Google DeepMind CEO Demis Hassabis on AI, Creativity, and a Golden Age of Science | All-In Summit

(0:00) Introducing Sir Demis Hassabis, reflecting on his Nobel Prize win (2:39) What is Google DeepMind? How does it interact with Google and Alphabet? (4:01) Genie 3 world model (9:21) State of robotics models, form factors, and more (14:42) AI science breakthroughs, measuring AGI (20:49) Nano-Banana and the future of creative tools, democratization of creativity (24:44) Isomorphic Labs, probabilistic vs deterministic, scaling compute, a golden age of science Thanks to our partners for making this happen! Solana - Solana is the high performance network powering internet capital markets, payments, and crypto applications. Connect with investors, crypto founders, and entrepreneurs at Solana's global flagship event during Abu Dhabi Finance Week & F1: solana.com/breakpoint. https://solana.com/ OKX - The new way to build your crypto portfolio and use it in daily life. We call it the new money app. https://www.okx.com/ Google Cloud - The next generation of unicorns is building on Google Cloud's industry-leading, fully integrated AI stack: infrastructure, platform, models, agents, and data. https://cloud.google.com/ IREN - IREN AI Cloud, powered by NVIDIA GPUs, provides the scale, performance, and reliability to accelerate your AI journey. https://iren.com/ Oracle - Step into the future of enterprise productivity at Oracle AI Experience Live. https://www.oracle.com/ Circle - The America-based company behind USDC — a fully-reserved, enterprise-grade stablecoin at the core of the emerging internet financial system. https://www.circle.com/ BVNK - Building stablecoin-powered financial infrastructure that helps businesses send, store, and spend value instantly, anywhere in the world. https://www.bvnk.com/ Polymarket: https://www.polymarket.com/ Athletic Brewing: https://athleticbrewing.com/ Follow Demis: https://x.com/demishassabis Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Speaker 00

  A genius who may hold the cards of our future.

• 0:05Speaker 01

  CEO of Google DeepMind, which is the engine of the company's artificial intelligence.

• 0:09Speaker 00

  After his Nobel and a knighthood from King Charles, he became a pioneer of artificial intelligence.

• 0:17Speaker 02

  We were the first ones to start doing it seriously in the modern era. AlphaGo was the big watershed moment, I think, not just for DeepMind and my company, but for AI in general. This was always my aim with AI from a kid, which is to use it to accelerate scientific discovery.

• 0:32Speaker 03

  Ladies and gentlemen, please welcome Google DeepMind's Demis Hassabis.

• 0:43David Friedberg?

  Welcome.

• 0:44Speaker 02

  Great to be here.

• 0:44David Friedberg?

  Thanks for following Tucker, Mark Cuban, at all. First off, congrats on winning the Nobel Prize.

• 0:52Speaker 02

  Thank you very much.

• 0:58David Friedberg?

  For the incredible breakthrough of AlphaFold, maybe you may have done this before, but I know everyone here would love to hear your recounting of how you, where you were when you won the Nobel Prize. How did you find out?

• 1:09Speaker 02

  Well, it was a very surreal moment, obviously. You know, everything about it is surreal. The way they tell you, they tell you like 10 minutes before it all goes live. It's just, you know, you can't really, you're sort of shell shocked when you get that call from Sweden. It's the call that every scientist dreams about. And then the ceremony is a whole week in Sweden with the royal family. It's amazing, obviously. It's been going for 120 years. And the most amazing bit is they bring out this Nobel book from the vaults in the safe, and you get to sign your name next to, you know, all the other greats. So it's quite an incredible moment, sort of leafing back to the other pages and seeing Feynman and Marie Curie and Einstein and Niels Bohr. And you just carry on going backwards and you get to put your name in that book. It's incredible.

• 1:54David Friedberg?

  Did you have an inkling you had been nominated and that this might be coming your way?

• 1:58Speaker 02

  Well, you you you get you hear rumors. It's amazingly locked down, actually, in today's age, how they keep it so so quiet. But it's sort of like a national treasure for Sweden. And as you hear, you know, maybe Alpha Fold is the kind of thing that that would be worthy of that recognition. And it has they look for impact as well as the scientific breakthrough impact in the real world. And that can take 20, 30 years to to arrive. So you just never know, you know, whether how soon it's going to be and and whether it's going to be at all. So it's a surprise.

• 2:30David Friedberg?

  Well, congrats.

• 2:30Speaker 02

  Yeah, thank you.

• 2:31David Friedberg?

  And thank you. You let me take a picture with it a few weeks ago. That's something I'll cherish. What is DeepMind within Alphabet? Alphabet is a sprawling organization, sprawling business units. What is DeepMind? What are you responsible for?

• 2:45Speaker 02

  Well, we sort of see DeepMind now and Google DeepMind as it's become. We sort of merged a couple of years back, all of the different AI efforts across Google and Alphabet, including DeepMind, put it all together, the kind of bringing the strengths of all the different groups together into one division. And really, the way I describe it now is that we're the engine room of the whole of Google and the whole of Alphabet. So Gemini, our main model that we're building, but also many of the other models that we also build, the video models and interactive world models, we plug them in all across Google now. So pretty much every product, every surface area has one of our AI models in it. So, you know, billions of people now interact with Gemini models, whether that's through AI

• 3:29Speaker 02

  overview, AI mode, or the Gemini app. And that's just the beginning, you know, we're kind of incorporating into Workspace, into Gmail, and so on. So it's a fantastic opportunity, really, for us to do cutting edge research, but then immediately ship it to billions of users.

• 3:44David Friedberg?

  And how many people? What's the profile? Are these scientists, engineers? What's the makeup of the organization?

• 3:50Speaker 02

  Yeah, there's around 5,000 people in my org, in Google DeepMind. And, you know, it's predominantly, I guess, 80% plus engineers and PhD researchers. So, yeah, about, you know, three, three or four thousand.

• 4:02David Friedberg?

  So there's an evolution of models, a lot of new models coming out, and also new classes of models. The other day... You released this Genie world model.

• 4:11Speaker 02

  Yes.

• 4:11David Friedberg?

  So what is the Genie world model? And I think we got a video of it. Is it worth looking at and we can talk about it live?

• 4:18Speaker 02

  Yeah, we can watch it, sure.

• 4:20David Friedberg?

  I think you have to see it to understand it because it's so extraordinary. Can we pull up the video? And then Demis can narrate a little bit about what we're looking at.

• 4:28Speaker 05

  What you're seeing are not games or videos. They're worlds. Each one of these is an interactive environment generated by Genie 3, a new frontier for world models. With Genie 3, you can use natural language to generate a variety of worlds and explore them interactively, all with a single text prompt.

• 4:48Speaker 02

  Yeah, so all of these videos, all these interactive worlds that you're seeing. So you're seeing someone actually can control the video. It's not a static video. It's just being generated by a text prompt. And then people are able to control the 3D environment using the arrow keys and the spacebar. So everything you're seeing here is being fully, all these pixels are being generated on the fly. They don't exist until the player or the person interacting with it goes to that part of the world. So all of this richness. And then you'll see in a second. So this is fully generated. This is not a real video. This has generated someone painting their room. And they're painting some stuff on the wall. And then the player is going to look to the right and then look back.

• 5:31Speaker 02

  So now, this part of the world didn't exist before, so now it exists. And then they look back and they see the same painting marks they left just earlier. And again, this is fully, every pixel you can see is fully generated. And then you can type things like "person in a chicken suit" or "jet ski", and it will just, in real time, include them in the scene. So, you know, it's quite mind blowing, really.

• 5:57David Friedberg?

  But I think what's hard to grok when looking at this, because we've all played video games that have a 3D element to them when you're in an immersive world.

• 6:04Speaker 02

  Yep.

• 6:05David Friedberg?

  But there's no objects that have been created. There's no rendering engine. You're not using Unity or Unreal, which are the 3D rendering engines.

• 6:13Speaker 02

  Yep.

• 6:13David Friedberg?

  This is actually just 2D images that are being rendered, like created on the floor. By the AI.

• 6:19Speaker 02

  This model is reverse engineering intuitive physics. So you know, it's watched many millions of videos and YouTube videos and other things about the world. And just from that, it's kind of reversed engineered how a lot of the world works. It's not perfect yet, but it can generate a consistent minute or two of interaction as you as the user. And many, many different worlds, there are some videos later on where you can control, you know, a dog on a beach or a jellyfish, or it does not limited to just human things.

• 6:49David Friedberg?

  Because the way a 3D rendering engine works is you type in, the programmer programs all the laws of physics. How does light reflect off of an object? You create a 3D object, light reflects off. And then, so what I see visually is rendered by the software because it's got all the programming on how to create physics, how to do physics. But this, this was just trained off of video and it figured it all out.

• 7:13Speaker 02

  Yeah, it was trained off of video and some synthetic data from, from game engines. And it's just reverse engineered it. And for me, it's, it's, it's very close to my heart, this project, but it's also quite mind blowing. Because in the nineties of my early career, I used to write video games and AI for video games and graphics engines. And I remember how hard it was to do this by hand, program all the polygons and the physics engines. And it's amazing to just see this do it effortlessly. All of the reflections on the water and the, the way materials flow and, and, and objects behave. And it's just doing that all out of the box.

• 7:49David Friedberg?

  I think it's hard to describe like how much complexity was solved for with that model. It's, it's, it's really, really, really mind blowing. Where does this lead us? So fast forward this model to Gen 5.

• 8:03Speaker 02

  Yeah, so, so the reason we're building these kind of models is we feel, and we've always felt, we're obviously progressing on the normal language models like with our Gemini model. But from the beginning with Gemini, we wanted it to be multimodal. So we wanted it to input and take any kind of input, images, audio, video, and it can output anything. And, and, and so we've been very interested in this because for an AI to be truly general. AGI, we feel that the AGI system needs to understand the world around us. And the physical world around us, not just the abstract world of languages or mathematics. And of course, that's what's critical for robotics to work, it's probably what's missing from it today. And also things like smart glasses, a smart glass system that helps you in your everyday life.

• 8:47Speaker 02

  It's got to understand the physical context that you're in and and how the world, the intuitive physics of the world works. So we think that building these types of models, these genie models and also VO, our best text to video models. Um, those are expressions of us uh building world models that understand the dynamics of the world, the physics of the world. If you can generate it, then um that's that's an expression of your system understanding uh those dynamics.

• 9:13David Friedberg?

  And that leads to a world of robotics ultimately. Um, one, one, one aspect, one application, but maybe we can talk about that. What is the state of the art with the vision, language, action models today? So a generalized system, a box, a machine.

• 9:29Speaker 02

  Yeah.

• 9:30David Friedberg?

  That can observe the world with a camera. And then I can use language, I can use text or speech to tell it, I want you to do it. And then it knows how to act physically to do something in the physical world.

• 9:40Speaker 02

  Yeah, that's right. So if you, if you look at our uh Gemini, Gemini live version of of Gemini, where you can hold up your phone to the world around you. Uh, I'd recommend any of you try it. It's kind of magical what it already understands about the physical world. Um, you can think of the next step as as incorporating that in some sort of more handy device like glasses. Um, and then it will be an everyday assistant, it'll be able to recommend things to you uh as you're walking the streets. Or we can embed it into Google Maps. Um, and then with robotics, uh, we've, we've built uh something called Gemini robotics models, which are sort of fine tuned Gemini with extra robotics data. And what's really cool about that is, and and we released some demos of this over the summer was,

• 10:21Speaker 02

  um, you can have, you know, we've got these tabletop setups of two hands uh interacting with objects on a table, two robotic hands. And you can just talk to the robot. So you can say, you know, put the the yellow object into the red bucket or whatever it is. And uh it will just, it will, it will. Interpret that instruction, that language instruction into motor movements. And that's the power of a multimodal model, rather than just a robotic-specific model.

• 10:46David Friedberg?

  Right.

• 10:46Speaker 02

  Is that it will be able to bring in real-world understanding to the way you interact with it. So in the end, it'll be the UI UX that you need for, as well as the understanding the robotic, the robots need to navigate the world safely.

• 10:59David Friedberg?

  I asked Sundar this, does that mean that ultimately you could build what would be the equivalent of, call it either a Unix, like an operating system layer, or like an Android for generalized robotics, at which point if it works well enough across enough devices, there will be a proliferation of robotics devices and companies and products that will suddenly take off in the world.

• 11:23Speaker 02

  Yeah.

• 11:23David Friedberg?

  Because this software exists to do this generally.

• 11:26Speaker 02

  Exactly. That's certainly one strategy we're pursuing is a kind of Android play, if you like, a cross, as a kind of robotics, almost an OS layer, cross robotics. But there's also some quite interesting things about vertically integrating our latest models with specific robot types and robot designs, and some kind of end-to-end learning of that, too. So both are actually pretty interesting, and we're pursuing both strategies.

• 11:51David Friedberg?

  Do you think that there's humanoid robots as a good kind of form factor? Does that make sense in the world?

• 11:57Speaker 02

  Yeah.

• 11:58David Friedberg?

  Because some folks have criticized it as being good for humans because we're meant to do lots of different things.

• 12:02Speaker 02

  Yeah.

• 12:02David Friedberg?

  But if we want to solve a problem, there may be a different form factor to fold laundry or do dishes or clean the house or whatever.

• 12:08Speaker 02

  Yeah, I think there's going to be a place for both. So actually, I used to be of the opinion, maybe five, 10 years ago, that we'll have form-specific robots for certain tasks. And I think in industry, industrial robots will definitely be like that, where you can optimize the robot for the specific task, whether it's a laboratory or a production line. You'd want quite different types of robots. On the other hand, for general use or personal use robotics and just interacting with the ordinary world, the humanoid form factor could be pretty important because, of course, we've designed the physical world around us to be for humans. And so steps, doorways. All the things that we've designed for ourselves, rather than changing all of those

• 12:53Speaker 02

  in the real world, it might be easier to design the form factor to work seamlessly with the way we've already designed the world. So I think there's an argument to be made that the humanoid form factor could be very important for for those types of tasks. But I think there is a place also for specialized robotic forms.

• 13:08David Friedberg?

  Do you have a view on hundreds of millions, millions, thousands over the next five years, seven years? I mean, do you have a, like, in your head, do you have a vision on?

• 13:15Speaker 02

  Yeah, I do, and I spend quite a lot of time on this, and I think we're still, I feel we're still a little bit early on robotics. I think in the next couple of years, there'll be a sort of real wow moment with robotics. But I think the algorithms need a bit more development. The general purpose models that these robotics models are built on still need to be better and more reliable and better understanding the world around it. And I think that will come in the next couple of years. And then also on the on the hardware side, the key is, I think eventually we will have millions of robots helping helping helping society and increasing productivity. But the key there is, when you talk to hardware experts, is at what point do you have the

• 13:58Speaker 02

  right level of hardware to go for the scaling option? Because effectively, when you start building factories around trying to make tens of thousands, hundreds of thousands of a particular robot type, you know, it's harder for you to update, quickly iterate the the robot design. So it's one of those kind of questions where if you call it too early, then then then the next generation of robot might be invented in six months' time that's just more reliable and better and more dexterous.

• 14:24David Friedberg?

  Sounds like, using a computing analogy, we're kind of in the 70s-era PC DOS kind of.

• 14:29Speaker 02

  Yeah, potentially. But of course, I think the maybe that's where we are, but I think the, except that 10 years happens in one year probably.

• 14:36David Friedberg?

  Right. 1984 might be one of those years.

• 14:39Speaker 02

  Right, exactly.

• 14:40David Friedberg?

  Yeah. So let's talk about other applications, particularly in science, true to your heart as a as a scientist, as the Nobel Prize-winning scientist. I always felt like the greatest thing, things that we would be able to do with AI would be the problems that are intractable to humans with our current technology and capabilities and our brains and whatnot, and we can unlock all of this potential. What are the areas of science and breakthroughs in science that you're most excited about? And what kinds of models do we use to get there?

• 15:15Speaker 02

  Yeah, I mean, AI to accelerate scientific discovery and help with things like human health is the reason I spent my whole career on AI. And I think it's the most important thing we can do with AI. And I feel like if we build AGI in the right way, it will be the ultimate tool for science. And I think we've been showing at DeepMind a lot of the way of that, obviously AlphaFold most famously, but actually, we've applied our AI systems to many branches of science, whether it's material design, helping with controlling plasma infusion reactors, predicting the weather, solving, you know, math Olympiad math problems. And the same types of systems with some extra fine tuning can basically solve a lot of

• 16:00Speaker 02

  these complex problems. So I think we're just scratching the surface of what AI will be able to do, and there are some things that are missing. So AI today, I would say, doesn't have true creativity in the sense that it can't come up with a new conjecture yet or new hypothesis. It can maybe prove something that you give it, but it's not able to come up with a sort of new idea or new theory itself. So I think that would be one of the tests actually for AGI.

• 16:25David Friedberg?

  What is that, creativity as a human?

• 16:28Speaker 02

  Yeah.

• 16:29David Friedberg?

  What is creativity then?

• 16:30Speaker 02

  Well, I think it's this sort of intuitive leaps that we often celebrate with the best scientists in history and artists, of course. And, you know, maybe it's done through analogy or analogical reasoning. There are many theories in psychology and neuroscience as to how we as human scientists do it. But a good test for it would be something like, give one of these modern AI systems a knowledge cutoff of 1901 and see if it can come up with special relativity like Einstein did in 1905, right? If it's able to do that, then I think we're on to something really, really important. Perhaps we're nearing an AGI. Another example would be with our AlphaGo programme that beat the world champion at Go.

• 17:11Speaker 02

  Not only did it win in, you know, back 10 years ago, it invented new strategies that had never been seen before for the game of Go. This famously move 37 in game two that is now studied. But can an AI system come up with a game as elegant, as satisfying, as aesthetically beautiful as Go? Not just a new strategy. And the answer to those things at the moment is no. So that's one of the things I think that's missing from a true general system, an AGI system, is it should be able to do those kinds of things as well.

• 17:42David Friedberg?

  Can you break down what's missing and maybe relate it to the point of view shared by Dario, Sam, others about AGIs a few years away?

• 17:52Speaker 02

  Yeah.

• 17:52David Friedberg?

  Do you not subscribe to that belief? And maybe help us understand what is it in your understanding of structure? And your understanding of the system architecture, what's lacking?

• 18:03Speaker 02

  Well, so I think the fundamental aspect of this is can we mimic these intuitive leaps rather than incremental advances? That the best human scientists seem to be able to do. I always say like what separates a great scientist from a good scientist is they're both technically very capable, of course, but the great scientist is more creative. And so maybe they'll spot some pattern from another subject area that can be can sort of have an analogy or some sort of pattern matching to the area they're trying to solve. And I think one day AI will be able to do this, but it doesn't have the reasoning capabilities and some of the thinking capabilities that are going to be needed to make that kind of breakthrough.

• 18:47Speaker 02

  I also think that we're lacking consistency. So you often hear some of our competitors talk about, you know, these modern systems that we have today are PhD intelligences. I think that's a nonsense. They're not, they're not PhD intelligences. They have some capabilities that are PhD level, but they're not in general capable. And that's what exactly what general intelligence should be of performing across the board at the PhD level. In fact, as we all know, interacting with today's chatbots, if you pose the question in a certain way, they can make simple mistakes with even like high school maths and simple counting. So that shouldn't be possible for a true AGI system.

• 19:28Speaker 02

  So I think that we are maybe, you know, I would say sort of five to 10 years away from having an AGI system that's capable of doing those things. Another thing that's missing is continual learning, this ability to like online teach the system something new or some, or adjust its behavior in some way. And so a lot of these, I think, core capabilities are still missing. And maybe scaling will get us there, but I feel If I was to bet, I think there are probably one or two missing breakthroughs that are still required. And will come over the next five or so years.

• 20:00David Friedberg?

  In the meantime, some of the reports and the scoring systems that are used seem to be demonstrating two things. One, perhaps, and tell me if we're wrong on this, a convergence of performance of large language models. And number two, perhaps, is a slowing down or a flatlining of improvements in performance on each generation. Are those two statements generally true or not so much?

• 20:22Speaker 02

  No, I mean, we're not seeing that internally and we're still seeing a huge rate of progress. But also, we're sort of looking at things more broadly. You see with our genie models and VO models and

• 20:35David Friedberg?

  Nano Banana is insane. It's bananas.

• 20:38Speaker 02

  Yes.

• 20:39David Friedberg?

  Can I see who's used it? Has anyone used Nano Banana? It's incredible, right? I mean, I'm a nerd who used to use Adobe Photoshop as a kid and Kai's Power Tools. And I was telling you Bryce 3D, still like the graphic systems and like recognizing what's going on there was just like mind blowing.

• 20:57Speaker 02

  Well, I think that's the future of a lot of these creative tools is you're just going to sort of vibe with it or just talk to them. And it'll be consistent enough where like with Nano Banana, what's amazing about it is that it's an image generator. It's best in, you know, it's state of the art and best in class. But it's one of the things that makes it so great is its consistency. It's able to instruction follow what you want changed and keep everything else the same. And so you can iterate with it and eventually get the kind of output that you want. And that's, I think, what the future of a lot of these creative tools is going to be. And sort of signals the direction and people love it and they love creating with it.

• 21:34David Friedberg?

  So democratization of creativity, I think is really powerful. I remember having to buy books on Adobe Photoshop as a kid and then you'd read them to learn how to remove something from an image. And how to fill it in and feather and all this stuff. Now anyone can do it with Nano Banana and just they can explain to the software what they want it to do and it just does it.

• 21:53Speaker 02

  Yeah, I think you're going to see two things, which is that this sort of democratization of these tools for everybody to just use and create with without. Having to learn incredibly complex UXs and UIs like we had to do in the past. But on the other hand, I think we're also collaborating with filmmakers and top creatives and artists. So they're helping us design what these new tools should be, what features would they want? People like the director Darren Aronofsky, who's a good friend of mine, amazing director. And he's been making, and his team have been making films using VO and some of our other tools. And we're learning a lot by observing them and collaborating them. And what we find is that it also superpowers and turbocharges the best professionals too.

• 22:36Speaker 02

  Because they're suddenly, the best creatives, the professional creatives, they're suddenly able to be 10x, 100x more productive. They can just try out all sorts of ideas they have in mind, you know, very low cost, and then get to the beautiful thing that they wanted. So I actually think it's sort of both things are true. We're democratizing it for everyday use, for YouTube creators and so on. But on the other hand, at the high end, the people who understand these tools, and it's not everyone can get the same output out of these tools, there's a skill in that, as well as the vision and the storytelling and the narrative style of the top creatives. And I think it just allows them, they really enjoy using these tools, it allows them to iterate way faster.

• 23:16David Friedberg?

  Do we get to a world where each individual describes what sort of content they're interested in? Play me music like Dave Matthews and it'll play some new track?

• 23:27Speaker 02

  Yes.

• 23:27David Friedberg?

  Or I want to play a video game set, you know, in the movie Braveheart and I want to be in that movie?

• 23:32Speaker 02

  Yes.

• 23:32David Friedberg?

  And I just have that experience? Do we end up there? Or do we still have a one-to-many creative process in society? How important culturally, and I know this is a little bit philosophical, but it's interesting to me, which is, are we still going to have storytelling where we have one story that we all share because someone made it?

• 23:48Speaker 02

  Yeah.

• 23:48David Friedberg?

  Or are we each going to start to develop and pull on our own kind of virtual?

• 23:52Speaker 02

  I actually foresee a world, and I think a lot about this, having started in the games industry as a game designer and programmer, is that in the 90s, is that, you know, I think the future of entertainment, this is what we're seeing, is the beginning of the future of entertainment. Maybe some new genre or new art form. And where there's a bit of co-creation, I still think that you'll have the top creative visionaries. They will be creating these compelling experiences and dynamic storylines, and they'll be of higher quality, even if they're using the same tools than the everyday person can do. But also, and so millions of people will... Potentially dive into those worlds, but maybe they'll also be able to create, co-create certain parts of those worlds. And perhaps that, you know, the, the, the main creative person is almost an editor of that world.

• 24:36Speaker 02

  So that's the kind of things I'm foreseeing in the next few years. And I'd actually like to explore ourselves with, with, with, with technologies like Genie.

• 24:44David Friedberg?

  Right, incredible. And how are you spending your time? Are you at, maybe you can describe Isomorphic.

• 24:50Speaker 02

  Of course.

• 24:50David Friedberg?

  What Isomorphic is and are you spending a lot of your time there?

• 24:52Speaker 02

  I am. So, so I also run Isomorphic, which is our spin out company to revolutionize drug discovery, building on our alpha fold breakthrough in, in protein folding. And of course, knowing the structure of a protein is only one step in the drug discovery process. So Isomorphic, you can think of it as building many adjacent alpha folds to help with things like designing chemical compounds that don't have any side effects, but bind to the right place on the protein. And I think we could reduce down drug discovery from taking years, sometimes a decade to do, down to maybe weeks or even days over the next 10 years.

• 25:30David Friedberg?

  It's incredible. Do you think that's in clinic soon or is that still in the discovery phase?

• 25:35Speaker 02

  We're building up the platform right now and it's, we have great partnerships with Eli Lilly. I think you had the CEO speaking earlier and Novartis, which are fantastic, and our own internal drug programs. And I think we'll be entering sort of pre-clinical phase sometime next year.

• 25:48David Friedberg?

  So candidates get handed over to the pharma company and they then take them forward?

• 25:51Speaker 02

  That's right. And we're working on cancers and immunology and oncology. And we're working with places like MD Anderson.

• 25:59David Friedberg?

  How much of this requires, and I just want to go back to your point about AGI as it relates to what you just said. Models can be probabilistic or deterministic and tell me if I'm reducing this down too simplistically, that the model takes an input and it outputs something very specific, like it's got a logical algorithm and it outputs the same thing every time. And it could be probabilistic where it can change things and make selections. The probability is 80%, I'll select this letter, 90% I'll select this letter, next, et cetera.

• 26:27David Friedberg?

  How much do we have to kind of develop deterministic models that sync up with, for example, the, the physics or the chemistry underlying the molecular interactions as you do your drug discovery modeling? How much are you building novel deterministic models?

• 26:42Speaker 02

  Yeah.

• 26:42David Friedberg?

  That work with the models that are probabilistic trained on data?

• 26:45Speaker 02

  Yeah, it's a great question. Actually, for the moment, and I think probably for the next five years or so, we're building what maybe you could call hybrid models. So AlphaFold itself is a hybrid model where you have the learning component, this probabilistic component you're talking about, which is based on neural networks and transformers and things, and that's learning from the data you give it, you know, any data you have available. But also, in a lot of cases with biology and chemistry, there isn't enough data to learn from. So you also have to build in some of the rules about chemistry and physics that you already know about. So for example, with AlphaFold, the angle of bonds between atoms. So and make sure that the AlphaFold understood you couldn't have atoms overlapping with each other and things like that.

• 27:28Speaker 02

  Now, in theory, it could learn that, but it would waste a lot of the learning capacity. So actually, it's better to kind of have that

• 27:33David Friedberg?

  Constrain it.

• 27:34Speaker 02

  Yeah, as a constraint in there. Now, the trick is, with all hybrid systems, is, and AlphaGo was another hybrid system, where there's a neural network learning about the game of Go and what kind of patterns are good. And then we had Monte Carlo Tree Search on top, which was doing the planning. And so the trick is, how do you marry up a learning system with a more handcrafted system, bespoke system, and actually have them work well together? And that's pretty tricky to do.

• 28:00David Friedberg?

  Does that sort of architecture ultimately lead to the breakthroughs needed for AGI, do you think? Are there deterministic components that need to be solved?

• 28:07Speaker 02

  Well, I think ultimately, what you want to do is, when you figure out something where this one of these hybrid systems, what you ultimately want to do is upstream it into the learning component. So it's always better if you can do end-to-end learning and directly predict the thing that you're after from the data that you're given. So so once you've figured out something using one of these hybrid systems, you then try and go back and reverse engineer what you've done and see if you can incorporate that learning, that information into the learning system. And this is sort of what we did with AlphaZero, the more general form of AlphaGo. So AlphaGo had some Go-specific knowledge in it, but then with AlphaZero, we got rid of that, including the human data, human games that we learned from, and actually just did self-learning from scratch.

• 28:52Speaker 02

  And of course, then it was able to learn any game, not just Go.

• 28:56David Friedberg?

  A lot of hype and hoopla has been made about the demand for energy arising from AI. This is a big part of the AI summit we held in Washington DC a few weeks ago. It seems to be the number one topic everyone talks about in tech nowadays. Where's all this power going to come from? But I ask the question of you, are there changes in the architecture of the models, or the hardware, or the relationship between the models and the hardware that brings down the energy per token of output, or the cost per token of output, that ultimately maybe say mutes the energy demand curve that's in front of us? Or do you not think that that's the case and we're still going to have a pretty kind of geometric energy demand curve?

• 29:35Speaker 02

  Well, look, interestingly, again, I think both cases are true in the sense that, especially us at Google and at DeepMind, we focus a lot on very efficient models that are powerful. Because we have our own internal use cases, of course, where we need to serve, say, AI overviews to billions of users every day. And it has to be extremely efficient, extremely low latency, and very cheap to serve. And so we've kind of pioneered many techniques that allow us to do that, like distillation, where you sort of have a bigger model internally that trains the smaller model, right? So you train the smaller model to mimic the bigger model. And over time, if you look at the progress of the last two years, the model efficiencies are like 10x, you know, even 100x better for the same performance.

• 30:17Speaker 02

  Now, the reason that that isn't reducing demand is because we're still not got to AGI yet. So also, the frontier models, you keep wanting to train and experiment with new ideas at larger and larger scale, whilst at the same time, at the serving side, things are getting more and more efficient. So both things are true. And in the end, I think that from the energy perspective, I think AI systems will give back a lot more to energy and climate change and these kind of things than they take in terms of efficiency of grid systems and electrical systems, material design, new types of properties, new energy sources. I think AI will help with all of that over the next 10 years that will far outweigh the energy that it uses today.

• 31:00David Friedberg?

  As the last question, describe the world 10 years from now.

• 31:05Speaker 02

  Wow, okay, well, I mean, you know, 10 years, even 10 weeks is a lifetime in AI.

• 31:11David Friedberg?

  Yeah, right. The Brownian field of 10 years for you.

• 31:14Speaker 02

  Right, right, but I do feel like if we will have AGI in the next 10 years, you know, full AGI, and I think that will usher in a new golden era of science, so a kind of new renaissance, and I think we'll see the benefits of that right across from energy to human health.

• 31:31David Friedberg?

  Amazing. Please join me in thanking Nobel laureate, Dennis. Thank you.

• 31:37Speaker 02

  Thank you.

• 31:38David Friedberg?

  Thank you.

• 31:39Speaker 02

  Thank you.