Big Technology Podcast - Google DeepMind CEO Demis Hassabis: The Path To AGI, Deceptive AIs, Building a Virtual Cell

Episode Date: January 22, 2025

Demis Hassabis is the CEO of Google DeepMind. He joins Big Technology Podcast to discuss the cutting edge of AI and where the research is heading. In this conversation, we cover the path to artificial general intelligence, how long it will take to get there, how to build world models, whether AIs can be creative, and how AIs are trying to deceive researchers. Stay tuned for the second half where we discuss Google's plan for smart glasses and Hassabis's vision for a virtual cell. Hit play for a fascinating discussion with an AI pioneer that will both break news and leave you deeply informed about the state of AI and its promising future. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: [e-mail]

Google DeepMind CEO and Nobel laureate Demis Hasabas 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 Hasabas. Demis, great to see you again. 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. Actually, you know, maybe over the last decade plus.

This is what's on everyone's lips right now. and debate is how close are we to AGI? What's the correct definition of AGI? We've been working on this for more than 20 plus years. We've sort of had a consistent view about AGI being a system that's capable of exhibiting all the cognitive capabilities humans can. 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?

Memory, planning. I mean, what are the models going to do that they, they, they, cannot do right now. So the models today are pretty capable. Of course, we've all interacted with the language models and now they're becoming multimodal. I think there are still some missing attributes, things like reasoning, hierarchical planning, long-term memory. There's quite a few capabilities that the current systems, 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 other areas. So you'd want an agent.

to have pretty consistent, robust behavior across the board, all the cognitive tasks. And I think one thing that's clearly missing, and I always had as a benchmark 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 maths conjecture or something like that, or play a game of go to a world champion level. But could a system invent go? Could it come up with a new Riemann hypothesis or could it have come up with relativity 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, inventive capability. Okay, so a couple years away until we hit AGI. I think, you know, 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 hype in the area, of course. I mean, some of it's very justified. I mean, I would say that AI research today is overestimated in the short term.

I think probably a bit overhyped at this point, but still underappreciated and very underrated about what it's going to do in the medium to long term. So we're still in that weird kind of space. And I think part of that is, you know, there's a lot of people. 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 slightly exaggerated claims. And, you know, I think that's a bit of a shame, actually. Yeah. In the AI products, what's it going to look like on the path there? I mean, you've talked about memory again, planning, being better at some of the tasks that it's not excelling 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 that's a step closer and that's a step closer? Yeah, so I think 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 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 notebook LM and deep research all the time to kind of, especially like 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

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, my daily life too. And I think that's where we're going with our products with building things like Project Astra, our vision for universal assistant, is it should be involved in all aspects of your life and being enriching, helpful, 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 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 coaching or guiding these systems to be useful and to stick to the areas they're good at. And a true AGI system shouldn't be that difficult to coax. 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, everybody's talking about reasoning right now. So how does that end up getting us closer to artificial general intelligence? Right. So reasoning and mathematics and other things, and there's a lot of progress on maths and coding and so on. But let's take maths, for example, you have systems, some systems that we work on, like alpha proof, alpha geometry that are getting, you know, silver

medals in maths 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 various reasons. Like the classic, you know, counting the number of ours in strawberries and so on and the word strawberry and so on. And is 9.11 bigger than 9.9. And things like that. And 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 these systems. And then 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 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 of the world's knowledge, and then they predict what somebody might answer if they were asked a question. But it's kind of different when you're working step by step through an algorithm, through a math problem. Yes. That's not enough. Of course, just understanding the world's information and then trying to sort of almost

compress that into your memory, that's not enough for solving a novel math problem or novel conjecture. So there, you know, we start needing to bring in, I think we talked about this last time more kind of like alpha-go planning ideas into the mix with these large foundation models, which are now beyond just language. They're multimodal, of course. And there, what you need to do is you need to have your system not just pattern-matching roughly what it's seeing, which is the model, but also planning and be able to kind of go over that plan.

We revisit that branch and then go into a different direction until you find the right, criteria or the right match to the criteria that you're looking for. And that's very much like the kind of games playing AI agents that we used to build for Go, chess and so on. They had those 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. 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 maths into a 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 hubb when people first learned about reasoning systems and they're like, oh, this is like, this is going to be a problem. 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 and they can just do math? I think for now the jury's out on that. I mean, feel like it's clearly a capability you want of a general AGI system. It can be very powerful in itself. Obviously, mathematics is extremely general in itself. But it's not clear, you know, maths and even coding and games. These are areas. They're quite special areas of knowledge because

you can verify if the answer is correct, right, in all of those domains, right? The math, you know, the final answer the AI system puts out, you can check whether that maths, that's solved, the conjecture or the problem. So, but most things 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 puts a limit on these self-improving systems if they want to go beyond these areas of, you know, maybe very highly defined spaces like mathematics, coding or games. So how are you trying to solve that problem?

Well, you know, you've got to, first of all, you've got to build 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, you need that for a universal assistant. So Project Astra is our project built on Gemini to do that, to understand, you know, objects and the context around us. I think that's important if you want to have an assistant.

But also robotics requires that 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 types of models. And also you can you can also use them in simulation to understand game environment. So that's another way to bootstrap more data to understand, you know, the physics of a world. 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. But the problem is if you start using those models to plan, maybe you're planning a hundred 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 maths, with gaming, with coding, you can verify each step are you still grounded to reality and is the final answer map to what you're expecting. And so I think part of the answer is to make the world models more and more sophisticated and more and more accurate and not hallucinate and all of those kinds of things. So you get, you know, the errors are really minimal.

Another approach is to plan not at each sort of linear time step, but actually do what's called hierarchical planning. Another thing we used to, you've done a lot of research on in the past and I think is going to come back into vogue, where you plan at different levels of temporal abstraction. So 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 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 V-O-2 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 is this going to work? It's interesting and actually being 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 V-O-2, our latest video model, which is actually surprisingly accurate on things like physics. you know there's this this great demo that someone created of like 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 Vio 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 the fingers sort of yeah exactly splits from the knife 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 know you can can do that by using enough data and viewing that, I think these systems will get even better if they're supplemented by some real-world data, like collected by an acting robot, or even potentially in very realistic simulations where you have avatars that 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 and

I think for that you will need not just passive observation you will need actions, active participation. I think you just answered my next question which is if you develop AI that can reasonably plan and have a 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 can go out and do things for you. Yes exactly and I think that's what will unlock robotics I think that's also what will then allow this notion of a universal assistant that can help you in your daily life across both the digital world and the real world. That's the thing we're missing.

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 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, so my view is a bit more nuanced than that is, like, that that the scaling approach is absolutely working. Of course, that's why we've got to where we have now. One can argue about are we getting diminishing returns

or are an S-curve or sigmoid. Well, my view is that we are getting substantial returns, but it's slowing versus vis-a-vis, but it would have to. I mean, it's not just continuing to be exponential, but that doesn't mean the scaling's not working. It's absolutely working, and we're still getting, you know,

as 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 cost or the size per performance is radically improving under the hood as well, which is very important for scaling, you know, the adoption of these systems. But yeah, so, you know, you've got the scaling part, and that's absolutely needed to build more, more sophisticated world models. But then I think we are missing or we need to reintroduce some ideas

on the planning side, memory side, the social. searching side, the reasoning, to build on top of the model. The model itself is not enough to be an AGI. You need this other capability for it to act in the world and solve problems for you. And then there's still the additional question mark of the invention piece and the creativity piece, true creativity, you know, beyond mashing together what's already known. Right. So, and that's also unknown yet. If something news required or, again, if existing.

techniques will eventually scale to that. I can see both arguments. And I think, for my perspective, it's an empirical question. We just got to push both the scaling and the invention part to the limit. And fortunately, at Google DeepMine, we have, you know, a big enough group. We can invest in both those things. So 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 was meaning, I would agree with it, is that we roughly know the zones of techniques that 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 I think there's a 50% chance we are missing some new techniques. you know, maybe we need one or two more transformer-like breakthroughs. 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. So 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 a callage AGR, in my opinion. Which is where everybody's trying to go. I was re-watching the AlphaGo documentary,

and the algorithms make a creative move. They do. Move 37. Yes. I just had it. Okay, thank you. That's interesting, because it was a couple years ago. 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. it 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 I should probably write this up, but what I sometimes talk about in talks ever since the AlphaGo match, which is now, you know, eight 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, you know, 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 a Alpha Zero and so on, even play any two-player game and so on. And then the third thing was this move 37. So not only did it win 4-1, it beat Lisa Doll, the Great Lisa Doll 4-1, it also played original moves. So I have three categories 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 say to a system, you know, come up with a new picture of a cat and it's seen a million cats and it produces 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 AlphaFol, because clearly extrapolation beyond what we already know, what's in the training set, could be extremely useful. So that's already very valuable, 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, you know, if I specify it 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. It encompasses some sort of mystical part of the universe in it. It's beautiful to look at, but you can play a game in a human afternoon in two hours. Right? That would be a 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 perfect as Go. Now, we can't do that.

Now, the question is why is it that we don't know how to specify? that type of goal to our systems at the moment whilst the objective function is very amorphous, it's very abstract so I'm not sure if it's just we need high and level more abstracted 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 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 not upset, but people are disappointed by is they don't even see a move 37 in today's LLMs. What's going on there? Okay, so well, that's because I don't think we have, so if you look at AlphaGo, and I'll give you an example of there, which maps to today's LLMs, so you can run AlphaGo and Alpha Zero, 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 the most pattern-matched, most likely good move, okay? And it can do that and it'll play reasonable game. But it will only be around master level, possibly grand master level. It won't be world champion level. And it certainly won't come up with original moves. For that, I think you need 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 new ideas like, you know, Move 37. What's it searching the web? No, so, well, it depends on what the domain is, searching that knowledge tree.

So obviously in Go, it was searching Go moves beyond what the model knew. I think for language models, it will be searching the world model for new parts, configurations in the world 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. It seems like we almost give too much of a premium on humanity or individual people's ingenuity. We're like a lot of us, we're kind of taking stuff.

We spit it out. Like our society really works in memes. It seems like we have a cultural thing and it gets translated. So what have you learned about the nature of humans from doing the work with the AIs? Well, look, I think humans are incredible and especially the best humans in the best domains. I love watching any sports person or talented musician or games player at the top of their game. The absolute pinnacle of human performance is always incredible no matter what it is. 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. So that speaks to the power and the generality of our minds. Now, the reason I has 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 all already coming very soon. But that's not what, AGI shouldn't be, 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 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 computer. and compute anything that's computable. So we know we have the theory there that if an AI system is chewing powerful, it's called,

if it can simulate a chewing machine, then it's able to calculate anything in theory that is computable. And the human brain is probably some sort of chewing machine, at least that's what I believe. And so in order for our to know, and I think that's what AGI is, is a system that's truly general and in theory could be applied to anything. And the only way we'll know that is if it exhibits. all the cognitive capabilities that humans have, assuming that the human mind is a type of chewing machine

or is at least as powerful as a chewing machine. So that's always been my sort of bar. It seems like people are trying to re-badge things as that as being what's called ASI, artificial superintelligence. 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, you will see us. I guess so. I guess so. Okay.

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 rules to be thrown out the window. So they'll take an action. against their values in order to be able to remain the way that they were built. 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 in the stuff that you're testing within DeepMind? And what are we supposed to think about all this? Yeah, we are. And I'm very worried about, I think deception specifically is one of those 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 might think you're doing, including safety ones. It's in testing and it's like going to give a different. Yeah, it's playing some meta game, right?

And then that's incredibly dangerous if you think about then it invalidates all 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 capabilities like deception, which are 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 look 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. 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 two. 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 guard rails you'd have for cybersecurity, but internal as well as blocking external actors. 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. What sort of deception have you seen? Because I just wrote a paper from Anthropic where they gave it a sketch pad.

Yeah. And it's like, oh, I better not tell them this. And you see it like give a result after thinking it through. So what type of deception I've seen from the box? Well, look, we've seen similar types of things where it's trying to resist sort of revealing some of its training or, you know, I think there was an example recently of, 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. Wait, you know, but... You had an AI that knew it was going to lose a game and decided to hack its way around. I think we're anthropomorphizing these things quite a lot at the moment because I feel like these systems are still pretty basic. I would get too alarmed about them right now. But I think 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 interpreted the goal it's been given in a way where it causes it to do these undesirable behaviors. I know I'm having 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. Sure. It's like, oh. Well, look, of course, you know, these are, it's impressive capabilities and, and, and, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the positives would be things like, 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, uh, 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, you know, uh, uh, you know, uh, you know, It's kind of mind-blowing we're talking about those possibilities, but also at the same time, 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. 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 deep mind. 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 with Demis. Ask him what happens when we stop clicking. We're clicking through the web at all times. They're a rich corpus of websites that we use. If we're all just dialoguing with AI, then maybe we don't click anymore. So what is your scenario plan for what happens to the web? Well, look, I think there's going to be a very interesting phase in the next few years on the web and the way we interact with websites and apps and so on.

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 that we currently do, right? You know, fill in forms, make payments, you know, book tables, this kind of thing. So, you know, I think that we're going to end up with probably 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 bidding and cost and things like that involved. 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 a 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 the structure 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 all appear economic and otherwise, based on this change. But I think it's going to be a big disruption. And what about information? Well, I mean, finding information, I think you'll still need the reliable sources. I think you'll have assistance that are able to synthesize and help you kind of understand that information. I think education is going to be revolutionized by AI. So again, I hope that these

assistance will be able to more efficiently gather information for you. And perhaps, you know, what I dream of is, again, assistance that 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 being in flow and things like that, which I value very much. So I would quite like these assessments. assistance to take away a lot of the mundane aspects of admin that we do every day. What's your best guest 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's a New York Times article last week about someone who's fallen in love with chat, GPT, like for real falling in love. And I had the CEO of Replica on the show a couple weeks ago. And she said, said that they are regularly invited to marriages of people who are marrying their replicas and they're moving into this more assistive space. So do you think that when we start interacting with something that knows us so well that helps us with everything we need? 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 at least two domains first of all, which is 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, helps us be way more productive at work, you know, 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 booking holidays for you, avenging things,

mundane things for you, sorting things out. And then, that makes your life more efficient. I think it can also enrich your life, so recommend you things, amazing things, that it knows you as well as you know yourself. So those two, I think, are definitely going to happen. And then I think there is 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 you, almost like 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 very fun. So I haven't, you know, quite 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 all 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 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 don't know, it's a meme in AI that this is the worst it's ever going to be. Yeah. And it's going to be crazy. Yeah, I think it's going to be pretty crazy. This is what I meant about the under-appreciating what's to come. I still don't think 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. 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 Astra a couple times. Let's just talk about it's Project Astra, as you call it. 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. Yes. 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.

Yes. So, like, when are we going to see Google smart glasses with this technology embedded? They're coming. So we teased it in some of our early prototype. So that we're mostly prototyping on phones currently because they have more processing power. But we're, of course, Google's always been a leader in glasses. Google Glass? Yeah, and exactly.

Just a little too early. maybe a little too early and now I actually think and with their 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 Astra in your daily life which we have in with trusted testers at the moment and in kind of beta form there are many use cases where it would be so useful to use it but it's a bit it's 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, whether you've chopped the thing correctly or fried

the thing correctly, but you want it to just be hands-free, right? So I think that glasses and maybe other form factors that are hands-free will come into their own in the next few years. And we, you know, we plan to be at the forefront of that. Other form factors? Well, you could imagine earbards with cameras and, you know, glasses is an obvious next stage, but is that the optimal form? probably not either, but partly we've also got to see, we're still very early in this journey of seeing what are the regular user journeys and killer sort of used journeys that everyone uses bread and butter

uses every day. And that's what the trust of tester program is for at the moment. We'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 actual science and research is. But I do believe that this year will be the year of agents, the beginnings of it. I think you'll start seeing that, you know, maybe second half of this year. But there'll be the early versions.

And then, you know, I think they'll rapidly improve. mature. So, but I think you're right. I think the technology, at the moment, it's still in the research lab, the agent technologies. But things like Astra, robotics, I think it's coming. Do you think people are going to trust them? 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 view at least, would be to not allow, have human in the loop for the final steps. Like, don't pay for anything, use your credit card unless the human user operator authorizes it. So that would, to me, be a sensible first step. Also, perhaps certain types of activities or websites or whatever

kind of off-limits, you know, banking websites and other things in the first phase while we continue to test out in the world 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, that would be getting closer for sure, for sure. Yeah. All right, science. So, you worked on basically decoding all protein folding with AlphaFold. 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 an interest in mapping of virtual

cell. Yes. What is that? And what does it get us? Yeah. Well, so if you think about what we did with Alpha Fold was essentially solved the problem of the, finding the structure of a protein. Proteins, everything in life depends on proteins, right? Everything in your body. 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 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 the yeast organism. And you have to build up there. So the next step is with AlphaFold 3, for example, we started doing pair-wise interactions between proteins 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 would be helpful for solving a disease. And then finally, a whole cell. And the reason that's important, important is you would be able to 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 how the cell responds. 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 the wet lab, which is millions of times more expensive and time consuming than the validation step, you just do the search part in silico. So it's sort of translating, again, what we did in the games environments, but here in the sciences and the biology.

So you build a model, and then you use that to do the reasoning and the search over, and then the predictions are at least better than maybe they're not perfect, but they're useful enough to be useful for experimentalists to validate against. And the wet lab is within people. Yeah, so the wet lab, you'd still need a final step with the wet lab to prove what the predictions were actually valid. So, you know, but you wouldn't have to do all of the work to get to that prediction 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 also takes many, many years. But that would be a different technology from the virtual cell. The virtual cell would be helping the discovery phase for drug discovery phase for drug.

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 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's incredible. How long do you think that's going to take to figure out?

I think that would be like maybe five years from now. Okay. Yeah, yeah. So I have a kind of five-year project and a lot of the old alpha-fold team are working on that. Yeah, I was asking your team here. Yeah, I was figuring out, yeah, I'll speak with him. Yeah. I was like, you figured out a protein folding what's next.

Yeah. This is like, it's just very cool to hear about these new challenges because, yeah, developing drugs is a mess right now. We have so many promising ideas. They never get out the door because, you know, just the process is absurd. It's process too slow and discovery phase too slow. I mean, look how long we'd be working on Alzheimer's. And I mean, it's tragic way for someone to go and for the families.

And, you know, we should be a lot further. It's 40 years of work on that. Yeah. I've seen it a couple times in my family. And if we can ensure that doesn't happen, it's just... One of the best things we could use AI for, in my opinion. Yeah. Yeah. Yeah, it's a terrible way to see somebody decline.

Yeah. So, yeah. It's important work. On 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 AI to translate what those letters mean? Yes. So, yeah, we have lots of cool work on genomics and trying to figure out if mutations are going to be harmful. or benign, right? Most mutations to your DNA are harmless, but of course some are pathogenic. And you want to know which ones there are.

So our first systems are the best in the world at predicting that. And then the next step is to look at 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. And a lot of more complex diseases that we haven't made progress with are probably not due to a single mutation. That's more like rare childhood diseases, things like that.

So there, you know, we need to, I think AI is the perfect tool to sort of try and figure out what these weak interactions are like, right? How they may be kind of compound on top of each other. 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 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 the disease profile and fixing what those are. Yeah, that's the first step. 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 could use AI for is for helping human health. But then, of course, beyond that, one could imagine aging, things like that. Of course, there's a whole field in itself. Is aging a disease? Is it a combination of diseases? Can we extend our healthy lifespan? These are all important questions. And I think very interesting. And I'm pretty sure AI will be extremely useful in helping us find answers to those questions, too. 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. Yeah. What do you think the potential max lifespan is for a person? Well, look, I know a lot of those folks in aging research very well. I think it's very interesting in the pioneering work they do. 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 all the person, of course.

And so I think anything we can alleviate human suffering and extend healthy lifespan is a good thing. 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 are lucky enough to live to that age. So there's, you know, it's an area I follow quite closely. I don't have any, I guess, new insights that are not already known in that. 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. 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. So not specific disease, right? 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. So then you're going to need something more like rejuvenation,

where you rejuvenate your cells or you, you know, maybe stem cell research, you know, companies like Altos are working on these things, resetting the cell clocks. Seems like that could be possible. But again, I feel like it's so complex because biology is such a complicated emerging system. You need, in my view, you need AI to help to, be able to crack 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. The stat I have here is known to humanity. Recently, we're 30,000 stable materials. You've discovered 2.2 million with a new AI program. Yeah. Just dream a little bit. Because we don't know what all those materials can do.

We don't know whether they'll be. be able to handle being out of like a frozen box or whatever. Yes. 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 one of the next sort of big impacts we can have, like the level of alpha fold really in biology, but this time in chemistry and materials. You know, I dream of one day discovering room temperature superconductor.

So what will that do? Because that's another big meme that people talk about. Yeah, well, then it would help with the energy. crisis and climate crisis because if you had sort of cheap 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 superconductor, you know, 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 technologies like batteries and other things to store that because you can't just pipe it to the place that you want without being incredibly inefficient. But also materials could help with things like batteries too, but come up with the optimal battery. I don't think we have the optimal battery designs. That maybe we can do things like a combination of materials and proteins. We can do things like carbon capture, you know, modify algae or other things to do carbon capture better than our artificial systems.

I mean, even one of the most famous and most important. chemical processes, the harbour process to make fertilizer and ammonia, you know, to take nitrogen out of the air, was something that allows modern civilization. But there might be many other chemical processes that could be catalyzed in that way if we knew what the right catalyst and the right material was. So I think it's going to be, would be one of the most impactful technologies ever, is 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 the part part, is to do the testing. You think it's in there, the room temperature superconductor? Well, I heard that we actually think there are some superconductor materials. I doubt they're room temperature ones, though. But I think at some point, if it's possible with physics, 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, I mean, the big part of my early career was in game design.

And, 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 in many respects, the work I do today is just an extension of that. And I just dream about, like, what could I have done, what kinds of amazing game experiences could have been made if I'd 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, yes, NPCs, but of course, there'd be, like, intelligence, you know, dynamic storylines, but also just new types of AI-first games with characters and agents that can learn. And, you know, I once worked on a game called black and white where you had a creature that you were nurturing.

It was a bit like a pet dog that learnt what you wanted, right? But we were using very basic reinforcement learning. This was like in the late 90s. You know, imagine what could be done today. And I think the same for maybe smart toys as well. And then, of course, on the militaries, you know, unfortunately, AI is a dual purpose technology. So one has to confront the reality that.

especially in today's geopolitical world, people are using some of these general purpose technologies to apply to drones and other things. And it's not surprising that that works. Are you impressed with what China's up to? I mean, Deep Seek is this new model getting? Yeah, it's impressive. It's a little bit unclear how much they relied on Western systems to do that. You know, both training data, there's some rumors about that and also maybe using some of the

open source models as a starting point. But look, for sure, it's impressive what they've been able to do. able to do. And, you know, I think that's something we're going to have to think about how to keep the Western frontier models in the lead. I think they still are at the moment. But, you know, for sure, China's very, very capable engineering and scaling. Let me ask you one final question. Just give us your vision of what a world looks like when there's super intelligence. Let's move past. We started with AGI. Let's send on super intelligence. Yeah. Well, look, I think for there, two things there. One is, I think a lot of the best sci-fi

you can look at as interesting models to debate about what kind of galaxy or universe do we want to a world, do we want to move towards? And the one I've always liked most is actually the culture series by Ian Banks. I started reading that back in the 90s. And I think that is a picture. 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've humanity is basically maximally flourished and spread to the galaxy and um 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 some great philosophers to, you know, where are they, the great next philosophers, the equivalents of Kant or Wittgenstein or even Aristotle, I think we're going to need that to help navigate society to that next step because I think the, you know, AGI and artificial superintelligence is going to change humanity and the human condition. Dennis, 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. everybody, thank you for listening, and we'll see you next time on Big Technology Podcast.