A genius who may hold the cards of our future. CEO of Google DeepMind, which is the engine of the company's artificial intelligence. After his Nobel and a knighthood from King Charles, he became a pioneer of artificial intelligence. 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 at 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.
Ladies and gentlemen, please welcome Google DeepMind's Demis Hassabis.
Welcome. Great to be here.
Thanks. Thanks for following Tucker, Mark Cuban at all. First off, congrats on winning the Nobel Prize.
Thanks 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 were when you won the Nobel Prize. How'd you find out?
Well, it was a very surreal moment obviously. Everything about it is surreal. The way they tell you, they tell you like 10 minutes before it all goes live. 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 seal ceremonies, the 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 the Nobel book from the vaults in the safe and you get to sign your name next to all the other greats. So it's quite an incredible moment, 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.
Did you have an inkling you had been nominated and that this might be coming your way?
Well, you hear rumors. It's amazingly locked down actually in today's age how they keep it so quiet, but it's sort of like a national treasure for Sweden. And so you hear, maybe AlphaFold is the kind of thing 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 arrive. So you just never know how soon it's going to be and whether it's going to be at all. So it's a surprise.
Well, congrats. Thank you. And thank you. You let me take a picture with it a few weeks ago, so 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?
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, 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 billions of people now interact with Gemini models, whether that's through AI Overview, AI Mode, or the Gemini app. And that's just the beginning. 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.
And how many people? What's the profile? Are these scientists, engineers? What's the makeup of your...
There's around 5,000 people in Google DeepMind. And it's predominantly, I guess, 80% plus engineers and PhD researchers. So about 3 or 4,000.
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.
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?
Yeah, we can watch. Sure. Because 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. 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.
Yeah. So all of these videos, all these interactive worlds that you're seeing, 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 is 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. 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 'a jet ski' and it will just in real time include them in the scene. So I think it's quite mind-blowing really.
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, 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. This is actually just 2D images that are being rendered, created on the fly by the AI. This model is reverse engineering intuitive physics. So it's watched many millions of videos and YouTube videos and other things about the world. And just from that, it's kind of reverse 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 the user in many different worlds. There's some videos later on where you can control a dog on a beach or a jellyfish. That's not limited to just human things. 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 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 was just trained off of video and it figured it all out.
Yeah, it was trained off of video and some synthetic data from game engines and it's just reverse engineered it. For me, it's very close to my heart, this project, but it's also quite mind-blowing because in the 90s, in 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 way materials flow, and objects behave. And it's just doing that all out of the box. I think it's hard to describe how much complexity was solved for with that model. It's really, really mind-blowing.
Where does this lead us? So fast forward this model to Gen 5.
So the reason we're building these kind of models is we feel, and we've always felt, 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 any kind of input, images, audio, video, and it can output anything. And so we've been very interested in this because for an AI to be truly general, to build 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 assistant that helps you in your everyday life. It's got to understand the physical context that you're in and how the intuitive physics of the world works. So we think that building these types of models, these Genie models and also Veo, our best text-to-video models, those are expressions of us building world models that understand the dynamics of the world, the physics of the world. If you can generate it, then that's an expression of your system understanding those dynamics, and that leads to a world of robotics ultimately, 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 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 for me.
That's right. So if you look at our Gemini, Gemini Live version of Gemini where you can hold up your phone to the world around you, I'd recommend any of you try it. It's kind of magical what it already understands about the physical world. You can think of the next step as incorporating that in some sort of more handy device like glasses. And then it will be an everyday assistant and it'll be able to recommend things to you as you're walking the streets, or we can embed it into Google Maps. And then with robotics, we've built something called Gemini Robotics models which are sort of fine-tuned Gemini with extra robotics data. And what's really cool about that, and we released some demos of this over the summer, was we've got these tabletop setups of two hands interacting with objects on a table, two robotic hands, and you can just talk to the robot. So you can say 'put the yellow object into the red bucket' or whatever it is, and 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, is that it will be able to bring in real world understanding to the way you interact with it. So in the end it will be the UI/UX that you need for, as well as the understanding the robots need to navigate the world safely.
I asked Sundar this: does that mean that ultimately you could build what would be the equivalent of, call it either a Unix-like 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 because this software exists to do this generally?
Exactly. That's certainly one strategy we're pursuing, a kind of Android play if you like, a cross-robotics almost an OS layer. 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.
Do you think that humanoid robots are a good form factor? Does that make sense in the world? Because some folks have criticized it as being good for humans because we're meant to do lots of different things, 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.
Yeah, I think there's going to be a place for both. Actually, I used to be of the opinion maybe 5, 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 for humans. And so steps, doorways, all the things that we've designed for ourselves, rather than changing all of those 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 those types of tasks. But I think there is a place also for specialized robotic forms.
Do you have a view on hundreds of millions, millions, thousands over the next 5 years, 7 years? Do you have a vision?
