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A genius who may hold the cards of our future. CEO of Google DeepMind, which is the
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engine of the company's artificial intelligence. After his Nobel and a nighthood from King Charles, he became a pioneer of
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artificial intelligence. We were the first ones to start doing it seriously in the modern era. Alph Go was
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the big watershed moment, I think, not just for Deep Mind at my company, but for AI in general. This was always my
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aim with AI from a kid, which is to to use it to accelerate scientific discovery.
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Ladies and gentlemen, please welcome Google Deep Minds Deis Hassabis.
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[Applause] Welcome. Great to be here.
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Thanks. Thanks for following Tucker, Mark Cuban at all. Um, first off,
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congrats on winning the Nobel Prize. Thank you. Um, thanks
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for the incredible breakthrough of AlphaFold. Maybe you may have done this before, but I know everyone here would
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love to hear your recounting of how you where you were when you won the Nobel Prize. How'd you find out?
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Well, it was very surreal moment obviously. Um, you know, it's that everything about it is surreal. The way
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they tell you, they tell you like 10 minutes before it all goes live. It's just, you know, you can't really it's
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you're sort of shell shocked when you get that call from Sweden. It's the call that every scientist dreams about. And
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um and then the seal ceremonies the whole week in Sweden with the royal family. It's amazing. Obviously, it's
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been going for 120 years. Uh and the most amazing bit is they bring out the this Nobel book from the from the vaults
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in the safe and you get to sign your name next to you know all the other greats. So it's quite an incredible
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moment sort of leafing back to the other pages and seeing Fineman and Mary Cury
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and Einstein and Neils Bore and you just carry on going backwards and you get to put your name on that in that book. It's
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incredible. Did you have an inkling you had been nominated and that this might be coming your way? Well, you you you get you hear rumors.
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It's amazingly locked down actually in today's age how they keep it so so quiet, but um it's sort of like a a
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national treasure for Sweden. And um and so you hear, you know, maybe Alpha Fold
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is the kind of thing that that would be uh worthy of that recognition. And it has they look for impact as well as the
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scientific breakthrough uh impact in the real world. And that can take 20 30 years to to arrive. So you just never
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know, you know, whether how soon it's going to be and and and whether it's going to be at all. So it's a surprise.
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Well, congrats. Yeah. Thank you. Um and thank you. You let me take a picture with it a few weeks ago when we
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So that's something I'll cherish. Um what is Deep Mind within Alphabet?
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Alphabet is a sprawling organization, sprawling business units. What is Deep Mind? What are you responsible for?
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Well, we sort of see DeepMind now and Google Deep Mind as it's become. We sort of merged a couple of years back all of
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the different AI efforts across Google and Alphabet including Deep Mind. Put it all together, the kind of bringing the
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the strengths of all the different groups together into one division. And um really the way I describe it now is
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that we're the engine room of the whole of Google and the whole of Alphabet. So Gemini, our main model that we're
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building, but also many of the other models that we also build, the the video models and interactive world models, uh
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we plug them in all across Google now. So pretty much every product, every surface area has um uh one of our ai
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models in it. So you know, billions of people now interact with Gemini models, whether that's through AI overview, AI
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mode, or the Gemini app. Uh and that's just the beginning. You know, we're kind of incorporating into workspace into
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Gmail and so on. So it's a fantastic opportunity really for us to do cutting
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edge research, but then immediately ship it to billions of users. And uh how many people what's the
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profile? Are these scientists, engineers? What's the makeup of your There's around 5,000 people in in in my
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or in Google Deep Mind and and you know, it's predominantly I guess 80% plus engineers and PhD researchers. So, uh
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yeah, about you know, three three or 4 thousand. So, there's an evolution of models, a lot of new models coming out and also
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new classes of models. Um the other day you released this Genie World model.
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Yes. So, what is the Genie World model and um I think we got a video of it. Is it
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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
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extraordinary. Um, can we pull up the video and then uh Demis can narrate a little bit about what we're looking at. What you're seeing are not games or
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videos, they're worlds. Each one of these is an interactive environment generated by Genie 3, a new
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frontier for world models. With Genie3, you can use natural language to generate a variety of worlds
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and explore them interactively. All with a single text prompt. Yeah. So all of these videos, all these
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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
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text prompt. And then people are able to control the 3D environment using the arrow keys and the spacebar. So
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everything you're seeing here is being fully all these pixels are being generated on the fly. They don't exist
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until the player or the the the person interacting with it goes to that part of the world. So, um, all of this richness,
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um, and then you'll see in a second. So, this is fully generated. This is not a real video. This is generated someone
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painting their room and they're painting some stuff on the wall. And then the the player is going to look to the right.
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Uh, and then look back. So, now this part of the world didn't exist before, so now it exists. And then
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they look back and they see the same painting marks they they left just earlier. And again, this is fully every
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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
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will just uh in real time uh include them in the scene. So um I think
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you know it's quite mind-blowing really. I think what's hard to gro when looking at this because we've all played video
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games that have a 3D element to them when you're in an immersive world, but there's no objects that have been
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created. There's no rendering engine. You're not using Unity or Unreal which are the 3D rendering engines.
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Yeah, this is actually just 2D images that are being rendered like created on the fly
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by the AI. This model is reverse engineering intuitive physics. So, you know, it's
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watched many millions of videos and YouTube videos and other things about the world. And just from that, it's kind
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of reverse engineered how a lot of the world works. It's not perfect yet, but it can generate um a consistent minute
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or two of um interaction as you as the user uh in many many different worlds.
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There there's some videos later on where you can control, you know, a dog on a beach or a jellyfish or that's not
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limited to just human things cuz the way a 3D rendering engine works is you type in the programmer programs
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all the laws of physics. How does light reflect off of an object? You create a 3D object, light reflects off, and then
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so what I see visually is rendered by the software because it's got all the programming on how to create physics,
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how to do physics. But this this was just trained off of video and it figured it all out.
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Yeah, it was trained off of video and some synthetic data from from game engines and it's just reverse engineered
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it. 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 90s, in my early career, I used to write uh
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video games and AI for video games and graphics engines. And I remember how hard it was to do this by hand, program
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all the polygons and the physics engines. Um, and it's amazing to just see this, do it effortlessly, all of the
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reflections on the water and the the way materials flow, um, and and and objects
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behave. And it's just doing that all out of the box. I think it's hard to describe like how much complexity was
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solved for with that model. Uh it's it's it's really really really mind-blowing.
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Where does this lead us? So fast forward this model to gen five. Yeah. So so the reason we're building
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these kind of models is um we feel and we've always felt obviously progressing
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on the normal language models like with our Gemini model but from the beginning with Gemini we wanted it to be
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multimodal. So we wanted it to input any take any kind of input images audio
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video and it can output anything and uh and and so we've been very interested in this because you for an AI to be truly
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general to build AGI we feel that the AGI system needs to understand the world around us and the physical world around
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us not just the abstract world of languages or mathematics and of course that's what's critical for robotics to
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work. It's probably what's missing from it today. And also things like smart glasses, a smart glass assistant that
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helps you in your everyday life. It's got to understand the physical context that you're in and and how the world the
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intuitive physics of the world works. So we think that building these types of models uh these genie models and also VO
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our the best text to video models um those are expressions of us uh building
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world models that understand the dynamics of the world the physics of the world. If you can generate it then um
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that's that's an expression of your system understanding uh those dynamics and that leads to a world of robotics
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ultimately um one one one aspect one application but maybe we can talk about
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that what is the state-of-the-art with the vision language action models today
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so a generalized system a box a machine that can observe the world with a camera
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and then I can use language I can use text or speech to tell I want you to do it. And then it knows how to act
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physically to do something in the physical world for me. That's right. So, if you if you look at our uh Gemini Gemini live version of of
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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
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of magical what it already understands about the physical world. Um you can think of the next step as as
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incorporating that in some sort of more handy device like glasses. Um and then it will be an everyday assistant. and
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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
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then with robotics uh we've we've built a something called Gemini robotics models which are sort of fine-tuned
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Gemini with extra robotics data. And what's really cool about that is and and we released some demos of this over the
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summer was um you can have you know we've got these tabletop setups of two hands uh interacting with objects on a
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table, two robotic hands and you can just talk to the robot. So you can say you know put the the yellow object into
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the red bucket or whatever it is and it will just it will it will interpret that instruction that language instruction
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into motor movements and that's the power of a multimodal model rather than just a robotic specific model is that it
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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
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UX that you you need for as well as the understanding the robotic the robots
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need to to navigate the world safely. I asked Sundar this, does that mean that ultimately you could build what would be
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the equivalent of call it either a Unix like an operating system layer or like an Android for generalized robotics at
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which point if it works well enough across enough devices, there will be a proliferation of robotics uh devices and
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and companies and products that will suddenly take off in the world because this software exists to do this
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generally. Exactly. That's certainly one strategy we're pursuing is a is a kind of Android play if you like a cross as a kind of
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robotics almost an OS layer cross robotics. Um but there's also some quite interesting things about vertically
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integrating our latest models uh with specific robot uh uh types and robot
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designs and some kind of endto-end learning of that too. So both are actually pretty interesting and we're
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pursuing uh both strategies. Do you think that there's humanoid robots as a good kind of um form factor?
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Is that does that make sense in the world? Because some folks have criticized it as being good for humans cuz we're meant to do lots of different
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things, but if we want to solve a problem, there may be a different form factor to fold laundry or do dishes or
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clean the house or whatever. Yeah, I think I think there's going to be a place for both. So, so actually I used to be of the opinion maybe five 5
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10 years ago that we'll have form specific robots for certain tasks and I
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think in industry industrial robots will definitely be like that where you can optimize the robot for the specific task
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whether it's a laboratory or a production line you'd want quite different types of robots. Uh on the
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other hand, for um uh uh general use or personal use robotics um and just
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interacting with the the ordinary world, uh the humanoid form factor could be pretty important because of course we've
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designed the physical world around us uh to be for for humans. And so steps,
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doorways, all the things that we've designed for ourselves, um rather than changing all of those in the real world,
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it might be easier to design the form factor to work seamlessly uh with the way we've already designed the world. So
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I think there's an argument to be made that the humanoid form factor could be very important for for those types of tasks. Um but I think there is a place
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also for specialized robotic forms. Do you have a view on hundreds of millions, millions, thousands over the
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next 5 years, seven years? I mean, do you have a like in your head, do you have a vision on Yeah, I I do and I spend quite a lot of time on this and I think we're we're
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we're still I I feel we're still a little bit early on robotics. I think in the next couple of years there'll be a
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sort of real wow moment with robotics, but um I think the algorithms need a bit
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more development. Uh the general purpose uh uh uh models that these these these robotics models are built on still need
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to be better and more reliable uh and and better understanding the world around it. Um and I think that will come
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in the next couple of years. And then also on the on the hardware side, the key is I think eventually we will have
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millions of robots uh helping helping helping society and and increasing productivity. But the key there is when
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you talk to hardware experts is at what point uh do you have the right level of hardware to go for the scaling uh option
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because effectively when you start building factories around trying to make tens of thousands hundreds of thousands
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of particular robot type um you know it's harder for you to update quickly iterate the the robot design. So, it's
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one of those kind of questions where if you call it too early, uh, then then then the next generation of robot might
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be invented in 6 months time that's just more reliable and better and more dextrous. Sounds like using a computing analogy,
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we're kind of in the 70s era PC DOSs kind of uh, yeah, potentially. But of course, I think the the the the maybe that's where
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we are, but I think the except that 10 years happens in one year probably. So, right
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one of those years, right? Exactly. Yeah. Um so let's talk about other
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applications um particularly in in science. Uh true to your heart as as as
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a as a scientist as the Nobel Prize winning scientist um I always felt like
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the greatest thing things that we would be able to do with AI would be the problems that are intractable to humans
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with our current technology and capabilities and our brains and whatnot and we can unlock all of this potential.
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what are the areas of science and breakthroughs in science that you're most excited about and what kinds of
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models do we use to get there? Yeah, I mean a AI to accelerate scientific discovery and and help with things like
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human health is the reason I spent my whole career on AI and I think um it's
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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
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tool for science and I think we've been showing at Deep Mind a lot of the way of that obviously Alphafold uh most
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famously but actually we've we've um applied uh our AI systems to many branches of science whether it's
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material design um helping with controlling plasma and fusion reactors, predicting the weather, um solving, you
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know, mass olympiad uh uh uh math problems and um the same types of
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systems uh with some uh extra finetuning can basically uh solve a lot of these
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complex problems. So I think we're just scratching the surface of what AI will be able to do and there are some things
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that are missing. So uh AI today I would say doesn't have true creativity in the sense that it can't come up with a new
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conjecture yet or new hypothesis. It can maybe prove something uh that you give it uh but it's not able to come up with
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a sort of new idea or new theory itself. So I think that would be one of the tests actually for
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AGI. What is that creativity as a human? Yeah. What is creativity? I think it's this sort of intuitive
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leaps that we often celebrate with the best scientists in history and and and artists of course. Um and you know maybe
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it's done through analogy or analogical reasoning. There are many theories in psychology and neuroscience and as to how uh we as human scientists do it. But
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a good test for it would be something like um give one of these modern AI systems a knowledge cutoff of 1901 and
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see if it can come up with special relativity like Einstein did in 1905.
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Right? If it's able to do that then I think we're on to something really uh really important where perhaps we're
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nearing an AGI. Another example would be with our Alpha Go program that beat the
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world champion at Go. Um, not only did it win in, you know, back 10 years ago, it it invented new strategies that had
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never been seen before uh for the game of Go, this famously move 37 in game two
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that is now studied. But can an AI system come up with a game as elegant,
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as satisfying, as aesthetically beautiful as go, not just a new strategy? And the answer to those things
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at the moment is no. So that's one of the things I think that's missing uh from uh a true general system an AGI
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system is it should be able to do uh those kinds of things as well. Can you break down what's missing and
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maybe related to the point of view shared by Daario Sam others about AGI is
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a few years away. Do you not subscribe to that belief and maybe help us understand what is it
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in your understanding of structure in your understanding of the system architecture what what's lacking
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well so I think the fundamental aspect of this is um can we mimic these
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intuitive leaps rather than incremental uh advances that that the best human
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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
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course. Um but the great scientist is more creative and so maybe they'll spot some pattern from another subject area
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that can be uh uh can sort of have an analogy or some sort of pattern matching
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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
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uh capabilities and and some of the um uh uh thinking capabilities that um are
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going to be needed to to make that kind of breakthrough. Um I also think that we're lacking consistency. So you often
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hear some of our competitors talk about uh you know these modern systems that we have today are PhD intelligences. I
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think that's a nonsense. They're not they're not PhD intelligences. They have some capabilities that are PhD level. um
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but they're not in general uh capable and that's what exactly what general intelligence should be of of performing
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across the board at the PhD level. In fact, as we all know interacting with today's chat bots, if you pose the
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question in a certain way, they can make simple mistakes with even like high school maths um and and simple counting.
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So, uh that shouldn't be possible for a true AGI system. So I think that we are
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maybe you know I would say sort of five to 10 years away um from having an AGI system that's capable of doing those
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things. Um another thing that's missing is continual learning. This ability to like online teach the system something
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new um or or some or adjust its behavior in some way. And so a lot of these I
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think core capabilities are still missing and maybe scaling will get us there but I feel if I was to bet I think
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there are probably one or two missing breakthroughs that are still required um and will come over the next uh five five
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or so years. In the meantime some of the reports and the the scoring systems that
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are used seem to be demonstrating two things. One perhaps, and tell me if we're wrong on this, a convergence of
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performance of large language models, and number two perhaps is a slowing down or a flatlining of improvements in
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performance on each generation. Are those two statements generally true or not so much? No, I mean, we're not we're not seeing
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that internally and and um we're still seeing a huge rate of progress. Um but
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also uh we're sort of looking at things more broadly. You see with our Genie models and VO models and nano banana is
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insane. It's bananas. It's bananas. Has anyone here can Can I see who's used it? Has anyone used Nano Banana?
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It's incredible, right? I mean, I'm I'm a nerd who used to use Adobe Photoshop as a kid and Kai's power tools and I was
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telling you Bryce 3D. So, like the graphic systems and like recognizing what's going on there was just like
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mind-blowing. Well, I think that's the future of uh a lot of these creative tools is you're just going to sort of vibe with it or just talk to them and
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they'll be consistent enough where like with Nana Banana, what's amazing about it is it's an image generator. It's best in best, you know, it's state-of-the-art
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and bestin-class, but it's one of the things that makes it so great is it's consistency. It's able to un instruction
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follow what you want changed and keep everything else the same. And so you can iterate with it uh and eventually get
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the kind of output that you want. And that's um I think what the future of a lot of these creative tools is going to
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be um and and sort of signals the direction and people love it and and they love creating with it.
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So democratization of creativity I I think is really powerful. I remember
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having to buy books on Adobe Photoshop as a kid and then you'd read them to learn how to remove something from a from an image and how to fill it in and
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feather and all this stuff. Now anyone can do it with Nano Banana and just they can explain to the software what they
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want it to do and it just does it. Yeah. I think you're going to see two things which is that um uh this sort of democratization of these tools for
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everybody to just use and create with without having to learn, you know, incredibly complex UX's and UIs uh like
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like we had to do in the past. But on the other hand, I think we and we're also collaborating with filmmakers and
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top creators and artists. Um so they're helping us design what these new tools should be, what features would they
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want. people like the director Darren Aronowski who's a good friend of mine, an amazing director and and he's been
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making and his team making films using VO and some of our other tools and we're learning a lot by observing them and and
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collaborating them. And what we find is that it's it also superpowers and turbocharges the best professionals too
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cuz they're suddenly um the best creatives, the professional creatives, they're suddenly able to be 10x, 100x
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more productive. they can just try out all sorts of ideas they have in mind, you know, very low cost and then get to
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the beautiful thing that they wanted. So, I actually think it's sort of both things are true. We're we're democratizing it for everyday use, uh,
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for YouTube creators and so on. But on the other hand, at the high end, um, the people who are who understand these
00:23:00
tools and it's and it's not everyone can get the same output out of these tools, there's a skill in that as well as um,
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the vision and the storytelling and the narrative style of uh, the top creatives. And I think it just allows
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them, they really enjoy using these tools. It allows them to iterate way faster. Do we get to a world where each
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individual describes what sort of content they're interested in? Play me music like Dave Matthews and it'll play
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some new track. Yes. Or I want to play a video game set, you know, in the movie Braveheart and I want
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to be in that movie. Yes. And I just have that experience. Do we end up there or do we still have a one
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to many creative process in society? how important culturally and I know this is a little bit philosophical but it's
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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
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or we each going to start to develop and pull on our own kind of virtual I I actually foresee a world and I think
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a lot about this having started in the games industry as a game designer and programmer is that uh in the '90s is
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that you know I think the future of enter this is what we're seeing is the beginning of the future of entertainment
00:24:05
maybe some new genre or new art form and where there's a bit of co-creation. I
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still think that you'll have the top creative visionaries. Um they will be creating these compelling experiences
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and dynamic story lines and they'll be of higher quality even if they're using the same tools than the everyday person
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can do. But also and so millions of people will potentially dive into those
00:24:25
worlds, but maybe they'll also be able to create co-create certain parts of those worlds and perhaps that you know
00:24:32
the the the main creative uh person is almost an editor of that world. So that's the kind of things I'm foreseeing
00:24:39
in the next few years and I'd actually like to explore ourselves with with with technologies like Genie.
00:24:44
Right. Incredible. And how are you spending your time? Are you at is and maybe you can describe Isomorphic?
00:24:50
Of course. What isomorphic is and are you spending a lot of your time there? I am. So, so I also run Isomorphic which
00:24:55
is our spinout company uh to revolutionize drug discovery building on our alpha fold breakthrough in in
00:25:02
protein folding and of course um pro knowing the structure of a protein is only one step in the drug discovery
00:25:08
process. So isomorphic you can think of it as building many uh adjacent alpha folds to help with things like designing
00:25:15
chemical compounds that don't have any side effects but bind to the right place on the protein. And um I think we could
00:25:22
reduce down drug discovery from taking years, sometimes a decade to do down to
00:25:27
maybe weeks or even days uh over the next 10 years. It's incredible. Do you think that's in
00:25:32
clinic soon or is that still in the discovery phase? And we're building up the platform right now and it's uh we have great partnerships
00:25:38
with Eli Liy. I think you had the CEO speaking earlier and and Novartis which are fantastic and our own internal drug
00:25:44
programs and I think we'll be entering sort of pre-clinical phase sometime next year. So candidates get handed over to
00:25:50
the pharma company and they then take them forward. That's right. And we're working on cancers and immunology and oncology and
00:25:55
we're working with uh uh uh uh places like MD Anderson. How much of this requires and I just
00:26:00
want to go back to your point about AGI as it relates to what you just said. models can be probabilistic or
00:26:07
deterministic and tell me if I'm reducing this down too simplistically that the model takes an input and it
00:26:12
outputs something very specific like it's got a logical algorithm and it outputs the same thing every time and it
00:26:18
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
00:26:25
etc um how much do we have to kind of develop deterministic models that sync
00:26:30
up with for example the the the physics or the chemistry underlying the molecular interactions as
00:26:38
you do your drug discovery modeling. How much are you building novel deterministic models that work with the
00:26:43
models that are probabilistic trained on data? Yeah, it's a great question. Actually, we for the moment and I think probably for the next 5 years or so, we're
00:26:50
building what maybe you could call hybrid models. So, alphafold itself is a hybrid model where you have the learning
00:26:56
component, this probabilistic component you're talking about which is you know based on neuronet networks and transformers and things and that's
00:27:02
learning from the data you give it uh you know any data you have available but also to in a lot of cases with biology
00:27:09
and chemistry there isn't enough data to learn from. So you also have to build in some of the rules about chemistry and
00:27:16
physics that you already know about. So for example with alpha fold um the angle of bonds between atoms. um so and make
00:27:23
sure that the the alpha fold understood you couldn't have atoms overlapping with each other and things like that. Now in
00:27:28
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 as a as a yeah as a as a constraint
00:27:36
in there. Now the trick is with all hybrid systems is and and Alpha Go was another hybrid system where a neural
00:27:42
network learning about the game of Go and what's what kind of patterns are good and then we had Monte Carlo research on top which was doing the
00:27:48
planning and so the trick is how do you marry up a learning system with a a more
00:27:54
handcrafted system bespoke system and actually have them work well together. Uh and that's that's pretty tricky to
00:28:00
do. Does that sort of architecture ultimately lead to the breakthroughs needed for AGI do you think? Are there
00:28:05
deterministic components that need to be solved? I think ultimately you what you want to do is um when you figure out something
00:28:10
where this one of these hybrid systems, what you want what you ultimately want to do is upstream it into the learning component. So it's always better if you
00:28:17
can do endto-end learning and and and directly predict the thing that you're after from the data that you you you're
00:28:23
given. So um so once you figured out something uh using one of these hybrid systems, you then try and go back and
00:28:30
reverse engineer what you've done and see if you can incorporate that learning uh that that that that information into
00:28:37
the learning system. And this is sort of what we did with Alpha Zero, the more general form of Alph Go. So Alph Go had
00:28:42
some uh Go specific knowledge in it. But then with Alpha Zero, we we got rid of that including the human data human
00:28:49
games that we learned from and actually just did self-learning from scratch. And of course then it was able to learn any
00:28:54
game not just go. A lot of hype and hoopla has been made about the demand for energy arising from
00:29:00
AI. Uh this is a big part of the AI summit we held in Washington DC a few weeks ago and it seems to be the number
00:29:07
one topic everyone talks about in tech nowadays. Where's all this power going to come from? But I ask the question of
00:29:12
you, are there changes in the architecture of the models or the hardware or the relationship between the
00:29:19
models and the hardware that brings down the energy per token of output or the cost per token of output that ultimately
00:29:26
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
00:29:32
still going to have a pretty kind of geometric energy demand curve? Well, look, interestingly again, I think both
00:29:37
cases are true in the sense that uh especially us at Google and at DeepMind, we we focus a lot on very efficient
00:29:44
models uh that are powerful because we have our own internal use cases of course where we need to serve say AI
00:29:49
overviews to billions of users uh every day and it has to be extremely efficient, extremely low latency and
00:29:55
very cheap to serve and and so we've we've kind of pioneered many um
00:30:00
techniques that allow us to do that like distillation where you sort of have a bigger model internally that trains the
00:30:05
smaller model, right? So, you train the smaller model to mimic the bigger model. And over time, you look at the progress of the last 2 years, uh the model
00:30:12
efficiencies are like 10x, you know, even 100x better for the same performance. Now, the the reason that
00:30:18
that isn't reducing demand is because we're still not got to AGI yet. So, also the frontier models, you keep wanting to
00:30:25
train and experiment with uh new ideas at larger and larger scale, whilst at the same time at the serving side, uh
00:30:32
things are getting more and more efficient. So both things are true and I and in in the end I think that from the
00:30:37
energy perspective um I think AI systems will give back a lot more to energy uh
00:30:43
uh and climate change and these kind of things than they take in terms of efficiency of of of grid systems and
00:30:49
electrical systems material design new types of properties new energy sources. I think AI uh will help with all of that
00:30:56
over the next 10 years that will far outweigh um the energy that it uses today. As the last question, describe
00:31:02
the world 10 years from now. Wow. Okay. Well, I mean, you know, 10 years uh even even 10 weeks is is a
00:31:10
lifetime in AI. So, um field of 10 years, right? But I do feel like if we will
00:31:16
have AGI in the next 10 years, you know, full AGI and um I think that will usher
00:31:22
in a new golden era of science. So, a kind of new renaissance. Um, and I think
00:31:27
we'll see the benefits of that right across from from energy to to human health. Amazing. Please join me in thanking
00:31:34
Nobel laurate Dennis. Thank you. That was great. Thank you.
00:31:42
[Applause]