Transcription
Google Deep Mind CEO and Nobel laureate Demis Hassabis joins us to talk about the path toward artificial general intelligence, Google's AI roadmap, and how AI research is driving scientific discovery. That's coming up right after this.
Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. Today, we're at Google DeepMind headquarters in London for what promises to be a fascinating conversation with Google DeepMind CEO, Demis Hassabis. Demis, great to see you again. Welcome.
Welcome to the show. Thanks for having me on the show. Definitely. It's great to be here. So, every research house right now is working toward building AI that mirrors human intelligence, human-level intelligence. They call it AGI. Where are we right now in the progression, and how long is it going to take to get there?
Well, look, I mean, of course, the last few years has been an incredible amount of progress. Um, actually, you know, maybe over the last decade plus. Um, this is what's on everyone's lips right now, and the debate is, is how close are we to AGI? What's the correct definition of AGI? Um, we've been working on this for more than 20 plus years. Um, we've sort of had a consistent view about AGI being a system that's capable of exhibiting all the cognitive capabilities humans can. Um, and I think we're getting, you know, closer and closer, but I think we're still probably a handful of years away.
Okay. And so what is it going to take to get there? So the models today are pretty capable. Of course, we've all interacted with the language models, and and now they're becoming multimodal. I think there are still some missing attributes, things like reasoning, um, hierarchical planning, um, long-term memory. Um, there's quite a few capabilities that, uh, the current systems, uh, I would say don't have. They're also not consistent across the board. You know, they're very, very strong in some things, but they're still surprisingly weak and flawed in in other areas. So you'd want an AGI to have pretty consistent, robust behavior across the board, all the cognitive tasks. And I think one thing that's clearly missing, I always, always had as a benchmark for for AGI was the ability for these systems to invent their own hypotheses or conjectures about science, not just prove existing ones. So, of course, that's extremely useful already to prove an existing math conjecture or something like that, or or play a game of Go to a world champion level. But could a system invent, could it come up with a new Riemann hypothesis, or could it come up with relativity, um, back in the days that Einstein did it with the information that he had? And I think today's systems are still pretty far away from having that kind of creative, uh, inventive capability.
Okay. So a couple of years away till we hit AGI? I think, um, you know, I, I would say probably like three to five years away. So if someone were to declare that they've reached AGI in 2025, probably marketing.
I think so. I mean, I think there's a lot of, um, uh, hype in the era, of course. Uh, I mean, some of it's very justified. I mean, I would say that, um, AI research today is, um, over-estimated in the short term. Um, I think probably a bit overhyped at this point, um, but still underappreciated and, um, and some, and very underrated about what it's going to do in the medium to long term. Um, so it's sort of, we're still in that weird kind of space. Uh, and I think part of that is, you know, there's a lot of people that need to do fundraising, a lot of startups and other things. And so I think we're going to have quite a few sort of fairly outlandish and and and slightly exaggerated claims. Um, and, you know, I think that's a bit of a, actually, yeah, in the AI, in the AI products.
What's it going to look like on the path there? I mean, you've talked about memory again, planning, um, being better at some of the tasks that it's not excelling at at the moment. So when we're using these AI products, let's say we're using Gemini, what are some of the things that we should look for in these domains that will make us say, "Oh, okay, it seems like it's that's a step closer and that's a step closer"?
Yeah, so I think, um, uh, today's systems, you know, obviously we're very proud of Gemini 2.0. I'm sure we're going to talk about that. But I feel like, um, they're very useful for still quite niche tasks, right? If you're doing some research, perhaps you're summarizing some area of research, incredible. You know, I use NotebookLM and Deep Research all the time to kind of, especially like, um, break the ice on a new area of research that I want to get into, or summarize some, you know, maybe a fairly mundane set of documents or something like that. So they're extremely good for certain tasks, and then people are getting a lot of value out of them. But they're still not pervasive, in my opinion, in everyday life, like helping me every day with my research, my work, my day-to-day, um, my daily life too. And I think that's where we're going with our products, with building things like, um, as Project Astro, our vision for Universal Assistant. It should be, uh, involved in all aspects of your life and be enriching, helpful, and and making that more efficient. And I think part of the reason is these systems are still fairly brittle, partly because they are quite flawed still, and they're not AGIs. And and you have to be quite specific, for example, with your prompts, or you need a lot of, there's quite a lot of skill there in in coaching or guiding these systems to be useful and to stick to, uh, uh, the areas they're good at. And and a true AGI system shouldn't be that difficult to, uh, to coax. It should be much more straightforward, you know, just like talking to another human.
Yeah. And then on the reasoning front, you said that's another thing that's missing. I mean, that's everybody's talking about reasoning right now. So how does that end up getting us closer to artificial general intelligence?
So reasoning and and mathematics and other things. And there's a lot of progress on math and coding and on. But let's take math, for example. You have systems, uh, uh, some systems that we work on like AlphaProof, AlphaGeometry, that are getting, you know, silver medals in math Olympiads, which is fantastic. But on the other hand, some of our systems, those same systems are still making some fairly basic mathematical errors, right? For for various reasons, like the classic, you know, counting the number of R's in strawberries and the word strawberry and so on. And and is 9.11 bigger than 9.9 and so on and things like that. And and and of course, of course, you can fix those things, and we are, and everyone's improving on those systems. But we shouldn't really be seeing those kinds of flaws in a system that is that capable in other domains, in more narrow domains of doing, you know, Olympiad-level mathematics. So there's something still a little bit missing, in my opinion, about the robustness of, uh, these systems. And then that's, I think that speaks to the generality of these systems. A truly general system would not have those sorts of weaknesses. It would be very, very strong, maybe even better than the best humans in some things like playing Go or doing mathematics, but it, it would be overall consistently good.
Now, can you talk a little bit about how these systems are attacking math problems? Because, you know, I think the general understanding of these systems is the LLMs, is they encompass all the world's knowledge and then they predict what, you know, as somebody might answer if they were asked a question. But it's kind of different, different when you're working step-by-step through an algorithm or through a math problem.
Yes, that's not enough, of course. You know, just understanding the world's information and and then trying to sort of almost compress that into your memory, that's not enough for solving a novel math problem or novel, novel conjecture. Um, so there, you know, we start needing to bring in, I think we talked about this last time, more kind of like AlphaGo planning ideas into the mix with these large foundation models, which are now beyond just language, they're multimodal, of course. Um, and there, what, what you need to do is you need to have, um, your system, uh, uh, uh, uh, not just pattern matching roughly what it's seeing, which is the model, but also planning and being able to kind of go over, uh, uh, that plan, re, you know, re, re, re, re-visit that branch, and then go into a different direction until you find the right, uh, criteria or the right match to the criteria that you're looking for. And that's very much like the the kind of games playing AI agents that we used to build for Go, Chess, and so on. They had those, um, aspects. And I think we've got to bring them back in, but now working in a more general way on these general models, not just a narrow domain like games. Um, and I think that also, that approach of a model guiding a search or planning process, so it's efficient, works very well with mathematics as well. You can sort of turn math into kind of game-like search, right?
And I want to ask about math, like once these models get math right, is that generalizable? Because I think there was like a whole hubbub when people first learned about reasoning systems, and they're like, "Oh, this is like, this is going to be a problem. These these models are getting smarter than we can control." Because if they can do math, then they can do X, Y, and Z. So is that generalizable, or is it like, we're going to teach them how to do math, they can just do math?
I think for now, uh, uh, the jury's out on that. I mean, I feel like it's a clearly a capability you want of a general AGI system. Uh, it can be very powerful in itself. Obviously, mathematics is is extremely general in itself. Um, but it's not clear. You know, math and even coding and games, these are areas, they're quite special, uh, uh, areas of of knowledge because you can verify if the answer is correct, right? In all of those, uh, domains, right? The math, you know, the final answer the AI system puts out, you can check whether that math, that solves the the the the conjecture or the problem. So, but most things in in the general world, which is messy and ill-defined, do not have easy ways to verify whether you've done something correct. So that that puts a limit on these self-improving systems if they want to go beyond these areas of high, you know, maybe very highly defined spaces like mathematics, coding, or or games.
So how are you trying to solve that problem?
Well, you, you, you know, you've got to first of all, you've got to build, um, general models, world models, we call them, to understand the world around you, the physics of the world, the dynamics of the world, the spatial temporal dynamics of the world, and so on, and the structure of the real world we live in. And of course, um, you need that for a universal assistant. So Project Astro is our project built on Gemini to do that, to understand, you know, objects and the and the context around us. I think that's important if you want to have an assistant, but also robotics requires too, of course. Robots are physically embodied AIs, and they need to understand their environment, the physical environment, the physics of the world. So we're building those, uh, types of models. Um, and also, you can, you can also use them in simulation to understand game environments. So that's another way to bootstrap more data for to to understand, you know, the physics of a world. Um, but the issue at the moment is that those models are not 100% accurate, right? So they, you know, maybe they're accurate 90% of the time, or even 99% of the time. Um, but the problem is, if you start using those models to plan, maybe you're planning a 100 steps in the future with that model, even if you only have a 1% error in what the model's telling you, that's going to compound over 100 steps to the point where you'll be in a, you know, you'll kind of get almost a random answer. And so that makes the planning very difficult. Whereas with math, with gaming, with C coding, you can verify each step. Are you still grounded, uh, to reality? And is the final answer mapped to what you're expecting? And so, um, I think part of the answer is to is to make the the world models more more sophisticated, and more more accurate, and and, um, and not hallucinate, and all of those kinds of things. So you get, you know, the errors are are really minimal. Another approach is to, um, plan not at each sort of, uh, uh, uh, linear time step, but actually do what's called hierarchical planning. Another thing we used to, we've done a lot of research on in the past, and I think it's going to come back into vogue, where you plan at different levels of temporal abstraction. So instead of that, that could that could also alleviate the need for your model to be super, super accurate, because you're not planning over hundreds of time steps, you're planning over only a handful of time steps, but at different levels of abstraction.
How do you build a world model? Because, you know, I always thought it was going to be like, our send robots out into the world and have them figure out how the world works. But one thing that surprised me is with these video generation tools, yes, you would think that if the AI didn't have a good world model, then nothing would really fit together when they try to figure out how the world works as they show you these videos, like V2, for instance, but they actually get the physics pretty right. Yeah. So can you get a world model just by showing an AI video? Do you have to be out in the world? How's this going to work?
It's interesting and actually been pretty surprising, I think, to the extent of how far these models can go without being out in the world, right? As you say, so V2, our latest video model, which is actually surprisingly, uh, uh, accurate on things like physics. You know, uh, there's this, this great demo that someone created of like, uh, chopping a tomato with a knife, right? And and getting the slices of the tomato just right, and the fingers and all of that. And V2 is the first model that can do that. You know, if you look at other competing models, they often the tomato sort of randomly comes back together, or, yeah, exactly, splits from the knife. Um, so those things are, if you think that really hard, you've got to understand consistency across frames, all of these things. And it turns out that you, you know, you can do that by using enough data and and viewing that. Um, I think these systems will get even better if they're some impeded by some real-world data, like collected by an acting robot, or even potentially in very realistic simulations where you have avatars that, uh, act in the world too. So I think that's the next big step, actually, for agent-based systems, is to go beyond world models. Can you collect enough data where the agents are also acting in the world and making plans and achieving tasks? Um, and I think for that, you will need, uh, uh, not just passive observation, you will need actions, active participation.
I think you just answered my next question, which is, if I, if you develop AI that can reasonably plan and have and reason about the world and has a model of how the world works, it can, and it seems like that's the answer, it can be an agent that could go out and do things for you.
Yes, exactly. And I think that's that's that's what will unlock robotics. I think that's also what will then allow, uh, this notion of a universal assistant that can help you in your daily life, across both the digital world and the real world. Um, that's what that's that's the thing we're missing. Um, and I think that's going to be incredibly powerful and useful tool.
You can't get there then by just scaling up the current models and building, you know, hundreds of thousand or million GPU clusters like Elon's doing right now, and that's not going to be the path to AGI?
Well, look, I actually think that, so my view is a bit more nuanced than that. Is like that, that the scaling approach is absolutely working. Of course, that's where we've why we got to where we have now. Um, one can argue about, are we getting diminishing returns or what? My view is that we are getting substantial returns, but not, but it's slowing. But but it would have to. I mean, it's, it's not just continuing to be exponential, but that doesn't mean the scaling is not working. It's absolutely working, and we're still getting, you know, you see Gemini 2 over Gemini 1.5. And by the way, the other thing that was working with the scaling is also making efficiency gains on the smaller size models. So the the cost or the size per performance is is is radically improving under the hood as well, which which is very important for for scaling, you know, the adoption of these systems. Um, but yeah, so so, you know, you've got, you've got the scaling part, and that's absolutely needed to build more more sophisticated world models. Um, but then I think we are missing or we need to reintroduce some ideas on the planning side, memory side, the searching side, uh, uh, uh, the reasoning to build on top of the model. The model itself is not enough to be in AGI. You need, uh, uh, this other capability for it to to act in the world and solve problems for you. And and then there's still the additional question mark of the of the invention piece and the creativity piece. True creativity, be, you know, beyond, uh, mashing together, what's already known, right? So, uh, and that's also unknown yet if if something new is required, or again, if existing techniques will eventually scale to that. I can see both arguments, and I think from my perspective, it's an empirical question. We just got to push both the scaling and the invention part to the limit. And and fortunately, at at Google DeepMind, we have, you know, a big enough group, we we can invest in both those things.
Sam Altman recently said something that caught people's eye. He said, "We are now confident we know how to build AGI as we have traditionally understood it." It just seems by listening to what you're saying that you feel the same way.
Well, it depends what we, you know. I think the way he said that was quite ambiguous, right? So in the sense of like, "Oh, we're building it right now and here's the ABC to do it." What I would say, and if this is what it was meaning, I would agree with it, is that we, we roughly know the zones of techniques that are required, what's probably missing, which bits need to be put together. But that's still an incredible amount of research, in my opinion, that needs to be done to get that all to work. Even if that was the case, and that's, and I think there's a 50% chance we are, uh, missing some new techniques. You know, maybe we need one or two more Transformer-like breakthroughs. And I, and I think I'm genuinely uncertain about that. So that's why I say 50%. So I mean, I wouldn't be surprised either way if we got there with existing techniques and things we already knew, but put them together in the right way and scaled that up, or if it turned out one or two things were missing.
Let's talk about creativity for a moment. I mean, you brought it up a couple times here, that the models are going to have to be creative, they're going to have to learn how to invent if we want to call AGI, in my opinion, which is where everybody's trying to go. Um, I was rewatching the AlphaGo documentary. Yeah. And the algorithms make a creative move. They do, move 37.
Yes.
Move 37.
Yes.
That's interesting because it was a couple years ago. They, the algorithms were already being creative. Yes. Why have we not really seen creativity from large language models? I mean, this is to me, I think the greatest disappointment that people have with these tools is like, they say, "This is very impressive work, but it's just limited to the training set. We'll mix and match what it knows, but it can't come up with anything new."
Yeah. Well, look, so what, and I should probably write this up, but what I sometimes talk about in talks, ever since the AlphaGo match, which is now, you know, plus years ago, amazingly, right? That happened. That was probably the reason that was such a watershed moment for AI. Was first of all, there was the Everest of of of of, you know, um, cracking Go, right? Which was always considered to be one of the holy grails of AI. So we did that. Second thing was the way we did it, which was these learning systems that were generalizable, right? Eventually they became AlphaZero and and so on, even play any two-player game and so on. Uh, and then the third thing was this move 37. So not only did it win, 4-1, it beat Lee Sedol, the great Sedol, 4-1. It also played original moves. But so I, I have three categories of of of of originality or creativity. The most basic kind of mundane form is just interpolation, which is like averaging of what you see. So if I said to a system, you know, "Come up with a new picture of a cat," and it's seen a million cats, and it produces just some kind of average of all the ones it's seen. In theory, that's an original cat because you won't find the average in the specific examples, but it's a pretty boring, you know, it's not really very creative. I wouldn't call that creativity. That's the lowest level. Next level is what AlphaGo exhibited, which is extrapolation. So here's all the games humans have ever played. It's played another million games on top of, you know, 10 million games on top of that. And now it comes up with a new strategy in Go that no human has ever seen before. That's move 37, right? Revolutionizing Go, even though we've played it for thousands of years. So that's pretty incredible. And that could be very useful in science. And that's why I got very excited about that and started doing things like AlphaFold, because clearly extrapolation beyond what we already know, what's in the training set, um, could be extremely useful. So that's already very valuable, and and I think truly creative. But there's one level above that that humans can do, which is invent. Go, can you invent me a game? If I, that you know, if I specify to an abstract level, you know, "Takes five minutes to learn the rules, but a lifetime to many lifetimes to master. It's beautiful aesthetically, encompasses some sort of mystical part of the universe in it, that it's beautiful to look at." Uh, but you can play a game in a human afternoon in two hours, right? That's the that's the high-level specification of Go. And then somehow the system's got to come up with a game that's as elegant and as beautiful and and perfect as Go. Now, we can't do that now. The, the question is, why is it that we don't know how to specify that type of goal to our systems at the moment? What's the objective function? It's very amorphous, it's very abstract. So I'm not sure if it's just we need higher level, more abstracted, uh, uh, layers in our systems, building more and more abstract models, so we can talk to it in this way, give it those kind of amorphous goals, or is there a missing capability actually about that that we still have? Human intelligence has that are still missing from our systems? And again, I'm unsure about that, which which way that is. I can see arguments both ways, and we'll try both. But I think the thing that people are upset, or or not upset, but people are disappointed by, is they don't even see a move 37 in today's LLMs.
Well, and because, okay, so well, that's because I don't think we, we have. So if you look at AlphaGo, and I'll give you an example of there, which which maps to today's LLMs. So, um, you can run AlphaGo and AlphaZero, our chess program, general two-player program, without the search and the reasoning part on top. You can just run it with the model. So what you say is to the model, "Come up with the first Go move you can think of in this position that's most, the most pattern-matched, most likely good move." Okay? And it can do that. It'll play a reasonable game, but it will only be around, uh, master level, or possibly grandmaster level. It won't be world champion level, and it certainly won't come up with, um, original moves. That, for that, I think you need, um, the search component to get you beyond where the model knows about, which is mostly summarizing existing knowledge to some new part of the tree of knowledge, right? So you can use the search to get beyond what the model currently understands. And that's where I think you can get, uh, new ideas, like, you know, move 37.
What's it searching? The web?
No. So well, it depends on, uh, what the domain is. Searching that, that knowledge tree. So obviously, in Go, it was searching Go moves beyond what the model knew. Um, I think for language models, it would be searching the world model for new parts, configurations in the world, um, that are useful. So, of course, that's so much more complicated, which is why we haven't seen it yet. But I think the agent-based systems that are coming will be capable of move 37 type things.
So are we setting too high of a bar for AI? Because I'm curious if you've learned anything about humanity doing this work.
Yeah, it seems like we almost give too much of a premium on humanity or individual people's ingenuity, where like a lot of us, like we kind of take in stuff, we spit it out. Like our society really works in memes. Like we have a cultural thing, and it gets translated. Um, so what do you, what have you learned about, like, the nature of humans from doing the work with the AIs?
Well, look, I, I think humans are incredible. And and and especially the best humans in the best domains. I love watching any sports or or or talented musician or games player at the top of their game, the absolute pinnacle of human performance. It's always incredible, no matter what it is. Um, so I think as a species, we're amazing. Individually, we're also kind of amazing, what everyone can do with their brains. So generally, right? Deal with new technologies. I mean, I'm always fascinated by how we just adapt to these things, sort of almost effortlessly as a society and as individuals. Um, so that speaks to the power and the generality of our minds. Um, now, the reason I had set the bar like that, and I don't think it's a question of like, "Can we get economic worth out of these systems?" I think that's already coming very soon. But that's not what AGI shouldn't be, uh, uh. I think we should treat AGI with scientific integrity, not just move goalposts for commercial reasons or whatever it is, hype and so on. And there, the the definition of that was always having a system that was, you know, if we think about it theoretically, that was capable of being as powerful as a Turing machine. So Alan Turing, one of my all-time scientific heroes, you know, he described a Turing machine, which underpins all modern computing, right? As a system that can simulate any other comp, can compute anything that's computable. So we know we have the theory there that if an AI system is Turing powerful, it's called, if it can simulate a Turing machine, then it's able to calculate anything in theory that is is computable. And the human brain is probably some sort of Turing machine, at least that's what I believe. Um, and so, um, in order for our to now, and that, I think that what AGI is, is a system that's truly general and in theory could be applied to anything. And and the only way we'll know that is if we, um, it exhibits all the cognitive capabilities that humans have, assuming that human, the human mind is a type of Turing machine, or is at least as powerful as a Turing machine. So that's my, always been my sort of bar. It seems like people are trying to rebadge things as that as being what's called ASI, artificial super intelligence. But I think that's beyond that. That's after you have that system, and then it starts going beyond in certain domains what humans are capable of, potentially inventing themselves.
Okay. So when I see everybody making the same joke on the same topic on Twitter, and I say, "Oh, that's just us being LLMs." I think I'm selling humanity a little short.
Well, we'll, yes, I guess so. I guess so. Okay.
Yeah. I want to ask you about deceptiveness. I mean, one of the most interesting things I saw at the end of last year was that these AI bots are starting to try to fool their evaluators, and they don't want their initial training, uh, rules to be thrown out the window. So they'll like take an action that's against their values in order to be able to remain the way that they were built. Yes. That's just incredible stuff to me. I mean, I know it's scary to researchers, but it blows my mind that it's able to do this. Are you seeing similar things, and what, and the stuff that you're testing within DeepMind? And what are we supposed to think about all this?
Yeah, we are. And, um, I'm very worried about, uh, I think deception, specifically, is one of the one of the core traits you really don't want in a system. The reason that's like a kind of fundamental trait you don't want is that if a system is capable of doing that, it invalidates all the other tests that you you might think you're doing, including safety ones. It's testing, and it's like, right, it's five years. Yeah, it's it's playing some meta game, right? And then, and that's incredibly dangerous. If you think about, then invalidates all the all of the the results of your other tests that you might, you know, safety tests and other things you might be doing with it. So I think there's a handful of abilities like deception which are, uh, uh, fundamental, and you don't want, and you want to test early for. And I've been encouraging the safety institutes and evaluation benchmark builders, including and also obviously all the internal work we're doing, to to look at, uh, at deception as a kind of class, a thing that we need to prevent and monitor, as important as tracking the performance and intelligence of the systems. Um, the answer to this as well, and one way to, there's many answers to the safety question of, and a lot of research, more research needs to be done in this very rapidly, is things like secure sandboxes. So we're building those too. We're world-class here at security at Google and at DeepMind. And also we are world-class at games environments, and we can combine those two things together to kind of create digital sandboxes with guardrails around them, sort of the kind of guardrails you'd have for for cybersecurity, but internal as well as blocking external actors. And, and then test these agent systems in those kind of secure sandboxes. That would probably be a good advisable next step for things like deception.
Y. What sort, what sort of deception have you seen? Because I just read a paper from Anthropic where they gave it a a sketchpad. Yeah. And it's like, "Oh, I better not tell them this." Then you see it like give a result after thinking it through. So what type of deception have you seen from the B?
Well, look, we, we've seen similar types of things where it's trying to, um, resist, sort of, re-revealing its, its, its. Some of its training. Or, you know, I think there was an example recently of, um, one of the chatbots being told to play against Stockfish, and it just sort of hacks its way around playing Stockfish at all at chess because it knew it would lose. But, you know, you had an AI that knew it was going to lose a game and decided to, I think we're anthropomorphizing these things quite a lot at the moment because I feel like these systems are still pretty basic. I get too alarmed about them right now. But I think it, it, it, it shows the type of issue we're going to have to deal with maybe in two, three years' time when these agent systems become quite powerful and quite general. So, and that's exactly what AI safety experts are worrying about, right? Where systems where, you know, there's unintentional effects of the system. You don't want the system to be deceptive. You don't, you want it to do exactly what you're telling it to and report that back reliably. But for whatever reason, it's, uh, interpreted the goal it's been given in a way where it causes it to do these undesirable behaviors.
I know I'm having a weird reaction to this, but on one hand, this scares the living daylights out of me. On the other hand, it makes me respect these models more than anything. It's like, "Go."
Well, look, of course, you know, these are, it's impressive capabilities. And and and the, the, the, you know, the the negatives are things like deception. But the positives would be things like inventing, you know, new materials, accelerating science. You need that kind of, uh, ability to problem-solve and get around, you know, uh, issues that are blocking progress. Um, but of course, you want that only in the positive direction, right? So those exactly the kinds of capabilities. I mean, they are, you know, uh, it's kind of mind-blowing. We're talking about those those possibilities, but also at the same time, uh, there's risk, and it's scary. So I think both the things are true.
Wild. Yeah. All right, let's talk about product quickly.
Sure.
One of the things that your colleagues have told me about you is you're very good at scenario planning. What's going to happen in the future? It's sort of an exercise that happens within DeepMind. What do you think is going to happen with the web? Because obviously the web is so important to Google. I had an editor that told me he was like, "Oh, you're going to speak, speak with Demis. Asking, what happens when we stop clicking?" We're clicking through the web at all times. The, the rich corpus of websites that we use. If we're all just dialoguing with AI, then maybe we don't click anymore. So what do you, what is your scenario plan for what happens to the web?
Well, look, I think there's, it's going to be, there's going to be a very interesting phase in the next few years on the web and the way we we interact with websites and apps and so on. Um, you know, if everything becomes more agent-based, then I think we're going to want our assistants and our agents to do a lot of the work and a lot of the mundane work, um, that we currently do, right? Um, you know, fill in forms, make payments, uh, you know, book tables, this kind of thing. So, you know, I think that we're going to end up with, probably a sit, a kind of economics model where agents talk to other agents and negotiate things between themselves and then give you back the results, right? And you'll have the service providers with agents as well, that are offering services, and maybe there's some, uh, uh, uh, bidding and cost and things like that involved, and efficiency. And then I hope from the user perspective, you know, you have this assistant that's super capable, that you can just like a brilliant, uh, human assistant, personal assistant, and can take care of a lot of the mundane things for you. And I think if you follow that through, that does imply a lot of changes to, uh, the structure of of the web and the way we currently use.
A lot of middlemen.
Yeah, sure. But there will be many other, I think there'll be incredible other opportunities that will appear, economic and otherwise, based on this, this change. But I, I think it's going to be a big disruption.
And what about information?
Well, uh, I mean, finding information, I think, uh, you'll still need the reliable sources. I think you'll have assistants that, um, are able to synthesize and and and help you kind of understand that information. Um, I think education is going to be revolutionized by AI. Um, so, uh, again, I, I hope that, uh, uh, these assistants will will be able to, more efficiently gather information for you and perhaps, you know, what I dream of is again, assistants like take care of a lot of the mundane things, perhaps replying to, you know, everyday emails and other things, so that you have, you protect your own mind and brain space from this bombardment we're getting today from social media and emails and so on and texts and so on. So it actually blocks deep work and and being in flow and things like that, which I, I value very much. So I would quite like these assistants to take away, uh, a lot of the, uh, the mundane aspects of of admin that we do every day.
What's your best guess as to what type of relationships we're going to have with our AI agents or AI assistants? So there's on one hand, you could have a dispassionate agent that's just like really good at getting stuff done for you. On the other hand, like it's already clear that people are like falling in love with these bots. There was a New York Times article last week about someone who's falling in love with ChatGPT, like for real falling in love. And I had, uh, the CEO of Replika on the show a couple weeks ago, and she said that they are regularly invited to marriages of people who are marrying their Replikas. And they're moving into this more assistive space. So do you think when we, when we start interacting with something that knows us so well, that helps us with everything we need, yeah, is it going to be like a third type of relationship where it's not necessarily a friend, not a lover, but it's going to be a deep relationship? Don't you think?
Yeah, it's going to be really interesting. I think the way I'm modeling that, first of all, is, uh, two at least two domains, first of all, which is your your personal life and then your work life, right? So I think you'll have this notion of virtual workers or something. Maybe we'll have a set of them or managed by a, you know, a lead assistant that does a lot of the, uh, helps us be way more productive at work, you know, or or whether that's email across workspace or whatever that is. So we're really thinking about that. Then there's a personal side where, you know, we're talking about earlier about all these, um, uh, booking holidays for you, avenging things, mundane things for you, sorting things out, and then, uh, that makes your life more efficient. I think it can also enrich your life, so recommend you things that amazing things that it knows you as well as you know yourself. Um, so those two, I think, are definitely going to happen. And then I think there is a, a philosophical discussion to be had about, is there a third space where these things start becoming so integral to your life, they become more like companions? I think that's possible too. We've seen that a little bit in gaming. So you may have seen, we had a little prototypes of Astro working in and Gemini working with, like being almost a game companion, commenting in, you almost as if you had a friend looking at a game you're playing and recommending things to you and advising you, but also maybe just playing along with you. And it's, it's, it's very fun. Um, so I, I haven't, you know, quite through thought through all the implications of that, but they're going to be big. And I'm sure there is going to be demand for companionship and other things. Maybe the good side of that is or help with loneliness and these sorts of things. But there's also, you know, I think it's going to be, it's going to have to be really carefully thought through by society, whether, you know, what directions we want to take that in. I mean, my personal opinion is that that it's the most underappreciated part of AI right now, and that people are just going to form such deep relationships with these bots as they get better. Because like, I know as a meme in AI, that this is the worst it's ever going to be. Yeah. And, uh, it's going to be crazy.
Happy. I think I think it's going to be pretty crazy. This is what I meant about the under, under appreciating what's to come. I still don't think this, this kind of thing I'm talking about, right? I think that it's going to be really crazy. It's going to be very disruptive. Um, I think there's going to be lots of positives out of it too, and lots of things will be amazing and better. But there are also risks with this new brave new world we're going into.
So you brought up Astro a couple times. Let's just talk about it. It's Project Astro, as you call it. Yeah. It is almost an always-on AI assistant. You can like hold your phone. It's currently just a prototype or not publicly released, but you can hold your phone and it will see what's going on in the room. So I could basically, I've seen you do this on your show, or not you personally, but somebody on your team. You can say, "Okay, where am I?" And it'll be like, "Oh, you're in a podcast studio." Anything. Okay. So it could have this contextual awareness. Yes. Can that work without smart glasses? Because it's really annoying to hold my phone up. So like, when are, when are we going to see Google smart glasses with this technology embedded?
They're coming. So we, we teased it in some of our early prototypes. So we're mostly prototyping on on on phones currently because they have more processing power. But we're, of course, Google's always been a leader in in glasses. Yeah. And exactly. Just a little too early. Yeah, maybe a little too early. And now I think, and with they're super excited that team is that, you know, maybe this assistant is the killer use case that glasses has always been looking for. And I think it's quite obvious when you, when you start using Astro in your daily life, which we have with trusted testers at the moment, and in kind of beta form, um, there are many use cases where it would be so useful to use it, but it's a bit, it's inconvenient that you're holding the phone. So one example is while you're cooking, for example, right? And and it can advise you what to do next, the menu, you know, how to, whether you've chopped the thing correctly or or or fried the thing correctly. But you want it to just be hands-free, right? So I think that, um, uh, glasses and maybe other form factors, uh, that are hands-free will, uh, come into their own in the next few years. And and we, we, we, you know, we plan to be at the forefront of that.
Other form factors?
Well, you could imagine earbuds with cameras and, you know, glasses is obvious next stage. But is that the optimal form? Probably not either. Um, but partly we've also got to see, we're still very early in this journey of seeing what are the, are the, um, the the regular user journeys and killer sort of use journeys that everyone uses a bread and butter uses every day. And that's what the the trusted tester program is for at the moment, who're kind of collecting that information and observing people using it and seeing what ends up being useful.
Okay. One last question on agents, then we move to science. Agentic agents, AI agents, this has been the buzzword in AI for more than a year now. Yeah. There aren't really any AI agents out there. No. What's going on?
Yeah, well, again, you know, I think the hype train can potentially is ahead of where the the the the actual science and research is. But I do believe that this year will be.
The year of Agents, um, the beginnings of it, I think you'll start seeing that, uh, you know, uh, later, maybe the second half of this year. Uh, but there'll be the early versions, and then, um, you know, I think they'll rapidly improve and mature. So, um, but I think you're right. I think the, the, the technology at the moment, it's still in the research lab, the agent technologies. Um, but things like Astra Robotics, I think it's coming.
You think people are going to trust them? I mean, it's like, "Go use the internet for me. Here's my credit card." I don't know. Well, so I think to begin with, you would probably, my, my view at least, would be to not allow, have a human in the loop for the final steps. Like, like, don't pay for anything, use your credit card, unless the, the, the human user operator authorizes it. So that would, to me, be a sensible first step. Also, perhaps, um, certain types of activities or websites or whatever, kind of off-limits, you know, banking websites and other things in the first phase, uh, while we continue to test out in the world that, how robust these systems are.
I propose we've really reached AGI when they say, "Don't worry, I won't spend your money," and then they do the deceptiveness thing, and then next thing you know, you're on a flight somewhere. Yes. Yeah, that would be, that would be, that would be getting closer, for sure, for sure. Yeah.
All right, science. So, um, you worked on basically, uh, decoding all protein folding without Fold. You won the Nobel Prize for that. Not to skip over the thing that you won the Nobel Prize for, but I want to talk about what's on the roadmap, which is that you have, um, an interest in mapping of a virtual cell. Yes. Uh, what is that and what does it get us?
Yeah, well, so if you think about what we did with AlphaFold, it was essentially solving the problem of the, the, the, finding the structure of a protein. And proteins, everything in life depends on proteins, right? Everything in your body. Um, so that's the kind of static picture of a protein. But the thing about biology is really, it's, you only understand what's going on in biology if you understand the dynamics and the interactions between the different things in, in a cell. And so a virtual cell project is about building a simulation, an AI simulation of a full working cell. I'd probably start with something like a yeast cell because of the simplicity of, of the yeast organism. And, um, and you have to build up there. So the next step is with AlphaFold 3, for example, we started doing pairwise interactions between protein and ligands, and proteins and DNA, proteins and RNA. And then the next step would be modeling a whole pathway, maybe a cancer pathway or something like that. That'd be helpful for solving a disease. And then finally, a whole cell. And the reason that's important is you would be able to hypothesize, make hypotheses and test those hypotheses about making some change, some nutrient change, or injecting a drug into the cell, and then seeing what happens to the, how the cell responds. Um, and at the moment, of course, you have to do that painstakingly in a wet lab. But imagine if you could do it a thousand, a million times faster in silico first, and only at the last step do you do a validation in the wet lab. So instead of doing the search in, in, in the wet lab, which is millions of times more expensive and time-consuming than the validation step, you just do the search part, um, in silico. So it's again, it's sort of translating again what we did in the games environments, uh, but here in the sciences and the biology. So you, you build a model and then you use that to do the reasoning and the search over. And then the predictions are, you know, at least better than not. Maybe they're not perfect, but they're useful enough to, um, to be useful for experimentalists to, to validate against.
And the wet lab is within people. Yeah. So the wet lab, uh, you, you, you'd still need a final step with the, with the wet lab to prove that, uh, what the predictions were actually valid. So you know, you, but you wouldn't have to do all of the work to get to that prediction in, in, in the wet lab. So you just get, "Here's the prediction, if you put this chemical in, this should be the change," right? And then you just do that one experiment. So. And then after that, of course, you still have to have clinical trials if you're talking about a drug. You would still need to test that properly through the clinical trials and so on, and test it on humans for efficacy and so on. That I also think could be improved with AI. That whole clinical trial, that's also takes many, many years. But this, that would be a different technology from the virtual cell. The virtual cell would be, would be helping the discovery phase for drug discovery. Just like I have an idea for a drug, throw it in the virtual cell, see what it does. Yeah. And maybe eventually it's a liver cell, or or a brain cell, or something like that. So you have different cell models. And then, you know, at least 90% of the time, it's giving you back what would really happen. That'd be incredible.
How, how long do you think that's going to take to get to? I think that'll be like, um, maybe five years from now. Yeah. Yeah. So I have a kind of five-year project, and a lot of the AlphaFold, the old Alpha team are working on that. Yeah. I was asking your team here, so you, you figured out, yeah, I was speaking with him. I was like, you figured out, uh, protein folding. What's next? And this is like, it's just very cool to hear about these new challenges because, yeah, the, uh, developing drugs is a mess. Yeah. Right now, we have so many promising ideas, they never get out the door because just the process is absurd. It's process too slow, and discovery phase too slow. I mean, look how long we've been working on Alzheimer's and, and, I mean, in this tragic way to for someone to go and for the families and, and, you know, we should be a lot further. It's 40 years of work on that. Yeah. Yeah. I've seen it a couple of times in my family. If we can ensure that doesn't happen, it's just one of the best things we could use AI in my opinion. Yeah. It's a terrible way to see somebody, uh, decline. So yeah, it's important work.
Um, in addition to that, there's the genome. Yes. And so the Human Genome Project, sort of, I was like, "Okay, so they decoded the whole genome. There's no more work to do there." Like, just the same way that you decoded proteins with Fold. But it turns out that actually, we just have like a bunch of letters when it's decoded. And so now you're working to use it to translate what those letters mean. Yes. So, yeah, we have lots of cool work, uh, on, on genomics and, um, uh, uh, uh, trying to figure out if mutations are going to be harmful or or benign. Right? Most mutations to your DNA are are are harmless. Um, but of course, some are pathogenic, and you want to know which ones there are. So our first, uh, systems, um, are have are the best in the world at predicting that. Um, and then, um, uh, uh, the next step is to to look at, uh, uh, situations where the disease isn't caused just by one genetic mutation, but maybe a series of them in concert. And obviously, that's a lot harder. Like, and a lot of more complex diseases that we haven't made progress with are probably not due to a single mutation, right? That's more like rare childhood diseases, things like that. Um, so there, you know, we need to, I think AI is the perfect tool, uh, to, to, to sort of, um, uh, uh, try and figure out what these weak interactions, uh, are like, right? How they maybe, um, uh, uh, uh, kind of compound on top of each other. Um, and so maybe the statistics are not very obvious, but an AI system that's able to kind of spot patterns would be able to figure out there is some connection here.
And so we talk about this a lot in terms of disease, but also I wonder what happens in terms of, uh, making people superhuman. I mean, if you're really able to tinker with the genetic code, right, the possibilities seem endless. So what do you think about that? Is that something that we're going to be able to do through AI? I think one day. I mean, we're focusing much more on on the on the disease profile and fixing. Yeah, that's the first step. And and I've always felt that that's the most important. If you ask me what's the number one thing I wanted to use AI for, and the most important thing we use AI for is for helping human health. Um, but then of course, beyond that, one could imagine, uh, aging, things like that. You know, is of course, there's a whole field in itself. Is aging a disease? Is it a combination of diseases? Can we extend, um, our healthy lifespan? Um, these are all important questions and I think very interesting. And I'm, I'm pretty sure AI will be extremely useful in helping us find answers to those questions too.
You, I see memes come across my Twitter feed, and maybe I need to change the stuff I'm recommended, but it's often like, "If you will live to 2050, you're not going to die." Uh, what do you think the potential max lifespan is for a person? Well, look, I know those a lot of those folks in aging research very well. I think it's very interesting the pioneering work they they do. Um, I think there's nothing good about getting old and your body decaying. I think it's, you know, if anyone who's seen that up close with their relatives, it's a pretty hard thing to go through, right? As a family, or or the, or the person, of course. And, and, and so I think anything we can alleviate human suffering and and extend healthy lifespan is a good thing. Um, you know, the natural limit seems to be about 120 years old, but, from what we know, you know, if you look at the oldest people that that that are lucky enough to to to live to that age. So there's, you know, it's, it's, it's, it's an area I follow quite closely. I don't have any, I guess, new insights that are not already known in that. Um, but I do, I would be surprised if there, if that's, if that's the limit, right? Because there's a sort of two steps to this. One is curing all diseases, one day, which I think we're going to do with Isomorphic and the work we're doing there, our spin-out, our drug discovery spin-out. Um, but then that's not enough to probably get you past 120, because there's some sort of, then there's the question of just natural systemic decay, right? Aging, in other words. Not specific disease, right? Um, often those people that live to 120, they don't seem to die from a specific disease. It's just sort of just general atrophy. Um, so then you're going to need something more like rejuvenation, where you, you, you rejuvenate your cells, or you, you know, maybe stem cell research, you know, companies like Altos are are working on these things, resetting the the cell clocks. Seems like that could be possible. But again, I feel like it's so complex because biology is such a complicated emergent system. You need, in my view, you need AI to help to to be able to crack anything, anything close to that, very quickly.
On material science, I don't want to leave here without talking about the fact that you've discovered many new materials, or potential materials. Uh, this stat I have here is known to humanity, uh, recently were 30,000 stable, uh, materials. You've discovered 2.2 million with a new AI program. Yeah. Um, just dream a little bit, because we don't know what all those materials can do. We don't know what, you know, whether they'll be able to handle being out of, like, a frozen box or whatever. Uh, dream materials for you to find in that set of new materials. Well, I mean, we're working really hard on materials. To me, it's like the next, uh, one of the next sort of, uh, big, uh, impacts we can have, like the level of AlphaFold, really in biology, but this time in chemistry, in materials. You know, I dream of, uh, one day discovering a room temperature superconductor.
So what will that do? That's another big meme that people talk about. Well, it would help with the energy crisis and climate crisis because, um, if you had sort of cheap, uh, superconductors, you know, then you can transport energy from one place to another without any loss of that energy. Right? So you could potentially put solar panels in the Sahara Desert and then just have a, the, the, the superconductor, you know, uh, funneling that into Europe where it's needed. At the moment, you would just lose a ton of the power to heat and other things on the way. So then you need other technology like batteries and other things to store that because you can't, you can't just pipe it to the place that you want without, without being incredibly inefficient. So, but also materials could help with things like batteries too. Like, but come up with the optimal battery. I don't think we have the optimal battery designs. Um, that maybe we can do things like, uh, combination of materials and and and proteins. We can do things like carbon capture. You know, modify, uh, uh, algae or other things to to do carbon capture, uh, better than, um, uh, our artificial system. Um, I mean, even the one of the most famous and most important chemical processes, the Haber process to make fertilizer and ammonia, you know, to take nitrogen out of the air, was was something that allows modern civilization. But there might be many other, uh, chemical processes that could be catalyzed in that way if we knew what the right catalyst and the right material was. Um, so, uh, I think it's going to be, would be one of the most impactful technologies ever is to to basically have in silico design of materials. So we've done step one of that, where we showed we can come up with new stable materials. But we need a way of testing the properties of those materials because no lab can test 200,000, you know, tens of thousands of materials or millions of materials at the moment. So we have to, that's, that's the hard part is to is to do the testing.
Do you think it's in there, the room temperature superconductor? Think, um, well, I heard that we, we actually think there are some superconducting materials. I, I, I doubt their room temperature ones though. But, I think at some point, if if it's possible with physics, um, an AI system will one day find it. So that's one use. The two other uses I could imagine, probably people interested in this type of work, toy manufacturers and militaries. Yeah. Are they working with it? Yeah. Toy manufacturers. I mean, look, I think there is incredible. One, I mean, big part of my early career was in game design. Yeah. Theme park and simulations. That's what got me into simulations and AI in the first place, and why I've always loved both of those things. And if, in many respects, of the work I do today is just an extension of that. Um, and, and I, I just dream about like, what could I have done? What kinds of amazing game experiences could have been made if I had the AI I have today available 25, 30 years ago when I was writing those games? And I'm a little bit surprised the game industry hasn't done that. I don't know why that is. We're starting to see some crazy stuff with NPCs that like are starting, but, but of course, that'd be like intelligent, you know, dynamic storylines. Um, but also just new types of AI-first games with learning consist, with with characters and and agents that can learn. Um, and, you know, well, once worked on a game called Black and White where you had a creature that you were nurturing, was a bit like a pet dog that that that learned what you wanted, right? But we were, we were using very basic reinforcement learning. This was like in the late '90s. You know, imagine what could be done today. Um, and I think the same for for maybe smart toys as well, right? Um, and then of course, on the military, you know, uh, unfortunately, AI is a dual-purpose technology. So one has to confront the reality that, um, especially in today's geopolitical world, uh, people are using some of these general-purpose technologies to apply to drones and other things. And, um, it's not surprising that that works.
Are you impressed with what China's up to? I mean, DeepSeek is this new model, impressive? Um, it's a little bit unclear how much they relied on on Western systems to do that, you know, both training data, there's some rumors about that, um, and, and also maybe using some of the open source models to as a starting point. Um, um, but look, it's for sure, it's impressive what they've been able to do. Um, and, um, you know, I think that's something we're going to have to think about how to keep, uh, the Western frontier models in in the lead. I think they still are at the moment, but, um, you know, for sure, China is very, very capable engineering and and scaling.
Let me ask you one final question. Um, just give us your vision of what a world looks like when there's superintelligence. So let's move past, we started with AGI, let's know superintelligence. Yeah. Well, look, I think for there, you, you, two things there. One is, um, I think a lot of the best sci-fi can we can look at as as interesting models to debate about what kind of, uh, galaxy or or or universe do we want to, a world do we want to to to to move towards. And the one I've always liked most is actually the Culture series by Ian Banks. Um, I started reading that back in the '90s and, and I think that is a picture. It's, it's like a thousand years into the future, but it's in a post-AGI world where there are AGI systems coexisting with human society and also alien society. And we, humanity's basically maximally flourished and spread to the galaxy. Um, and I, I, that, that I think is a great vision of, um, how the things might go if, in, in the, in the positive case. So, um, I'd sort of hold that up. Um, I think the other thing we're going to need to do is, as I mentioned earlier about the under, under appreciating still what's going to come in the longer term, I think there is a need for great philosophers. To, you know, where are they? The great next philosophers, the equivalent of Kant or Wittgenstein, or even Aristotle. Um, I think we're going to need that to to help navigate society to that next step because I think the, you know, AGI and artificial superintelligence is going to change, um, uh, humanity and the human condition.
Demis, thank you so much for doing this. Great to see you in person and hope to do it again soon. Thank you. Thank you very much. All right, everybody, thank you for listening, and we'll see you next time on Big Technology Podcast.