relevant for scientific this is super relevant for scientific discovery or pushing met science and discovery or pushing met science and discovery or pushing met science and medicine forward, which we want to do medicine forward, which we want to do medicine forward, which we want to do with these systems. And you can actually with these systems. And you can actually with these systems. And you can actually bolt on some uh fairly simple search bolt on some uh fairly simple search bolt on some uh fairly simple search systems on top of these models and get systems on top of these models and get systems on top of these models and get you into a new region of space. Of you into a new region of space. Of you into a new region of space. Of course, you also have to um make sure course, you also have to um make sure course, you also have to um make sure that you're not searching that space that you're not searching that space that you're not searching that space totally randomly. It would be too big.

totally randomly. It would be too big. totally randomly. It would be too big. So, you have to have some objective So, you have to have some objective So, you have to have some objective function that you're trying to optimize function that you're trying to optimize function that you're trying to optimize and hill climb towards and that guides and hill climb towards and that guides and hill climb towards and that guides that search. But there's some mechanism that search. But there's some mechanism that search. But there's some mechanism of evolution that are interesting maybe of evolution that are interesting maybe of evolution that are interesting maybe in the space of programs. But then the in the space of programs. But then the in the space of programs. But then the space of programs is an extremely space of programs is an extremely space of programs is an extremely important space because you can probably important space because you can probably important space because you can probably generalize to to everything you know for generalize to to everything you know for generalize to to everything you know for example mutation. example mutation. example mutation. So it's not just Monte Carlo tree search So it's not just Monte Carlo tree search So it's not just Monte Carlo tree search where it's like a search. where it's like a search. where it's like a search. >> You could every once in a while >> You could every once in a while >> You could every once in a while >> combine things. Yeah. >> combine things. Yeah. >> combine things. Yeah. >> Combine things alter like sub like a >> Combine things alter like sub like a >> Combine things alter like sub like a components of a thing. Yes. So then you components of a thing. Yes. So then you components of a thing. Yes. So then you know what evolution is really good at is know what evolution is really good at is know what evolution is really good at is not just the natural selection. not just the natural selection. not just the natural selection. It's combining things and building It's combining things and building It's combining things and building increasingly complex hierarchical increasingly complex hierarchical increasingly complex hierarchical systems. systems. systems. >> So that component is super interesting >> So that component is super interesting >> So that component is super interesting especially like with alpha evolve in the especially like with alpha evolve in the especially like with alpha evolve in the space of programs. space of programs. space of programs. >> Yeah. Exactly. So there's a you can get >> Yeah. Exactly. So there's a you can get >> Yeah. Exactly. So there's a you can get a bit of an extra property out of a bit of an extra property out of a bit of an extra property out of evolutionary systems which is some new evolutionary systems which is some new evolutionary systems which is some new emergent capability may come about but emergent capability may come about but emergent capability may come about but of course like happened with life. of course like happened with life. of course like happened with life. Interestingly, with naive uh sort of Interestingly, with naive uh sort of Interestingly, with naive uh sort of traditional evolutionary computing traditional evolutionary computing traditional evolutionary computing methods without LLMs and the modern AI, methods without LLMs and the modern AI, methods without LLMs and the modern AI, the problem with them, there was they the problem with them, there was they the problem with them, there was they were very well studied in the 90s and were very well studied in the 90s and were very well studied in the 90s and and and and early 2000s and some and and and early 2000s and some and and and early 2000s and some promising results, but the problem was promising results, but the problem was promising results, but the problem was they could never work out how to evolve they could never work out how to evolve they could never work out how to evolve new properties, new emergent properties.

new properties, new emergent properties. new properties, new emergent properties. You always had a sort of subset of the You always had a sort of subset of the You always had a sort of subset of the properties that you put into the system. properties that you put into the system. properties that you put into the system. But maybe if we combine them with these But maybe if we combine them with these But maybe if we combine them with these foundation models, perhaps we can foundation models, perhaps we can foundation models, perhaps we can overcome that limitation. Obviously uh overcome that limitation. Obviously uh overcome that limitation. Obviously uh natural evolution clearly did because it natural evolution clearly did because it natural evolution clearly did because it it did evolve new capabilities right so it did evolve new capabilities right so it did evolve new capabilities right so bacteria to where we are now. So clearly bacteria to where we are now. So clearly bacteria to where we are now. So clearly that it must be possible with that it must be possible with that it must be possible with evolutionary systems to generate uh new evolutionary systems to generate uh new evolutionary systems to generate uh new patterns you know going back to the patterns you know going back to the patterns you know going back to the first thing we talked about and uh new first thing we talked about and uh new first thing we talked about and uh new capabilities and emergent properties and capabilities and emergent properties and capabilities and emergent properties and maybe we're on the cusp of discovering maybe we're on the cusp of discovering maybe we're on the cusp of discovering how to do that. how to do that. how to do that. >> Yeah listen uh alpha evolve is one of >> Yeah listen uh alpha evolve is one of >> Yeah listen uh alpha evolve is one of the coolest things I've ever seen. I've the coolest things I've ever seen. I've the coolest things I've ever seen. I've I've on my desk at home, you know, most I've on my desk at home, you know, most I've on my desk at home, you know, most of my time is spent behind that of my time is spent behind that of my time is spent behind that computers just programming. And next to computers just programming. And next to computers just programming. And next to the the three screens is a skull of a the the three screens is a skull of a the the three screens is a skull of a tectalic, which is one of the early tectalic, which is one of the early tectalic, which is one of the early organisms that crawled out of the water organisms that crawled out of the water organisms that crawled out of the water onto land. And I just kind of watch that onto land. And I just kind of watch that onto land. And I just kind of watch that little guy. little guy. little guy. It's like you whatever the computation It's like you whatever the computation It's like you whatever the computation mechanism of evolution is is quite mechanism of evolution is is quite mechanism of evolution is is quite incredible. It's truly truly incredible.

incredible. It's truly truly incredible. incredible. It's truly truly incredible. Now whether that's exactly the thing we Now whether that's exactly the thing we Now whether that's exactly the thing we need to do to do our search but never need to do to do our search but never need to do to do our search but never dismiss the power of nature what it did dismiss the power of nature what it did dismiss the power of nature what it did here. here. here. >> Yeah. And it's amazing um which is a >> Yeah. And it's amazing um which is a >> Yeah. And it's amazing um which is a relatively simple algorithm right relatively simple algorithm right relatively simple algorithm right effectively and it can generate all of effectively and it can generate all of effectively and it can generate all of this immense complexity emerges this immense complexity emerges this immense complexity emerges obviously running over you know 4 obviously running over you know 4 obviously running over you know 4 billion years of time but but it's it's billion years of time but but it's it's billion years of time but but it's it's it's you know you can think about that it's you know you can think about that it's you know you can think about that as again a pro a search process that ran as again a pro a search process that ran as again a pro a search process that ran over the physics substrate of the over the physics substrate of the over the physics substrate of the universe for a long amount of universe for a long amount of universe for a long amount of computational time but then it generated computational time but then it generated computational time but then it generated all this incredible uh rich diversity. all this incredible uh rich diversity. all this incredible uh rich diversity. >> So uh so many questions I want to ask >> So uh so many questions I want to ask >> So uh so many questions I want to ask you. But one, you do have a dream. One you. But one, you do have a dream. One you. But one, you do have a dream. One of the natural systems you want to uh of the natural systems you want to uh of the natural systems you want to uh try to model is a is a cell. try to model is a is a cell. try to model is a is a cell. >> Yes, >> Yes, >> Yes, >> that's a beautiful dream. Uh I could ask >> that's a beautiful dream. Uh I could ask >> that's a beautiful dream. Uh I could ask you about that. I also just for that you about that. I also just for that you about that. I also just for that purpose on the AI scientist front just purpose on the AI scientist front just purpose on the AI scientist front just broadly. So there's a essay uh from broadly. So there's a essay uh from broadly. So there's a essay uh from Daniel Cocatalio, Scott Alexander, and Daniel Cocatalio, Scott Alexander, and Daniel Cocatalio, Scott Alexander, and others that outlines steps along the way others that outlines steps along the way others that outlines steps along the way to get to ASI and has a lot of to get to ASI and has a lot of to get to ASI and has a lot of interesting ideas in it. one of which is interesting ideas in it. one of which is interesting ideas in it. one of which is uh including a superhuman coder and a uh including a superhuman coder and a uh including a superhuman coder and a superhuman AI researcher superhuman AI researcher superhuman AI researcher and in that there's a term of research and in that there's a term of research and in that there's a term of research taste that's really interesting. So in taste that's really interesting. So in taste that's really interesting. So in everything you've seen, do you think everything you've seen, do you think everything you've seen, do you think it's possible for AI systems to have it's possible for AI systems to have it's possible for AI systems to have research taste to help you in the way research taste to help you in the way research taste to help you in the way that AI co-scientist does to help steer that AI co-scientist does to help steer that AI co-scientist does to help steer human um human brilliant scientists and

human um human brilliant scientists and human um human brilliant scientists and then potentially by itself to figure out then potentially by itself to figure out then potentially by itself to figure out what are the directions what are the directions what are the directions where you want to generate truly novel where you want to generate truly novel where you want to generate truly novel ideas because that seems to be like a ideas because that seems to be like a ideas because that seems to be like a really important component how to do really important component how to do really important component how to do great science. Yeah, I think that's great science. Yeah, I think that's great science. Yeah, I think that's going to be one of the hardest things to going to be one of the hardest things to going to be one of the hardest things to to uh mimic or model is is this this to uh mimic or model is is this this to uh mimic or model is is this this idea of taste or or judgment. I think idea of taste or or judgment. I think idea of taste or or judgment. I think that's what separates the you know the that's what separates the you know the that's what separates the you know the the great scientists from the good the great scientists from the good the great scientists from the good scientists like all all professional scientists like all all professional scientists like all all professional scientists are good technically right scientists are good technically right scientists are good technically right otherwise they wouldn't have made it otherwise they wouldn't have made it otherwise they wouldn't have made it that far in in academia and things like that far in in academia and things like that far in in academia and things like that but then do you have the taste to that but then do you have the taste to that but then do you have the taste to sort of sniff out what the right sort of sniff out what the right sort of sniff out what the right direction is what the right experiment direction is what the right experiment direction is what the right experiment is what the right question is. So the is what the right question is. So the is what the right question is. So the it's the it's picking the right question it's the it's picking the right question it's the it's picking the right question is is the hardest part of science. Um is is the hardest part of science. Um is is the hardest part of science. Um and and making the right hypothesis and and and making the right hypothesis and and and making the right hypothesis and um that's what you know today's systems um that's what you know today's systems um that's what you know today's systems definitely they can't do. So you know I definitely they can't do. So you know I definitely they can't do. So you know I often say it's harder to come up with a often say it's harder to come up with a often say it's harder to come up with a conjecture a really good conjecture than conjecture a really good conjecture than conjecture a really good conjecture than it is to solve it. So we may have it is to solve it. So we may have it is to solve it. So we may have systems soon that can solve pretty hard systems soon that can solve pretty hard systems soon that can solve pretty hard conjectures. um you know I I um mass conjectures. um you know I I um mass conjectures. um you know I I um mass Olympiad problems where we we you know Olympiad problems where we we you know Olympiad problems where we we you know alpha proof last year our system got you alpha proof last year our system got you alpha proof last year our system got you know silver medal in that really hard know silver medal in that really hard know silver medal in that really hard problems maybe eventually we'll be able problems maybe eventually we'll be able problems maybe eventually we'll be able to solve a millennium prize kind of to solve a millennium prize kind of to solve a millennium prize kind of problem but could a system have come up problem but could a system have come up problem but could a system have come up with a conjecture worthy of study that with a conjecture worthy of study that with a conjecture worthy of study that someone like Terren Tower would have someone like Terren Tower would have someone like Terren Tower would have gone you know what that's a really deep gone you know what that's a really deep gone you know what that's a really deep question about the nature of maths or question about the nature of maths or question about the nature of maths or the nature of numbers or the nature of the nature of numbers or the nature of the nature of numbers or the nature of physics and that is far harder type of physics and that is far harder type of physics and that is far harder type of creativity and we don't really Oh, creativity and we don't really Oh, creativity and we don't really Oh, systems clearly can't do that and we're

systems clearly can't do that and we're systems clearly can't do that and we're not quite sure what that mechanism would not quite sure what that mechanism would not quite sure what that mechanism would be. This kind of leap of imagination be. This kind of leap of imagination be. This kind of leap of imagination like like Einstein had when he came up like like Einstein had when he came up like like Einstein had when he came up with, you know, special relativity and with, you know, special relativity and with, you know, special relativity and then general relativity with the then general relativity with the then general relativity with the knowledge he had at the time. knowledge he had at the time. knowledge he had at the time. >> As for conjecture, >> As for conjecture, >> As for conjecture, the you want to come up with a thing the you want to come up with a thing the you want to come up with a thing that's interesting and amenable to that's interesting and amenable to that's interesting and amenable to proof. proof. proof. >> Yes. >> Yes. >> Yes. >> So like it's easy to come up with a >> So like it's easy to come up with a >> So like it's easy to come up with a thing that's extremely difficult. thing that's extremely difficult. thing that's extremely difficult. >> Yeah. >> Yeah. >> Yeah. >> It's easy to come up with a thing that's >> It's easy to come up with a thing that's >> It's easy to come up with a thing that's extremely easy. at that at that very extremely easy. at that at that very extremely easy. at that at that very edge, edge, edge, >> that sweet spot, right, of of basically >> that sweet spot, right, of of basically >> that sweet spot, right, of of basically advancing the science and splitting the advancing the science and splitting the advancing the science and splitting the hypothesis space into two ideally, hypothesis space into two ideally, hypothesis space into two ideally, right? Whether if it's true or not true, right? Whether if it's true or not true, right? Whether if it's true or not true, you you've learned something really you you've learned something really you you've learned something really useful and um and and that's hard and useful and um and and that's hard and useful and um and and that's hard and and and and making something that's also and and and making something that's also and and and making something that's also uh you know falsifiable and within sort uh you know falsifiable and within sort uh you know falsifiable and within sort of the technologies that you have you of the technologies that you have you of the technologies that you have you currently have available. So it's a very currently have available. So it's a very currently have available. So it's a very creative process actually highly creative process actually highly creative process actually highly creative process that um I think just a creative process that um I think just a creative process that um I think just a kind of naive search on top of a model kind of naive search on top of a model kind of naive search on top of a model won't be enough for that. won't be enough for that. won't be enough for that. >> Okay. The idea of splitting the >> Okay. The idea of splitting the >> Okay. The idea of splitting the hypothesis space in two is super hypothesis space in two is super hypothesis space in two is super interesting. So uh I've heard you say interesting. So uh I've heard you say interesting. So uh I've heard you say that there's basically no failure in or that there's basically no failure in or that there's basically no failure in or failure is extremely valuable if it's failure is extremely valuable if it's failure is extremely valuable if it's done if you construct the questions done if you construct the questions done if you construct the questions right if you construct the experiments right if you construct the experiments right if you construct the experiments right if you design them right that right if you design them right that right if you design them right that failure success are both useful. So failure success are both useful. So failure success are both useful. So perhaps because it splits the hypothesis perhaps because it splits the hypothesis perhaps because it splits the hypothesis basically two, it's like a binary basically two, it's like a binary basically two, it's like a binary search.

search. search. >> That's right. So when you do like, you >> That's right. So when you do like, you >> That's right. So when you do like, you know, real blue sky research, there's no know, real blue sky research, there's no know, real blue sky research, there's no such thing as failure really as long as such thing as failure really as long as such thing as failure really as long as you're picking experiments and you're picking experiments and you're picking experiments and hypotheses that that that that hypotheses that that that that hypotheses that that that that meaningfully spit the hypothesis space. meaningfully spit the hypothesis space. meaningfully spit the hypothesis space. So you know, and you learn something, So you know, and you learn something, So you know, and you learn something, you can learn something kind of equally you can learn something kind of equally you can learn something kind of equally valuable from an experiment that doesn't valuable from an experiment that doesn't valuable from an experiment that doesn't work. That should tell you, if you've work. That should tell you, if you've work. That should tell you, if you've designed the experiment well and your designed the experiment well and your designed the experiment well and your hypothesis are interesting, it should hypothesis are interesting, it should hypothesis are interesting, it should tell you a lot about where to go next. tell you a lot about where to go next. tell you a lot about where to go next. and um and then it's you're effectively and um and then it's you're effectively and um and then it's you're effectively doing a search process um and using that doing a search process um and using that doing a search process um and using that information in in you know very helpful information in in you know very helpful information in in you know very helpful ways. So to go to your dream ways. So to go to your dream ways. So to go to your dream of uh modeling a cell uh what are the of uh modeling a cell uh what are the of uh modeling a cell uh what are the big challenges that lay ahead for us to big challenges that lay ahead for us to big challenges that lay ahead for us to make that happen? We should maybe make that happen? We should maybe make that happen? We should maybe highlight that alpha I mean there's just highlight that alpha I mean there's just highlight that alpha I mean there's just so many leaps. so many leaps. so many leaps. >> Yeah. >> Yeah. >> Yeah. >> So AlphaFold solved if it's fair to say >> So AlphaFold solved if it's fair to say >> So AlphaFold solved if it's fair to say protein folding and there's so many protein folding and there's so many protein folding and there's so many incredible things we could talk about incredible things we could talk about incredible things we could talk about there including the open sourcing uh the there including the open sourcing uh the there including the open sourcing uh the everything you've released. Alpha Fold 3 everything you've released. Alpha Fold 3 everything you've released. Alpha Fold 3 is doing protein, RNA, DNA interactions, is doing protein, RNA, DNA interactions, is doing protein, RNA, DNA interactions, >> which is super complicated and and >> which is super complicated and and >> which is super complicated and and fascinating. That's amendable to fascinating. That's amendable to fascinating. That's amendable to modeling. Alpha genome uh predicts uh modeling. Alpha genome uh predicts uh modeling. Alpha genome uh predicts uh how small genetic changes like if we how small genetic changes like if we how small genetic changes like if we think about single mutations, how they think about single mutations, how they think about single mutations, how they link to actual uh function. So um those link to actual uh function. So um those link to actual uh function. So um those are it seems like it's creeping along to are it seems like it's creeping along to are it seems like it's creeping along to sophistic to to much more complicated u sophistic to to much more complicated u sophistic to to much more complicated u things like a cell but a cell has a lot things like a cell but a cell has a lot things like a cell but a cell has a lot of really complicated components.

of really complicated components. of really complicated components. >> Yeah. So what I've tried to do >> Yeah. So what I've tried to do >> Yeah. So what I've tried to do throughout my career is I have these throughout my career is I have these throughout my career is I have these really grand dreams and then I try to as really grand dreams and then I try to as really grand dreams and then I try to as you've noticed and then I try to break you've noticed and then I try to break you've noticed and then I try to break but I try to break them down any you but I try to break them down any you but I try to break them down any you know it's easy to have a kind of a crazy know it's easy to have a kind of a crazy know it's easy to have a kind of a crazy ambitious dream but the the the trick is ambitious dream but the the the trick is ambitious dream but the the the trick is how do you break it down into manageable how do you break it down into manageable how do you break it down into manageable achievable uh interim steps that are achievable uh interim steps that are achievable uh interim steps that are meaningful and useful in their own right meaningful and useful in their own right meaningful and useful in their own right and so virtual cell which is what I call and so virtual cell which is what I call and so virtual cell which is what I call the project of modeling a cell I've had the project of modeling a cell I've had the project of modeling a cell I've had this idea you know of wanting to do that this idea you know of wanting to do that this idea you know of wanting to do that for maybe more like 25 is and I used to for maybe more like 25 is and I used to for maybe more like 25 is and I used to talk with Paul Nurse who is a bit of a talk with Paul Nurse who is a bit of a talk with Paul Nurse who is a bit of a mentor of mine in biology. He runs the mentor of mine in biology. He runs the mentor of mine in biology. He runs the the you know founded the Craig Institute the you know founded the Craig Institute the you know founded the Craig Institute and and won the Nobel Prize in in 2001. and and won the Nobel Prize in in 2001. and and won the Nobel Prize in in 2001. uh is is we've been talking about it uh is is we've been talking about it uh is is we've been talking about it since you know before the you know in since you know before the you know in since you know before the you know in the '90s and um and I come used to come the '90s and um and I come used to come the '90s and um and I come used to come back to every 5 years is like what would back to every 5 years is like what would back to every 5 years is like what would you need to model the full internals of you need to model the full internals of you need to model the full internals of a cell so that you could do experiments a cell so that you could do experiments a cell so that you could do experiments on the virtual cell and what those on the virtual cell and what those on the virtual cell and what those experiment you know in silicone and experiment you know in silicone and experiment you know in silicone and those predictions would be useful for those predictions would be useful for those predictions would be useful for you to save you a lot of time in the wet you to save you a lot of time in the wet you to save you a lot of time in the wet lab right that would be the dream maybe lab right that would be the dream maybe lab right that would be the dream maybe you could 100x speed up experiments by you could 100x speed up experiments by you could 100x speed up experiments by doing most of it in silicone the search doing most of it in silicone the search doing most of it in silicone the search in silicico and then you do the in silicico and then you do the in silicico and then you do the validation step in the wet lab. That validation step in the wet lab. That validation step in the wet lab. That would be that's the that's the dream.

would be that's the that's the dream. would be that's the that's the dream. And so u but maybe now finally uh so I And so u but maybe now finally uh so I And so u but maybe now finally uh so I was trying to build these components was trying to build these components was trying to build these components alpha fold being one that that would alpha fold being one that that would alpha fold being one that that would allow you eventually to model the full allow you eventually to model the full allow you eventually to model the full interaction a full simulation of a cell interaction a full simulation of a cell interaction a full simulation of a cell and I'd probably start with a yeast cell and I'd probably start with a yeast cell and I'd probably start with a yeast cell and partly that's what Paul nurse and partly that's what Paul nurse and partly that's what Paul nurse studied because a yeast cell is like a studied because a yeast cell is like a studied because a yeast cell is like a full organism that's a single cell right full organism that's a single cell right full organism that's a single cell right so it's the kind of simplest single cell so it's the kind of simplest single cell so it's the kind of simplest single cell organism and so it's not just a cell organism and so it's not just a cell organism and so it's not just a cell it's a full organism and um and yeast is it's a full organism and um and yeast is it's a full organism and um and yeast is very well understood And so that would very well understood And so that would very well understood And so that would be a good candidate for uh a a kind of be a good candidate for uh a a kind of be a good candidate for uh a a kind of full simulated model. Now alpha fold is full simulated model. Now alpha fold is full simulated model. Now alpha fold is the is the solution to the kind of the is the solution to the kind of the is the solution to the kind of static picture of what does a what does static picture of what does a what does static picture of what does a what does a protein look 3D structure protein look a protein look 3D structure protein look a protein look 3D structure protein look like a static picture of it. But we know like a static picture of it. But we know like a static picture of it. But we know that biology all the interesting things that biology all the interesting things that biology all the interesting things happen with the dynamics the happen with the dynamics the happen with the dynamics the interactions and that's what alpha 3 is interactions and that's what alpha 3 is interactions and that's what alpha 3 is is the first step towards is modeling is the first step towards is modeling is the first step towards is modeling those interactions. So first of all those interactions. So first of all those interactions. So first of all pairwise you know proteins with proteins pairwise you know proteins with proteins pairwise you know proteins with proteins proteins with RNA and DNA but then um proteins with RNA and DNA but then um proteins with RNA and DNA but then um the next step after that would be the next step after that would be the next step after that would be modeling maybe a whole pathway maybe modeling maybe a whole pathway maybe modeling maybe a whole pathway maybe like the to pathway that's involved in like the to pathway that's involved in like the to pathway that's involved in cancer or something like this and then cancer or something like this and then cancer or something like this and then eventually you might be able to model eventually you might be able to model eventually you might be able to model you know a whole cell you know a whole cell you know a whole cell >> also there's another complexity here >> also there's another complexity here >> also there's another complexity here that stuff in a cell happens at that stuff in a cell happens at that stuff in a cell happens at different time scales is that tricky different time scales is that tricky different time scales is that tricky like there you know protein uh folding like there you know protein uh folding like there you know protein uh folding is you know super fast is you know super fast is you know super fast >> yes >> yes >> yes >> um I don't know all the bi ological >> um I don't know all the bi ological >> um I don't know all the bi ological mechanisms, but some of them take a long mechanisms, but some of them take a long mechanisms, but some of them take a long time. And so is that that's an level. So time. And so is that that's an level. So time. And so is that that's an level. So the levels of interaction has a the levels of interaction has a the levels of interaction has a different temporal scale that you have different temporal scale that you have different temporal scale that you have to be able to model.

to be able to model. to be able to model. >> So that would be hard. So you'd probably >> So that would be hard. So you'd probably >> So that would be hard. So you'd probably need several simulated systems that can need several simulated systems that can need several simulated systems that can interact at these different temporal interact at these different temporal interact at these different temporal dynamics or at least maybe it's like a dynamics or at least maybe it's like a dynamics or at least maybe it's like a hierarchical system. So um you can jump hierarchical system. So um you can jump hierarchical system. So um you can jump up and down the the different temporal up and down the the different temporal up and down the the different temporal stages. So can you avoid I mean one of stages. So can you avoid I mean one of stages. So can you avoid I mean one of the challenges here is the challenges here is the challenges here is not avoid simulating for example the the not avoid simulating for example the the not avoid simulating for example the the the quantum mechanical aspects of any of the quantum mechanical aspects of any of the quantum mechanical aspects of any of this right you want to not overm model this right you want to not overm model this right you want to not overm model you can skip ahead to just model the you can skip ahead to just model the you can skip ahead to just model the really highlevel things that get you a really highlevel things that get you a really highlevel things that get you a really good estimate of what's going to really good estimate of what's going to really good estimate of what's going to happen happen happen >> so you you got to make a decision when >> so you you got to make a decision when >> so you you got to make a decision when you're modeling any natural system what you're modeling any natural system what you're modeling any natural system what is the cutoff level of the granularity is the cutoff level of the granularity is the cutoff level of the granularity that you're going to model it to that that you're going to model it to that that you're going to model it to that then captures the dynamics that you're then captures the dynamics that you're then captures the dynamics that you're interested in. So probably for a cell I interested in. So probably for a cell I interested in. So probably for a cell I would hope that would be the protein would hope that would be the protein would hope that would be the protein level uh and that one wouldn't have to level uh and that one wouldn't have to level uh and that one wouldn't have to go down to the atomic level. Um so you go down to the atomic level. Um so you go down to the atomic level. Um so you know of course that's where alpha volt know of course that's where alpha volt know of course that's where alpha volt stock kicks in. So that would be kind of stock kicks in. So that would be kind of stock kicks in. So that would be kind of the basis and then you'd build these um the basis and then you'd build these um the basis and then you'd build these um uh higher level simulations that um take uh higher level simulations that um take uh higher level simulations that um take those as building blocks and then you those as building blocks and then you those as building blocks and then you get the emergent behavior. Apologize for get the emergent behavior. Apologize for get the emergent behavior. Apologize for the pthead questions ahead of time, but the pthead questions ahead of time, but the pthead questions ahead of time, but uh will do you think uh we'll be able to uh will do you think uh we'll be able to uh will do you think uh we'll be able to simulate and model the origin of life.

simulate and model the origin of life. simulate and model the origin of life. So being able to simulate the first from So being able to simulate the first from So being able to simulate the first from from non-living organisms the the birth from non-living organisms the the birth from non-living organisms the the birth of a living organism. of a living organism. of a living organism. >> I think that's a one of the of course >> I think that's a one of the of course >> I think that's a one of the of course one of the deepest and most fascinating one of the deepest and most fascinating one of the deepest and most fascinating questions. Um I love that area of questions. Um I love that area of questions. Um I love that area of biology. you know, uh, people like biology. you know, uh, people like biology. you know, uh, people like there's a great book by Nick Lane, one there's a great book by Nick Lane, one there's a great book by Nick Lane, one of the top top experts in this area of the top top experts in this area of the top top experts in this area called the the 10 great inventions of of called the the 10 great inventions of of called the the 10 great inventions of of of evolution. I think it's fantastic and of evolution. I think it's fantastic and of evolution. I think it's fantastic and it also speaks to what the great filters it also speaks to what the great filters it also speaks to what the great filters might be, you know, prior or are they might be, you know, prior or are they might be, you know, prior or are they ahead of us. I think I think they're ahead of us. I think I think they're ahead of us. I think I think they're most likely in the past if you read that most likely in the past if you read that most likely in the past if you read that book of how unlikely to go, you know, book of how unlikely to go, you know, book of how unlikely to go, you know, have any life at all and then single have any life at all and then single have any life at all and then single cell to multisell seems an unbelievably cell to multisell seems an unbelievably cell to multisell seems an unbelievably big jump that took like a billion years, big jump that took like a billion years, big jump that took like a billion years, I think, on Earth to do, right? So it I think, on Earth to do, right? So it I think, on Earth to do, right? So it shows you how hard it was, right? shows you how hard it was, right? shows you how hard it was, right? >> Bacteria were super happy for a very >> Bacteria were super happy for a very >> Bacteria were super happy for a very long time, long time, long time, >> a very long time before they captured >> a very long time before they captured >> a very long time before they captured mitochondria somehow, right? I don't see mitochondria somehow, right? I don't see mitochondria somehow, right? I don't see why not why AI couldn't help with that why not why AI couldn't help with that why not why AI couldn't help with that some kind of simulation. Again, it's some kind of simulation. Again, it's some kind of simulation. Again, it's again, it's a bit of a search process again, it's a bit of a search process again, it's a bit of a search process through a combinatorial space. Here's through a combinatorial space. Here's through a combinatorial space. Here's like all the chem, you know, the like all the chem, you know, the like all the chem, you know, the chemical soup that that you start with, chemical soup that that you start with, chemical soup that that you start with, the primordial soup that, you know, the primordial soup that, you know, the primordial soup that, you know, maybe was on Earth near these hot vents.

maybe was on Earth near these hot vents. maybe was on Earth near these hot vents. Here's some initial conditions. Can you Here's some initial conditions. Can you Here's some initial conditions. Can you uh generate something that looks like a uh generate something that looks like a uh generate something that looks like a cell? So perhaps that would be a next cell? So perhaps that would be a next cell? So perhaps that would be a next stage after the virtual cell project is stage after the virtual cell project is stage after the virtual cell project is well how how could you actually um well how how could you actually um well how how could you actually um something like that emerge from the something like that emerge from the something like that emerge from the chemical soup? chemical soup? chemical soup? >> Well, I would love it if there was a >> Well, I would love it if there was a >> Well, I would love it if there was a move 37 for the origin of life. Yeah, move 37 for the origin of life. Yeah, move 37 for the origin of life. Yeah, >> I think that's one of the sort of great >> I think that's one of the sort of great >> I think that's one of the sort of great mysteries. I think ultimately what we mysteries. I think ultimately what we mysteries. I think ultimately what we will figure out is their continuum. will figure out is their continuum. will figure out is their continuum. There's no such thing as a line between There's no such thing as a line between There's no such thing as a line between non-living and living. But if we can non-living and living. But if we can non-living and living. But if we can make that rigorous Yes. make that rigorous Yes. make that rigorous Yes. >> that that the very thing from the be big >> that that the very thing from the be big >> that that the very thing from the be big bang to today has been the same process. bang to today has been the same process. bang to today has been the same process. If we can break down that wall that If we can break down that wall that If we can break down that wall that we've constructed in our minds of the we've constructed in our minds of the we've constructed in our minds of the actual origin of from non-living to actual origin of from non-living to actual origin of from non-living to living and it's not a line that it's a living and it's not a line that it's a living and it's not a line that it's a continuum that connects physics and continuum that connects physics and continuum that connects physics and chemistry and biology. There's no line. chemistry and biology. There's no line. chemistry and biology. There's no line. >> I mean this is my whole reason why I've >> I mean this is my whole reason why I've >> I mean this is my whole reason why I've worked on AI and AGI my whole life worked on AI and AGI my whole life worked on AI and AGI my whole life because I think it can be the ultimate because I think it can be the ultimate because I think it can be the ultimate tool to help us answer these kind of tool to help us answer these kind of tool to help us answer these kind of questions. And I don't really understand questions. And I don't really understand questions. And I don't really understand why um you know the average person why um you know the average person why um you know the average person doesn't think like worry about this doesn't think like worry about this doesn't think like worry about this stuff more like how how can we not have stuff more like how how can we not have stuff more like how how can we not have a good definition of life and not and a good definition of life and not and a good definition of life and not and not living and non-living and the nature not living and non-living and the nature not living and non-living and the nature of time and let alone consciousness and of time and let alone consciousness and of time and let alone consciousness and gravity and all these things. It's it's gravity and all these things. It's it's gravity and all these things. It's it's just and quantum mechanics weirdness.

just and quantum mechanics weirdness. just and quantum mechanics weirdness. It's just to me it's I've always had It's just to me it's I've always had It's just to me it's I've always had this sort of screaming at me in my face this sort of screaming at me in my face this sort of screaming at me in my face the whole and that it's getting louder the whole and that it's getting louder the whole and that it's getting louder you It's like how what is going on here? you It's like how what is going on here? you It's like how what is going on here? You know, in in I mean that in the You know, in in I mean that in the You know, in in I mean that in the deepest sense like in the you know the deepest sense like in the you know the deepest sense like in the you know the nature of reality which has to be the nature of reality which has to be the nature of reality which has to be the ultimate question uh that would answer ultimate question uh that would answer ultimate question uh that would answer all of these things. It's sort of crazy all of these things. It's sort of crazy all of these things. It's sort of crazy if you think about we can stare at each if you think about we can stare at each if you think about we can stare at each other and all these living things all other and all these living things all other and all these living things all the time. We can inspect it with the time. We can inspect it with the time. We can inspect it with microscopes and take it apart uh almost microscopes and take it apart uh almost microscopes and take it apart uh almost down to the atomic level and yet we down to the atomic level and yet we down to the atomic level and yet we still can't answer that clearly in a still can't answer that clearly in a still can't answer that clearly in a simple way that question of how do you simple way that question of how do you simple way that question of how do you define living? define living? define living? >> Yeah, >> Yeah, >> Yeah, >> it's kind of amazing. Yeah, living you >> it's kind of amazing. Yeah, living you >> it's kind of amazing. Yeah, living you can kind of talk your way out of can kind of talk your way out of can kind of talk your way out of thinking about but like consciousness thinking about but like consciousness thinking about but like consciousness like we have this very obviously like we have this very obviously like we have this very obviously subjective conscious experience like subjective conscious experience like subjective conscious experience like we're at the center of our own world and we're at the center of our own world and we're at the center of our own world and it it feels like something and then h it it feels like something and then h it it feels like something and then h how how are you not screaming how how are you not screaming how how are you not screaming >> at the mystery of it all I mean but >> at the mystery of it all I mean but >> at the mystery of it all I mean but really humans have been contending with really humans have been contending with really humans have been contending with the mystery of the world around them for the mystery of the world around them for the mystery of the world around them for long there's a lot of mysteries like long there's a lot of mysteries like long there's a lot of mysteries like what's up with the sun and and the rain, what's up with the sun and and the rain, what's up with the sun and and the rain, >> like what's that about? And then like >> like what's that about? And then like >> like what's that about? And then like last year we had a lot of rain and this last year we had a lot of rain and this last year we had a lot of rain and this year we don't have rain. Like what did year we don't have rain. Like what did year we don't have rain. Like what did we do wrong? Humans have been asking we do wrong? Humans have been asking we do wrong? Humans have been asking that question for a long time.

that question for a long time. that question for a long time. >> Exactly. So we're quite I guess we've >> Exactly. So we're quite I guess we've >> Exactly. So we're quite I guess we've developed a lot of mechanisms to cope developed a lot of mechanisms to cope developed a lot of mechanisms to cope with this these deep mysteries that we with this these deep mysteries that we with this these deep mysteries that we can't fully we can see but we can't can't fully we can see but we can't can't fully we can see but we can't fully understand and we have to have to fully understand and we have to have to fully understand and we have to have to just get on with daily life and and and just get on with daily life and and and just get on with daily life and and and we get we keep ourselves busy right in a we get we keep ourselves busy right in a we get we keep ourselves busy right in a way. Do we keep ourselves distracted? way. Do we keep ourselves distracted? way. Do we keep ourselves distracted? I mean weather is one of the most I mean weather is one of the most I mean weather is one of the most important questions of human history. We important questions of human history. We important questions of human history. We still that's that's the go-to small talk still that's that's the go-to small talk still that's that's the go-to small talk direction of of the weather direction of of the weather direction of of the weather >> especially in England >> especially in England >> especially in England >> and then it's which is you know famously >> and then it's which is you know famously >> and then it's which is you know famously is an extremely difficult system to is an extremely difficult system to is an extremely difficult system to model and uh even that system uh Google model and uh even that system uh Google model and uh even that system uh Google deep mind has made progress on. Yes, deep mind has made progress on. Yes, deep mind has made progress on. Yes, we've yeah, we've created the the best we've yeah, we've created the the best we've yeah, we've created the the best weather prediction systems in the world weather prediction systems in the world weather prediction systems in the world and they're better than traditional and they're better than traditional and they're better than traditional fluid dynamics sort of systems that fluid dynamics sort of systems that fluid dynamics sort of systems that usually calculated on massive usually calculated on massive usually calculated on massive supercomputers takes days to calculate supercomputers takes days to calculate supercomputers takes days to calculate it. And we've managed to model a lot of it. And we've managed to model a lot of it. And we've managed to model a lot of the weather dynamics with neural network the weather dynamics with neural network the weather dynamics with neural network systems with our weather next system. systems with our weather next system. systems with our weather next system. And again, it's interesting that those And again, it's interesting that those And again, it's interesting that those kinds of dynamics can be modeled even kinds of dynamics can be modeled even kinds of dynamics can be modeled even though they're very complicated, almost though they're very complicated, almost though they're very complicated, almost bordering on chaotic systems in some bordering on chaotic systems in some bordering on chaotic systems in some cases. A lot of the interesting aspects cases. A lot of the interesting aspects cases. A lot of the interesting aspects of that um can be modeled by these of that um can be modeled by these of that um can be modeled by these neural network systems, including very neural network systems, including very neural network systems, including very recently we had, you know, cyclone recently we had, you know, cyclone recently we had, you know, cyclone prediction of where, you know, paths of prediction of where, you know, paths of prediction of where, you know, paths of hurricanes might go. of course super hurricanes might go. of course super hurricanes might go. of course super useful super important for the world and useful super important for the world and useful super important for the world and and and it's super important to do that and and it's super important to do that and and it's super important to do that very timely and very quickly and as well very timely and very quickly and as well very timely and very quickly and as well as accurately and uh I think it's very as accurately and uh I think it's very as accurately and uh I think it's very promising direction again of you know promising direction again of you know promising direction again of you know simulating and uh uh so that you can run simulating and uh uh so that you can run simulating and uh uh so that you can run forward predictions and simulations of

forward predictions and simulations of forward predictions and simulations of very complicated real world systems. very complicated real world systems. very complicated real world systems. >> I should mention that uh I've got a >> I should mention that uh I've got a >> I should mention that uh I've got a chance in uh Texas to meet a community chance in uh Texas to meet a community chance in uh Texas to meet a community of folks called the stormchasers. of folks called the stormchasers. of folks called the stormchasers. >> Yes. And what's really incredible about >> Yes. And what's really incredible about >> Yes. And what's really incredible about them, I need to talk to them more, is them, I need to talk to them more, is them, I need to talk to them more, is they're extremely tech-savvy because they're extremely tech-savvy because they're extremely tech-savvy because what they have to do is they have to use what they have to do is they have to use what they have to do is they have to use models to predict where the storm is. So models to predict where the storm is. So models to predict where the storm is. So they're it's just it's it's this they're it's just it's it's this they're it's just it's it's this beautiful mix of like crazy enough to beautiful mix of like crazy enough to beautiful mix of like crazy enough to like go into the eye of the storm and like go into the eye of the storm and like go into the eye of the storm and like like like >> in order to protect your life and >> in order to protect your life and >> in order to protect your life and predict where the extreme events are predict where the extreme events are predict where the extreme events are going to be, they have to have going to be, they have to have going to be, they have to have increasingly sophisticated models of of increasingly sophisticated models of of increasingly sophisticated models of of weather. weather. weather. >> Yeah. >> Yeah. >> Yeah. >> Yeah. It's it's a a beautiful balance of >> Yeah. It's it's a a beautiful balance of >> Yeah. It's it's a a beautiful balance of like being in it as living organisms and like being in it as living organisms and like being in it as living organisms and the the cutting edge of science. So they the the cutting edge of science. So they the the cutting edge of science. So they actually might be using uh deep mind actually might be using uh deep mind actually might be using uh deep mind system. So that's system. So that's system. So that's >> Yeah, they hopefully they are and I I'd >> Yeah, they hopefully they are and I I'd >> Yeah, they hopefully they are and I I'd love to join them on one of those love to join them on one of those love to join them on one of those chases. They look amazing, right? To chases. They look amazing, right? To chases. They look amazing, right? To actually experience it one time. actually experience it one time. actually experience it one time. >> Exactly. And then also to experience the >> Exactly. And then also to experience the >> Exactly. And then also to experience the correct prediction where something will correct prediction where something will correct prediction where something will come and how it's going to evolve. It's come and how it's going to evolve. It's come and how it's going to evolve. It's incredible. incredible. incredible. >> Yeah. >> Yeah. >> Yeah. >> You've estimated that we'll have AGI by >> You've estimated that we'll have AGI by >> You've estimated that we'll have AGI by 2030.

2030. 2030. Um so there's interesting questions Um so there's interesting questions Um so there's interesting questions around that. How will we actually know around that. How will we actually know around that. How will we actually know that we got there? Uh and uh what maybe that we got there? Uh and uh what maybe that we got there? Uh and uh what maybe the move quote move 37 of AGI. the move quote move 37 of AGI. the move quote move 37 of AGI. >> My estimate is sort of 50% chance by in >> My estimate is sort of 50% chance by in >> My estimate is sort of 50% chance by in the next 5 years. So you know by 2030 the next 5 years. So you know by 2030 the next 5 years. So you know by 2030 let's say and uh so I think there's a let's say and uh so I think there's a let's say and uh so I think there's a good chance that that could happen. Part good chance that that could happen. Part good chance that that could happen. Part of it is what what is your definition of of it is what what is your definition of of it is what what is your definition of AGI? Of course, people are arguing about AGI? Of course, people are arguing about AGI? Of course, people are arguing about that now and and uh mine's quite a high that now and and uh mine's quite a high that now and and uh mine's quite a high bar and always has been of like can we bar and always has been of like can we bar and always has been of like can we match the cognitive functions that the match the cognitive functions that the match the cognitive functions that the brain has, right? So, we know our brains brain has, right? So, we know our brains brain has, right? So, we know our brains are pretty much general cheuring are pretty much general cheuring are pretty much general cheuring machines approximate. And of course, machines approximate. And of course, machines approximate. And of course, we've created incredible modern we've created incredible modern we've created incredible modern civilization with our minds. So, that civilization with our minds. So, that civilization with our minds. So, that also speaks to how general the brain is. also speaks to how general the brain is. also speaks to how general the brain is. And um for us to know we have a true And um for us to know we have a true And um for us to know we have a true AGI, we would have to like make sure AGI, we would have to like make sure AGI, we would have to like make sure that it has all those capabilities. it that it has all those capabilities. it that it has all those capabilities. it isn't kind of a jagged intelligence isn't kind of a jagged intelligence isn't kind of a jagged intelligence where some things it's really good at where some things it's really good at where some things it's really good at like today's systems but other things like today's systems but other things like today's systems but other things it's really uh flawed at and and that's it's really uh flawed at and and that's it's really uh flawed at and and that's what we currently have with today's what we currently have with today's what we currently have with today's systems they're not consistent so you'd systems they're not consistent so you'd systems they're not consistent so you'd want that consistency of intelligence want that consistency of intelligence want that consistency of intelligence across the board and then we have some across the board and then we have some across the board and then we have some missing I think capabilities like sort missing I think capabilities like sort missing I think capabilities like sort of uh the true invention capabilities of uh the true invention capabilities of uh the true invention capabilities and creativity that we were talking and creativity that we were talking and creativity that we were talking about earlier so you'd want to see those about earlier so you'd want to see those about earlier so you'd want to see those how you test that um I think you just how you test that um I think you just how you test that um I think you just test it one way to do it would be a kind test it one way to do it would be a kind test it one way to do it would be a kind of brute force test of tens of thousand of brute force test of tens of thousand of brute force test of tens of thousand thousand of cognitive tasks that um you thousand of cognitive tasks that um you thousand of cognitive tasks that um you know we know that humans can do uh and know we know that humans can do uh and know we know that humans can do uh and maybe also make the system available to

maybe also make the system available to maybe also make the system available to uh a few hundred of the world's top uh a few hundred of the world's top uh a few hundred of the world's top experts uh the terren towers of each experts uh the terren towers of each experts uh the terren towers of each each subject area and see if they can each subject area and see if they can each subject area and see if they can find you know give them give them a find you know give them give them a find you know give them give them a month or two and see if they can find an month or two and see if they can find an month or two and see if they can find an obvious flaw in the system and if they obvious flaw in the system and if they obvious flaw in the system and if they can't then I think you're you're pretty can't then I think you're you're pretty can't then I think you're you're pretty uh you know pretty you can be pretty uh you know pretty you can be pretty uh you know pretty you can be pretty confident we have a a fully general confident we have a a fully general confident we have a a fully general system system system >> maybe to push back a little bit it seems >> maybe to push back a little bit it seems >> maybe to push back a little bit it seems like humans are really incredible as the like humans are really incredible as the like humans are really incredible as the the intelligence improves across all the intelligence improves across all the intelligence improves across all domains to take it for granted. domains to take it for granted. domains to take it for granted. >> Mhm. >> Mhm. >> Mhm. >> Uh like you mentioned Terrence Tao >> Uh like you mentioned Terrence Tao >> Uh like you mentioned Terrence Tao uh these brilliant experts they might uh these brilliant experts they might uh these brilliant experts they might quickly in a span of weeks take for quickly in a span of weeks take for quickly in a span of weeks take for granted all the incredible things it can granted all the incredible things it can granted all the incredible things it can do and then focus in well haha right do and then focus in well haha right do and then focus in well haha right there. You know I I consider myself uh there. You know I I consider myself uh there. You know I I consider myself uh first of all human. first of all human. first of all human. >> Yeah. >> Yeah. >> Yeah. >> Uh second I identify as human. Um >> Uh second I identify as human. Um >> Uh second I identify as human. Um I you know some people listen to me talk I you know some people listen to me talk I you know some people listen to me talk and they're like that guy is not good at and they're like that guy is not good at and they're like that guy is not good at talking the stuttering the you know so talking the stuttering the you know so talking the stuttering the you know so like even humans have obvious across like even humans have obvious across like even humans have obvious across domains limits even just outside of domains limits even just outside of domains limits even just outside of mathematics and physics and so on it I I mathematics and physics and so on it I I mathematics and physics and so on it I I I wonder if it will take something like I wonder if it will take something like I wonder if it will take something like a move 37 so on the positive side versus a move 37 so on the positive side versus a move 37 so on the positive side versus like like like >> a barrage of 10,000 cognitive tasks >> a barrage of 10,000 cognitive tasks >> a barrage of 10,000 cognitive tasks where it would be one or two where it's where it would be one or two where it's where it would be one or two where it's like yes, holy this is like yes, holy this is like yes, holy this is >> I think exactly. So I think there's the >> I think exactly. So I think there's the >> I think exactly. So I think there's the sort of blanket testing to just make sort of blanket testing to just make sort of blanket testing to just make sure you've got the consistency, but I sure you've got the consistency, but I sure you've got the consistency, but I think there are the sort of lighthouse

think there are the sort of lighthouse think there are the sort of lighthouse moments like the move 37 that I would be moments like the move 37 that I would be moments like the move 37 that I would be looking for. So one would be inventing a looking for. So one would be inventing a looking for. So one would be inventing a new conjecture or new hypothesis about new conjecture or new hypothesis about new conjecture or new hypothesis about physics like Einstein did. So maybe you physics like Einstein did. So maybe you physics like Einstein did. So maybe you could even run the back test of that could even run the back test of that could even run the back test of that very rigorously like have a cut off of very rigorously like have a cut off of very rigorously like have a cut off of knowledge cutff of 1900 and then give knowledge cutff of 1900 and then give knowledge cutff of 1900 and then give the system everything that was you know the system everything that was you know the system everything that was you know that was written up to 1900 and then and that was written up to 1900 and then and that was written up to 1900 and then and then see if it could come up with then see if it could come up with then see if it could come up with special relativity and general special relativity and general special relativity and general relativity right like Einstein did that relativity right like Einstein did that relativity right like Einstein did that that would be an interesting test that would be an interesting test that would be an interesting test another one would be can it invent a another one would be can it invent a another one would be can it invent a game like go not just come up with move game like go not just come up with move game like go not just come up with move 37 a new strategy but can it invent a 37 a new strategy but can it invent a 37 a new strategy but can it invent a game that's as deep as aesthetically game that's as deep as aesthetically game that's as deep as aesthetically beautiful as elegant as go and those are beautiful as elegant as go and those are beautiful as elegant as go and those are the sorts of things I would be looking the sorts of things I would be looking the sorts of things I would be looking out for. Uh and probably a system being out for. Uh and probably a system being out for. Uh and probably a system being able to do uh uh several of those able to do uh uh several of those able to do uh uh several of those things, right, for it to be very things, right, for it to be very things, right, for it to be very general. Um not just one domain. And so general. Um not just one domain. And so general. Um not just one domain. And so I think that would be the signs at least I think that would be the signs at least I think that would be the signs at least that I would be looking for that we've that I would be looking for that we've that I would be looking for that we've got a system that's a GI level. And then got a system that's a GI level. And then got a system that's a GI level. And then maybe to fill that out, you would also maybe to fill that out, you would also maybe to fill that out, you would also check the consistency, you know, make check the consistency, you know, make check the consistency, you know, make sure there's no holes in that system sure there's no holes in that system sure there's no holes in that system either.

either. either. >> Yeah. Something like a new conjecture or >> Yeah. Something like a new conjecture or >> Yeah. Something like a new conjecture or scientific discovery. That would be a scientific discovery. That would be a scientific discovery. That would be a cool feeling. Yeah, that would be cool feeling. Yeah, that would be cool feeling. Yeah, that would be amazing. So, it's not not just helping amazing. So, it's not not just helping amazing. So, it's not not just helping us do that, but actually coming up with us do that, but actually coming up with us do that, but actually coming up with something brand new something brand new something brand new >> and you would be in the room for that. >> and you would be in the room for that. >> and you would be in the room for that. So, it would be like probably 2 or 3 So, it would be like probably 2 or 3 So, it would be like probably 2 or 3 months before announcing it. months before announcing it. months before announcing it. >> Mhm. >> Mhm. >> Mhm. >> And you would just be sitting there >> And you would just be sitting there >> And you would just be sitting there trying not to tweet trying not to tweet trying not to tweet >> something like that. Exactly. It's like >> something like that. Exactly. It's like >> something like that. Exactly. It's like what is this amazing new you know what is this amazing new you know what is this amazing new you know physics idea? And then we would probably physics idea? And then we would probably physics idea? And then we would probably check it with world experts in that check it with world experts in that check it with world experts in that domain, right? and validate it and kind domain, right? and validate it and kind domain, right? and validate it and kind of go through its workings and it I of go through its workings and it I of go through its workings and it I guess it would be explaining its guess it would be explaining its guess it would be explaining its workings too. Um yeah be an amazing workings too. Um yeah be an amazing workings too. Um yeah be an amazing moment. moment. moment. >> Do you worry that we as humans even >> Do you worry that we as humans even >> Do you worry that we as humans even expert humans like you might miss it expert humans like you might miss it expert humans like you might miss it might miss might miss might miss >> it may be pretty complicated. So it >> it may be pretty complicated. So it >> it may be pretty complicated. So it could be the analogy I give there is I could be the analogy I give there is I could be the analogy I give there is I don't think it will be um uh uh totally don't think it will be um uh uh totally don't think it will be um uh uh totally mysterious to the to the best human mysterious to the to the best human mysterious to the to the best human scientists but it may be a bit like for scientists but it may be a bit like for scientists but it may be a bit like for example in chess if I was to talk to example in chess if I was to talk to example in chess if I was to talk to Gary Kasparov or Magnus Carlson and play Gary Kasparov or Magnus Carlson and play Gary Kasparov or Magnus Carlson and play a game with them and they make a a game with them and they make a a game with them and they make a brilliant move I might not be able to brilliant move I might not be able to brilliant move I might not be able to come up with that move but they could come up with that move but they could come up with that move but they could explain why afterwards that move made explain why afterwards that move made explain why afterwards that move made sense and we were to understand it to sense and we were to understand it to sense and we were to understand it to some degree not to the level they do but some degree not to the level they do but some degree not to the level they do but in you know if they were good at in you know if they were good at in you know if they were good at explaining which is actually part of explaining which is actually part of explaining which is actually part of intellg igence too is being able to intellg igence too is being able to intellg igence too is being able to explain in a simple way that what you're explain in a simple way that what you're explain in a simple way that what you're thinking about. Um uh I I think that thinking about. Um uh I I think that thinking about. Um uh I I think that that would be very possible for the best that would be very possible for the best that would be very possible for the best human scientists.

human scientists. human scientists. >> But I wonder maybe you can you can >> But I wonder maybe you can you can >> But I wonder maybe you can you can educate me on the side of go. I wonder educate me on the side of go. I wonder educate me on the side of go. I wonder if there's moves for Agnes or Gary where if there's moves for Agnes or Gary where if there's moves for Agnes or Gary where they at first will dismiss it as a bad they at first will dismiss it as a bad they at first will dismiss it as a bad move. move. move. >> Yeah, sure. It could be. But then >> Yeah, sure. It could be. But then >> Yeah, sure. It could be. But then afterwards they'll figure out with their afterwards they'll figure out with their afterwards they'll figure out with their intuition that that this why this works. intuition that that this why this works. intuition that that this why this works. And then and then and then empirically And then and then and then empirically And then and then and then empirically the nice thing about games is one of the the nice thing about games is one of the the nice thing about games is one of the great things about games is you can it's great things about games is you can it's great things about games is you can it's a sort of scientific test. Does it do a sort of scientific test. Does it do a sort of scientific test. Does it do you win the game or not win? And then um you win the game or not win? And then um you win the game or not win? And then um that tells you okay that move in the end that tells you okay that move in the end that tells you okay that move in the end was good. That strategy was good. And was good. That strategy was good. And was good. That strategy was good. And then you can go back and analyze that then you can go back and analyze that then you can go back and analyze that and and and and explain even to yourself and and and and explain even to yourself and and and and explain even to yourself a little bit more why explore around it. a little bit more why explore around it. a little bit more why explore around it. And that's how chess analysis and things And that's how chess analysis and things And that's how chess analysis and things like that works. So perhaps that's why like that works. So perhaps that's why like that works. So perhaps that's why my brain works like that cuz I I've been my brain works like that cuz I I've been my brain works like that cuz I I've been doing that since I was four and you're doing that since I was four and you're doing that since I was four and you're train you know it's sort of hardcore train you know it's sort of hardcore train you know it's sort of hardcore training in that way. But even even now training in that way. But even even now training in that way. But even even now like when I generate code like when I generate code like when I generate code there there is this kind of nuanced there there is this kind of nuanced there there is this kind of nuanced fascinating con contention that's fascinating con contention that's fascinating con contention that's happening where I might at first happening where I might at first happening where I might at first identify as a set of generated code is identify as a set of generated code is identify as a set of generated code is incorrect in in some interesting nuanced incorrect in in some interesting nuanced incorrect in in some interesting nuanced ways but then I'm always have to ask the ways but then I'm always have to ask the ways but then I'm always have to ask the question is there a deeper insight here question is there a deeper insight here question is there a deeper insight here that that I'm the one who's incorrect that that I'm the one who's incorrect that that I'm the one who's incorrect >> and that's going to as the systems get >> and that's going to as the systems get >> and that's going to as the systems get more and more intelligent you're going more and more intelligent you're going more and more intelligent you're going to have to contend with that. It's like to have to contend with that. It's like to have to contend with that. It's like what what what do you is this a bug or a what what what do you is this a bug or a what what what do you is this a bug or a feature of what you just came up with?

feature of what you just came up with? feature of what you just came up with? >> Yeah. And they're going to be pretty >> Yeah. And they're going to be pretty >> Yeah. And they're going to be pretty complicated to do. But of course it will complicated to do. But of course it will complicated to do. But of course it will be you can imagine also AI systems that be you can imagine also AI systems that be you can imagine also AI systems that are producing that code or whatever that are producing that code or whatever that are producing that code or whatever that is and then human programmers looking at is and then human programmers looking at is and then human programmers looking at it but also not unaded with the help of it but also not unaded with the help of it but also not unaded with the help of AI tools as well. So it's going to be AI tools as well. So it's going to be AI tools as well. So it's going to be kind of an interesting you know maybe kind of an interesting you know maybe kind of an interesting you know maybe different AI tools to the ones that the different AI tools to the ones that the different AI tools to the ones that the more you know kind of monitoring tools more you know kind of monitoring tools more you know kind of monitoring tools to the ones that generated it. So if we to the ones that generated it. So if we to the ones that generated it. So if we look at a AGI system, look at a AGI system, look at a AGI system, sorry to bring it back up, but alpha sorry to bring it back up, but alpha sorry to bring it back up, but alpha evolve, evolve, evolve, super cool. So Alpha Evolve enables on super cool. So Alpha Evolve enables on super cool. So Alpha Evolve enables on the programming side something like the programming side something like the programming side something like recursive self-improvement uh recursive self-improvement uh recursive self-improvement uh potentially like what who can imagine potentially like what who can imagine potentially like what who can imagine what that AGI system maybe not the first what that AGI system maybe not the first what that AGI system maybe not the first version but a few versions beyond that. version but a few versions beyond that. version but a few versions beyond that. What does that actually look like? Do What does that actually look like? Do What does that actually look like? Do you think it would be simple? You think you think it would be simple? You think you think it would be simple? You think it'll be something like a self-improving it'll be something like a self-improving it'll be something like a self-improving program in a simple one? program in a simple one? program in a simple one? >> I mean, potentially that's possible. I >> I mean, potentially that's possible. I >> I mean, potentially that's possible. I would say um I'm not sure it's even would say um I'm not sure it's even would say um I'm not sure it's even desirable because that's a kind of like desirable because that's a kind of like desirable because that's a kind of like hard takeoff scenario. But but you you hard takeoff scenario. But but you you hard takeoff scenario. But but you you these current systems like Alpha Evolve, these current systems like Alpha Evolve, these current systems like Alpha Evolve, they have, you know, human in the loop they have, you know, human in the loop they have, you know, human in the loop deciding on various things. They're deciding on various things. They're deciding on various things. They're separate hybrid systems that interact.

separate hybrid systems that interact. separate hybrid systems that interact. Uh one could imagine eventually doing Uh one could imagine eventually doing Uh one could imagine eventually doing that end to end. I don't see why that that end to end. I don't see why that that end to end. I don't see why that wouldn't be possible but right now um wouldn't be possible but right now um wouldn't be possible but right now um you know I think the systems are not you know I think the systems are not you know I think the systems are not good enough to do that in terms of good enough to do that in terms of good enough to do that in terms of coming up with the architecture of the coming up with the architecture of the coming up with the architecture of the code. Um and again it's a little bit code. Um and again it's a little bit code. Um and again it's a little bit connected to this idea of coming up with connected to this idea of coming up with connected to this idea of coming up with a new conjectural hypothesis. How like a new conjectural hypothesis. How like a new conjectural hypothesis. How like they're good if you give them very they're good if you give them very they're good if you give them very specific instructions about what you're specific instructions about what you're specific instructions about what you're trying to do. Um, but if you give them a trying to do. Um, but if you give them a trying to do. Um, but if you give them a very vague high level instruction, that very vague high level instruction, that very vague high level instruction, that wouldn't work currently. Like, uh, and I wouldn't work currently. Like, uh, and I wouldn't work currently. Like, uh, and I think that's related to this idea of think that's related to this idea of think that's related to this idea of like invent a game as good as go, right? like invent a game as good as go, right? like invent a game as good as go, right? Imagine that was the prompt. That's Imagine that was the prompt. That's Imagine that was the prompt. That's that's pretty underspecified. And so the that's pretty underspecified. And so the that's pretty underspecified. And so the current systems wouldn't know, I think, current systems wouldn't know, I think, current systems wouldn't know, I think, what to do with that, how to narrow that what to do with that, how to narrow that what to do with that, how to narrow that down to something tractable. And I think down to something tractable. And I think down to something tractable. And I think there's similar like, look, just make a there's similar like, look, just make a there's similar like, look, just make a better version of yourself that's too better version of yourself that's too better version of yourself that's too that's too unconstrained. But we've done that's too unconstrained. But we've done that's too unconstrained. But we've done it in, you know, and as you know with it in, you know, and as you know with it in, you know, and as you know with Alpha Evolve, like things like faster Alpha Evolve, like things like faster Alpha Evolve, like things like faster matrix multiplication. So when you when matrix multiplication. So when you when matrix multiplication. So when you when you hone it down to very specific thing you hone it down to very specific thing you hone it down to very specific thing you want um it's very good at you want um it's very good at you want um it's very good at incrementally improving that but at the incrementally improving that but at the incrementally improving that but at the moment these are more like incremental moment these are more like incremental moment these are more like incremental improvements sort of small iterations improvements sort of small iterations improvements sort of small iterations whereas if you know if you wanted a big whereas if you know if you wanted a big whereas if you know if you wanted a big leap in uh understanding you need a you leap in uh understanding you need a you leap in uh understanding you need a you need a much larger uh advance.

need a much larger uh advance. need a much larger uh advance. >> Yeah. But it could also be sort of to >> Yeah. But it could also be sort of to >> Yeah. But it could also be sort of to push back against hard takeoff scenario. push back against hard takeoff scenario. push back against hard takeoff scenario. It could be just a sequence of um It could be just a sequence of um It could be just a sequence of um incremental improvements like matrix incremental improvements like matrix incremental improvements like matrix multiplication like it has to sit there multiplication like it has to sit there multiplication like it has to sit there for days thinking how to incrementally for days thinking how to incrementally for days thinking how to incrementally improve a thing and that it does so improve a thing and that it does so improve a thing and that it does so recursively and as you do more and more recursively and as you do more and more recursively and as you do more and more improvement it'll slow down so there'll improvement it'll slow down so there'll improvement it'll slow down so there'll be like a like uh the path to AGI won't be like a like uh the path to AGI won't be like a like uh the path to AGI won't be like a it'll be a gradual improvement be like a it'll be a gradual improvement be like a it'll be a gradual improvement over time. over time. over time. >> Yes. If it was just incremental >> Yes. If it was just incremental >> Yes. If it was just incremental improvements that's how it would look. improvements that's how it would look. improvements that's how it would look. So the question is could it come up with So the question is could it come up with So the question is could it come up with a new leap like the transformers a new leap like the transformers a new leap like the transformers architecture right could it have done architecture right could it have done architecture right could it have done that back in 2017 when you know we did that back in 2017 when you know we did that back in 2017 when you know we did it and brain did it and it's it's not it and brain did it and it's it's not it and brain did it and it's it's not clear that that these systems something clear that that these systems something clear that that these systems something like Alpha wouldn't be able to do make like Alpha wouldn't be able to do make like Alpha wouldn't be able to do make such a big leap so for sure these such a big leap so for sure these such a big leap so for sure these systems are good we have systems I think systems are good we have systems I think systems are good we have systems I think that can do incremental hill climbing that can do incremental hill climbing that can do incremental hill climbing and that's a kind of bigger question and that's a kind of bigger question and that's a kind of bigger question about is that all that's needed from about is that all that's needed from about is that all that's needed from here or do we actually need one or two here or do we actually need one or two here or do we actually need one or two more um uh big breakthroughs more um uh big breakthroughs more um uh big breakthroughs >> and can the same kind of systems provide >> and can the same kind of systems provide >> and can the same kind of systems provide the breakthroughs also. So make it a the breakthroughs also. So make it a the breakthroughs also. So make it a bunch of scurves like incremental bunch of scurves like incremental bunch of scurves like incremental improvement but also every once in a improvement but also every once in a improvement but also every once in a while leaps.

while leaps. while leaps. >> Yeah. I don't think anyone has systems >> Yeah. I don't think anyone has systems >> Yeah. I don't think anyone has systems that can have shown unequivocally those that can have shown unequivocally those that can have shown unequivocally those big leaps that the the right. We have a big leaps that the the right. We have a big leaps that the the right. We have a lot of systems that do the hill climbing lot of systems that do the hill climbing lot of systems that do the hill climbing of the S-curve that you're currently on. of the S-curve that you're currently on. of the S-curve that you're currently on. >> Yeah. And that would be the move 37 is a >> Yeah. And that would be the move 37 is a >> Yeah. And that would be the move 37 is a leap. leap. leap. >> Yeah. I think would be a leap. Um >> Yeah. I think would be a leap. Um >> Yeah. I think would be a leap. Um something like that. Uh do you think the something like that. Uh do you think the something like that. Uh do you think the scaling laws are holding strong on the scaling laws are holding strong on the scaling laws are holding strong on the pre-training, post- training, test time, pre-training, post- training, test time, pre-training, post- training, test time, compute? Uh do you uh on the flip side compute? Uh do you uh on the flip side compute? Uh do you uh on the flip side of that anticipate AI progress hitting a of that anticipate AI progress hitting a of that anticipate AI progress hitting a wall? wall? wall? >> We certainly feel there's a lot more >> We certainly feel there's a lot more >> We certainly feel there's a lot more room just in the scaling. So um actually room just in the scaling. So um actually room just in the scaling. So um actually all steps pre-training, post-training all steps pre-training, post-training all steps pre-training, post-training and inference time. So uh there's sort and inference time. So uh there's sort and inference time. So uh there's sort of three scalings that are happening of three scalings that are happening of three scalings that are happening concurrently. Um and we again there it's concurrently. Um and we again there it's concurrently. Um and we again there it's about how innovative you can be and we about how innovative you can be and we about how innovative you can be and we you know we pride ourselves on having you know we pride ourselves on having you know we pride ourselves on having the broadest and um deepest research the broadest and um deepest research the broadest and um deepest research bench. uh we have amazing you know bench. uh we have amazing you know bench. uh we have amazing you know incredible uh researchers and uh people incredible uh researchers and uh people incredible uh researchers and uh people like Nam Shazir who you know came up like Nam Shazir who you know came up like Nam Shazir who you know came up with transformers and and Dave Silva you with transformers and and Dave Silva you with transformers and and Dave Silva you know who led the Alph Go project and so know who led the Alph Go project and so know who led the Alph Go project and so on and um it's it's it's that research on and um it's it's it's that research on and um it's it's it's that research base means that if some new new base means that if some new new base means that if some new new breakthrough is required like an Alph Go breakthrough is required like an Alph Go breakthrough is required like an Alph Go or Transformers uh I would back us to be or Transformers uh I would back us to be or Transformers uh I would back us to be the place that does that. So I'm the place that does that. So I'm the place that does that. So I'm actually quite like it when the terrain actually quite like it when the terrain actually quite like it when the terrain gets harder, right? Because then it gets harder, right? Because then it gets harder, right? Because then it veers more from just engineering to to veers more from just engineering to to veers more from just engineering to to true research and you know re or true research and you know re or true research and you know re or research plus engineering and that's our research plus engineering and that's our research plus engineering and that's our sweet spot. And I I think that's harder sweet spot. And I I think that's harder sweet spot. And I I think that's harder it's harder to invent things than to it's harder to invent things than to it's harder to invent things than to than to um you know fast follow. And um

than to um you know fast follow. And um than to um you know fast follow. And um so you know we don't know I would say so you know we don't know I would say so you know we don't know I would say it's a it's kind of 50/50 whether new it's a it's kind of 50/50 whether new it's a it's kind of 50/50 whether new things are needed or whether the scaling things are needed or whether the scaling things are needed or whether the scaling the existing stuff is going to be the existing stuff is going to be the existing stuff is going to be enough. And so in true kind of empirical enough. And so in true kind of empirical enough. And so in true kind of empirical fashion, we're pushing both of those as fashion, we're pushing both of those as fashion, we're pushing both of those as hard as possible. The new blue sky ideas hard as possible. The new blue sky ideas hard as possible. The new blue sky ideas and you know maybe about half our and you know maybe about half our and you know maybe about half our resources are on that and then and then resources are on that and then and then resources are on that and then and then uh scaling to the max the the current uh scaling to the max the the current uh scaling to the max the the current the current capabilities and um we're the current capabilities and um we're the current capabilities and um we're still seeing some you know fantastic still seeing some you know fantastic still seeing some you know fantastic progress on uh each different version of progress on uh each different version of progress on uh each different version of Gemini. That's interesting the way you Gemini. That's interesting the way you Gemini. That's interesting the way you put it in terms of the deep bench that put it in terms of the deep bench that put it in terms of the deep bench that if uh progress towards AGI is more than if uh progress towards AGI is more than if uh progress towards AGI is more than just scaling compute so the engineering just scaling compute so the engineering just scaling compute so the engineering side of the problem and is more on the side of the problem and is more on the side of the problem and is more on the scientific side where there's scientific side where there's scientific side where there's breakthroughs needed then you feel breakthroughs needed then you feel breakthroughs needed then you feel confident deep mind as well Google deep confident deep mind as well Google deep confident deep mind as well Google deep mind is well positioned to kick kick ass mind is well positioned to kick kick ass mind is well positioned to kick kick ass in that domain in that domain in that domain >> well I mean if you look at the history >> well I mean if you look at the history >> well I mean if you look at the history of the last decade or 15 years um it's of the last decade or 15 years um it's of the last decade or 15 years um it's been I you know maybe I don't know 80 been I you know maybe I don't know 80 been I you know maybe I don't know 80 90% of the breakthroughs that that 90% of the breakthroughs that that 90% of the breakthroughs that that underpins modern AI field today was from underpins modern AI field today was from underpins modern AI field today was from you know originally Google brain Google you know originally Google brain Google you know originally Google brain Google research and deep mind so yeah I would research and deep mind so yeah I would research and deep mind so yeah I would back that to continue hopefully back that to continue hopefully back that to continue hopefully >> uh so on the data side are you concerned >> uh so on the data side are you concerned >> uh so on the data side are you concerned about running out of highquality data about running out of highquality data about running out of highquality data especially high quality human data especially high quality human data especially high quality human data >> I'm not very worried about that partly >> I'm not very worried about that partly >> I'm not very worried about that partly because I think there's enough data uh because I think there's enough data uh because I think there's enough data uh or and it's been proven to get the or and it's been proven to get the or and it's been proven to get the systems to be pretty good and this goes systems to be pretty good and this goes systems to be pretty good and this goes back to simulations again if you do you back to simulations again if you do you back to simulations again if you do you have enough data to make simulations or

have enough data to make simulations or have enough data to make simulations or so that you can create more synthetic so that you can create more synthetic so that you can create more synthetic data that are from the right data that are from the right data that are from the right distribution. Obviously, that's the key. distribution. Obviously, that's the key. distribution. Obviously, that's the key. So, you need enough real world data in So, you need enough real world data in So, you need enough real world data in order to be able to uh uh create those order to be able to uh uh create those order to be able to uh uh create those kinds of generator data generators and kinds of generator data generators and kinds of generator data generators and um I think that we're at that step at um I think that we're at that step at um I think that we're at that step at the moment. the moment. the moment. >> Yeah, you've done a lot of incredible >> Yeah, you've done a lot of incredible >> Yeah, you've done a lot of incredible stuff on the side of science and biology stuff on the side of science and biology stuff on the side of science and biology doing a lot with not so much data. doing a lot with not so much data. doing a lot with not so much data. >> Yeah. >> Yeah. >> Yeah. >> I mean, it's still a lot of data, but I >> I mean, it's still a lot of data, but I >> I mean, it's still a lot of data, but I guess enough guess enough guess enough >> take off that going. Exactly. Yeah. >> take off that going. Exactly. Yeah. >> take off that going. Exactly. Yeah. >> So exactly >> So exactly >> So exactly >> uh how crucial is the scaling of compute >> uh how crucial is the scaling of compute >> uh how crucial is the scaling of compute to building AGI? This is a question to building AGI? This is a question to building AGI? This is a question that's an engineering question. It's a that's an engineering question. It's a that's an engineering question. It's a almost geopolitical question almost geopolitical question almost geopolitical question >> because it also integrated into that is >> because it also integrated into that is >> because it also integrated into that is the supply chains and energy a thing the supply chains and energy a thing the supply chains and energy a thing that you care a lot about which is um that you care a lot about which is um that you care a lot about which is um potentially fusion. So innovating on the potentially fusion. So innovating on the potentially fusion. So innovating on the side of energy also. Do you think we're side of energy also. Do you think we're side of energy also. Do you think we're going to keep scaling compute? going to keep scaling compute? going to keep scaling compute? >> I think so for several reasons. I think >> I think so for several reasons. I think >> I think so for several reasons. I think compute there's there's the amount of compute there's there's the amount of compute there's there's the amount of compute you have for training often it compute you have for training often it compute you have for training often it needs to be colloccated so actually even needs to be colloccated so actually even needs to be colloccated so actually even like you know uh bandwidth constraints like you know uh bandwidth constraints like you know uh bandwidth constraints between data centers can affect that so between data centers can affect that so between data centers can affect that so it's it's it's there's additional it's it's it's there's additional it's it's it's there's additional constraints even there and that that's constraints even there and that that's constraints even there and that that's important for training obviously the important for training obviously the important for training obviously the largest models you can but there's also largest models you can but there's also largest models you can but there's also because now AI systems are in products because now AI systems are in products because now AI systems are in products and being used by billions of people and being used by billions of people and being used by billions of people around the world you need a ton of around the world you need a ton of around the world you need a ton of inference compute now um and then on top inference compute now um and then on top inference compute now um and then on top of that there's the thinking systems, of that there's the thinking systems, of that there's the thinking systems, the new paradigm uh of the last year the new paradigm uh of the last year the new paradigm uh of the last year that uh where they get smarter the that uh where they get smarter the that uh where they get smarter the longer amount of inference time you give longer amount of inference time you give longer amount of inference time you give them at test time. So all of those

them at test time. So all of those them at test time. So all of those things need a lot of compute and I don't things need a lot of compute and I don't things need a lot of compute and I don't really see that slowing down. Um and as really see that slowing down. Um and as really see that slowing down. Um and as AI systems become better, they'll become AI systems become better, they'll become AI systems become better, they'll become more useful and there'll be more demand more useful and there'll be more demand more useful and there'll be more demand for them. So both from the training for them. So both from the training for them. So both from the training side, the training side actually is is side, the training side actually is is side, the training side actually is is only just one part of that. It may even only just one part of that. It may even only just one part of that. It may even become the smaller part of of what's become the smaller part of of what's become the smaller part of of what's needed um uh in the overall compute that needed um uh in the overall compute that needed um uh in the overall compute that that's required. Yeah, that's one sort that's required. Yeah, that's one sort that's required. Yeah, that's one sort of almost memey kind of thing which is of almost memey kind of thing which is of almost memey kind of thing which is like the success and the incredible like the success and the incredible like the success and the incredible aspects of V3 there people kind of make aspects of V3 there people kind of make aspects of V3 there people kind of make fun of like the more successful it fun of like the more successful it fun of like the more successful it becomes the you know the servers are becomes the you know the servers are becomes the you know the servers are sweating. sweating. sweating. >> Yes, exactly the difference in >> Yes, exactly the difference in >> Yes, exactly the difference in >> Yeah. Yeah. Exactly. We did a little >> Yeah. Yeah. Exactly. We did a little >> Yeah. Yeah. Exactly. We did a little video of of the servers frying eggs and video of of the servers frying eggs and video of of the servers frying eggs and things and um that's right and and and things and um that's right and and and things and um that's right and and and we're going to have to figure out how to we're going to have to figure out how to we're going to have to figure out how to do that. Um there's a lot of interesting do that. Um there's a lot of interesting do that. Um there's a lot of interesting hardware innovations that we do as you hardware innovations that we do as you hardware innovations that we do as you know we have our own TPU line and we're know we have our own TPU line and we're know we have our own TPU line and we're looking at like inference only things looking at like inference only things looking at like inference only things inference only chips and how we can make inference only chips and how we can make inference only chips and how we can make those more efficient. We're also very those more efficient. We're also very those more efficient. We're also very interested in building AI systems and we interested in building AI systems and we interested in building AI systems and we have done the help with energy usage so have done the help with energy usage so have done the help with energy usage so help um data center energy like for the help um data center energy like for the help um data center energy like for the cooling systems be efficient um grid cooling systems be efficient um grid cooling systems be efficient um grid optimization optimization optimization um and then eventually things like um and then eventually things like um and then eventually things like helping with plasma containment fusion helping with plasma containment fusion helping with plasma containment fusion reactors. We've done lots of work on reactors. We've done lots of work on reactors. We've done lots of work on that with Commonwealth Fusion and also that with Commonwealth Fusion and also that with Commonwealth Fusion and also uh one could imagine reactor design. Um uh one could imagine reactor design. Um uh one could imagine reactor design. Um and then material design I think is one and then material design I think is one and then material design I think is one of the most exciting new types of solar of the most exciting new types of solar of the most exciting new types of solar material solar panel material super room material solar panel material super room material solar panel material super room temperature superconductors has always temperature superconductors has always temperature superconductors has always been on my list of dream breakthroughs been on my list of dream breakthroughs been on my list of dream breakthroughs and um optimal batteries and I think a and um optimal batteries and I think a and um optimal batteries and I think a solution to any you know one of those

solution to any you know one of those solution to any you know one of those things would be absolutely revolutionary things would be absolutely revolutionary things would be absolutely revolutionary for you know climate and energy usage for you know climate and energy usage for you know climate and energy usage and we're probably close you know again and we're probably close you know again and we're probably close you know again in the next 5 years to having AI systems in the next 5 years to having AI systems in the next 5 years to having AI systems that can materially help with those that can materially help with those that can materially help with those problems. If you were to bet, sorry for problems. If you were to bet, sorry for problems. If you were to bet, sorry for the ridiculous question, but what what the ridiculous question, but what what the ridiculous question, but what what is the main source of energy is the main source of energy is the main source of energy in like 20, 30, 40 years, do you think in like 20, 30, 40 years, do you think in like 20, 30, 40 years, do you think it's going to be nuclear fusion? it's going to be nuclear fusion? it's going to be nuclear fusion? >> I think fusion and solar are the two >> I think fusion and solar are the two >> I think fusion and solar are the two that I I would bet on. Um solar, I mean, that I I would bet on. Um solar, I mean, that I I would bet on. Um solar, I mean, you know, it's the fusion reactor in the you know, it's the fusion reactor in the you know, it's the fusion reactor in the sky, of course, and I think really the sky, of course, and I think really the sky, of course, and I think really the problem there is is is batteries and problem there is is is batteries and problem there is is is batteries and transmission. So you know as well as transmission. So you know as well as transmission. So you know as well as more efficient more and more efficient more efficient more and more efficient more efficient more and more efficient solar material perhaps eventually you solar material perhaps eventually you solar material perhaps eventually you know in space you know these kind of know in space you know these kind of know in space you know these kind of Dyson sphere type ideas and fusion I Dyson sphere type ideas and fusion I Dyson sphere type ideas and fusion I think is definitely doable seems uh if think is definitely doable seems uh if think is definitely doable seems uh if we have the right design of reactor and we have the right design of reactor and we have the right design of reactor and we can control the plasma and uh fast we can control the plasma and uh fast we can control the plasma and uh fast enough and so on and I think both of enough and so on and I think both of enough and so on and I think both of those things will actually get solved so those things will actually get solved so those things will actually get solved so we'll probably have at least those will we'll probably have at least those will we'll probably have at least those will probably be the two primary sources of probably be the two primary sources of probably be the two primary sources of renewable clean almost free or perhaps renewable clean almost free or perhaps renewable clean almost free or perhaps free energy What a time to be alive. If free energy What a time to be alive. If free energy What a time to be alive. If I uh traveled into the future with you I uh traveled into the future with you I uh traveled into the future with you 100 years from now, how much would you 100 years from now, how much would you 100 years from now, how much would you be surprised if we've passed a type one be surprised if we've passed a type one be surprised if we've passed a type one card scale civilization? I would not be card scale civilization? I would not be card scale civilization? I would not be that surprised if there was a like a that surprised if there was a like a that surprised if there was a like a 100redyear time scale from here. I mean, 100redyear time scale from here. I mean, 100redyear time scale from here. I mean, I think it's pretty clear if we crack I think it's pretty clear if we crack I think it's pretty clear if we crack the energy problems in one of the ways the energy problems in one of the ways the energy problems in one of the ways we've just discussed, fusion or or very we've just discussed, fusion or or very we've just discussed, fusion or or very efficient solar, um, then if energy is

efficient solar, um, then if energy is efficient solar, um, then if energy is kind of free and renewable and clean, kind of free and renewable and clean, kind of free and renewable and clean, um, then that solves a whole bunch of um, then that solves a whole bunch of um, then that solves a whole bunch of other problems. So, for example, the other problems. So, for example, the other problems. So, for example, the water access problem goes away because water access problem goes away because water access problem goes away because you can just use desalination. We have you can just use desalination. We have you can just use desalination. We have the technology, it's just too expensive. the technology, it's just too expensive. the technology, it's just too expensive. So, only, you know, fairly wealthy So, only, you know, fairly wealthy So, only, you know, fairly wealthy countries like Singapore and Israel and countries like Singapore and Israel and countries like Singapore and Israel and so on like actually use it. But but if so on like actually use it. But but if so on like actually use it. But but if it was uh cheap then every then you know it was uh cheap then every then you know it was uh cheap then every then you know all countries that have a coast could all countries that have a coast could all countries that have a coast could but also you'd have unlimited rocket but also you'd have unlimited rocket but also you'd have unlimited rocket fuel. You could just separate sea water fuel. You could just separate sea water fuel. You could just separate sea water out into hydrogen and oxygen using out into hydrogen and oxygen using out into hydrogen and oxygen using energy and that's rocket fuel. So uh energy and that's rocket fuel. So uh energy and that's rocket fuel. So uh combined with you know Elon's amazing combined with you know Elon's amazing combined with you know Elon's amazing self landing rockets then it could be self landing rockets then it could be self landing rockets then it could be like you sort of like a bus service to like you sort of like a bus service to like you sort of like a bus service to to space. So that opens up you know to space. So that opens up you know to space. So that opens up you know incredible new resources and domains. uh incredible new resources and domains. uh incredible new resources and domains. uh asteroid mining I think will become a asteroid mining I think will become a asteroid mining I think will become a thing and maximum human flourishing to thing and maximum human flourishing to thing and maximum human flourishing to the stars. That's what I uh dream about the stars. That's what I uh dream about the stars. That's what I uh dream about as well is like Carl Sean's sort of idea as well is like Carl Sean's sort of idea as well is like Carl Sean's sort of idea of bringing consciousness to the of bringing consciousness to the of bringing consciousness to the universe, waking up the universe. And I universe, waking up the universe. And I universe, waking up the universe. And I I think human civilization will do that I think human civilization will do that I think human civilization will do that in the full sense of time if we get AI in the full sense of time if we get AI in the full sense of time if we get AI right and uh and and and crack some of right and uh and and and crack some of right and uh and and and crack some of these problems with it.

these problems with it. these problems with it. >> Yeah. I wonder what it would look like >> Yeah. I wonder what it would look like >> Yeah. I wonder what it would look like if you just a tourist flying through if you just a tourist flying through if you just a tourist flying through space. You would probably notice Earth space. You would probably notice Earth space. You would probably notice Earth because if you solve the energy problem, because if you solve the energy problem, because if you solve the energy problem, you would see a lot of space rockets you would see a lot of space rockets you would see a lot of space rockets probably. So it would be like traffic probably. So it would be like traffic probably. So it would be like traffic here in London. here in London. here in London. >> But in space, >> But in space, >> But in space, >> just a lot of rockets >> just a lot of rockets >> just a lot of rockets >> and then you would probably see floating >> and then you would probably see floating >> and then you would probably see floating in space some kind of source of energy in space some kind of source of energy in space some kind of source of energy like solar. like solar. like solar. >> Yeah. >> Yeah. >> Yeah. >> Potentially. So earth would just look >> Potentially. So earth would just look >> Potentially. So earth would just look more on the surface more um more on the surface more um more on the surface more um technological technological technological and then then you would use the power of and then then you would use the power of and then then you would use the power of that energy then to preserve the natural that energy then to preserve the natural that energy then to preserve the natural >> yes >> yes >> yes >> like the rainforest and all that kind of >> like the rainforest and all that kind of >> like the rainforest and all that kind of stuff stuff stuff >> because for the first time in in human >> because for the first time in in human >> because for the first time in in human history we wouldn't be uh resource history we wouldn't be uh resource history we wouldn't be uh resource constrainted and I think that could be constrainted and I think that could be constrainted and I think that could be amazing new era for humanity where it's amazing new era for humanity where it's amazing new era for humanity where it's not zero sum right I have this land you not zero sum right I have this land you not zero sum right I have this land you don't have it or if we take you know if don't have it or if we take you know if don't have it or if we take you know if the tigers have their forest just then the tigers have their forest just then the tigers have their forest just then the the local villagers can't what are the the local villagers can't what are the the local villagers can't what are they going to use? I I I think that this they going to use? I I I think that this they going to use? I I I think that this will help a lot. No, it won't solve all will help a lot. No, it won't solve all will help a lot. No, it won't solve all problems because there's still other problems because there's still other problems because there's still other human foibless that will will will still human foibless that will will will still human foibless that will will will still exist, but it will at least remove one I exist, but it will at least remove one I exist, but it will at least remove one I think one of the big vectors which is think one of the big vectors which is think one of the big vectors which is scarcity of resources, you know, scarcity of resources, you know, scarcity of resources, you know, including land and more materials and including land and more materials and including land and more materials and energy and um you know, we should be I energy and um you know, we should be I energy and um you know, we should be I sometimes call it like and others call sometimes call it like and others call sometimes call it like and others call it about this kind of radical abundance it about this kind of radical abundance it about this kind of radical abundance era where um there's plenty of resources era where um there's plenty of resources era where um there's plenty of resources to go around. But of course the next big to go around. But of course the next big to go around. But of course the next big question is making sure that that's question is making sure that that's question is making sure that that's fairly you know shared fairly uh and fairly you know shared fairly uh and fairly you know shared fairly uh and everyone in society benefits from that.

everyone in society benefits from that. everyone in society benefits from that. So there is something about human nature So there is something about human nature So there is something about human nature where I go you know it's like borat like where I go you know it's like borat like where I go you know it's like borat like my neighbor like I like you start my neighbor like I like you start my neighbor like I like you start trouble we we we do start conflicts and trouble we we we do start conflicts and trouble we we we do start conflicts and that's why games throughout as I'm that's why games throughout as I'm that's why games throughout as I'm learning actually more and more even in learning actually more and more even in learning actually more and more even in ancient history serve the purpose of ancient history serve the purpose of ancient history serve the purpose of pushing people away from war actually pushing people away from war actually pushing people away from war actually hot war so maybe we can figure out hot war so maybe we can figure out hot war so maybe we can figure out increasingly sophisticated video games increasingly sophisticated video games increasingly sophisticated video games that pull us they that give us that uh that pull us they that give us that uh that pull us they that give us that uh scratch the itch of like conflict, scratch the itch of like conflict, scratch the itch of like conflict, whatever that is, about us, the human whatever that is, about us, the human whatever that is, about us, the human nature, and then avoid the actual hot nature, and then avoid the actual hot nature, and then avoid the actual hot wars that would come with increasingly wars that would come with increasingly wars that would come with increasingly sophisticated technologies because we're sophisticated technologies because we're sophisticated technologies because we're now long past the stage where the now long past the stage where the now long past the stage where the weapons we're able to create can weapons we're able to create can weapons we're able to create can actually just destroy all of human actually just destroy all of human actually just destroy all of human civilization. So, it's no longer um civilization. So, it's no longer um civilization. So, it's no longer um that's no longer a great way to to uh that's no longer a great way to to uh that's no longer a great way to to uh start with your neighbor. It's start with your neighbor. It's start with your neighbor. It's better to play a game of chess better to play a game of chess better to play a game of chess >> or football or football. Yeah.

>> or football or football. Yeah. >> or football or football. Yeah. >> And I think I mean I think that's what >> And I think I mean I think that's what >> And I think I mean I think that's what my modern sport is. So, and I love my modern sport is. So, and I love my modern sport is. So, and I love football watching it and and I just feel football watching it and and I just feel football watching it and and I just feel like uh and I used to play it a lot as like uh and I used to play it a lot as like uh and I used to play it a lot as well and it's it's it's it's very well and it's it's it's it's very well and it's it's it's it's very visceral and it's tribal and I think it visceral and it's tribal and I think it visceral and it's tribal and I think it does channel a lot of those energies does channel a lot of those energies does channel a lot of those energies into a which I think is a kind of human into a which I think is a kind of human into a which I think is a kind of human need to belong to some some group and um need to belong to some some group and um need to belong to some some group and um but into a into a into a fun way, a but into a into a into a fun way, a but into a into a into a fun way, a healthy way and and a not a not healthy way and and a not a not healthy way and and a not a not destructive way kind of constructive uh destructive way kind of constructive uh destructive way kind of constructive uh thing. And I think going back to games thing. And I think going back to games thing. And I think going back to games again is I think they're originally why again is I think they're originally why again is I think they're originally why they're so great as well for kids to they're so great as well for kids to they're so great as well for kids to play things like chess is they're great play things like chess is they're great play things like chess is they're great little microcosm simulations of the little microcosm simulations of the little microcosm simulations of the world. They are simulations of the world world. They are simulations of the world world. They are simulations of the world too. They're simplified versions of some too. They're simplified versions of some too. They're simplified versions of some real world situation, whether it's poker real world situation, whether it's poker real world situation, whether it's poker or or go or chess, different aspects or or or go or chess, different aspects or or or go or chess, different aspects or diplomacy, different aspects of of the diplomacy, different aspects of of the diplomacy, different aspects of of the real world. And allows you to practice real world. And allows you to practice real world. And allows you to practice at them, too. And and cuz, you know, how at them, too. And and cuz, you know, how at them, too. And and cuz, you know, how many times do you get to practice a many times do you get to practice a many times do you get to practice a massive decision moment in your life, massive decision moment in your life, massive decision moment in your life, you know, what job to take, what you know, what job to take, what you know, what job to take, what university to go to, you know, you get university to go to, you know, you get university to go to, you know, you get maybe, I don't know, a dozen or so key maybe, I don't know, a dozen or so key maybe, I don't know, a dozen or so key decisions one has to make, and you got decisions one has to make, and you got decisions one has to make, and you got to make those as best as you can. Um, to make those as best as you can. Um, to make those as best as you can. Um, and games is a kind of safe environment, and games is a kind of safe environment, and games is a kind of safe environment, repeatable environment where you can get repeatable environment where you can get repeatable environment where you can get better at your decision- making process.

better at your decision- making process. better at your decision- making process. Um, and it maybe has this additional Um, and it maybe has this additional Um, and it maybe has this additional benefit of channeling some energies into benefit of channeling some energies into benefit of channeling some energies into uh into more creative and constructive uh into more creative and constructive uh into more creative and constructive pursuits. pursuits. pursuits. >> Well, I think it's also really important >> Well, I think it's also really important >> Well, I think it's also really important to practice um losing and winning, to practice um losing and winning, to practice um losing and winning, >> right? >> right? >> right? >> Like losing is a really, you know, >> Like losing is a really, you know, >> Like losing is a really, you know, that's why I love games. That's why I that's why I love games. That's why I that's why I love games. That's why I love even um things like uh Brazilian love even um things like uh Brazilian love even um things like uh Brazilian jiu-jitsu. jiu-jitsu. jiu-jitsu. >> Yeah. >> Yeah. >> Yeah. >> Where you can get your ass kicked in a >> Where you can get your ass kicked in a >> Where you can get your ass kicked in a safe environment over and over. It safe environment over and over. It safe environment over and over. It reminds you about reminds you about reminds you about >> the way about physics, about the way the >> the way about physics, about the way the >> the way about physics, about the way the world works, about sometimes you lose, world works, about sometimes you lose, world works, about sometimes you lose, sometimes you win. You can still be sometimes you win. You can still be sometimes you win. You can still be friends with everybody. But that that friends with everybody. But that that friends with everybody. But that that feeling of losing, I mean, it's a weird feeling of losing, I mean, it's a weird feeling of losing, I mean, it's a weird one for us humans to like really like one for us humans to like really like one for us humans to like really like make sense of like that's just part of make sense of like that's just part of make sense of like that's just part of life. That is a fundamental part of life life. That is a fundamental part of life life. That is a fundamental part of life is losing. is losing. is losing. >> Yeah. And I think in martial arts as I >> Yeah. And I think in martial arts as I >> Yeah. And I think in martial arts as I understand it, but also in things like understand it, but also in things like understand it, but also in things like light chess is a at least the way I took light chess is a at least the way I took light chess is a at least the way I took it, it's a lot to do with it, it's a lot to do with it, it's a lot to do with self-improvement, self-nowledge. You self-improvement, self-nowledge. You self-improvement, self-nowledge. You know that, okay, so I did this thing. know that, okay, so I did this thing. know that, okay, so I did this thing. It's not about really being the other It's not about really being the other It's not about really being the other person. It's about maximizing your own person. It's about maximizing your own person. It's about maximizing your own potential. If you do it in a healthy potential. If you do it in a healthy potential. If you do it in a healthy way, you learn to use victory and losses way, you learn to use victory and losses way, you learn to use victory and losses in a way. Don't get carried away with in a way. Don't get carried away with in a way. Don't get carried away with victory and and think you're the just victory and and think you're the just victory and and think you're the just the best in the world. Keep and and and the best in the world. Keep and and and the best in the world. Keep and and and the losses keep you humble and always the losses keep you humble and always the losses keep you humble and always knowing there's always something more to knowing there's always something more to knowing there's always something more to learn. there's always a bigger expert learn. there's always a bigger expert learn. there's always a bigger expert that you can mentor you, you know, I that you can mentor you, you know, I that you can mentor you, you know, I think you learn that I'm pretty sure in think you learn that I'm pretty sure in think you learn that I'm pretty sure in martial arts and and and I think that's martial arts and and and I think that's martial arts and and and I think that's also uh the way that at least I was also uh the way that at least I was also uh the way that at least I was trained in chess. And so in the same way trained in chess. And so in the same way trained in chess. And so in the same way and it can be very hardcore and very and it can be very hardcore and very and it can be very hardcore and very important and of course you want to win, important and of course you want to win, important and of course you want to win, but you also need to learn how to deal but you also need to learn how to deal but you also need to learn how to deal with setbacks in a in a healthy way that

with setbacks in a in a healthy way that with setbacks in a in a healthy way that and and and and wire that that feeling and and and and wire that that feeling and and and and wire that that feeling that you have when you lose something that you have when you lose something that you have when you lose something into a constructive thing of next time into a constructive thing of next time into a constructive thing of next time I'm going to improve this, right? Or get I'm going to improve this, right? Or get I'm going to improve this, right? Or get better at this. There is something better at this. There is something better at this. There is something that's a source of happiness, a source that's a source of happiness, a source that's a source of happiness, a source of meaning, that improvement step. It's of meaning, that improvement step. It's of meaning, that improvement step. It's not about the winning or losing. not about the winning or losing. not about the winning or losing. >> Yes. The mastery. There's nothing more >> Yes. The mastery. There's nothing more >> Yes. The mastery. There's nothing more satisfying in a way is like, "Oh, wow. satisfying in a way is like, "Oh, wow. satisfying in a way is like, "Oh, wow. This thing I couldn't do before, now I This thing I couldn't do before, now I This thing I couldn't do before, now I can." And and and again, games and can." And and and again, games and can." And and and again, games and physical sports and and mental sports, physical sports and and mental sports, physical sports and and mental sports, they're way they're ways of measuring. they're way they're ways of measuring. they're way they're ways of measuring. They're beautiful because you can They're beautiful because you can They're beautiful because you can measure that that progress. measure that that progress. measure that that progress. >> Yeah. I mean there's something about >> Yeah. I mean there's something about >> Yeah. I mean there's something about this is why I love role playing games this is why I love role playing games this is why I love role playing games like the uh number go up of like my on like the uh number go up of like my on like the uh number go up of like my on the skill tree like literally that is a the skill tree like literally that is a the skill tree like literally that is a source of meaning for us humans whatever source of meaning for us humans whatever source of meaning for us humans whatever our our our >> yeah we're quite we're quite addicted to >> yeah we're quite we're quite addicted to >> yeah we're quite we're quite addicted to this sort of yeah these numbers going up this sort of yeah these numbers going up this sort of yeah these numbers going up and uh and and and and maybe that's why and uh and and and and maybe that's why and uh and and and and maybe that's why we made games like that because we made games like that because we made games like that because obviously that is something we're we're obviously that is something we're we're obviously that is something we're we're hill climbing systems ourselves right hill climbing systems ourselves right hill climbing systems ourselves right >> yeah it would be quite sad if we didn't >> yeah it would be quite sad if we didn't >> yeah it would be quite sad if we didn't have any mechanism by have any mechanism by have any mechanism by >> color belts all of the we do we do this >> color belts all of the we do we do this >> color belts all of the we do we do this everywhere right where we just have this everywhere right where we just have this everywhere right where we just have this thing that thing that thing that >> it's and I don't want to dismiss that >> it's and I don't want to dismiss that >> it's and I don't want to dismiss that that there is a source of deep meaning that there is a source of deep meaning that there is a source of deep meaning for us as humans. U so one of the for us as humans. U so one of the for us as humans. U so one of the incredible stories on the business on incredible stories on the business on incredible stories on the business on the leadership side is um what Google the leadership side is um what Google the leadership side is um what Google has done over the past year. So I uh I has done over the past year. So I uh I has done over the past year. So I uh I think it's fair to say that Google was think it's fair to say that Google was think it's fair to say that Google was losing on the LLM product side uh a year losing on the LLM product side uh a year losing on the LLM product side uh a year ago with Gemini 15 and now it's winning ago with Gemini 15 and now it's winning ago with Gemini 15 and now it's winning with Gemini 25 and you took the helm and with Gemini 25 and you took the helm and with Gemini 25 and you took the helm and you led this effort. What did it take to you led this effort. What did it take to you led this effort. What did it take to go from, let's say, quote unquote losing go from, let's say, quote unquote losing go from, let's say, quote unquote losing to quote unquote winning in the in in

to quote unquote winning in the in in to quote unquote winning in the in in the span of a year? the span of a year? the span of a year? >> Yeah. Well, firstly, it's absolutely >> Yeah. Well, firstly, it's absolutely >> Yeah. Well, firstly, it's absolutely incredible team that we have, you know, incredible team that we have, you know, incredible team that we have, you know, led by Cory and Jeff Dean and and Oral led by Cory and Jeff Dean and and Oral led by Cory and Jeff Dean and and Oral and the amazing team we have on Gemini. and the amazing team we have on Gemini. and the amazing team we have on Gemini. Absolutely world class. So, you can't do Absolutely world class. So, you can't do Absolutely world class. So, you can't do it without the best talent. Um, and of it without the best talent. Um, and of it without the best talent. Um, and of course, you have, you know, we have a course, you have, you know, we have a course, you have, you know, we have a lot of great compute as well. But then lot of great compute as well. But then lot of great compute as well. But then it's the research culture we've created, it's the research culture we've created, it's the research culture we've created, right? and basically coming together right? and basically coming together right? and basically coming together both different groups in in Google you both different groups in in Google you both different groups in in Google you know there was Google brain world-class know there was Google brain world-class know there was Google brain world-class team and and then the old deep mind and team and and then the old deep mind and team and and then the old deep mind and pulling together all the best people and pulling together all the best people and pulling together all the best people and the best ideas and gathering around to the best ideas and gathering around to the best ideas and gathering around to make the absolute greatest system we make the absolute greatest system we make the absolute greatest system we could could could hard um but we're all very competitive hard um but we're all very competitive hard um but we're all very competitive uh and we you know love research this is uh and we you know love research this is uh and we you know love research this is so fun to do um and we you know it's so fun to do um and we you know it's so fun to do um and we you know it's great to see our trajectory wasn't a great to see our trajectory wasn't a great to see our trajectory wasn't a given but we're very pleased with um the given but we're very pleased with um the given but we're very pleased with um the the where we are in the rate of progress the where we are in the rate of progress the where we are in the rate of progress is the most important thing. So if you is the most important thing. So if you is the most important thing. So if you look at where we've come two from 2 look at where we've come two from 2 look at where we've come two from 2 years ago to one year ago to now you years ago to one year ago to now you years ago to one year ago to now you know I think our we call it relentless know I think our we call it relentless know I think our we call it relentless progress along with relentless shipping progress along with relentless shipping progress along with relentless shipping of that progress is um being very of that progress is um being very of that progress is um being very successful and you know um it's successful and you know um it's successful and you know um it's unbelievably competitive uh the whole unbelievably competitive uh the whole unbelievably competitive uh the whole space the whole AI space with some of space the whole AI space with some of space the whole AI space with some of the greatest entrepreneurs and leaders the greatest entrepreneurs and leaders the greatest entrepreneurs and leaders uh and companies in the world all uh and companies in the world all uh and companies in the world all competing now because everyone's competing now because everyone's competing now because everyone's realized how important AI is um and it's realized how important AI is um and it's realized how important AI is um and it's very you know been pleasing for us to very you know been pleasing for us to very you know been pleasing for us to see that progress see that progress see that progress you know, Google's a gigantic company.

you know, Google's a gigantic company. you know, Google's a gigantic company. Uh can you speak to the natural things Uh can you speak to the natural things Uh can you speak to the natural things that happen in that case is the that happen in that case is the that happen in that case is the bureaucracy that emerges like you want bureaucracy that emerges like you want bureaucracy that emerges like you want to be careful like you know like that to be careful like you know like that to be careful like you know like that the natural kind of there's there's the natural kind of there's there's the natural kind of there's there's meetings and there's managers and that meetings and there's managers and that meetings and there's managers and that like what what are some of the like what what are some of the like what what are some of the challenges from a leadership perspective challenges from a leadership perspective challenges from a leadership perspective breaking through that in order to like breaking through that in order to like breaking through that in order to like you said ship like the the number of you said ship like the the number of you said ship like the the number of products products products >> Gemini related products that's been >> Gemini related products that's been >> Gemini related products that's been shipped over the past year is just shipped over the past year is just shipped over the past year is just insane insane insane >> right it is yeah exactly that's that's >> right it is yeah exactly that's that's >> right it is yeah exactly that's that's what relentlessness looks like um I what relentlessness looks like um I what relentlessness looks like um I think it's it's a question of like any think it's it's a question of like any think it's it's a question of like any big company you know ends up having uh a big company you know ends up having uh a big company you know ends up having uh a lot of layers of management and things lot of layers of management and things lot of layers of management and things like that is sort of the nature of how like that is sort of the nature of how like that is sort of the nature of how it works. Um but I still operate and I it works. Um but I still operate and I it works. Um but I still operate and I was always operating with old Deep Mind was always operating with old Deep Mind was always operating with old Deep Mind as a as a startup still large one but as a as a startup still large one but as a as a startup still large one but still as a startup and that's what we still as a startup and that's what we still as a startup and that's what we still act like today as with Google Deep still act like today as with Google Deep still act like today as with Google Deep Mind and acting with decisiveness and Mind and acting with decisiveness and Mind and acting with decisiveness and the energy that you get from the best the energy that you get from the best the energy that you get from the best smaller organizations and we try to get smaller organizations and we try to get smaller organizations and we try to get the best of both worlds where we have the best of both worlds where we have the best of both worlds where we have this incredible billions of users this incredible billions of users this incredible billions of users surfaces uh incredible products that we surfaces uh incredible products that we surfaces uh incredible products that we can power up with our AI and our and our can power up with our AI and our and our can power up with our AI and our and our research. Um, and that's amazing. And research. Um, and that's amazing. And research. Um, and that's amazing. And you can, you know, that's very few you can, you know, that's very few you can, you know, that's very few places in the world you can get that do places in the world you can get that do places in the world you can get that do incredible world-class research on the incredible world-class research on the incredible world-class research on the one hand and then plug it in and improve one hand and then plug it in and improve one hand and then plug it in and improve billions of people's lives the next day.

billions of people's lives the next day. billions of people's lives the next day. Uh, that's a pretty amazing combination. Uh, that's a pretty amazing combination. Uh, that's a pretty amazing combination. And we're continually fighting and And we're continually fighting and And we're continually fighting and cutting away bureaucracy to allow the cutting away bureaucracy to allow the cutting away bureaucracy to allow the research culture and the relentless research culture and the relentless research culture and the relentless shipping culture to flourish. And I shipping culture to flourish. And I shipping culture to flourish. And I think we've got a pretty good balance think we've got a pretty good balance think we've got a pretty good balance whilst being responsible with it, you whilst being responsible with it, you whilst being responsible with it, you know, as you have to be as a large know, as you have to be as a large know, as you have to be as a large company and also uh with a number of, company and also uh with a number of, company and also uh with a number of, you know, uh huge product surfaces that you know, uh huge product surfaces that you know, uh huge product surfaces that we have. we have. we have. >> Uh so a funny thing you mentioned about >> Uh so a funny thing you mentioned about >> Uh so a funny thing you mentioned about like the the surface of the billion. I I like the the surface of the billion. I I like the the surface of the billion. I I had a conversation with a guy named um had a conversation with a guy named um had a conversation with a guy named um brilliant guy uh here at the British brilliant guy uh here at the British brilliant guy uh here at the British Museum called Irvin Fininkle. He's a Museum called Irvin Fininkle. He's a Museum called Irvin Fininkle. He's a world expert at Kuneaforms, which is a world expert at Kuneaforms, which is a world expert at Kuneaforms, which is a ancient writing on tablets. and he ancient writing on tablets. and he ancient writing on tablets. and he doesn't know about Chad GBT or Gemini. doesn't know about Chad GBT or Gemini. doesn't know about Chad GBT or Gemini. He doesn't even know anything about AI. He doesn't even know anything about AI. He doesn't even know anything about AI. But his first encounter with this AI But his first encounter with this AI But his first encounter with this AI >> is AI mode on Google. Yes. >> is AI mode on Google. Yes. >> is AI mode on Google. Yes. >> He's like, "Is that what you're talking >> He's like, "Is that what you're talking >> He's like, "Is that what you're talking about? This AI mode and you know, it's about? This AI mode and you know, it's about? This AI mode and you know, it's just it's just a reminder that there's a just it's just a reminder that there's a just it's just a reminder that there's a large part of the world that doesn't large part of the world that doesn't large part of the world that doesn't know about this AI thing." know about this AI thing." know about this AI thing." >> Yeah. I know. It's funny cuz if you live >> Yeah. I know. It's funny cuz if you live >> Yeah. I know. It's funny cuz if you live on uh X and Twitter and I mean it's sort on uh X and Twitter and I mean it's sort on uh X and Twitter and I mean it's sort of at least my feed it's all AI and and of at least my feed it's all AI and and of at least my feed it's all AI and and there's certain places where you know in there's certain places where you know in there's certain places where you know in the valley and certain pockets where the valley and certain pockets where the valley and certain pockets where everyone's just all they're thinking everyone's just all they're thinking everyone's just all they're thinking about is AI but a lot of the normal about is AI but a lot of the normal about is AI but a lot of the normal world hasn't hasn't come across it yet world hasn't hasn't come across it yet world hasn't hasn't come across it yet but but but >> that's a great responsibility to the >> that's a great responsibility to the >> that's a great responsibility to the their first interaction their first interaction their first interaction >> on the the the grand scale of the rural >> on the the the grand scale of the rural >> on the the the grand scale of the rural India or anywhere across the world like India or anywhere across the world like India or anywhere across the world like you get to you get to you get to >> right and we want it to be as good as >> right and we want it to be as good as >> right and we want it to be as good as possible and in a lot of cases it's just possible and in a lot of cases it's just possible and in a lot of cases it's just under the hood powering making something

under the hood powering making something under the hood powering making something like maps or search work better and um like maps or search work better and um like maps or search work better and um and it's ideally for a lot of those and it's ideally for a lot of those and it's ideally for a lot of those people should just be seamless. It's people should just be seamless. It's people should just be seamless. It's just new technology that makes their just new technology that makes their just new technology that makes their lives more, you know, productive and and lives more, you know, productive and and lives more, you know, productive and and and helps them. and helps them. and helps them. >> A bunch of folks on the Gemini product >> A bunch of folks on the Gemini product >> A bunch of folks on the Gemini product and engineering teams spoken extremely and engineering teams spoken extremely and engineering teams spoken extremely highly of you on another dimension that highly of you on another dimension that highly of you on another dimension that I almost didn't even expect cuz I kind I almost didn't even expect cuz I kind I almost didn't even expect cuz I kind of think of you as the like deep of think of you as the like deep of think of you as the like deep scientist and caring about these big scientist and caring about these big scientist and caring about these big research scientific questions. But they research scientific questions. But they research scientific questions. But they also said you're a great product guy also said you're a great product guy also said you're a great product guy like how to create a thing that a lot of like how to create a thing that a lot of like how to create a thing that a lot of people would use and enjoy using. So can people would use and enjoy using. So can people would use and enjoy using. So can you maybe speak to what it takes to you maybe speak to what it takes to you maybe speak to what it takes to create a a AI based product that a lot create a a AI based product that a lot create a a AI based product that a lot of people would enjoy using? of people would enjoy using? of people would enjoy using? >> Yeah. Well, I mean again that comes back >> Yeah. Well, I mean again that comes back >> Yeah. Well, I mean again that comes back from my game design days where I used to from my game design days where I used to from my game design days where I used to design games for millions of gamers. design games for millions of gamers. design games for millions of gamers. People would forget about that. I've had People would forget about that. I've had People would forget about that. I've had experience with cutting edge technology experience with cutting edge technology experience with cutting edge technology in product. That that that that is how in product. That that that that is how in product. That that that that is how games was in the '90s. And so I love games was in the '90s. And so I love games was in the '90s. And so I love actually the combination of cutting edge actually the combination of cutting edge actually the combination of cutting edge research and then being applied in a research and then being applied in a research and then being applied in a product and to power a new experience.

product and to power a new experience. product and to power a new experience. And so um I think it's the same skill And so um I think it's the same skill And so um I think it's the same skill really of of you know imagining what it really of of you know imagining what it really of of you know imagining what it would be like to use it viscerally um would be like to use it viscerally um would be like to use it viscerally um and having good taste. Coming back to and having good taste. Coming back to and having good taste. Coming back to earlier the same thing that's useful in earlier the same thing that's useful in earlier the same thing that's useful in science um I think is is can also be science um I think is is can also be science um I think is is can also be useful in in product design. And um I useful in in product design. And um I useful in in product design. And um I I've just had a very you know always I've just had a very you know always I've just had a very you know always been a sort of multi-disiplinary person. been a sort of multi-disiplinary person. been a sort of multi-disiplinary person. So I don't see uh the boundaries really So I don't see uh the boundaries really So I don't see uh the boundaries really between you know arts and sciences or between you know arts and sciences or between you know arts and sciences or product and research. It's it's a product and research. It's it's a product and research. It's it's a continuum for me. I mean I only work on continuum for me. I mean I only work on continuum for me. I mean I only work on I like working on products that are I like working on products that are I like working on products that are cutting edge. I wouldn't be able to you cutting edge. I wouldn't be able to you cutting edge. I wouldn't be able to you know have cutting edge technology under know have cutting edge technology under know have cutting edge technology under the hood. I wouldn't be excited about the hood. I wouldn't be excited about the hood. I wouldn't be excited about them if they were just run-of-the-mill them if they were just run-of-the-mill them if they were just run-of-the-mill products. Um so it requires this products. Um so it requires this products. Um so it requires this invention creativity capability. What invention creativity capability. What invention creativity capability. What are some specific things you kind of are some specific things you kind of are some specific things you kind of learned about when you um even on the learned about when you um even on the learned about when you um even on the LLM side, you're interacting with LLM side, you're interacting with LLM side, you're interacting with Gemini, you're like this doesn't feel Gemini, you're like this doesn't feel Gemini, you're like this doesn't feel like the layout, the the interface, like the layout, the the interface, like the layout, the the interface, >> maybe the trade-off between the latency, >> maybe the trade-off between the latency, >> maybe the trade-off between the latency, like how like how like how >> how to present to the user how long to >> how to present to the user how long to >> how to present to the user how long to wait wait wait >> and how that waiting is shown or the >> and how that waiting is shown or the >> and how that waiting is shown or the reasoning capabilities. There's some reasoning capabilities. There's some reasoning capabilities. There's some interesting things cuz like you said, interesting things cuz like you said, interesting things cuz like you said, it's the very cutting edge. We don't it's the very cutting edge. We don't it's the very cutting edge. We don't know know know >> how to present it, how to present it >> how to present it, how to present it >> how to present it, how to present it correctly. So is there some specific correctly. So is there some specific correctly. So is there some specific things you've you've learned?

things you've you've learned? things you've you've learned? >> I mean it's such a fast evolving space. >> I mean it's such a fast evolving space. >> I mean it's such a fast evolving space. We're evaluating this all the time, but We're evaluating this all the time, but We're evaluating this all the time, but where we are today is that you want to where we are today is that you want to where we are today is that you want to continually simplify things. Um the continually simplify things. Um the continually simplify things. Um the whether that's the interface or all the whether that's the interface or all the whether that's the interface or all the inter what you build on top of the inter what you build on top of the inter what you build on top of the model. You kind of want to get out of model. You kind of want to get out of model. You kind of want to get out of the way of the model. The model train is the way of the model. The model train is the way of the model. The model train is coming down the track and it's improving coming down the track and it's improving coming down the track and it's improving unbelievably fast. This relentless unbelievably fast. This relentless unbelievably fast. This relentless progress we talked about earlier. You progress we talked about earlier. You progress we talked about earlier. You know, you look at 2.5 versus 1.5 and know, you look at 2.5 versus 1.5 and know, you look at 2.5 versus 1.5 and it's just a gigantic improvement. And we it's just a gigantic improvement. And we it's just a gigantic improvement. And we expect that again for the future expect that again for the future expect that again for the future versions. And so the models are becoming versions. And so the models are becoming versions. And so the models are becoming more capable. So you've got the more capable. So you've got the more capable. So you've got the interesting thing about the design space interesting thing about the design space interesting thing about the design space in in in today's world these AI first in in in today's world these AI first in in in today's world these AI first products is you got to design not for products is you got to design not for products is you got to design not for what the thing can do today the what the thing can do today the what the thing can do today the technology can do today but in a year's technology can do today but in a year's technology can do today but in a year's time. So you actually have to be a very time. So you actually have to be a very time. So you actually have to be a very technical product person because uh you technical product person because uh you technical product person because uh you got to kind of have a good intuition for got to kind of have a good intuition for got to kind of have a good intuition for and feel for okay that thing that I'm and feel for okay that thing that I'm and feel for okay that thing that I'm dreaming about now can't be done today dreaming about now can't be done today dreaming about now can't be done today but is the research track on schedule to but is the research track on schedule to but is the research track on schedule to basically intercept that in 6 months or basically intercept that in 6 months or basically intercept that in 6 months or a year's time. So you kind of got to a year's time. So you kind of got to a year's time. So you kind of got to intercept where this highly changing intercept where this highly changing intercept where this highly changing technology is going as well as that um technology is going as well as that um technology is going as well as that um uh uh new capabilities are coming online uh uh new capabilities are coming online uh uh new capabilities are coming online all the time that you didn't realize all the time that you didn't realize all the time that you didn't realize before that can allow like D research to before that can allow like D research to before that can allow like D research to work or now we got video generation what work or now we got video generation what work or now we got video generation what do we do with that um this multimodal do we do with that um this multimodal do we do with that um this multimodal stuff you know is it one question I have stuff you know is it one question I have stuff you know is it one question I have is is it really going to be the current is is it really going to be the current is is it really going to be the current UI that we have today these textbox UI that we have today these textbox UI that we have today these textbox chats seems very unlikely given once you chats seems very unlikely given once you chats seems very unlikely given once you think about these super multimodal uh uh think about these super multimodal uh uh think about these super multimodal uh uh systems Shouldn't it be something more

systems Shouldn't it be something more systems Shouldn't it be something more like Minority Report where you're you're like Minority Report where you're you're like Minority Report where you're you're sort of vibing with it in a in a in a sort of vibing with it in a in a in a sort of vibing with it in a in a in a kind of collaborative way? Right? It kind of collaborative way? Right? It kind of collaborative way? Right? It seems very restricted today. I think seems very restricted today. I think seems very restricted today. I think we'll look back on today's interfaces we'll look back on today's interfaces we'll look back on today's interfaces and products and systems as quite and products and systems as quite and products and systems as quite archaic in maybe in just a couple of archaic in maybe in just a couple of archaic in maybe in just a couple of years. So I think there's a lot of space years. So I think there's a lot of space years. So I think there's a lot of space actually for innovation to happen on the actually for innovation to happen on the actually for innovation to happen on the product side as well as the the research product side as well as the the research product side as well as the the research side. And then we're offline talking side. And then we're offline talking side. And then we're offline talking about this keyboard is the the open about this keyboard is the the open about this keyboard is the the open question is how when and how much will question is how when and how much will question is how when and how much will we move to audio as the primary way of we move to audio as the primary way of we move to audio as the primary way of interacting with the machines around us interacting with the machines around us interacting with the machines around us versus typing stuff. Yeah, I mean typing versus typing stuff. Yeah, I mean typing versus typing stuff. Yeah, I mean typing is a very low bandwidth way of doing is a very low bandwidth way of doing is a very low bandwidth way of doing even if you're very fast, you know, even if you're very fast, you know, even if you're very fast, you know, typer and I think we're going to have to typer and I think we're going to have to typer and I think we're going to have to start utilizing other devices whether start utilizing other devices whether start utilizing other devices whether that's smart glasses, you know, audio, that's smart glasses, you know, audio, that's smart glasses, you know, audio, earbuds, um, and eventually maybe some earbuds, um, and eventually maybe some earbuds, um, and eventually maybe some sorts of neural devices where we can sorts of neural devices where we can sorts of neural devices where we can increase the the input and the output increase the the input and the output increase the the input and the output bandwidth to something uh, you know, bandwidth to something uh, you know, bandwidth to something uh, you know, maybe 100x of what is today. maybe 100x of what is today. maybe 100x of what is today. >> I think that you know underappreciated >> I think that you know underappreciated >> I think that you know underappreciated art form is the interface design. But I art form is the interface design. But I art form is the interface design. But I think you can not unlock the power of think you can not unlock the power of think you can not unlock the power of the intelligence of a system if you the intelligence of a system if you the intelligence of a system if you don't have the right interface. The don't have the right interface. The don't have the right interface. The interface is really the way you unlock interface is really the way you unlock interface is really the way you unlock its power. It's such an interesting its power. It's such an interesting its power. It's such an interesting question of how to do that. So h how question of how to do that. So h how question of how to do that. So h how >> you would think like getting out of the >> you would think like getting out of the >> you would think like getting out of the way isn't real art form.

way isn't real art form. way isn't real art form. >> Yes. You know, it's the sort of thing >> Yes. You know, it's the sort of thing >> Yes. You know, it's the sort of thing that I guess Steve Jobs always talked that I guess Steve Jobs always talked that I guess Steve Jobs always talked about, right? It's simplicity, beauty, about, right? It's simplicity, beauty, about, right? It's simplicity, beauty, and elegance that we want, right? And and elegance that we want, right? And and elegance that we want, right? And we're not there. Nobody's there yet in we're not there. Nobody's there yet in we're not there. Nobody's there yet in my opinion. And that's what I would like my opinion. And that's what I would like my opinion. And that's what I would like us to get to. Again, it sort of speaks us to get to. Again, it sort of speaks us to get to. Again, it sort of speaks to like Go again, right? As a game, the to like Go again, right? As a game, the to like Go again, right? As a game, the most elegant, beautiful game. Can you, most elegant, beautiful game. Can you, most elegant, beautiful game. Can you, you know, that can you make an interface you know, that can you make an interface you know, that can you make an interface as beautiful as that? And actually, I as beautiful as that? And actually, I as beautiful as that? And actually, I think we're going to enter an era of AI think we're going to enter an era of AI think we're going to enter an era of AI generated interfaces that are probably generated interfaces that are probably generated interfaces that are probably personalized to you so it fits the way personalized to you so it fits the way personalized to you so it fits the way that you your aesthetic, your feel, the that you your aesthetic, your feel, the that you your aesthetic, your feel, the way that your brain works. And um and way that your brain works. And um and way that your brain works. And um and and and the AI kind of generates that and and the AI kind of generates that and and the AI kind of generates that depending on the task. You know, that depending on the task. You know, that depending on the task. You know, that feels like that's probably the direction feels like that's probably the direction feels like that's probably the direction we'll end up in. we'll end up in. we'll end up in. >> Yeah. Because some people are power >> Yeah. Because some people are power >> Yeah. Because some people are power users and they want every single users and they want every single users and they want every single parameter on screen, everything, parameter on screen, everything, parameter on screen, everything, everything based like perhaps me with a everything based like perhaps me with a everything based like perhaps me with a key keyboard based navigation. I like to key keyboard based navigation. I like to key keyboard based navigation. I like to have shortcuts for everything. And some have shortcuts for everything. And some have shortcuts for everything. And some people like the minimalism people like the minimalism people like the minimalism >> just hide all of that complexity. >> just hide all of that complexity. >> just hide all of that complexity. Exactly. Exactly. Exactly. >> Yeah. Uh well, I'm glad you have a Steve >> Yeah. Uh well, I'm glad you have a Steve >> Yeah. Uh well, I'm glad you have a Steve Jobs mode in you as well. This is great. Jobs mode in you as well. This is great. Jobs mode in you as well. This is great. Einstein mode, Steve Jobs mode. Um all Einstein mode, Steve Jobs mode. Um all Einstein mode, Steve Jobs mode. Um all right, let me try to trick you into right, let me try to trick you into right, let me try to trick you into answering a question. When when will answering a question. When when will answering a question. When when will Gemini 3 come out? Is it before or after Gemini 3 come out? Is it before or after Gemini 3 come out? Is it before or after GTA 6? The world waits for both. And GTA 6? The world waits for both. And GTA 6? The world waits for both. And what does it take to go from 25 what does it take to go from 25 what does it take to go from 25 to 3 0? Because it seems like there's to 3 0? Because it seems like there's to 3 0? Because it seems like there's been a lot of releases of 25 which are been a lot of releases of 25 which are been a lot of releases of 25 which are already leaps in performance.

already leaps in performance. already leaps in performance. >> So what what does it even mean to go to >> So what what does it even mean to go to >> So what what does it even mean to go to a new version? Is it about performance? a new version? Is it about performance? a new version? Is it about performance? Is this about a completely different Is this about a completely different Is this about a completely different flavor of an experience? flavor of an experience? flavor of an experience? >> Yeah. Well, so the way it works with our >> Yeah. Well, so the way it works with our >> Yeah. Well, so the way it works with our different uh version numbers is we you different uh version numbers is we you different uh version numbers is we you know we try to collect so maybe it takes know we try to collect so maybe it takes know we try to collect so maybe it takes you know roughly 6 months or something you know roughly 6 months or something you know roughly 6 months or something to to do a new kind of full run and the to to do a new kind of full run and the to to do a new kind of full run and the full productization of a new version and full productization of a new version and full productization of a new version and during that time lots of new interesting during that time lots of new interesting during that time lots of new interesting research iterations and ideas come up research iterations and ideas come up research iterations and ideas come up and we sort of collect them all together and we sort of collect them all together and we sort of collect them all together that you know you could imagine the last that you know you could imagine the last that you know you could imagine the last 6 months worth of interesting ideas on 6 months worth of interesting ideas on 6 months worth of interesting ideas on the architecture front uh maybe it's on the architecture front uh maybe it's on the architecture front uh maybe it's on on the data front. It's like many on the data front. It's like many on the data front. It's like many different possible things and we collect different possible things and we collect different possible things and we collect package that all up, test which ones are package that all up, test which ones are package that all up, test which ones are likely to be useful for the next likely to be useful for the next likely to be useful for the next iteration and then bundle that all iteration and then bundle that all iteration and then bundle that all together and then we start the new you together and then we start the new you together and then we start the new you know giant hero training run right and know giant hero training run right and know giant hero training run right and and then uh and then of course that gets and then uh and then of course that gets and then uh and then of course that gets monitored uh and then at the end then monitored uh and then at the end then monitored uh and then at the end then there's the of the pre-training then there's the of the pre-training then there's the of the pre-training then there's all the post- training there's there's all the post- training there's there's all the post- training there's many different ways of doing that many different ways of doing that many different ways of doing that different ways of patching it so there's different ways of patching it so there's different ways of patching it so there's a whole experimental phase there which a whole experimental phase there which a whole experimental phase there which you can also get a lot of gains out and you can also get a lot of gains out and you can also get a lot of gains out and that's where you see the version numbers that's where you see the version numbers that's where you see the version numbers usually referring to the base model, the usually referring to the base model, the usually referring to the base model, the pre-trained model. And then the interim pre-trained model. And then the interim pre-trained model. And then the interim versions of 2.5, you know, and the versions of 2.5, you know, and the versions of 2.5, you know, and the different sizes and the different little different sizes and the different little different sizes and the different little additions, they're often uh patches or additions, they're often uh patches or additions, they're often uh patches or post-training ideas that can be done post-training ideas that can be done post-training ideas that can be done afterwards off the same basic afterwards off the same basic afterwards off the same basic architecture. And then of course on top architecture. And then of course on top architecture. And then of course on top of that, we also have different sizes, of that, we also have different sizes, of that, we also have different sizes, pro and flash and flashlight that are

pro and flash and flashlight that are pro and flash and flashlight that are often distilled from the biggest ones, often distilled from the biggest ones, often distilled from the biggest ones, you know, the flash model from the Pro you know, the flash model from the Pro you know, the flash model from the Pro model. And that means we have a range of model. And that means we have a range of model. And that means we have a range of different choices if you are the different choices if you are the different choices if you are the developer of do you want to prioritize developer of do you want to prioritize developer of do you want to prioritize performance or speed right and cost. And performance or speed right and cost. And performance or speed right and cost. And we like to think of this parto frontier we like to think of this parto frontier we like to think of this parto frontier of of you know on the one hand uh the y- of of you know on the one hand uh the y- of of you know on the one hand uh the y- axis is you know like performance and axis is you know like performance and axis is you know like performance and then the the the x-axis is you know cost then the the the x-axis is you know cost then the the the x-axis is you know cost or latency and and speed uh basically or latency and and speed uh basically or latency and and speed uh basically and we we have models that completely and we we have models that completely and we we have models that completely define the frontier. So whatever your define the frontier. So whatever your define the frontier. So whatever your trade-off is that you want as an trade-off is that you want as an trade-off is that you want as an individual user or as a as a developer, individual user or as a as a developer, individual user or as a as a developer, you should find one of our models you should find one of our models you should find one of our models satisfies that constraint. satisfies that constraint. satisfies that constraint. >> So behind the version changes, there is >> So behind the version changes, there is >> So behind the version changes, there is a big hero run. a big hero run. a big hero run. >> Yes. >> Yes. >> Yes. >> And then there's uh just an insane >> And then there's uh just an insane >> And then there's uh just an insane complexity of productization. complexity of productization. complexity of productization. Then there's the distillation of the Then there's the distillation of the Then there's the distillation of the different sizes along that predator different sizes along that predator different sizes along that predator front. And then as with each step you front. And then as with each step you front. And then as with each step you take, you realize there might be a cool take, you realize there might be a cool take, you realize there might be a cool product. There's side quests.

product. There's side quests. product. There's side quests. >> Yes. >> Yes. >> Yes. >> Exactly. >> Exactly. >> Exactly. >> But and then you also don't want to take >> But and then you also don't want to take >> But and then you also don't want to take too many side quests because then you too many side quests because then you too many side quests because then you have a million versions of million have a million versions of million have a million versions of million products. It's very unclear, but you products. It's very unclear, but you products. It's very unclear, but you also get super excited because it's also get super excited because it's also get super excited because it's super cool. Like how does even you look super cool. Like how does even you look super cool. Like how does even you look at VO at VO at VO how does it fit into the bigger thing? how does it fit into the bigger thing? how does it fit into the bigger thing? >> Exactly. Exactly. And then you're >> Exactly. Exactly. And then you're >> Exactly. Exactly. And then you're constantly this process of converging constantly this process of converging constantly this process of converging upstream we call it you know ideas from upstream we call it you know ideas from upstream we call it you know ideas from the from the product surfaces or or or the from the product surfaces or or or the from the product surfaces or or or from the post training and and even from the post training and and even from the post training and and even further downstream than that you you further downstream than that you you further downstream than that you you kind of upstream that into the the core kind of upstream that into the the core kind of upstream that into the the core model training for the next run. Right. model training for the next run. Right. model training for the next run. Right. So then the main model the main Gemini So then the main model the main Gemini So then the main model the main Gemini track becomes more and more general and track becomes more and more general and track becomes more and more general and eventually you know AGI eventually you know AGI eventually you know AGI >> one hero run at a time. >> one hero run at a time. >> one hero run at a time. >> Yes. Exactly. A few hero runs later. >> Yes. Exactly. A few hero runs later. >> Yes. Exactly. A few hero runs later. >> Uh yeah. So sometimes when you release >> Uh yeah. So sometimes when you release >> Uh yeah. So sometimes when you release these new versions or every version these new versions or every version these new versions or every version really really really are benchmarks um productive or are benchmarks um productive or are benchmarks um productive or counterproductive for showing the counterproductive for showing the counterproductive for showing the performance of a model you need them and performance of a model you need them and performance of a model you need them and and but it's important that you don't and but it's important that you don't and but it's important that you don't overfitit to them right so there overfitit to them right so there overfitit to them right so there shouldn't be the end the be all and end shouldn't be the end the be all and end shouldn't be the end the be all and end all so there's there's lmina or it used all so there's there's lmina or it used all so there's there's lmina or it used to be calledis that's one of them that to be calledis that's one of them that to be calledis that's one of them that turned out sort of organically to be one turned out sort of organically to be one turned out sort of organically to be one of the the main ways people like to test of the the main ways people like to test of the the main ways people like to test these systems at least the chat bots um these systems at least the chat bots um these systems at least the chat bots um obviously there's loads of academic obviously there's loads of academic obviously there's loads of academic benchmarks on from from that test benchmarks on from from that test benchmarks on from from that test mathematics and coding ability, general mathematics and coding ability, general mathematics and coding ability, general language ability, science ability and so language ability, science ability and so language ability, science ability and so on. And then we have our own internal on. And then we have our own internal on. And then we have our own internal benchmarks that we care about. It's a benchmarks that we care about. It's a benchmarks that we care about. It's a kind of multi-objective, kind of multi-objective, kind of multi-objective, you know, optimization problem, right?

you know, optimization problem, right? you know, optimization problem, right? You you don't want to be good at just You you don't want to be good at just You you don't want to be good at just one thing. We're trying to build general one thing. We're trying to build general one thing. We're trying to build general systems that are good across the board systems that are good across the board systems that are good across the board and you try and make no regret uh and you try and make no regret uh and you try and make no regret uh improvements. though where you're improvements. though where you're improvements. though where you're improving like you know coding uh but it improving like you know coding uh but it improving like you know coding uh but it doesn't reduce your performance in other doesn't reduce your performance in other doesn't reduce your performance in other areas right so that's the hard part cuz areas right so that's the hard part cuz areas right so that's the hard part cuz you you can of course you could put more you you can of course you could put more you you can of course you could put more coding data in or you could put more um coding data in or you could put more um coding data in or you could put more um I don't know gaming data in but then I don't know gaming data in but then I don't know gaming data in but then does it make worse your language uh does it make worse your language uh does it make worse your language uh system or or uh in your translation system or or uh in your translation system or or uh in your translation systems and other things that you care systems and other things that you care systems and other things that you care about. So it's you've got to kind of about. So it's you've got to kind of about. So it's you've got to kind of continually monitor this increasingly continually monitor this increasingly continually monitor this increasingly larger and larger suite of of larger and larger suite of of larger and larger suite of of benchmarks. And also there's uh when you benchmarks. And also there's uh when you benchmarks. And also there's uh when you stick them into products these models stick them into products these models stick them into products these models you also care about the direct usage and you also care about the direct usage and you also care about the direct usage and the direct stats and the signals that the direct stats and the signals that the direct stats and the signals that you're getting from the end users you're getting from the end users you're getting from the end users whether they're coders or or or the whether they're coders or or or the whether they're coders or or or the average person using using the chat average person using using the chat average person using using the chat interfaces. interfaces. interfaces. >> Yeah. Because ultimately you want to >> Yeah. Because ultimately you want to >> Yeah. Because ultimately you want to measure the usefulness but it's so hard measure the usefulness but it's so hard measure the usefulness but it's so hard to convert that into a number right. to convert that into a number right. to convert that into a number right. It's it's really vibe based benchmarks It's it's really vibe based benchmarks It's it's really vibe based benchmarks across a large number of users and it's across a large number of users and it's across a large number of users and it's hard to know and I it would be just hard to know and I it would be just hard to know and I it would be just terrifying to me to you know you have a terrifying to me to you know you have a terrifying to me to you know you have a much smarter model but it's just much smarter model but it's just much smarter model but it's just something vibe based. It's not not not something vibe based. It's not not not something vibe based. It's not not not quite working. That's such a scary cuz quite working. That's such a scary cuz quite working. That's such a scary cuz and everything you just said it has to and everything you just said it has to and everything you just said it has to be smart and useful across so many be smart and useful across so many be smart and useful across so many domains. So you you get super excited domains. So you you get super excited domains. So you you get super excited because it's all of a sudden solving because it's all of a sudden solving because it's all of a sudden solving programming problems you've never been programming problems you've never been programming problems you've never been able to solve before.

able to solve before. able to solve before. >> But now it's crappy poetry or something >> But now it's crappy poetry or something >> But now it's crappy poetry or something and it's just I don't know that's a and it's just I don't know that's a and it's just I don't know that's a stressful that's so difficult stressful that's so difficult stressful that's so difficult >> um to balance and because you can't >> um to balance and because you can't >> um to balance and because you can't really trust the benchmarks you really really trust the benchmarks you really really trust the benchmarks you really have to trust the end users. have to trust the end users. have to trust the end users. >> Yeah. And then other things that are >> Yeah. And then other things that are >> Yeah. And then other things that are even more esoteric come into play like even more esoteric come into play like even more esoteric come into play like um you know the style of the persona of um you know the style of the persona of um you know the style of the persona of the the the system you know how it you the the the system you know how it you the the the system you know how it you know is it verbose is it succinct is it know is it verbose is it succinct is it know is it verbose is it succinct is it humorous you know and and different humorous you know and and different humorous you know and and different people like different things so um you people like different things so um you people like different things so um you know it's very interesting it's almost know it's very interesting it's almost know it's very interesting it's almost like cutting edge part of psychology like cutting edge part of psychology like cutting edge part of psychology research or person personality research research or person personality research research or person personality research you know I used to do that in my PhD you know I used to do that in my PhD you know I used to do that in my PhD like five factor personality what do we like five factor personality what do we like five factor personality what do we actually want our assistance to be like actually want our assistance to be like actually want our assistance to be like and different people will like different and different people will like different and different people will like different things as well. So, these are all just things as well. So, these are all just things as well. So, these are all just sort of new problems in product space sort of new problems in product space sort of new problems in product space that I don't think have ever really been that I don't think have ever really been that I don't think have ever really been tackled before, but um we're going to tackled before, but um we're going to tackled before, but um we're going to sort of rapidly have to deal with now. I sort of rapidly have to deal with now. I sort of rapidly have to deal with now. I think is a super fascinating space think is a super fascinating space think is a super fascinating space developing the character of the thing developing the character of the thing developing the character of the thing and in so doing it puts a mirror to and in so doing it puts a mirror to and in so doing it puts a mirror to ourselves what are the kind of things um ourselves what are the kind of things um ourselves what are the kind of things um that we like cuz prompt engineering that we like cuz prompt engineering that we like cuz prompt engineering allows you to control a lot of those allows you to control a lot of those allows you to control a lot of those elements but can the product elements but can the product elements but can the product uh make it easier for you to uh control uh make it easier for you to uh control uh make it easier for you to uh control the different flavors of those the different flavors of those the different flavors of those experiences the different characters experiences the different characters experiences the different characters that you interact with.

that you interact with. that you interact with. >> Yeah, exactly. So >> Yeah, exactly. So >> Yeah, exactly. So >> So what's the probability of Google Deep >> So what's the probability of Google Deep >> So what's the probability of Google Deep Mai winning? Well, I don't see it as Mai winning? Well, I don't see it as Mai winning? Well, I don't see it as sort of winning. I mean, I think we need sort of winning. I mean, I think we need sort of winning. I mean, I think we need to think winning is the wrong way to to think winning is the wrong way to to think winning is the wrong way to look at it given how important and look at it given how important and look at it given how important and consequential what it is we're building. consequential what it is we're building. consequential what it is we're building. So, funnily enough, I don't I try not to So, funnily enough, I don't I try not to So, funnily enough, I don't I try not to view it like a game or competition, even view it like a game or competition, even view it like a game or competition, even though that's a lot of my mindset. It's though that's a lot of my mindset. It's though that's a lot of my mindset. It's it's about in my view, all of us have it's about in my view, all of us have it's about in my view, all of us have those of us at the leading edge have a those of us at the leading edge have a those of us at the leading edge have a responsibility to um steward this responsibility to um steward this responsibility to um steward this unbelievable technology that could be unbelievable technology that could be unbelievable technology that could be used for incredible good, but also has used for incredible good, but also has used for incredible good, but also has risks. um steward it safely into the risks. um steward it safely into the risks. um steward it safely into the world for the benefit of humanity. world for the benefit of humanity. world for the benefit of humanity. That's always um what I've um uh uh I That's always um what I've um uh uh I That's always um what I've um uh uh I dreamed about and what we've always dreamed about and what we've always dreamed about and what we've always tried to do and I hope that's what tried to do and I hope that's what tried to do and I hope that's what eventually the community maybe the eventually the community maybe the eventually the community maybe the international community will rally international community will rally international community will rally around when it becomes obvious that as around when it becomes obvious that as around when it becomes obvious that as we get closer and closer to to AGI that we get closer and closer to to AGI that we get closer and closer to to AGI that um that's what's needed. um that's what's needed. um that's what's needed. >> I agree with you. I think that's >> I agree with you. I think that's >> I agree with you. I think that's beautifully put. You've said that um you beautifully put. You've said that um you beautifully put. You've said that um you talk to and are on good terms with the talk to and are on good terms with the talk to and are on good terms with the leads of some of these uh labs as the leads of some of these uh labs as the leads of some of these uh labs as the competition heats up. Um how hard is it competition heats up. Um how hard is it competition heats up. Um how hard is it to maintain sort of those relationships?

to maintain sort of those relationships? to maintain sort of those relationships? It's been okay so far. I try to pride It's been okay so far. I try to pride It's been okay so far. I try to pride myself in being uh collaborative. I'm a myself in being uh collaborative. I'm a myself in being uh collaborative. I'm a collaborative person. Research is a collaborative person. Research is a collaborative person. Research is a collaborative endeavor. Science is a collaborative endeavor. Science is a collaborative endeavor. Science is a collaborative endeavor. Right? It's all collaborative endeavor. Right? It's all collaborative endeavor. Right? It's all good for humanity in the end if you cure good for humanity in the end if you cure good for humanity in the end if you cure incredible, you know, terrible diseases incredible, you know, terrible diseases incredible, you know, terrible diseases and you come with an incredible cure. and you come with an incredible cure. and you come with an incredible cure. this is net win for humanity and the this is net win for humanity and the this is net win for humanity and the same with energy. All of the things that same with energy. All of the things that same with energy. All of the things that I'm interested in in in helping solve I'm interested in in in helping solve I'm interested in in in helping solve with AI. So I just want that technology with AI. So I just want that technology with AI. So I just want that technology to exist in the world and be used for to exist in the world and be used for to exist in the world and be used for the right things and and and the the the right things and and and the the the right things and and and the the kind of the benefits of that the kind of the benefits of that the kind of the benefits of that the productivity benefits of that being productivity benefits of that being productivity benefits of that being shared for every the benefit of shared for every the benefit of shared for every the benefit of everyone. So I try to maintain good everyone. So I try to maintain good everyone. So I try to maintain good relations with all the leading lab uh relations with all the leading lab uh relations with all the leading lab uh people. They have very interesting people. They have very interesting people. They have very interesting characters many of them as you might characters many of them as you might characters many of them as you might expect. Um, but yeah, I'm on good terms. expect. Um, but yeah, I'm on good terms. expect. Um, but yeah, I'm on good terms. I I hope with pretty much all of them. I I hope with pretty much all of them. I I hope with pretty much all of them. And uh I I think that's going to be And uh I I think that's going to be And uh I I think that's going to be important when when things get even more important when when things get even more important when when things get even more serious than they are now. Uh that there serious than they are now. Uh that there serious than they are now. Uh that there are those communication channels and uh are those communication channels and uh are those communication channels and uh that's what will facilitate uh that's what will facilitate uh that's what will facilitate uh cooperation or collaboration if that's cooperation or collaboration if that's cooperation or collaboration if that's what is required especially on things what is required especially on things what is required especially on things like safety.

like safety. like safety. >> Yeah, I hope there's some collaboration >> Yeah, I hope there's some collaboration >> Yeah, I hope there's some collaboration on stuff that's uh sort of less high on stuff that's uh sort of less high on stuff that's uh sort of less high stakes and in so doing serves as a stakes and in so doing serves as a stakes and in so doing serves as a mechanism for maintaining friendships mechanism for maintaining friendships mechanism for maintaining friendships and relationships. So, for example, I and relationships. So, for example, I and relationships. So, for example, I think the internet would love it if you think the internet would love it if you think the internet would love it if you and Elon somehow collaborated on and Elon somehow collaborated on and Elon somehow collaborated on creating a video game. That kind of creating a video game. That kind of creating a video game. That kind of thing that I think that enables thing that I think that enables thing that I think that enables camaraderie in good terms and also you camaraderie in good terms and also you camaraderie in good terms and also you two are legit gamers. So, it's just fun two are legit gamers. So, it's just fun two are legit gamers. So, it's just fun to Yeah. to Yeah. to Yeah. >> fun to create. >> fun to create. >> fun to create. >> Yeah, that would be awesome. And we've >> Yeah, that would be awesome. And we've >> Yeah, that would be awesome. And we've talked about that in the past and it may talked about that in the past and it may talked about that in the past and it may be a cool thing that that you know we be a cool thing that that you know we be a cool thing that that you know we can do. And I agree with you. It'd be can do. And I agree with you. It'd be can do. And I agree with you. It'd be nice to have um kind of side projects in nice to have um kind of side projects in nice to have um kind of side projects in a way where where one can just lean into a way where where one can just lean into a way where where one can just lean into the collaboration aspect of it and it's the collaboration aspect of it and it's the collaboration aspect of it and it's a sort of uh win-win for both sides and a sort of uh win-win for both sides and a sort of uh win-win for both sides and it's um and it kind of builds up that it's um and it kind of builds up that it's um and it kind of builds up that that that uh collaborative muscle. that that uh collaborative muscle. that that uh collaborative muscle. >> I see the scientific endeavor as that >> I see the scientific endeavor as that >> I see the scientific endeavor as that kind of side project for humanity and I kind of side project for humanity and I kind of side project for humanity and I I think deep Google deep mind has been I think deep Google deep mind has been I think deep Google deep mind has been really pushing that. I would love it if really pushing that. I would love it if really pushing that. I would love it if to see other labs do more scientific to see other labs do more scientific to see other labs do more scientific stuff and then collaborate cuz it just stuff and then collaborate cuz it just stuff and then collaborate cuz it just seems like easier to collaborate on the seems like easier to collaborate on the seems like easier to collaborate on the big scientific questions. I agree and I big scientific questions. I agree and I big scientific questions. I agree and I would love to see a lot of people a lot would love to see a lot of people a lot would love to see a lot of people a lot of the other labs talk about science but of the other labs talk about science but of the other labs talk about science but I think we're really the only ones using I think we're really the only ones using I think we're really the only ones using it for science and doing that and that's it for science and doing that and that's it for science and doing that and that's why projects like Alpha Fold are so why projects like Alpha Fold are so why projects like Alpha Fold are so important to me and I think to our important to me and I think to our important to me and I think to our mission is to show uh how AI can vis you mission is to show uh how AI can vis you mission is to show uh how AI can vis you know be clearly used in a very concrete know be clearly used in a very concrete know be clearly used in a very concrete way for the benefit of humanity and and way for the benefit of humanity and and way for the benefit of humanity and and also we spun out companies like also we spun out companies like also we spun out companies like isomorphic off the back of Alphafold to isomorphic off the back of Alphafold to isomorphic off the back of Alphafold to do drug discovery and it's going really do drug discovery and it's going really do drug discovery and it's going really well and build sort of you know you can well and build sort of you know you can well and build sort of you know you can think of build additional alpha fold think of build additional alpha fold think of build additional alpha fold type type systems to go into chemistry

type type systems to go into chemistry type type systems to go into chemistry space to help accelerate drug design and space to help accelerate drug design and space to help accelerate drug design and the examples I think we need to show uh the examples I think we need to show uh the examples I think we need to show uh and society needs to understand what AI and society needs to understand what AI and society needs to understand what AI can bring these huge benefits. can bring these huge benefits. can bring these huge benefits. >> Well, from the bottom of my heart, thank >> Well, from the bottom of my heart, thank >> Well, from the bottom of my heart, thank you for pushing the scientific efforts you for pushing the scientific efforts you for pushing the scientific efforts forward wi with rigor, with fun, with forward wi with rigor, with fun, with forward wi with rigor, with fun, with humility, all of it. I just love to see humility, all of it. I just love to see humility, all of it. I just love to see and still talking about P equals NP. I and still talking about P equals NP. I and still talking about P equals NP. I mean, it's just incredible. So, I love mean, it's just incredible. So, I love mean, it's just incredible. So, I love it. Uh there there's been uh seemingly a it. Uh there there's been uh seemingly a it. Uh there there's been uh seemingly a war for talent. Some of it is meme, I war for talent. Some of it is meme, I war for talent. Some of it is meme, I don't know. Um, what do you think about don't know. Um, what do you think about don't know. Um, what do you think about Meta buying up talent with huge salaries Meta buying up talent with huge salaries Meta buying up talent with huge salaries and and the heating up of this battle and and the heating up of this battle and and the heating up of this battle for talent? And I I should say that I for talent? And I I should say that I for talent? And I I should say that I think a lot of people see Deep Mind is a think a lot of people see Deep Mind is a think a lot of people see Deep Mind is a really great place to do uh cutting edge really great place to do uh cutting edge really great place to do uh cutting edge work for the reasons that you've work for the reasons that you've work for the reasons that you've outlined is like there's this vibrant outlined is like there's this vibrant outlined is like there's this vibrant scientific culture. scientific culture. scientific culture. >> Yeah. Well, look, of course, um, you >> Yeah. Well, look, of course, um, you >> Yeah. Well, look, of course, um, you know, there's a strategy that that Meta know, there's a strategy that that Meta know, there's a strategy that that Meta is taking right now. I think that um is taking right now. I think that um is taking right now. I think that um from my perspective at least I think the from my perspective at least I think the from my perspective at least I think the people that are real uh believers in the people that are real uh believers in the people that are real uh believers in the mission of AGI and what it can do and mission of AGI and what it can do and mission of AGI and what it can do and understand the real consequences both understand the real consequences both understand the real consequences both good and bad from that and what's what good and bad from that and what's what good and bad from that and what's what that responsibility entails I think that responsibility entails I think that responsibility entails I think they're mostly doing it to be like they're mostly doing it to be like they're mostly doing it to be like myself to be on the frontier of that myself to be on the frontier of that myself to be on the frontier of that research so you know they can help research so you know they can help research so you know they can help influence the way that goes and steward influence the way that goes and steward influence the way that goes and steward that technology safely into the world that technology safely into the world that technology safely into the world and you know meta right now are not at and you know meta right now are not at and you know meta right now are not at the frontier maybe they'll they'll the frontier maybe they'll they'll the frontier maybe they'll they'll manage to get back on there and um you manage to get back on there and um you manage to get back on there and um you know it's probably rational what they're know it's probably rational what they're know it's probably rational what they're doing from their perspective because doing from their perspective because doing from their perspective because they're behind and they need to do they're behind and they need to do they're behind and they need to do something but I think um there's more

something but I think um there's more something but I think um there's more important things than than just money. important things than than just money. important things than than just money. Of course one has to pay you know people Of course one has to pay you know people Of course one has to pay you know people their market rates and all of these their market rates and all of these their market rates and all of these things and that continues to go up. Um things and that continues to go up. Um things and that continues to go up. Um but as pro and and and I was expecting but as pro and and and I was expecting but as pro and and and I was expecting this because more and more people are this because more and more people are this because more and more people are finally realizing leaders of companies finally realizing leaders of companies finally realizing leaders of companies what I've always known for 30 plus years what I've always known for 30 plus years what I've always known for 30 plus years now which is that AGI is the most now which is that AGI is the most now which is that AGI is the most important technology probably that's important technology probably that's important technology probably that's ever going to be invented. So in some ever going to be invented. So in some ever going to be invented. So in some senses it's it's rational to be doing senses it's it's rational to be doing senses it's it's rational to be doing that. But I also think there's a much that. But I also think there's a much that. But I also think there's a much bigger question. I mean people in AI bigger question. I mean people in AI bigger question. I mean people in AI these days are very well paid. You know these days are very well paid. You know these days are very well paid. You know I I remember when we were starting out I I remember when we were starting out I I remember when we were starting out back in 2010 you know I didn't even pay back in 2010 you know I didn't even pay back in 2010 you know I didn't even pay myself for a couple of years because myself for a couple of years because myself for a couple of years because wasn't enough money. We couldn't raise wasn't enough money. We couldn't raise wasn't enough money. We couldn't raise any money. And these days interns are any money. And these days interns are any money. And these days interns are being paid you know the amount that we being paid you know the amount that we being paid you know the amount that we raised as our first entire seed round. raised as our first entire seed round. raised as our first entire seed round. So it's pretty funny. And I remember the So it's pretty funny. And I remember the So it's pretty funny. And I remember the days where we used I used to have to to days where we used I used to have to to days where we used I used to have to to work for free and and almost pay my own work for free and and almost pay my own work for free and and almost pay my own way to do an internship. right now it's way to do an internship. right now it's way to do an internship. right now it's all the other way around but that's just all the other way around but that's just all the other way around but that's just how it is. it's the new world and um but how it is. it's the new world and um but how it is. it's the new world and um but I think that you know we've been I think that you know we've been I think that you know we've been discussing like what happens post AGI discussing like what happens post AGI discussing like what happens post AGI and energy systems are solved and so on and energy systems are solved and so on and energy systems are solved and so on what is even money going to mean so I what is even money going to mean so I what is even money going to mean so I think uh you know and the economy and think uh you know and the economy and think uh you know and the economy and and we're going to have much bigger and we're going to have much bigger and we're going to have much bigger issues to work through and how does the issues to work through and how does the issues to work through and how does the economy function in that world and economy function in that world and economy function in that world and companies so I think you know it's a companies so I think you know it's a companies so I think you know it's a little bit of a side issue about uh uh little bit of a side issue about uh uh little bit of a side issue about uh uh salaries and things of like that today salaries and things of like that today salaries and things of like that today >> yeah when you're facing such gigantic >> yeah when you're facing such gigantic >> yeah when you're facing such gigantic consequences and and gigantic consequences and and gigantic consequences and and gigantic fascinating scientific questions fascinating scientific questions fascinating scientific questions >> which maybe only a few years away. So, >> which maybe only a few years away. So, >> which maybe only a few years away. So, >> so on the practical pragmatic sense, if >> so on the practical pragmatic sense, if >> so on the practical pragmatic sense, if we zoom in on jobs, we can look at

we zoom in on jobs, we can look at we zoom in on jobs, we can look at programmers because it seems like AI programmers because it seems like AI programmers because it seems like AI systems are currently doing incredibly systems are currently doing incredibly systems are currently doing incredibly well in programming and increasingly so. well in programming and increasingly so. well in programming and increasingly so. So, a lot of people that uh program for So, a lot of people that uh program for So, a lot of people that uh program for a living, love programming, are worried a living, love programming, are worried a living, love programming, are worried they will lose their jobs. How worried they will lose their jobs. How worried they will lose their jobs. How worried should they be, do you think? and what's should they be, do you think? and what's should they be, do you think? and what's the right way to uh sort of adjust to the right way to uh sort of adjust to the right way to uh sort of adjust to the new reality and ensure that you the new reality and ensure that you the new reality and ensure that you survive and thrive as a human in the survive and thrive as a human in the survive and thrive as a human in the programming world. programming world. programming world. >> Well, it's interesting that programming >> Well, it's interesting that programming >> Well, it's interesting that programming and it's again counterintuitive to what and it's again counterintuitive to what and it's again counterintuitive to what we thought years ago maybe that some of we thought years ago maybe that some of we thought years ago maybe that some of the skills that we think of as harder the skills that we think of as harder the skills that we think of as harder skills are turned out maybe to be the skills are turned out maybe to be the skills are turned out maybe to be the easier ones for various reasons but you easier ones for various reasons but you easier ones for various reasons but you know coding and math because you can know coding and math because you can know coding and math because you can create a lot of synthetic data and create a lot of synthetic data and create a lot of synthetic data and verify if that data is correct. So verify if that data is correct. So verify if that data is correct. So because of that nature of that it's because of that nature of that it's because of that nature of that it's easier to make things like synthetic easier to make things like synthetic easier to make things like synthetic data to train from. Um it's also an area data to train from. Um it's also an area data to train from. Um it's also an area of course we're all interested in of course we're all interested in of course we're all interested in because as programmers right to help us because as programmers right to help us because as programmers right to help us and get faster at it and more and get faster at it and more and get faster at it and more productive. So I think the for the next productive. So I think the for the next productive. So I think the for the next era like the next 5 10 years I think era like the next 5 10 years I think era like the next 5 10 years I think what we're going to find is people who what we're going to find is people who what we're going to find is people who are kind of embrace these technologies are kind of embrace these technologies are kind of embrace these technologies become almost at one with them um become almost at one with them um become almost at one with them um whether that's in the creative whether that's in the creative whether that's in the creative industries or the technical industries industries or the technical industries industries or the technical industries will become sort of superhumanly will become sort of superhumanly will become sort of superhumanly productive I think. So the great productive I think. So the great productive I think. So the great programmers will be even better but programmers will be even better but programmers will be even better but they'll be even 10x even what they are they'll be even 10x even what they are they'll be even 10x even what they are today and because there you'll be able today and because there you'll be able today and because there you'll be able to use their skills to utilize the the to use their skills to utilize the the to use their skills to utilize the the tools to the maximum uh you exploit them tools to the maximum uh you exploit them tools to the maximum uh you exploit them to the maximum and um so I think that's to the maximum and um so I think that's to the maximum and um so I think that's what we're going to see in the next what we're going to see in the next what we're going to see in the next domain um so that's going to cause quite domain um so that's going to cause quite domain um so that's going to cause quite a lot of change right and so that's

a lot of change right and so that's a lot of change right and so that's coming a lot of people benefit from that coming a lot of people benefit from that coming a lot of people benefit from that so I think one example of that is if so I think one example of that is if so I think one example of that is if coding becomes easier um it becomes coding becomes easier um it becomes coding becomes easier um it becomes available to many more creatives to do available to many more creatives to do available to many more creatives to do more uh and uh but I think the top more uh and uh but I think the top more uh and uh but I think the top programmers will still have huge programmers will still have huge programmers will still have huge advantages as terms of specifying going advantages as terms of specifying going advantages as terms of specifying going back to specifying what the architecture back to specifying what the architecture back to specifying what the architecture should be the question should be how to should be the question should be how to should be the question should be how to guide these um uh coding assistants in a guide these um uh coding assistants in a guide these um uh coding assistants in a way that's useful and you know check way that's useful and you know check way that's useful and you know check whether the code they produce is good so whether the code they produce is good so whether the code they produce is good so I think there's plenty of um uh headroom I think there's plenty of um uh headroom I think there's plenty of um uh headroom there for the foreseeable you know next there for the foreseeable you know next there for the foreseeable you know next few years few years few years >> so I think there's there's several >> so I think there's there's several >> so I think there's there's several interesting things there one is there's interesting things there one is there's interesting things there one is there's a lot of imperative to just get better a lot of imperative to just get better a lot of imperative to just get better and better consistently of using these and better consistently of using these and better consistently of using these tools. So they they're riding the wave tools. So they they're riding the wave tools. So they they're riding the wave of the improvement improving models. of the improvement improving models. of the improvement improving models. >> Yes. >> Yes. >> Yes. >> Versus like competing against them. >> Versus like competing against them. >> Versus like competing against them. >> But sadly, but that's the the nature of >> But sadly, but that's the the nature of >> But sadly, but that's the the nature of of life on earth. Um there could be a of life on earth. Um there could be a of life on earth. Um there could be a huge amount of value to certain kinds of huge amount of value to certain kinds of huge amount of value to certain kinds of programming at the cutting edge and less programming at the cutting edge and less programming at the cutting edge and less value to other kinds. For example, it value to other kinds. For example, it value to other kinds. For example, it could be like, you know, front end could be like, you know, front end could be like, you know, front end >> web design might uh be more amendable to >> web design might uh be more amendable to >> web design might uh be more amendable to to to as as you mentioned to generation to to as as you mentioned to generation to to as as you mentioned to generation >> uh by AI systems and maybe for example >> uh by AI systems and maybe for example >> uh by AI systems and maybe for example game engine design or something like game engine design or something like game engine design or something like this or backhand design or or guiding this or backhand design or or guiding this or backhand design or or guiding systems in high performance situations, systems in high performance situations, systems in high performance situations, high performance programming type of high performance programming type of high performance programming type of design decisions that might be extremely design decisions that might be extremely design decisions that might be extremely valuable. But it it will shift where the

valuable. But it it will shift where the valuable. But it it will shift where the humans are needed most and that's scary humans are needed most and that's scary humans are needed most and that's scary for people to adjust. I can I think for people to adjust. I can I think for people to adjust. I can I think that's right that the any time where that's right that the any time where that's right that the any time where there's a lot of disruption and change there's a lot of disruption and change there's a lot of disruption and change you know and we've had this it's not you know and we've had this it's not you know and we've had this it's not just this time we've had this in many just this time we've had this in many just this time we've had this in many times in human history with the internet times in human history with the internet times in human history with the internet u mobile but before that obviously u mobile but before that obviously u mobile but before that obviously industrial revolution um and it's going industrial revolution um and it's going industrial revolution um and it's going to be one of those eras where there will to be one of those eras where there will to be one of those eras where there will be a lot of change I think there'll be be a lot of change I think there'll be be a lot of change I think there'll be new jobs we can't even imagine today new jobs we can't even imagine today new jobs we can't even imagine today just like the internet created and then just like the internet created and then just like the internet created and then those people with the right skill sets those people with the right skill sets those people with the right skill sets to ride that wave will become incredibly to ride that wave will become incredibly to ride that wave will become incredibly uh valuable right those skills but maybe uh valuable right those skills but maybe uh valuable right those skills but maybe people will have to relearn or adapt a people will have to relearn or adapt a people will have to relearn or adapt a bit uh their current skills. And it's bit uh their current skills. And it's bit uh their current skills. And it's the the thing that's going to be harder the the thing that's going to be harder the the thing that's going to be harder to deal with this time around is that I to deal with this time around is that I to deal with this time around is that I think what we're going to see is think what we're going to see is think what we're going to see is something like probably 10 times the something like probably 10 times the something like probably 10 times the impact the industrial revolution had and impact the industrial revolution had and impact the industrial revolution had and but 10 times faster as well. Right? So but 10 times faster as well. Right? So but 10 times faster as well. Right? So instead of 100 years, it takes 10 years. instead of 100 years, it takes 10 years. instead of 100 years, it takes 10 years. And so that's going to make it, you And so that's going to make it, you And so that's going to make it, you know, it's like a 100x uh the impact and know, it's like a 100x uh the impact and know, it's like a 100x uh the impact and the speed combined. So that's what's I the speed combined. So that's what's I the speed combined. So that's what's I think going to make it more difficult think going to make it more difficult think going to make it more difficult for society to to to deal with and it's for society to to to deal with and it's for society to to to deal with and it's there's a lot to think through and I there's a lot to think through and I there's a lot to think through and I think we need to be discussing that think we need to be discussing that think we need to be discussing that right now and I I you know I encourage right now and I I you know I encourage right now and I I you know I encourage top economists in the world and top economists in the world and top economists in the world and philosophers to start thinking about um philosophers to start thinking about um philosophers to start thinking about um uh how should is society going to be uh how should is society going to be uh how should is society going to be affected by this and what should we do affected by this and what should we do affected by this and what should we do including things like um you know including things like um you know including things like um you know universal basic provision or something universal basic provision or something universal basic provision or something like that where a lot of the um like that where a lot of the um like that where a lot of the um increased productivity increased productivity increased productivity uh gets shared out and distributed uh to uh gets shared out and distributed uh to uh gets shared out and distributed uh to society um and maybe in the form of

society um and maybe in the form of society um and maybe in the form of surface services and other things where surface services and other things where surface services and other things where if you want more than that you still go if you want more than that you still go if you want more than that you still go and get some incredibly rare skills and and get some incredibly rare skills and and get some incredibly rare skills and things like that um and and make things like that um and and make things like that um and and make yourself unique. Um but uh uh but yourself unique. Um but uh uh but yourself unique. Um but uh uh but there's a basic provision that is there's a basic provision that is there's a basic provision that is provided provided provided >> and if you think of government as a >> and if you think of government as a >> and if you think of government as a technology there's also interesting technology there's also interesting technology there's also interesting questions not just in economics but just questions not just in economics but just questions not just in economics but just politics. How do you design a system politics. How do you design a system politics. How do you design a system that's responding to the rapidly that's responding to the rapidly that's responding to the rapidly changing times such that you can changing times such that you can changing times such that you can represent the different pain that people represent the different pain that people represent the different pain that people feel from the different groups? And how feel from the different groups? And how feel from the different groups? And how do you reallocate resources in a way do you reallocate resources in a way do you reallocate resources in a way that um addresses that pain and that um addresses that pain and that um addresses that pain and represents the hope and the pain and the represents the hope and the pain and the represents the hope and the pain and the fears of different people uh in a way fears of different people uh in a way fears of different people uh in a way that doesn't lead to division because that doesn't lead to division because that doesn't lead to division because politicians are often really good at politicians are often really good at politicians are often really good at sort of fueling the division and using sort of fueling the division and using sort of fueling the division and using that to get elected the other that to get elected the other that to get elected the other defining the other and then saying defining the other and then saying defining the other and then saying that's bad and sort of based on that that's bad and sort of based on that that's bad and sort of based on that >> I think that's often counterproductive >> I think that's often counterproductive >> I think that's often counterproductive ive to leveraging a rapidly changing ive to leveraging a rapidly changing ive to leveraging a rapidly changing technology how to help the world technology how to help the world technology how to help the world flourish. So we almost need to improve flourish. So we almost need to improve flourish. So we almost need to improve our political systems as well rapidly if our political systems as well rapidly if our political systems as well rapidly if you think of them as a technology you think of them as a technology you think of them as a technology >> definitely and I think I think we'll >> definitely and I think I think we'll >> definitely and I think I think we'll need new governance structures need new governance structures need new governance structures institutions probably to help with this institutions probably to help with this institutions probably to help with this transition. So I think political transition. So I think political transition. So I think political philosophy and political science is philosophy and political science is philosophy and political science is going to be key uh to that. But I think going to be key uh to that. But I think going to be key uh to that. But I think the number one thing first of all is to the number one thing first of all is to the number one thing first of all is to create more abundance of resources right

create more abundance of resources right create more abundance of resources right then there's the so that's the number then there's the so that's the number then there's the so that's the number one thing increase productivity get more one thing increase productivity get more one thing increase productivity get more resources maybe eventually get out of resources maybe eventually get out of resources maybe eventually get out of the zero sum situation then the second the zero sum situation then the second the zero sum situation then the second question is how to use uh those question is how to use uh those question is how to use uh those resources and distribute those resources resources and distribute those resources resources and distribute those resources but yeah you can't do that without but yeah you can't do that without but yeah you can't do that without having that abundance first. Uh you having that abundance first. Uh you having that abundance first. Uh you mentioned to me uh the book the maniac mentioned to me uh the book the maniac mentioned to me uh the book the maniac uh by Benjamin Levitut a book on uh uh by Benjamin Levitut a book on uh uh by Benjamin Levitut a book on uh first of all about you there's a bio first of all about you there's a bio first of all about you there's a bio about you um strange yeah about you um strange yeah about you um strange yeah >> it's unclear yeah sure it's unclear how >> it's unclear yeah sure it's unclear how >> it's unclear yeah sure it's unclear how much is fiction how much is reality um much is fiction how much is reality um much is fiction how much is reality um but I think the central figure that is but I think the central figure that is but I think the central figure that is John vonman I would say it's a haunting John vonman I would say it's a haunting John vonman I would say it's a haunting and beautiful exploration of madness and and beautiful exploration of madness and and beautiful exploration of madness and genius and let's say the double-edged uh genius and let's say the double-edged uh genius and let's say the double-edged uh sword of discovery sword of discovery sword of discovery And you know for um people who don't And you know for um people who don't And you know for um people who don't know John vonman is a kind of legendary know John vonman is a kind of legendary know John vonman is a kind of legendary mind. He contributed to quantum mind. He contributed to quantum mind. He contributed to quantum mechanics. He was on the Manhattan mechanics. He was on the Manhattan mechanics. He was on the Manhattan project. He is widely considered to be project. He is widely considered to be project. He is widely considered to be the father of or pioneer the modern the father of or pioneer the modern the father of or pioneer the modern computer and AI and so on. So as many computer and AI and so on. So as many computer and AI and so on. So as many people say he's like one of the smartest people say he's like one of the smartest people say he's like one of the smartest humans ever. So it's just fascinating.

humans ever. So it's just fascinating. humans ever. So it's just fascinating. And what's also fascinating is as a And what's also fascinating is as a And what's also fascinating is as a person who saw nuclear science and person who saw nuclear science and person who saw nuclear science and physics become the atomic bomb. So you physics become the atomic bomb. So you physics become the atomic bomb. So you you got to see ideas become a thing that you got to see ideas become a thing that you got to see ideas become a thing that has a huge amount of impact on the has a huge amount of impact on the has a huge amount of impact on the world. He also foresaw the same thing world. He also foresaw the same thing world. He also foresaw the same thing for computing. for computing. for computing. >> Yeah. >> Yeah. >> Yeah. >> He he and that's the a little bit again >> He he and that's the a little bit again >> He he and that's the a little bit again beautiful and haunting aspect of the beautiful and haunting aspect of the beautiful and haunting aspect of the book. um than taking a leap forward and book. um than taking a leap forward and book. um than taking a leap forward and looking at this at least at all alpha looking at this at least at all alpha looking at this at least at all alpha zero alpha go alpha zero big moment that zero alpha go alpha zero big moment that zero alpha go alpha zero big moment that maybe John vonman's maybe John vonman's maybe John vonman's thinking was brought to to to to thinking was brought to to to to thinking was brought to to to to reality. So I I I guess the question is reality. So I I I guess the question is reality. So I I I guess the question is um what do you think if you got to hang um what do you think if you got to hang um what do you think if you got to hang out with John Noman now? What what would out with John Noman now? What what would out with John Noman now? What what would he say about what's going on? he say about what's going on? he say about what's going on? >> Well, that would be an amazing >> Well, that would be an amazing >> Well, that would be an amazing experience. you know, he's a fantastic experience. you know, he's a fantastic experience. you know, he's a fantastic mind and and I also love the where he he mind and and I also love the where he he mind and and I also love the where he he spent a lot of his time at Princeton at spent a lot of his time at Princeton at spent a lot of his time at Princeton at the Institute of Advanced Studies, a the Institute of Advanced Studies, a the Institute of Advanced Studies, a very special place for thinking and um very special place for thinking and um very special place for thinking and um it's amazing how much of a polymath he it's amazing how much of a polymath he it's amazing how much of a polymath he was in the the spread of things he was in the the spread of things he was in the the spread of things he helped invent including of course the helped invent including of course the helped invent including of course the vonoyman architecture that all the vonoyman architecture that all the vonoyman architecture that all the modern computers are based on. And um he modern computers are based on. And um he modern computers are based on. And um he had amazing foresight. I think he would had amazing foresight. I think he would had amazing foresight. I think he would have loved where we are today and he have loved where we are today and he have loved where we are today and he would have um I think he would have would have um I think he would have would have um I think he would have really enjoyed Alph Go being you know really enjoyed Alph Go being you know really enjoyed Alph Go being you know games he also did game theory. I think games he also did game theory. I think games he also did game theory. I think he foresaw a lot of what would happen he foresaw a lot of what would happen he foresaw a lot of what would happen with learning machines systems that that with learning machines systems that that with learning machines systems that that that are kind of grown I think he called

that are kind of grown I think he called that are kind of grown I think he called it rather than programmed. I'm not sure it rather than programmed. I'm not sure it rather than programmed. I'm not sure how even maybe he wouldn't even be that how even maybe he wouldn't even be that how even maybe he wouldn't even be that surprised this the fruition of what I surprised this the fruition of what I surprised this the fruition of what I think he already foresaw in the 1950s. think he already foresaw in the 1950s. think he already foresaw in the 1950s. >> I wonder what advice he would give. You >> I wonder what advice he would give. You >> I wonder what advice he would give. You got to see the building of the atomic got to see the building of the atomic got to see the building of the atomic bomb with the Manhattan project. I'm bomb with the Manhattan project. I'm bomb with the Manhattan project. I'm sure there's sure there's sure there's >> interesting stuff that maybe is not >> interesting stuff that maybe is not >> interesting stuff that maybe is not talked about enough. Maybe some talked about enough. Maybe some talked about enough. Maybe some bureaucratic aspect, maybe the influence bureaucratic aspect, maybe the influence bureaucratic aspect, maybe the influence of politicians, maybe of politicians, maybe of politicians, maybe >> maybe not enough of picking up the phone >> maybe not enough of picking up the phone >> maybe not enough of picking up the phone and talking to people that are called and talking to people that are called and talking to people that are called enemies by the said politicians. There enemies by the said politicians. There enemies by the said politicians. There might be some like deep wisdom that we might be some like deep wisdom that we might be some like deep wisdom that we just may have lost from that time just may have lost from that time just may have lost from that time actually. actually. actually. >> Yeah, I'm sure. I'm sure there is. I >> Yeah, I'm sure. I'm sure there is. I >> Yeah, I'm sure. I'm sure there is. I mean, I've we we you know studied I read mean, I've we we you know studied I read mean, I've we we you know studied I read a lot of books for that time as well. a lot of books for that time as well. a lot of books for that time as well. chronicle time um and some brilliant chronicle time um and some brilliant chronicle time um and some brilliant people involved. I I agree with you. I people involved. I I agree with you. I people involved. I I agree with you. I think maybe there needs to be more think maybe there needs to be more think maybe there needs to be more dialogue and understanding. Um I hope we dialogue and understanding. Um I hope we dialogue and understanding. Um I hope we can learn from those those times. I can learn from those those times. I can learn from those those times. I think the difference here is that the AI think the difference here is that the AI think the difference here is that the AI has so many it's a multi-use technology has so many it's a multi-use technology has so many it's a multi-use technology obviously we're trying to do things like obviously we're trying to do things like obviously we're trying to do things like that like solve you know all diseases um that like solve you know all diseases um that like solve you know all diseases um uh help with energy uh and scarcity uh help with energy uh and scarcity uh help with energy uh and scarcity these incredible things. This is why all these incredible things. This is why all these incredible things. This is why all of us and and myself, you know, I worked of us and and myself, you know, I worked of us and and myself, you know, I worked started on this journey 30 plus years started on this journey 30 plus years started on this journey 30 plus years ago. And um but of course there are ago. And um but of course there are ago. And um but of course there are risks too and probably vonoman my guess risks too and probably vonoman my guess risks too and probably vonoman my guess is he foraw both and um and I think he is he foraw both and um and I think he is he foraw both and um and I think he sort of said I think is to his wife that sort of said I think is to his wife that sort of said I think is to his wife that that that it would be this is computers that that it would be this is computers that that it would be this is computers would be even more impactful in the would be even more impactful in the would be even more impactful in the world and as we just discussed you know world and as we just discussed you know world and as we just discussed you know I think that's right. I think it's going I think that's right. I think it's going I think that's right. I think it's going to be 10 times at least of the to be 10 times at least of the to be 10 times at least of the industrial revolution. So I think he's

industrial revolution. So I think he's industrial revolution. So I think he's right. So I think he would have been I right. So I think he would have been I right. So I think he would have been I imagine fascinated by uh uh uh where we imagine fascinated by uh uh uh where we imagine fascinated by uh uh uh where we are now. are now. are now. >> And I think one of the maybe you can >> And I think one of the maybe you can >> And I think one of the maybe you can correct me but one of the takeaways from correct me but one of the takeaways from correct me but one of the takeaways from the book is that reason as uh said in the book is that reason as uh said in the book is that reason as uh said in the book mad dreams of reason. It's not the book mad dreams of reason. It's not the book mad dreams of reason. It's not enough for guiding humanity as we build enough for guiding humanity as we build enough for guiding humanity as we build these super powerful technology that these super powerful technology that these super powerful technology that there's something else. I mean there's there's something else. I mean there's there's something else. I mean there's also like a religious component. also like a religious component. also like a religious component. Whatever God, whatever religion gives it Whatever God, whatever religion gives it Whatever God, whatever religion gives it G, it pulls at us something in the human G, it pulls at us something in the human G, it pulls at us something in the human spirit that raw cold reason doesn't give spirit that raw cold reason doesn't give spirit that raw cold reason doesn't give us. us. us. >> And I I agree with that. I think we need >> And I I agree with that. I think we need >> And I I agree with that. I think we need to approach it with whatever you want to to approach it with whatever you want to to approach it with whatever you want to call it, the a spiritual dimension or call it, the a spiritual dimension or call it, the a spiritual dimension or humanist dimension. Doesn't have to be humanist dimension. Doesn't have to be humanist dimension. Doesn't have to be to do with religion, right? But this to do with religion, right? But this to do with religion, right? But this idea of of a soul, what makes us human, idea of of a soul, what makes us human, idea of of a soul, what makes us human, this spark that we have perhaps is to do this spark that we have perhaps is to do this spark that we have perhaps is to do with consciousness when we finally with consciousness when we finally with consciousness when we finally understand that. Um, I think that has to understand that. Um, I think that has to understand that. Um, I think that has to be at the heart of the endeavor. Um, and be at the heart of the endeavor. Um, and be at the heart of the endeavor. Um, and technology, I've always seen technology technology, I've always seen technology technology, I've always seen technology as the enabler, right? The tools that as the enabler, right? The tools that as the enabler, right? The tools that that enable us to to flourish and to that enable us to to flourish and to that enable us to to flourish and to understand more about the the world. And understand more about the the world. And understand more about the the world. And I I'm sort of with Fman on this, and he I I'm sort of with Fman on this, and he I I'm sort of with Fman on this, and he used to always talk about science and used to always talk about science and used to always talk about science and art being companions, right? You can art being companions, right? You can art being companions, right? You can understand it from both sides, the understand it from both sides, the understand it from both sides, the beauty of a flower, how beautiful it is, beauty of a flower, how beautiful it is, beauty of a flower, how beautiful it is, and also understand why the colors of and also understand why the colors of and also understand why the colors of the flower evolved like that, right?

the flower evolved like that, right? the flower evolved like that, right? That just makes it more beautiful, the That just makes it more beautiful, the That just makes it more beautiful, the the the just the intrinsic beauty of the the the just the intrinsic beauty of the the the just the intrinsic beauty of the flower. And and I've always sort of seen flower. And and I've always sort of seen flower. And and I've always sort of seen it like that. And maybe you know in the it like that. And maybe you know in the it like that. And maybe you know in the renaissance times the great discoverers renaissance times the great discoverers renaissance times the great discoverers then like people like Da Vinci you know then like people like Da Vinci you know then like people like Da Vinci you know they were I don't think he saw any they were I don't think he saw any they were I don't think he saw any difference between science and art uh difference between science and art uh difference between science and art uh and perhaps religion right they were and perhaps religion right they were and perhaps religion right they were everything was it's just part of being everything was it's just part of being everything was it's just part of being human and um being inspired about the human and um being inspired about the human and um being inspired about the world around us and that's what I the world around us and that's what I the world around us and that's what I the philosophy I tried to take and u one of philosophy I tried to take and u one of philosophy I tried to take and u one of my favorite philosophers is Spininoza my favorite philosophers is Spininoza my favorite philosophers is Spininoza and I think he combined that all very and I think he combined that all very and I think he combined that all very well you know this idea of trying to well you know this idea of trying to well you know this idea of trying to understand the universe and understand the universe and understand the universe and understanding our place in it and that understanding our place in it and that understanding our place in it and that was his kind of way of understanding was his kind of way of understanding was his kind of way of understanding religion and I think that's quite religion and I think that's quite religion and I think that's quite beautiful and for me every all of these beautiful and for me every all of these beautiful and for me every all of these things are related interrelated the things are related interrelated the things are related interrelated the technology and um what it means to be technology and um what it means to be technology and um what it means to be human and uh I think it's very important human and uh I think it's very important human and uh I think it's very important though that we remember that as when though that we remember that as when though that we remember that as when we're immersed in the technology and the we're immersed in the technology and the we're immersed in the technology and the the research I think a lot of the research I think a lot of the research I think a lot of researchers that I see in in our field researchers that I see in in our field researchers that I see in in our field are a little bit too narrow and only are a little bit too narrow and only are a little bit too narrow and only understand the technology and I think understand the technology and I think understand the technology and I think also that's why it's important important also that's why it's important important also that's why it's important important for this to be debated by society at for this to be debated by society at for this to be debated by society at large and I'm very supportive of things large and I'm very supportive of things large and I'm very supportive of things like this the AI summits that will like this the AI summits that will like this the AI summits that will happen and governments understanding it happen and governments understanding it happen and governments understanding it and I think that's one good thing about and I think that's one good thing about and I think that's one good thing about the chatbot era and the product era of the chatbot era and the product era of the chatbot era and the product era of AI is that everyday person can actually AI is that everyday person can actually AI is that everyday person can actually feel and and interact with cutting edge feel and and interact with cutting edge feel and and interact with cutting edge AI and and and feel feel it for AI and and and feel feel it for AI and and and feel feel it for themselves.

themselves. themselves. >> Yeah. Because they they force the >> Yeah. Because they they force the >> Yeah. Because they they force the technologist to have the human technologist to have the human technologist to have the human conversation. Yeah, for sure. That's the conversation. Yeah, for sure. That's the conversation. Yeah, for sure. That's the hopeful aspect of it. Like you said, hopeful aspect of it. Like you said, hopeful aspect of it. Like you said, it's a dual use technology that we're it's a dual use technology that we're it's a dual use technology that we're forcefully integrating the entire of forcefully integrating the entire of forcefully integrating the entire of humanity into it by into the discussion humanity into it by into the discussion humanity into it by into the discussion about AI because ultimately AI AGI will about AI because ultimately AI AGI will about AI because ultimately AI AGI will be used for the things that states use be used for the things that states use be used for the things that states use technologies for which is uh conflict technologies for which is uh conflict technologies for which is uh conflict and so on. And the more we uh integrate and so on. And the more we uh integrate and so on. And the more we uh integrate humans into this picture by having chats humans into this picture by having chats humans into this picture by having chats with them, the more it will guide with them, the more it will guide with them, the more it will guide >> Yeah. be able to adapt. society will be >> Yeah. be able to adapt. society will be >> Yeah. be able to adapt. society will be able to adapt to these technologies like able to adapt to these technologies like able to adapt to these technologies like we've always done in the past with with we've always done in the past with with we've always done in the past with with uh the incredible technologies we've uh the incredible technologies we've uh the incredible technologies we've invented in the past. invented in the past. invented in the past. >> Do you think there will be something >> Do you think there will be something >> Do you think there will be something like a Manhattan project like a Manhattan project like a Manhattan project where where where um there will be an escalation of the um there will be an escalation of the um there will be an escalation of the power of this technology and states in power of this technology and states in power of this technology and states in their old way of thinking will try to their old way of thinking will try to their old way of thinking will try to use it as weapons technologies and there use it as weapons technologies and there use it as weapons technologies and there will be this kind of escalation. will be this kind of escalation. will be this kind of escalation. >> I hope not. Um I think that would be uh >> I hope not. Um I think that would be uh >> I hope not. Um I think that would be uh very dangerous to do and I think also um very dangerous to do and I think also um very dangerous to do and I think also um you know not the right use of the you know not the right use of the you know not the right use of the technology. I I hope we'll end up with technology. I I hope we'll end up with technology. I I hope we'll end up with more something more collaborative if more something more collaborative if more something more collaborative if needed like more like a like a CERN needed like more like a like a CERN needed like more like a like a CERN project you know where um it's research project you know where um it's research project you know where um it's research focused and the best minds in the world focused and the best minds in the world focused and the best minds in the world come together to carefully complete the come together to carefully complete the come together to carefully complete the final steps and make sure it's final steps and make sure it's final steps and make sure it's responsibly done before you know like responsibly done before you know like responsibly done before you know like deploying it to the world. We'll see. I deploying it to the world. We'll see. I deploying it to the world. We'll see. I mean it's difficult with the current

mean it's difficult with the current mean it's difficult with the current geopolitical climate I think uh to to geopolitical climate I think uh to to geopolitical climate I think uh to to see cooperation but things can change see cooperation but things can change see cooperation but things can change and um I think at least on the and um I think at least on the and um I think at least on the scientific level it's important for the scientific level it's important for the scientific level it's important for the researchers to to to to keep in touch researchers to to to to keep in touch researchers to to to to keep in touch and and and keep close to each other on and and and keep close to each other on and and and keep close to each other on at least on those kinds of topics. at least on those kinds of topics. at least on those kinds of topics. >> Yeah. And I I personally believe on the >> Yeah. And I I personally believe on the >> Yeah. And I I personally believe on the education side and um immigration side, education side and um immigration side, education side and um immigration side, it would be great if both directions uh it would be great if both directions uh it would be great if both directions uh people from the west immigrated to China people from the west immigrated to China people from the west immigrated to China and China back. I mean there is some and China back. I mean there is some and China back. I mean there is some like family human aspect of people just like family human aspect of people just like family human aspect of people just intermixing. intermixing. intermixing. >> Yeah. >> Yeah. >> Yeah. >> And thereby those ties grow strong. So >> And thereby those ties grow strong. So >> And thereby those ties grow strong. So you can't sort of divide against each you can't sort of divide against each you can't sort of divide against each other this kind of old school way of other this kind of old school way of other this kind of old school way of thinking. And so uh multi- uh thinking. And so uh multi- uh thinking. And so uh multi- uh multicultural multid-disciplinary multicultural multid-disciplinary multicultural multid-disciplinary research teams working on scientific research teams working on scientific research teams working on scientific questions. That's like the hope. Don't questions. That's like the hope. Don't questions. That's like the hope. Don't don't let the the warm leaders that are don't let the the warm leaders that are don't let the the warm leaders that are warmongers because it divide us. I think warmongers because it divide us. I think warmongers because it divide us. I think science is the ultimately really science is the ultimately really science is the ultimately really beautiful connector. beautiful connector. beautiful connector. >> Yeah. Science has always been uh I think >> Yeah. Science has always been uh I think >> Yeah. Science has always been uh I think quite a a very collaborative endeavor quite a a very collaborative endeavor quite a a very collaborative endeavor and you know scientists know that it's and you know scientists know that it's and you know scientists know that it's it's a it's a collective endeavor as it's a it's a collective endeavor as it's a it's a collective endeavor as well and we can all learn from each well and we can all learn from each well and we can all learn from each other. So perhaps it could be a vector other. So perhaps it could be a vector other. So perhaps it could be a vector to get a bit of cooperation. What's your to get a bit of cooperation. What's your to get a bit of cooperation. What's your uh ridiculous question? What's your uh ridiculous question? What's your uh ridiculous question? What's your pdoom? Probability the human pdoom? Probability the human pdoom? Probability the human civilization destroys itself.

civilization destroys itself. civilization destroys itself. >> Well, look, I I don't have a >> Well, look, I I don't have a >> Well, look, I I don't have a it's a you know, I don't have a pdoom it's a you know, I don't have a pdoom it's a you know, I don't have a pdoom number. The reason I don't is because I number. The reason I don't is because I number. The reason I don't is because I think it's would imply a level of think it's would imply a level of think it's would imply a level of precision that is not there. So, like I precision that is not there. So, like I precision that is not there. So, like I don't know how people are getting their don't know how people are getting their don't know how people are getting their poom numbers. I think it's a kind of a poom numbers. I think it's a kind of a poom numbers. I think it's a kind of a little bit of a ridiculous notion little bit of a ridiculous notion little bit of a ridiculous notion because um what I would say is it's because um what I would say is it's because um what I would say is it's definitely nonzero and it's probably non definitely nonzero and it's probably non definitely nonzero and it's probably non negligible. So that in itself is pretty negligible. So that in itself is pretty negligible. So that in itself is pretty sobering and my my view is it's just sobering and my my view is it's just sobering and my my view is it's just hugely uncertain, right? What these hugely uncertain, right? What these hugely uncertain, right? What these technologies are going to be able to do, technologies are going to be able to do, technologies are going to be able to do, how fast are they going to take off, how how fast are they going to take off, how how fast are they going to take off, how controllable they going to be. Some controllable they going to be. Some controllable they going to be. Some things may turn out to be and hopefully things may turn out to be and hopefully things may turn out to be and hopefully like way easier than we thought, right? like way easier than we thought, right? like way easier than we thought, right? Um but it may be there's some really Um but it may be there's some really Um but it may be there's some really hard um uh uh problems that are harder hard um uh uh problems that are harder hard um uh uh problems that are harder than we guess today and I think uh we than we guess today and I think uh we than we guess today and I think uh we don't know that for sure and so in under don't know that for sure and so in under don't know that for sure and so in under those conditions of a lot of uncertainty those conditions of a lot of uncertainty those conditions of a lot of uncertainty but huge stakes both ways you know on but huge stakes both ways you know on but huge stakes both ways you know on the one hand we we could solve all the one hand we we could solve all the one hand we we could solve all diseases energy problems the not the the diseases energy problems the not the the diseases energy problems the not the the the scarcity problem and then travel to the scarcity problem and then travel to the scarcity problem and then travel to the stars and conscious of the stars and the stars and conscious of the stars and the stars and conscious of the stars and maximum human flourishing on the other maximum human flourishing on the other maximum human flourishing on the other hand is this sort of p doom scenarios so hand is this sort of p doom scenarios so hand is this sort of p doom scenarios so given the uncertainty around it and the given the uncertainty around it and the given the uncertainty around it and the importance of It's clear to me the only importance of It's clear to me the only importance of It's clear to me the only rational sensible approach is to proceed rational sensible approach is to proceed rational sensible approach is to proceed with cautious optimism. So we want the with cautious optimism. So we want the with cautious optimism. So we want the outcome. We want the um uh the benefits outcome. We want the um uh the benefits outcome. We want the um uh the benefits of course uh and uh all of the the of course uh and uh all of the the of course uh and uh all of the the amazing things that AI can bring and amazing things that AI can bring and amazing things that AI can bring and actually I would be really worried for actually I would be really worried for actually I would be really worried for humanity if I if given the other humanity if I if given the other humanity if I if given the other challenges that we have climate dis you

challenges that we have climate dis you challenges that we have climate dis you know aging uh resources all of that if I know aging uh resources all of that if I know aging uh resources all of that if I didn't know something like AI was coming didn't know something like AI was coming didn't know something like AI was coming down the line right how would we solve down the line right how would we solve down the line right how would we solve all those other problems I think it's all those other problems I think it's all those other problems I think it's hard um so I think we've you know it hard um so I think we've you know it hard um so I think we've you know it could be amazingly transformative for could be amazingly transformative for could be amazingly transformative for good um but on the other And you know good um but on the other And you know good um but on the other And you know there are these risks that we know are there are these risks that we know are there are these risks that we know are there but we can't quite quantify. So there but we can't quite quantify. So there but we can't quite quantify. So the the best thing to do is to use the the the best thing to do is to use the the the best thing to do is to use the scientific method to do more research to scientific method to do more research to scientific method to do more research to try and uh more precisely define those try and uh more precisely define those try and uh more precisely define those risks and of course address them. Um and risks and of course address them. Um and risks and of course address them. Um and I think that's what we're doing. I think I think that's what we're doing. I think I think that's what we're doing. I think there probably needs to be uh 10 times there probably needs to be uh 10 times there probably needs to be uh 10 times more effort on that than there is now as more effort on that than there is now as more effort on that than there is now as we're getting closer and closer to the we're getting closer and closer to the we're getting closer and closer to the to the to the AGI line. to the to the AGI line. to the to the AGI line. >> What would be the source of worry for >> What would be the source of worry for >> What would be the source of worry for you more? Would it be human caused or AI you more? Would it be human caused or AI you more? Would it be human caused or AI AGI caused? AGI caused? AGI caused? >> Humans abusing that technology versus >> Humans abusing that technology versus >> Humans abusing that technology versus AGI itself through mechanism that you've AGI itself through mechanism that you've AGI itself through mechanism that you've spoken about which is fascinating spoken about which is fascinating spoken about which is fascinating deception or this kind of stuff deception or this kind of stuff deception or this kind of stuff >> getting better and better and better >> getting better and better and better >> getting better and better and better secretly and then secretly and then secretly and then >> I think they they operate over different >> I think they they operate over different >> I think they they operate over different time scales and they're equally time scales and they're equally time scales and they're equally important to address. So there's just important to address. So there's just important to address. So there's just the the the the common garden of variety the the the the common garden of variety the the the the common garden of variety of like you know bad actors using new of like you know bad actors using new of like you know bad actors using new technology uh in this case general technology uh in this case general technology uh in this case general purpose technology and repurposing it purpose technology and repurposing it purpose technology and repurposing it for harmful ends and that's a huge uh for harmful ends and that's a huge uh for harmful ends and that's a huge uh risk and I think that has a lot of risk and I think that has a lot of risk and I think that has a lot of complications because generally you know complications because generally you know complications because generally you know I'm in huge favor of open science and I'm in huge favor of open science and I'm in huge favor of open science and open source and in fact we did it with open source and in fact we did it with open source and in fact we did it with all our science projects like AlphaFold all our science projects like AlphaFold all our science projects like AlphaFold and all of those things uh for the and all of those things uh for the and all of those things uh for the benefit of of of the scientific

benefit of of of the scientific benefit of of of the scientific community. Um but how does one restrict community. Um but how does one restrict community. Um but how does one restrict bad actors access access to these bad actors access access to these bad actors access access to these powerful systems whether they're powerful systems whether they're powerful systems whether they're individuals or even rogue states uh and individuals or even rogue states uh and individuals or even rogue states uh and but enable access at the same time to but enable access at the same time to but enable access at the same time to good actors to to maximally build on top good actors to to maximally build on top good actors to to maximally build on top of. It's pretty tricky problem that of. It's pretty tricky problem that of. It's pretty tricky problem that there's I've not heard a clear solution there's I've not heard a clear solution there's I've not heard a clear solution to. So there's the bad actor use case to. So there's the bad actor use case to. So there's the bad actor use case problem and then there's obviously uh as problem and then there's obviously uh as problem and then there's obviously uh as the systems become more agentic and and the systems become more agentic and and the systems become more agentic and and closer to AGI um and more autonomous how closer to AGI um and more autonomous how closer to AGI um and more autonomous how do we ensure the guard rails and they do we ensure the guard rails and they do we ensure the guard rails and they stick to what we want them to do uh and stick to what we want them to do uh and stick to what we want them to do uh and under our control. Yeah, I tend to maybe under our control. Yeah, I tend to maybe under our control. Yeah, I tend to maybe on my mind is limited worry more about on my mind is limited worry more about on my mind is limited worry more about the humans. So the bad actors the humans. So the bad actors the humans. So the bad actors >> and there it could be uh in part how do >> and there it could be uh in part how do >> and there it could be uh in part how do you not put destructive technology in you not put destructive technology in you not put destructive technology in the hands of bad actors but in another the hands of bad actors but in another the hands of bad actors but in another part from again geopolitical technology part from again geopolitical technology part from again geopolitical technology perspective how do you reduce the number perspective how do you reduce the number perspective how do you reduce the number of bad actors in the world that's that's of bad actors in the world that's that's of bad actors in the world that's that's also an interesting human problem. also an interesting human problem. also an interesting human problem. >> Yeah it's a hard problem. I mean, look, >> Yeah it's a hard problem. I mean, look, >> Yeah it's a hard problem. I mean, look, we we we can um maybe also use the we we we can um maybe also use the we we we can um maybe also use the technology itself to help um technology itself to help um technology itself to help um early warning on some of the bad actor early warning on some of the bad actor early warning on some of the bad actor use cases, right? Whether that's bio or use cases, right? Whether that's bio or use cases, right? Whether that's bio or nuclear or whatever it is, like AI could nuclear or whatever it is, like AI could nuclear or whatever it is, like AI could be potentially helpful there as long as be potentially helpful there as long as be potentially helpful there as long as the AI that you're using is itself the AI that you're using is itself the AI that you're using is itself reliable, right? So it's a sort of reliable, right? So it's a sort of reliable, right? So it's a sort of interlocking problem and that's what interlocking problem and that's what interlocking problem and that's what makes it very tricky and and again it makes it very tricky and and again it makes it very tricky and and again it may require some agreement may require some agreement may require some agreement internationally at least between China internationally at least between China internationally at least between China and the and and the US of of of some uh

and the and and the US of of of some uh and the and and the US of of of some uh basic standards right I have to ask you basic standards right I have to ask you basic standards right I have to ask you about the the book the maniac there about the the book the maniac there about the the book the maniac there there's this the the hand of God moment there's this the the hand of God moment there's this the the hand of God moment Lisa doll's move 78 Lisa doll's move 78 Lisa doll's move 78 >> that perhaps the last time a human did a >> that perhaps the last time a human did a >> that perhaps the last time a human did a move of sort of pure human genius and move of sort of pure human genius and move of sort of pure human genius and beat Alph Go or like broke its brain if beat Alph Go or like broke its brain if beat Alph Go or like broke its brain if sorry to anthropomorphize but it's an sorry to anthropomorphize but it's an sorry to anthropomorphize but it's an interesting moment cuz I think in so interesting moment cuz I think in so interesting moment cuz I think in so many domains it will keep happening. many domains it will keep happening. many domains it will keep happening. >> Yeah, it's a special moment and you know >> Yeah, it's a special moment and you know >> Yeah, it's a special moment and you know it was great for Lisa Doll and you know it was great for Lisa Doll and you know it was great for Lisa Doll and you know I think it's in a way they were kind of I think it's in a way they were kind of I think it's in a way they were kind of inspiring each other. We as a team were inspiring each other. We as a team were inspiring each other. We as a team were inspired by Lisa Doll's brilliance and inspired by Lisa Doll's brilliance and inspired by Lisa Doll's brilliance and nobleness and then maybe he got inspired nobleness and then maybe he got inspired nobleness and then maybe he got inspired by you know what AlphaGo was doing to by you know what AlphaGo was doing to by you know what AlphaGo was doing to then conjure this incredible then conjure this incredible then conjure this incredible inspirational moment. it's all you know inspirational moment. it's all you know inspirational moment. it's all you know captured very well in the in the captured very well in the in the captured very well in the in the documentary about it and um I think documentary about it and um I think documentary about it and um I think that'll continue in many domains where that'll continue in many domains where that'll continue in many domains where there's this at least for the for the there's this at least for the for the there's this at least for the for the again for the foreseeable uh future of again for the foreseeable uh future of again for the foreseeable uh future of like the humans bringing in their like the humans bringing in their like the humans bringing in their ingenuity um and asking the right ingenuity um and asking the right ingenuity um and asking the right question let's say uh and then utilizing question let's say uh and then utilizing question let's say uh and then utilizing these tools uh in a way that um then these tools uh in a way that um then these tools uh in a way that um then cracks a problem.

cracks a problem. cracks a problem. >> Yeah. What as the AI becomes smarter and >> Yeah. What as the AI becomes smarter and >> Yeah. What as the AI becomes smarter and smarter, one of the interesting smarter, one of the interesting smarter, one of the interesting questions we can ask ourselves is what questions we can ask ourselves is what questions we can ask ourselves is what makes humans special? It does feel I'm makes humans special? It does feel I'm makes humans special? It does feel I'm perhaps biased that we humans are deeply perhaps biased that we humans are deeply perhaps biased that we humans are deeply special. I don't know if it's our special. I don't know if it's our special. I don't know if it's our intelligence. intelligence. intelligence. It could be something else that that It could be something else that that It could be something else that that other thing that's outside the mad other thing that's outside the mad other thing that's outside the mad dreams of reason. I think that's what dreams of reason. I think that's what dreams of reason. I think that's what I've always imagined uh when I was a kid I've always imagined uh when I was a kid I've always imagined uh when I was a kid and starting on this journey of like um and starting on this journey of like um and starting on this journey of like um I was of course fascinated by things I was of course fascinated by things I was of course fascinated by things like consciousness did did a like consciousness did did a like consciousness did did a neuroscience PhD to look at how the neuroscience PhD to look at how the neuroscience PhD to look at how the brain works especially imagination and brain works especially imagination and brain works especially imagination and memory I focused on the hippocampus and memory I focused on the hippocampus and memory I focused on the hippocampus and it's sort of going to be interesting I it's sort of going to be interesting I it's sort of going to be interesting I always thought the best way of course always thought the best way of course always thought the best way of course one can kind of philosophize about it one can kind of philosophize about it one can kind of philosophize about it and have thought experiments and maybe and have thought experiments and maybe and have thought experiments and maybe even do actual experiments like you do even do actual experiments like you do even do actual experiments like you do in neuroscience on on real brains but in in neuroscience on on real brains but in in neuroscience on on real brains but in the end I always imagined that building the end I always imagined that building the end I always imagined that building AI a kind intelligent artifact and then AI a kind intelligent artifact and then AI a kind intelligent artifact and then comparing that to the human mind and comparing that to the human mind and comparing that to the human mind and seeing what the differences were uh seeing what the differences were uh seeing what the differences were uh would be the best way to uncover what's would be the best way to uncover what's would be the best way to uncover what's special about the human mind if indeed special about the human mind if indeed special about the human mind if indeed there is anything special and I suspect there is anything special and I suspect there is anything special and I suspect there probably is but it's going to be there probably is but it's going to be there probably is but it's going to be hard to def you know I think this hard to def you know I think this hard to def you know I think this journey we're on will help us uh journey we're on will help us uh journey we're on will help us uh understand that and define that and you understand that and define that and you understand that and define that and you know there may be a difference between know there may be a difference between know there may be a difference between carbon based substrates that we are and carbon based substrates that we are and carbon based substrates that we are and silicon ones when they process silicon ones when they process silicon ones when they process information you know one of the best information you know one of the best information you know one of the best definitions I like of of of definitions I like of of of definitions I like of of of consciousness is it's the information consciousness is it's the information consciousness is it's the information feels when we process it, right? Um, it feels when we process it, right? Um, it feels when we process it, right? Um, it could be I mean doesn't it's not a very could be I mean doesn't it's not a very could be I mean doesn't it's not a very helpful scientific explanation. I think helpful scientific explanation. I think helpful scientific explanation. I think it's kind of interesting intuition in it's kind of interesting intuition in it's kind of interesting intuition in intuitive one and um and so you know on

intuitive one and um and so you know on intuitive one and um and so you know on this this this journey this scientific this this this journey this scientific this this this journey this scientific journey we're on will I think um help journey we're on will I think um help journey we're on will I think um help uncover that mystery. uncover that mystery. uncover that mystery. >> Yeah. What I cannot create I do not >> Yeah. What I cannot create I do not >> Yeah. What I cannot create I do not understand. That's uh somebody you understand. That's uh somebody you understand. That's uh somebody you deeply admire Richard Feman like you deeply admire Richard Feman like you deeply admire Richard Feman like you mentioned. you also reach um for the the mentioned. you also reach um for the the mentioned. you also reach um for the the Wignner's dreams of universality that he Wignner's dreams of universality that he Wignner's dreams of universality that he saw in constraint domains but also saw in constraint domains but also saw in constraint domains but also broadly generally in in mathematics and broadly generally in in mathematics and broadly generally in in mathematics and so on. So so many aspects on which so on. So so many aspects on which so on. So so many aspects on which you're pushing towards you're pushing towards you're pushing towards >> not to start trouble at the end but uh >> not to start trouble at the end but uh >> not to start trouble at the end but uh Roger Penrose. Roger Penrose. Roger Penrose. >> Yes. >> Yes. >> Yes. >> Okay. >> Okay. >> Okay. So uh you know do do you think So uh you know do do you think So uh you know do do you think consciousness there's this hard problem consciousness there's this hard problem consciousness there's this hard problem of consciousness how information feels of consciousness how information feels of consciousness how information feels >> um do you think consciousness first of >> um do you think consciousness first of >> um do you think consciousness first of all is a computation and if it is if all is a computation and if it is if all is a computation and if it is if it's information processing like you it's information processing like you it's information processing like you said everything is said everything is said everything is >> is it something that could be modeled by >> is it something that could be modeled by >> is it something that could be modeled by a classical computer? a classical computer? a classical computer? >> Yeah. >> Yeah. >> Yeah. >> Or is it a quantum mechanical in nature? >> Or is it a quantum mechanical in nature? >> Or is it a quantum mechanical in nature? Well, look, Pero is an amazing thinker, Well, look, Pero is an amazing thinker, Well, look, Pero is an amazing thinker, one of the greatest of the modern era, one of the greatest of the modern era, one of the greatest of the modern era, and he we've had a lot of discussions and he we've had a lot of discussions and he we've had a lot of discussions about this. Of course, we cordily about this. Of course, we cordily about this. Of course, we cordily disagree, which is, you know, I I feel disagree, which is, you know, I I feel disagree, which is, you know, I I feel like um I mean, he collaborated with a like um I mean, he collaborated with a like um I mean, he collaborated with a lot of good neuroscientists to see if he lot of good neuroscientists to see if he lot of good neuroscientists to see if he could find mechanisms for quantum could find mechanisms for quantum could find mechanisms for quantum mechanics behavior in the brain. And mechanics behavior in the brain. And mechanics behavior in the brain. And they, to my knowledge, they haven't they, to my knowledge, they haven't they, to my knowledge, they haven't found anything um convincing yet. So my found anything um convincing yet. So my found anything um convincing yet. So my betting is there is is that that that betting is there is is that that that betting is there is is that that that it's mostly you know it is just it's mostly you know it is just it's mostly you know it is just classical computing that's going on in classical computing that's going on in classical computing that's going on in the brain which suggests that all the the brain which suggests that all the the brain which suggests that all the phenomena uh are modelable or mimickable phenomena uh are modelable or mimickable phenomena uh are modelable or mimickable by a classical computer but we'll see

by a classical computer but we'll see by a classical computer but we'll see you know there may be this final you know there may be this final you know there may be this final mysterious things of the feeling of mysterious things of the feeling of mysterious things of the feeling of consciousness the qualia these kinds of consciousness the qualia these kinds of consciousness the qualia these kinds of things that philosophers debate where things that philosophers debate where things that philosophers debate where it's unique to the substrate we may even it's unique to the substrate we may even it's unique to the substrate we may even come towards understanding that when if come towards understanding that when if come towards understanding that when if we do things like neural link and and we do things like neural link and and we do things like neural link and and have neural interfaces to the AI have neural interfaces to the AI have neural interfaces to the AI systems, which I think we probably will systems, which I think we probably will systems, which I think we probably will eventually um maybe to keep up with the eventually um maybe to keep up with the eventually um maybe to keep up with the AI systems. Uh we might actually be able AI systems. Uh we might actually be able AI systems. Uh we might actually be able to feel for ourselves what it's like to to feel for ourselves what it's like to to feel for ourselves what it's like to compute on silicon, right? So um and compute on silicon, right? So um and compute on silicon, right? So um and maybe that will tell us. Uh so I think maybe that will tell us. Uh so I think maybe that will tell us. Uh so I think it's it's going to be interesting. I had it's it's going to be interesting. I had it's it's going to be interesting. I had a debate once with the late Daniel a debate once with the late Daniel a debate once with the late Daniel Dennett about why do we think each other Dennett about why do we think each other Dennett about why do we think each other are conscious? Okay, so it's for two are conscious? Okay, so it's for two are conscious? Okay, so it's for two reasons. One is you're exhibiting the reasons. One is you're exhibiting the reasons. One is you're exhibiting the same behavior that I am. So that's one same behavior that I am. So that's one same behavior that I am. So that's one thing. behaviorally you seem like a thing. behaviorally you seem like a thing. behaviorally you seem like a conscious being if I am. But the second conscious being if I am. But the second conscious being if I am. But the second thing which is often overlooked is that thing which is often overlooked is that thing which is often overlooked is that we're running on the same substrate. So we're running on the same substrate. So we're running on the same substrate. So if you're behaving in the same way and if you're behaving in the same way and if you're behaving in the same way and we're running on the same substrate, we're running on the same substrate, we're running on the same substrate, it's most parsimonious to assume you're it's most parsimonious to assume you're it's most parsimonious to assume you're feeling the same experience that I'm feeling the same experience that I'm feeling the same experience that I'm feeling. But with an AI uh that's on feeling. But with an AI uh that's on feeling. But with an AI uh that's on silicon, we won't be able to rely on the silicon, we won't be able to rely on the silicon, we won't be able to rely on the second part. Even if it exhibits the second part. Even if it exhibits the second part. Even if it exhibits the first part, the behavior looks like a first part, the behavior looks like a first part, the behavior looks like a behavior of a conscious being. It might behavior of a conscious being. It might behavior of a conscious being. It might even claim it is. Um but we but but we even claim it is. Um but we but but we even claim it is. Um but we but but we wouldn't know how it actually felt. Um wouldn't know how it actually felt. Um wouldn't know how it actually felt. Um and it probably couldn't know we what we and it probably couldn't know we what we and it probably couldn't know we what we felt at least in the first stages. Maybe felt at least in the first stages. Maybe felt at least in the first stages. Maybe when we get to super intelligence and when we get to super intelligence and when we get to super intelligence and the technologies that builds perhaps the technologies that builds perhaps the technologies that builds perhaps we'll we'll be able to um bridge that.

we'll we'll be able to um bridge that. we'll we'll be able to um bridge that. >> No, I mean that's a huge test for >> No, I mean that's a huge test for >> No, I mean that's a huge test for radical empathy is to empathize with a radical empathy is to empathize with a radical empathy is to empathize with a different substrate. different substrate. different substrate. >> Right. Exactly. We never had to confront >> Right. Exactly. We never had to confront >> Right. Exactly. We never had to confront that before. that before. that before. >> Yeah. So maybe maybe through brain >> Yeah. So maybe maybe through brain >> Yeah. So maybe maybe through brain computer interfaces be able to truly computer interfaces be able to truly computer interfaces be able to truly empathize what it feels like to be a empathize what it feels like to be a empathize what it feels like to be a computer computer computer >> for information to be computed not on a >> for information to be computed not on a >> for information to be computed not on a carbon system. I mean that's deeply I carbon system. I mean that's deeply I carbon system. I mean that's deeply I mean some people kind of think about mean some people kind of think about mean some people kind of think about that with plants with other life forms that with plants with other life forms that with plants with other life forms which are different similar substrate which are different similar substrate which are different similar substrate but sufficiently far enough on the uh but sufficiently far enough on the uh but sufficiently far enough on the uh evolutionary tree evolutionary tree evolutionary tree >> that it's requires a radical empathy but >> that it's requires a radical empathy but >> that it's requires a radical empathy but to do that with a computer to do that with a computer to do that with a computer >> I mean Lou we sort of there are animal >> I mean Lou we sort of there are animal >> I mean Lou we sort of there are animal studies on this of like of course higher studies on this of like of course higher studies on this of like of course higher animals like you know killer whales and animals like you know killer whales and animals like you know killer whales and dolphins and dogs and and monkeys you dolphins and dogs and and monkeys you dolphins and dogs and and monkeys you know they have some and elephants you know they have some and elephants you know they have some and elephants you know they have some aspects certainly of know they have some aspects certainly of know they have some aspects certainly of consciousness right? Even though they're consciousness right? Even though they're consciousness right? Even though they're not might not be that that that smart on not might not be that that that smart on not might not be that that that smart on an IQ sense. So so we can already an IQ sense. So so we can already an IQ sense. So so we can already empathize with that and maybe even some empathize with that and maybe even some empathize with that and maybe even some of our systems one day like we built of our systems one day like we built of our systems one day like we built this thing called dolphin Gemma, you this thing called dolphin Gemma, you this thing called dolphin Gemma, you know, which can one a version of our know, which can one a version of our know, which can one a version of our system was trained on dolphin and whale system was trained on dolphin and whale system was trained on dolphin and whale sounds and maybe we'll be able to build sounds and maybe we'll be able to build sounds and maybe we'll be able to build a an interpreter or translator at some a an interpreter or translator at some a an interpreter or translator at some point which should be pretty cool.

point which should be pretty cool. point which should be pretty cool. >> What gives you hope for the future of >> What gives you hope for the future of >> What gives you hope for the future of human civilization? human civilization? human civilization? >> Well, what gives me hope is I think our >> Well, what gives me hope is I think our >> Well, what gives me hope is I think our almost limitless ingenuity first of all. almost limitless ingenuity first of all. almost limitless ingenuity first of all. I think the best of us and the best I think the best of us and the best I think the best of us and the best human minds are incredible. Um, and you human minds are incredible. Um, and you human minds are incredible. Um, and you know, I love, you know, meeting and know, I love, you know, meeting and know, I love, you know, meeting and watching any human that's the top of watching any human that's the top of watching any human that's the top of their game, whether that's sport or their game, whether that's sport or their game, whether that's sport or science or art. You know, it's it's it's science or art. You know, it's it's it's science or art. You know, it's it's it's just nothing more wonderful than that, just nothing more wonderful than that, just nothing more wonderful than that, seeing them in their element in flow. seeing them in their element in flow. seeing them in their element in flow. Um, I think it's almost limitless. You Um, I think it's almost limitless. You Um, I think it's almost limitless. You know, our brains are general systems, know, our brains are general systems, know, our brains are general systems, intelligent systems. So, I think it's intelligent systems. So, I think it's intelligent systems. So, I think it's almost limitless what we can potentially almost limitless what we can potentially almost limitless what we can potentially do with them. And then the other thing do with them. And then the other thing do with them. And then the other thing is our extreme adaptability. I think is our extreme adaptability. I think is our extreme adaptability. I think it's going to be okay in terms of it's going to be okay in terms of it's going to be okay in terms of there's going to be a lot of change. But there's going to be a lot of change. But there's going to be a lot of change. But but look where we are now with our but look where we are now with our but look where we are now with our effectively our hunter gatherer brains. effectively our hunter gatherer brains. effectively our hunter gatherer brains. How is it we can you know we can cope How is it we can you know we can cope How is it we can you know we can cope with the modern world, right? Flying on with the modern world, right? Flying on with the modern world, right? Flying on planes, doing podcasts, you know, planes, doing podcasts, you know, planes, doing podcasts, you know, playing computer games and virtual playing computer games and virtual playing computer games and virtual simulations. I mean, it's already given simulations. I mean, it's already given simulations. I mean, it's already given that was developed for, you know, that was developed for, you know, that was developed for, you know, hunting buffal hunting buffal hunting buffal [Music] societyy's already adapted to this societyy's already adapted to this mind-blowing AI technology we have today mind-blowing AI technology we have today mind-blowing AI technology we have today already. It's like, oh, I talk to chat already. It's like, oh, I talk to chat already. It's like, oh, I talk to chat bots. It's totally fine.

bots. It's totally fine. bots. It's totally fine. >> And it's uh very possible that this very >> And it's uh very possible that this very >> And it's uh very possible that this very podcast activity, which I'm here for, podcast activity, which I'm here for, podcast activity, which I'm here for, will be completely replaced by AI. I'm will be completely replaced by AI. I'm will be completely replaced by AI. I'm very replaceable and I'm waiting for very replaceable and I'm waiting for very replaceable and I'm waiting for >> not to the level that you can do it, >> not to the level that you can do it, >> not to the level that you can do it, Lex. So don't think Lex. So don't think Lex. So don't think >> Thank you. That's that's what we humans >> Thank you. That's that's what we humans >> Thank you. That's that's what we humans do to each other. We compliment. do to each other. We compliment. do to each other. We compliment. >> All right. And uh I'm uh deeply grateful >> All right. And uh I'm uh deeply grateful >> All right. And uh I'm uh deeply grateful for us humans to have this uh infinite for us humans to have this uh infinite for us humans to have this uh infinite capacity for curiosity, adaptability, capacity for curiosity, adaptability, capacity for curiosity, adaptability, like you said, and also compassion and like you said, and also compassion and like you said, and also compassion and ability to love. ability to love. ability to love. >> Exactly. >> Exactly. >> Exactly. >> All of those human >> All of those human >> All of those human >> all the things that are deeply human. >> all the things that are deeply human. >> all the things that are deeply human. >> Well, this is a huge honor, Demis. >> Well, this is a huge honor, Demis. >> Well, this is a huge honor, Demis. You're one of the truly special humans You're one of the truly special humans You're one of the truly special humans in the world. Uh thank you so much for in the world. Uh thank you so much for in the world. Uh thank you so much for doing what you do and for talking today. doing what you do and for talking today. doing what you do and for talking today. >> Well, thank you very much, Lex. >> Well, thank you very much, Lex. >> Well, thank you very much, Lex. Thanks for listening to this Thanks for listening to this Thanks for listening to this conversation with Demos. To support this conversation with Demos. To support this conversation with Demos. To support this podcast, please check out our sponsors podcast, please check out our sponsors podcast, please check out our sponsors in the description and consider in the description and consider in the description and consider subscribing to this channel. subscribing to this channel. subscribing to this channel. And now, let me answer some questions And now, let me answer some questions And now, let me answer some questions and try to articulate some things I've and try to articulate some things I've and try to articulate some things I've been thinking about. If you would like been thinking about. If you would like been thinking about. If you would like to submit questions, including in audio to submit questions, including in audio to submit questions, including in audio and video form, go to lexfreman.com/am.

I got a lot of amazing questions, I got a lot of amazing questions, thoughts, and requests from folks. I'll thoughts, and requests from folks. I'll thoughts, and requests from folks. I'll keep trying to pick some uh randomly and keep trying to pick some uh randomly and keep trying to pick some uh randomly and comment on it at the end of every comment on it at the end of every comment on it at the end of every episode. I got a note on May 21st this episode. I got a note on May 21st this episode. I got a note on May 21st this year that said, "Hi, Lux. 20 years ago year that said, "Hi, Lux. 20 years ago year that said, "Hi, Lux. 20 years ago today, David Foster Wallace delivered today, David Foster Wallace delivered today, David Foster Wallace delivered his famous this is water speech at uh his famous this is water speech at uh his famous this is water speech at uh Kenyan College. What do you think of Kenyan College. What do you think of Kenyan College. What do you think of this speech?" this speech?" this speech?" Well, first I think this is probably one Well, first I think this is probably one Well, first I think this is probably one of the greatest and most unique of the greatest and most unique of the greatest and most unique commencement speeches ever given. But of commencement speeches ever given. But of commencement speeches ever given. But of course, I have many favorites, including course, I have many favorites, including course, I have many favorites, including the one by Steve Jobs. And David Foster the one by Steve Jobs. And David Foster the one by Steve Jobs. And David Foster Wallace is one of my favorite writers Wallace is one of my favorite writers Wallace is one of my favorite writers and one of my favorite humans. and one of my favorite humans. and one of my favorite humans. There's a tragic honesty to his work. There's a tragic honesty to his work. There's a tragic honesty to his work. And it always felt as if he was engaging And it always felt as if he was engaging And it always felt as if he was engaging in a a constant battle with his own in a a constant battle with his own in a a constant battle with his own mind. and the writing, his writing were mind. and the writing, his writing were mind. and the writing, his writing were kind of his notes from the front lines kind of his notes from the front lines kind of his notes from the front lines of that battle.

of that battle. of that battle. Now, onto the speech. Let me quote some Now, onto the speech. Let me quote some Now, onto the speech. Let me quote some parts. There's of course the parable of parts. There's of course the parable of parts. There's of course the parable of the fish and the water that goes, "There the fish and the water that goes, "There the fish and the water that goes, "There are these two young fish swimming along, are these two young fish swimming along, are these two young fish swimming along, and they happen to meet an older fish and they happen to meet an older fish and they happen to meet an older fish swimming the other way who nods at them swimming the other way who nods at them swimming the other way who nods at them and says, "Morning boys. How's the and says, "Morning boys. How's the and says, "Morning boys. How's the water?" water?" water?" And the two young fish swim on for a bit And the two young fish swim on for a bit And the two young fish swim on for a bit and then eventually one of them looks and then eventually one of them looks and then eventually one of them looks over at the other and goes, "What the over at the other and goes, "What the over at the other and goes, "What the hell is water?" hell is water?" hell is water?" In the speech, David Foster Wallace goes In the speech, David Foster Wallace goes In the speech, David Foster Wallace goes on to say, "The point of the fish story on to say, "The point of the fish story on to say, "The point of the fish story is merely that the most obvious is merely that the most obvious is merely that the most obvious important realities are often the ones important realities are often the ones important realities are often the ones that are hardest to see and talk about." that are hardest to see and talk about." that are hardest to see and talk about." stated as an English sentence. Of stated as an English sentence. Of stated as an English sentence. Of course, this is just a banal platitude. course, this is just a banal platitude. course, this is just a banal platitude. But the fact is that in the dayto-day But the fact is that in the dayto-day But the fact is that in the dayto-day trenches of adult existence, bal trenches of adult existence, bal trenches of adult existence, bal platitudes can have a life or death platitudes can have a life or death platitudes can have a life or death importance. Or so I wish to suggest to importance. Or so I wish to suggest to importance. Or so I wish to suggest to you in this dry and lovely morning. I you in this dry and lovely morning. I you in this dry and lovely morning. I have several takeaways from this parable have several takeaways from this parable have several takeaways from this parable and the speech that follows. First, I and the speech that follows. First, I and the speech that follows. First, I think we must question everything and in think we must question everything and in think we must question everything and in particular the most basic assumptions particular the most basic assumptions particular the most basic assumptions about our reality, our life and the very about our reality, our life and the very about our reality, our life and the very nature of existence and that this nature of existence and that this nature of existence and that this project is a deeply personal one in some project is a deeply personal one in some project is a deeply personal one in some fundamental sense. Nobody can really fundamental sense. Nobody can really fundamental sense. Nobody can really help you in this process of discovery.

help you in this process of discovery. help you in this process of discovery. The call to action here, I think, from The call to action here, I think, from The call to action here, I think, from uh David Foster Wallace, as he puts it, uh David Foster Wallace, as he puts it, uh David Foster Wallace, as he puts it, is to quote, to be just a little less is to quote, to be just a little less is to quote, to be just a little less arrogant, to have just a little more arrogant, to have just a little more arrogant, to have just a little more critical awareness about myself and my critical awareness about myself and my critical awareness about myself and my certainties. certainties. certainties. Because a huge percentage of the stuff Because a huge percentage of the stuff Because a huge percentage of the stuff that I tend to be automatically certain that I tend to be automatically certain that I tend to be automatically certain of is, it turns out, totally wrong and of is, it turns out, totally wrong and of is, it turns out, totally wrong and deluded. deluded. deluded. All right, back to me. Lex speaking. All right, back to me. Lex speaking. All right, back to me. Lex speaking. Second takeaway is that the central Second takeaway is that the central Second takeaway is that the central spiritual battles of our life are not spiritual battles of our life are not spiritual battles of our life are not fought on a uh mountain top somewhere at fought on a uh mountain top somewhere at fought on a uh mountain top somewhere at a meditation retreat but it is fought in a meditation retreat but it is fought in a meditation retreat but it is fought in the mundane moments of daily life. the mundane moments of daily life. the mundane moments of daily life. Third takeaway is that we too easily Third takeaway is that we too easily Third takeaway is that we too easily give away our time and attention to the give away our time and attention to the give away our time and attention to the multitude of distractions that the world multitude of distractions that the world multitude of distractions that the world feeds us. the insatiable black holes of feeds us. the insatiable black holes of feeds us. the insatiable black holes of attention. attention. attention. David Foster Wallace's call to action in David Foster Wallace's call to action in David Foster Wallace's call to action in this case is to be deeply aware of the this case is to be deeply aware of the this case is to be deeply aware of the beauty in each moment and to find beauty in each moment and to find beauty in each moment and to find meaning in the mundane.

meaning in the mundane. meaning in the mundane. I often quote David Foster Wallace in I often quote David Foster Wallace in I often quote David Foster Wallace in his advice that the key to life is to be his advice that the key to life is to be his advice that the key to life is to be unorable. unorable. unorable. And I think this is exactly right. Every And I think this is exactly right. Every And I think this is exactly right. Every moment, every object, every experience moment, every object, every experience moment, every object, every experience when looked at closely enough contains when looked at closely enough contains when looked at closely enough contains within it infinite richness to explore. within it infinite richness to explore. within it infinite richness to explore. And since uh Deus Lasabus of this very And since uh Deus Lasabus of this very And since uh Deus Lasabus of this very podcast episode and I are such fans of podcast episode and I are such fans of podcast episode and I are such fans of Richard Feineman, allow me to uh also Richard Feineman, allow me to uh also Richard Feineman, allow me to uh also quote Mr. Fineman on this topic as well. quote Mr. Fineman on this topic as well. quote Mr. Fineman on this topic as well. quote, "I have a friend who's an artist quote, "I have a friend who's an artist quote, "I have a friend who's an artist and has sometimes taken a view which I and has sometimes taken a view which I and has sometimes taken a view which I don't agree with very well. He'll hold don't agree with very well. He'll hold don't agree with very well. He'll hold up a flower and say, "Look how beautiful up a flower and say, "Look how beautiful up a flower and say, "Look how beautiful it is." And I'll agree. Then he says, it is." And I'll agree. Then he says, it is." And I'll agree. Then he says, "I, as an artist, can see how beautiful "I, as an artist, can see how beautiful "I, as an artist, can see how beautiful this is, but you as a scientist take this is, but you as a scientist take this is, but you as a scientist take this all apart and it becomes a dull this all apart and it becomes a dull this all apart and it becomes a dull thing." And I think that's kind of thing." And I think that's kind of thing." And I think that's kind of nutty. nutty. nutty. First of all, the beauty that he sees is First of all, the beauty that he sees is First of all, the beauty that he sees is available to other people and to me too, available to other people and to me too, available to other people and to me too, I believe. Although I may not be quite I believe. Although I may not be quite I believe. Although I may not be quite as refined aesthetically as he is, I can as refined aesthetically as he is, I can as refined aesthetically as he is, I can appreciate the beauty of a flower. At appreciate the beauty of a flower. At appreciate the beauty of a flower. At the same time, I see much more about the the same time, I see much more about the the same time, I see much more about the flower than he sees. I can imagine the flower than he sees. I can imagine the flower than he sees. I can imagine the cells in there, the complicated actions cells in there, the complicated actions cells in there, the complicated actions inside which also have beauty. I mean inside which also have beauty. I mean inside which also have beauty. I mean it's not just beauty at this dimension it's not just beauty at this dimension it's not just beauty at this dimension at 1 cm. There's also beauty at the at 1 cm. There's also beauty at the at 1 cm. There's also beauty at the smaller dimensions. The inner structure

smaller dimensions. The inner structure smaller dimensions. The inner structure also the processes. The fact that the also the processes. The fact that the also the processes. The fact that the colors in the flower evolved in order to colors in the flower evolved in order to colors in the flower evolved in order to attract insects to pollinate it is attract insects to pollinate it is attract insects to pollinate it is interesting. It means that the insects interesting. It means that the insects interesting. It means that the insects can see the color. It adds a question. can see the color. It adds a question. can see the color. It adds a question. Does this aesthetic sense also exist in Does this aesthetic sense also exist in Does this aesthetic sense also exist in lower forms? Why is it aesthetic? all lower forms? Why is it aesthetic? all lower forms? Why is it aesthetic? all kinds of interesting questions which the kinds of interesting questions which the kinds of interesting questions which the science knowledge only adds to the science knowledge only adds to the science knowledge only adds to the excitement, the mystery and the awe of a excitement, the mystery and the awe of a excitement, the mystery and the awe of a flower. It only adds flower. It only adds flower. It only adds all right back to uh David Foster all right back to uh David Foster all right back to uh David Foster Wallace's speech. He has a great story Wallace's speech. He has a great story Wallace's speech. He has a great story in there that I particularly enjoy. in there that I particularly enjoy. in there that I particularly enjoy. It goes, "There are these two guys It goes, "There are these two guys It goes, "There are these two guys sitting together in a bar in the remote sitting together in a bar in the remote sitting together in a bar in the remote Alaskan wilderness. One of the guys is Alaskan wilderness. One of the guys is Alaskan wilderness. One of the guys is religious. The other is an atheist and religious. The other is an atheist and religious. The other is an atheist and the two are arguing about the existence the two are arguing about the existence the two are arguing about the existence of God with that special intensity that of God with that special intensity that of God with that special intensity that comes after about the fourth beer. And comes after about the fourth beer. And comes after about the fourth beer. And the atheist says, "Look, it's not like I the atheist says, "Look, it's not like I the atheist says, "Look, it's not like I don't have actual reasons for not don't have actual reasons for not don't have actual reasons for not believing in God. It's not like I believing in God. It's not like I believing in God. It's not like I haven't ever experimented with the whole haven't ever experimented with the whole haven't ever experimented with the whole God and prayer thing. Just last month, I God and prayer thing. Just last month, I God and prayer thing. Just last month, I got caught away from the camp in that got caught away from the camp in that got caught away from the camp in that terrible blizzard and I was totally lost terrible blizzard and I was totally lost terrible blizzard and I was totally lost and I couldn't see a thing and it was 50 and I couldn't see a thing and it was 50 and I couldn't see a thing and it was 50 below. And so I tried it. I fell to my below. And so I tried it. I fell to my below. And so I tried it. I fell to my knees in the snow and cried out, "Oh knees in the snow and cried out, "Oh knees in the snow and cried out, "Oh God, if there is a God, I'm lost in this God, if there is a God, I'm lost in this God, if there is a God, I'm lost in this blizzard and I'm going to die if you blizzard and I'm going to die if you blizzard and I'm going to die if you don't help me."

don't help me." don't help me." And now back in the bar, the religious And now back in the bar, the religious And now back in the bar, the religious guy looks at the atheist all puzzled. guy looks at the atheist all puzzled. guy looks at the atheist all puzzled. "Well, then you must believe now," he "Well, then you must believe now," he "Well, then you must believe now," he says. After all, there you are alive. says. After all, there you are alive. says. After all, there you are alive. The atheist just rolls his eyes. No, The atheist just rolls his eyes. No, The atheist just rolls his eyes. No, man. All that happened was a couple of man. All that happened was a couple of man. All that happened was a couple of Eskimos happened to be wandering by and Eskimos happened to be wandering by and Eskimos happened to be wandering by and show me the way back to the camp. show me the way back to the camp. show me the way back to the camp. All this, I think, teaches us that All this, I think, teaches us that All this, I think, teaches us that everything is a matter of perspective everything is a matter of perspective everything is a matter of perspective and that wisdom may arrive if we have and that wisdom may arrive if we have and that wisdom may arrive if we have the humility to keep shifting and the humility to keep shifting and the humility to keep shifting and expanding our perspective on the world. expanding our perspective on the world. expanding our perspective on the world. Thank you for allowing me to talk a bit Thank you for allowing me to talk a bit Thank you for allowing me to talk a bit about David Foster Wallace. He's one of about David Foster Wallace. He's one of about David Foster Wallace. He's one of my favorite writers and he's a beautiful my favorite writers and he's a beautiful my favorite writers and he's a beautiful soul. soul. soul. If I may, one more thing I wanted to If I may, one more thing I wanted to If I may, one more thing I wanted to briefly comment on. I found myself to be briefly comment on. I found myself to be briefly comment on. I found myself to be in this strange position of getting in this strange position of getting in this strange position of getting attacked online often from all sides, attacked online often from all sides, attacked online often from all sides, including being lied about sometimes including being lied about sometimes including being lied about sometimes through selective misrepresentation, but through selective misrepresentation, but through selective misrepresentation, but often through downright lies. I don't often through downright lies. I don't often through downright lies. I don't know how else to put it. This all breaks know how else to put it. This all breaks know how else to put it. This all breaks my heart, frankly, but I've come to my heart, frankly, but I've come to my heart, frankly, but I've come to understand that it's the way of the understand that it's the way of the understand that it's the way of the internet and the cost of the path I've internet and the cost of the path I've internet and the cost of the path I've chosen. There's been days when it's been chosen. There's been days when it's been chosen. There's been days when it's been rough on me mentally. It's not fun being rough on me mentally. It's not fun being rough on me mentally. It's not fun being lied about, especially when it's about lied about, especially when it's about lied about, especially when it's about things that are usually for a long time things that are usually for a long time things that are usually for a long time have been a source of happiness and joy have been a source of happiness and joy have been a source of happiness and joy for me. But again, that's life. I'll for me. But again, that's life. I'll for me. But again, that's life. I'll continue exploring the world of people

continue exploring the world of people continue exploring the world of people and ideas with empathy and rigor, and ideas with empathy and rigor, and ideas with empathy and rigor, wearing my heart on my sleeve as much as wearing my heart on my sleeve as much as wearing my heart on my sleeve as much as I can. For me, that's the only way to I can. For me, that's the only way to I can. For me, that's the only way to live. Anyway, a common attack on me is live. Anyway, a common attack on me is live. Anyway, a common attack on me is about my time at MIT and Drexel, two about my time at MIT and Drexel, two about my time at MIT and Drexel, two great universities I love and have great universities I love and have great universities I love and have tremendous respect for. Since a bunch of tremendous respect for. Since a bunch of tremendous respect for. Since a bunch of lies have accumulated online about me on lies have accumulated online about me on lies have accumulated online about me on these topics to a sad and at times these topics to a sad and at times these topics to a sad and at times hilarious degree, I thought I would once hilarious degree, I thought I would once hilarious degree, I thought I would once more state the obvious facts about my more state the obvious facts about my more state the obvious facts about my bio for the small number of you who may bio for the small number of you who may bio for the small number of you who may care. care. care. TLDDR, two things. First, as I say TLDDR, two things. First, as I say TLDDR, two things. First, as I say often, including in a recent podcast often, including in a recent podcast often, including in a recent podcast episode that somehow was listened to by episode that somehow was listened to by episode that somehow was listened to by many millions of people, I proudly went many millions of people, I proudly went many millions of people, I proudly went to Drexen University for my bachelor's, to Drexen University for my bachelor's, to Drexen University for my bachelor's, masters, and doctor degrees. masters, and doctor degrees. masters, and doctor degrees. Second, I am a research scientist at MIT Second, I am a research scientist at MIT Second, I am a research scientist at MIT and have been there in a paid research and have been there in a paid research and have been there in a paid research position for the last 10 years.

position for the last 10 years. position for the last 10 years. Allow me to elaborate a bit more on Allow me to elaborate a bit more on Allow me to elaborate a bit more on these two things now, but please skip if these two things now, but please skip if these two things now, but please skip if this is not at all interesting. So, like this is not at all interesting. So, like this is not at all interesting. So, like I said, a common attack on me is that I I said, a common attack on me is that I I said, a common attack on me is that I have no real affiliation with MIT. The have no real affiliation with MIT. The have no real affiliation with MIT. The accusation, I guess, is that I'm falsely accusation, I guess, is that I'm falsely accusation, I guess, is that I'm falsely claiming an MIT affiliation because I claiming an MIT affiliation because I claiming an MIT affiliation because I taught a lecture there once. taught a lecture there once. taught a lecture there once. Nope. That accusation against me is a Nope. That accusation against me is a Nope. That accusation against me is a complete lie. complete lie. complete lie. I have been at MIT for over 10 years in I have been at MIT for over 10 years in I have been at MIT for over 10 years in a paid research position from 2015 a paid research position from 2015 a paid research position from 2015 to today. To be extra clear, I'm a to today. To be extra clear, I'm a to today. To be extra clear, I'm a research scientist at MIT working in research scientist at MIT working in research scientist at MIT working in lids, the laboratory for information and lids, the laboratory for information and lids, the laboratory for information and decision systems in the college of decision systems in the college of decision systems in the college of computing. computing. computing. For now, since I'm still at MIT, you can For now, since I'm still at MIT, you can For now, since I'm still at MIT, you can uh see me in the directory and on the uh see me in the directory and on the uh see me in the directory and on the various lab pages. I have indeed given various lab pages. I have indeed given various lab pages. I have indeed given many lectures at MIT over the years, a many lectures at MIT over the years, a many lectures at MIT over the years, a small fraction of which I posted online.

small fraction of which I posted online. small fraction of which I posted online. Teaching for me always has been just for Teaching for me always has been just for Teaching for me always has been just for fun and not part of my research work. I fun and not part of my research work. I fun and not part of my research work. I personally think I suck at it, but I personally think I suck at it, but I personally think I suck at it, but I have always learned and grown from the have always learned and grown from the have always learned and grown from the experience. It's like Fineman spoke experience. It's like Fineman spoke experience. It's like Fineman spoke about, if you want to understand about, if you want to understand about, if you want to understand something deeply, it's good to try to something deeply, it's good to try to something deeply, it's good to try to teach it. teach it. teach it. But like I said, my main focus has But like I said, my main focus has But like I said, my main focus has always been on research. I published always been on research. I published always been on research. I published many peer-reviewed papers that you can many peer-reviewed papers that you can many peer-reviewed papers that you can see in my Google Scholar profile. For my see in my Google Scholar profile. For my see in my Google Scholar profile. For my first four years at MIT, I worked first four years at MIT, I worked first four years at MIT, I worked extremely intensively. Most weeks were extremely intensively. Most weeks were extremely intensively. Most weeks were 80 to 100 hour work weeks. After that, 80 to 100 hour work weeks. After that, 80 to 100 hour work weeks. After that, in 2019, I still kept my research in 2019, I still kept my research in 2019, I still kept my research scientist position, but I split my time scientist position, but I split my time scientist position, but I split my time taking a leap to pursue projects in AI taking a leap to pursue projects in AI taking a leap to pursue projects in AI and robotics outside MIT and to dedicate and robotics outside MIT and to dedicate and robotics outside MIT and to dedicate a lot of focus to the podcast. As I've a lot of focus to the podcast. As I've a lot of focus to the podcast. As I've said, I've been continuously surprised said, I've been continuously surprised said, I've been continuously surprised just how many hours preparing for an just how many hours preparing for an just how many hours preparing for an episode takes. There are many episodes episode takes. There are many episodes episode takes. There are many episodes of the podcast for which I have to read, of the podcast for which I have to read, of the podcast for which I have to read, write, and think for 100, 200 or more write, and think for 100, 200 or more write, and think for 100, 200 or more hours across multiple weeks and months.

hours across multiple weeks and months. hours across multiple weeks and months. Since 2020, I have not actively Since 2020, I have not actively Since 2020, I have not actively published research papers. Just like the published research papers. Just like the published research papers. Just like the podcast, I think it's something that's a podcast, I think it's something that's a podcast, I think it's something that's a serious full-time effort. But not serious full-time effort. But not serious full-time effort. But not publishing and doing full-time research publishing and doing full-time research publishing and doing full-time research has been eating at me because I love has been eating at me because I love has been eating at me because I love research and I love programming and research and I love programming and research and I love programming and building systems that test out building systems that test out building systems that test out interesting technical ideas, especially interesting technical ideas, especially interesting technical ideas, especially in the context of human AI or human in the context of human AI or human in the context of human AI or human robot interaction. robot interaction. robot interaction. I hope to change this in the coming I hope to change this in the coming I hope to change this in the coming months and years. months and years. months and years. What I've come to realize about myself What I've come to realize about myself What I've come to realize about myself is if I don't publish or if I don't is if I don't publish or if I don't is if I don't publish or if I don't launch systems that people use, I launch systems that people use, I launch systems that people use, I definitely feel like a piece of me is definitely feel like a piece of me is definitely feel like a piece of me is missing. It legitimately is a source of missing. It legitimately is a source of missing. It legitimately is a source of happiness for me. Anyway, I'm proud of happiness for me. Anyway, I'm proud of happiness for me. Anyway, I'm proud of my time at MIT. I was and am constantly my time at MIT. I was and am constantly my time at MIT. I was and am constantly surrounded by people much smarter than surrounded by people much smarter than surrounded by people much smarter than me, many of whom have become lifelong me, many of whom have become lifelong me, many of whom have become lifelong colleagues and friends. colleagues and friends. colleagues and friends. MIT is a place I go to escape the world, MIT is a place I go to escape the world, MIT is a place I go to escape the world, to focus on exploring fascinating to focus on exploring fascinating to focus on exploring fascinating questions at the cutting edge of science questions at the cutting edge of science questions at the cutting edge of science and engineering. This again makes me and engineering. This again makes me and engineering. This again makes me truly happy. And it does hit pretty hard truly happy. And it does hit pretty hard truly happy. And it does hit pretty hard on a psychological level when I'm on a psychological level when I'm on a psychological level when I'm getting attacked over this.

getting attacked over this. getting attacked over this. Perhaps I'm doing something wrong. If I Perhaps I'm doing something wrong. If I Perhaps I'm doing something wrong. If I am, I will try to do better. am, I will try to do better. am, I will try to do better. In all this discussion of academic work, In all this discussion of academic work, In all this discussion of academic work, I hope you know that I don't ever mean I hope you know that I don't ever mean I hope you know that I don't ever mean to say that I'm an expert at anything. to say that I'm an expert at anything. to say that I'm an expert at anything. In the podcast and in my private life, I In the podcast and in my private life, I In the podcast and in my private life, I don't claim to be smart. In fact, I don't claim to be smart. In fact, I don't claim to be smart. In fact, I often call myself an idiot and mean it. often call myself an idiot and mean it. often call myself an idiot and mean it. I try to make fun of myself as much as I try to make fun of myself as much as I try to make fun of myself as much as possible and in general to celebrate possible and in general to celebrate possible and in general to celebrate others instead. others instead. others instead. Now, to talk about Drexler University, Now, to talk about Drexler University, Now, to talk about Drexler University, which I also love, am proud of and am which I also love, am proud of and am which I also love, am proud of and am deeply grateful for my time there. As I deeply grateful for my time there. As I deeply grateful for my time there. As I said, I went to Drexil for my said, I went to Drexil for my said, I went to Drexil for my bachelor's, masters, and doctor degrees bachelor's, masters, and doctor degrees bachelor's, masters, and doctor degrees in computer science and electrical in computer science and electrical in computer science and electrical engineering. engineering. engineering. I've talked about Drexel many times, I've talked about Drexel many times, I've talked about Drexel many times, including, as I mentioned, at the end of including, as I mentioned, at the end of including, as I mentioned, at the end of a recent podcast, the Donald Trump a recent podcast, the Donald Trump a recent podcast, the Donald Trump episode, funny enough, that was listened episode, funny enough, that was listened episode, funny enough, that was listened to by many millions of people, where I to by many millions of people, where I to by many millions of people, where I answered a question about graduate answered a question about graduate answered a question about graduate school and explained my own journey at school and explained my own journey at school and explained my own journey at Drexel and how grateful I am for it. If Drexel and how grateful I am for it. If Drexel and how grateful I am for it. If it's at all interesting to you, please it's at all interesting to you, please it's at all interesting to you, please go listen to the end of that episode or go listen to the end of that episode or go listen to the end of that episode or watch the related clip. At Drexel, I met watch the related clip. At Drexel, I met watch the related clip. At Drexel, I met and worked with many brilliant and worked with many brilliant and worked with many brilliant researchers and mentors from whom I've researchers and mentors from whom I've researchers and mentors from whom I've learned a lot about engineering, learned a lot about engineering, learned a lot about engineering, science, and life. There are many science, and life. There are many science, and life. There are many valuable things I gained from my time at valuable things I gained from my time at valuable things I gained from my time at Drexel. First, I took a large number of Drexel. First, I took a large number of Drexel. First, I took a large number of very difficult math and theoretical

very difficult math and theoretical very difficult math and theoretical computer science courses. They taught me computer science courses. They taught me computer science courses. They taught me how to think deeply and rigorously, and how to think deeply and rigorously, and how to think deeply and rigorously, and also how to work hard and not give up also how to work hard and not give up also how to work hard and not give up even if it feels like I'm too dumb to even if it feels like I'm too dumb to even if it feels like I'm too dumb to find a solution to a technical problem. find a solution to a technical problem. find a solution to a technical problem. Second, I programmed a lot during that Second, I programmed a lot during that Second, I programmed a lot during that time, mostly C, C++. I programmed time, mostly C, C++. I programmed time, mostly C, C++. I programmed robots, optimization algorithms, robots, optimization algorithms, robots, optimization algorithms, computer vision systems, wireless computer vision systems, wireless computer vision systems, wireless network protocols, multimodal machine network protocols, multimodal machine network protocols, multimodal machine learning systems, and all kinds of learning systems, and all kinds of learning systems, and all kinds of simulations of physical systems. simulations of physical systems. simulations of physical systems. This is where I really develop a love This is where I really develop a love This is where I really develop a love for programming, including yes, Emacs for programming, including yes, Emacs for programming, including yes, Emacs and the Kinesis keyboard. and the Kinesis keyboard. and the Kinesis keyboard. Uh I also during that time read a lot. I Uh I also during that time read a lot. I Uh I also during that time read a lot. I played a lot of guitar, wrote a lot of played a lot of guitar, wrote a lot of played a lot of guitar, wrote a lot of crappy poetry and uh trained a lot of uh crappy poetry and uh trained a lot of uh crappy poetry and uh trained a lot of uh injudo and jiu-jitsu injudo and jiu-jitsu injudo and jiu-jitsu which I cannot sing enough praises to. which I cannot sing enough praises to. which I cannot sing enough praises to. Jiu-jitsu humbled me on a daily basis Jiu-jitsu humbled me on a daily basis Jiu-jitsu humbled me on a daily basis throughout my 20s and it still does to throughout my 20s and it still does to throughout my 20s and it still does to this very day whenever I get a chance to this very day whenever I get a chance to this very day whenever I get a chance to train.

train. train. Anyway, I hope that the folks who Anyway, I hope that the folks who Anyway, I hope that the folks who occasionally get swept up in enchanting occasionally get swept up in enchanting occasionally get swept up in enchanting online crowds that want to tear down online crowds that want to tear down online crowds that want to tear down others don't lose themselves in it too others don't lose themselves in it too others don't lose themselves in it too much. much. much. In the end, I still think there's more In the end, I still think there's more In the end, I still think there's more good than bad in people. But we're all, good than bad in people. But we're all, good than bad in people. But we're all, each of us, a mixed bag. I know I am each of us, a mixed bag. I know I am each of us, a mixed bag. I know I am very much flawed. I speak awkwardly. I very much flawed. I speak awkwardly. I very much flawed. I speak awkwardly. I sometimes say stupid I can get sometimes say stupid I can get sometimes say stupid I can get irrationally emotional. I can be too irrationally emotional. I can be too irrationally emotional. I can be too much of a dick when I should be kind. I much of a dick when I should be kind. I much of a dick when I should be kind. I can lose myself in a biased rabbit hole can lose myself in a biased rabbit hole can lose myself in a biased rabbit hole before I wake up to the bigger, more before I wake up to the bigger, more before I wake up to the bigger, more accurate picture of reality. accurate picture of reality. accurate picture of reality. I'm human and so are you. For better or I'm human and so are you. For better or I'm human and so are you. For better or for worse. for worse. for worse. And I do still believe we're in this And I do still believe we're in this And I do still believe we're in this whole beautiful mess together. whole beautiful mess together. whole beautiful mess together. I love you all.