Best of Big Technology: Demis Hassabis On AGI, Deceptive AIs, Building a Virtual Cell

26:16So they'll take an action that's 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.

26:53It's in testing and it's like going to give a difference. Yeah, it's playing some meta game, right? And that's incredibly dangerous if you think about then it invalidates all of 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 to look at deception as a kind of class A thing that we need to prevent and monitor.

27:31As 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 guardrails 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.

28:12That would probably be a good advisable next step for things like deception. What sort of deception have you seen? Because I just read a paper from Anthropic where they gave it a sketch pad. And it's like, oh, I better not tell them this. And you see it give a result after thinking it through. So what type of deception have you seen from the bots? 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.

28:51Wait, 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 won't 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. And that's exactly what AI safety experts are worrying about, right? Where systems where there's unintentional effects of the system. You don't want the system to be deceptive.

29:23You 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 that'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. Sure. Because it's like, oh. Well, look, of course, you know, these are – it's impressive capabilities. And the negatives are things like deception, but the positives would be things like inventing new materials, accelerating science.

29:58You need that kind of ability to problem solve and get around issues that are blocking progress. But of course, you want that only in the positive direction. So those are exactly the kinds of capabilities. I mean, 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 DeepMind.

30:34What 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. 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 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.

31:03You 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, 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 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 and efficiency.

31:43And then I hope from the user perspective, 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 it. There's a lot of middlemen. Yeah, sure. But there will be many other – I think there will be incredible other opportunities that will appear economic and otherwise based on this change. But I think it's going to be a big disruption. And what about information?

32:19Well, I mean finding information. I think you'll still need the reliable sources. I think you'll have assistants 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 assistants will be able to more efficiently gather information for you. And perhaps, you know, what I dream of is, again, assistants 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.

33:03So it actually blocks deep work and being in flow and things like that, which I value very much. So I would quite like these assistants to take away a lot of the mundane aspects 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 fallen in love with Chad GPT, like for real, fallen in love.

33:36And I had the CEO of Replica on the show a couple weeks ago, and she 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?

34:11So I think you'll have this notion of virtual workers or something. Maybe we'll have a set of them or managed by a lead assistant that does a lot of the – helps us be way more productive at work, 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, arranging 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 that amazing things that it knows you as well as you know yourself.

34:50So 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 Astra working in and Gemini working with like being almost a game companion, commenting and 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.

35:18And 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 it will 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.

35:56Yeah. And it's going to be crazy. Yeah, I think it's going to be pretty crazy. This is what I meant about the underappreciating 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 uh let's just talk about it's project astra 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 if 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 i'll be like oh you're in a podcast studio yeah anything okay so so it could have this contextual awareness.

36:44Yes. 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 prototypes. So we're mostly prototyping on phones currently because they have more processing power. But, of course, Google's always been a leader in glasses. Google Glass. Yeah, exactly. Just a little too early. Yeah, maybe a little too early. And now I actually think, and 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.

37:17And I think it's quite obvious when you start using Astra in your daily life, which we have 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 inconvenient that you're holding the phone. So one example is while you're cooking, for example, right? And it can advise you what to do next, whether you've chopped the thing correctly or fried the thing correctly. But you want it to just be hands-free. So I think that glasses and maybe other form factors that are hands-free will come into their own in the next few years.

37:53And 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. 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 use journeys that everyone uses, bread and butter uses every day. And that's what the Truster 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.

38:31Agentic 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, 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 maybe second half of this year. But there'll be the early versions. And then I think they'll rapidly improve and mature. But I think you're right. I think the technology at the moment is still in the research lab, the agent technologies.

39:11But things like Astra, robotics, I think it's coming. 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.

39:45while 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 getting closer for sure. For sure. Yeah. All right, science. So you worked on basically decoding all protein folding with alpha 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 an interest in mapping a virtual cell.

40:20Yes. What is that? And what does it get us? Yeah. Well, so if you think about what we did with AlphaFold was essentially solve the problem of 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 probably start with something like a yeast cell because of the simplicity of the yeast organism.

41:01And you have to build up there. So the next step is with AlphaFold3, for example, we started doing pairwise 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 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.

41:37And 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, you know, at least better than, maybe they're not perfect, but they're useful enough to be useful for experimentalists to validate against.

42:23And the wet lab is within people. Yeah. So the wet lab, you'd still need a final step with the wet lab to prove that 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.

42:57That 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. 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 at least 90 % of the time, it's giving you back what would really happen. That would be incredible. How long do you think that's going to take to figure out?

43:25I 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 AlphaFold team are working on that. Yeah, I was asking your team here. Yeah, John Jumper. I was speaking with him. I was like, you figured out protein folding, what's next? It's just very cool to hear about these new challenges because developing drugs is a mess right now. We have so many promising ideas, they never get out the door because just the process is absurd. The process is too slow and discovery phase is too slow. I mean, look how long we've been working on Alzheimer's.

43:59And I mean, it's a 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 of 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, it's a terrible way to see somebody decline. Yeah. So it's important work. In addition to that, there's the genome. Yes. And so the Human Genome Project sort of... Yes. I was like, okay, so they decoded the whole genome. There's no more work to do there. Like just same way that you decoded proteins with Fold.

44:32But 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.

45:17And 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, I think AI is the perfect tool to try and figure out what these weak interactions are like, how they maybe compound on top of each other. And so maybe the statistics are not very obvious, but an AI system that's able to 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.

46:01I 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 goes wrong. 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 use AI for is for helping human health. But then, of course, beyond that, one could imagine aging, things like that. You know, of course, there's a whole field in itself.

46:31Is 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. You know, 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 the pioneering work they do.

47:05I 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 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 and not specific disease Right.

48:13Often 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 emergent system. In my view, you need AI 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.

48:53The 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. Just dream a little bit. because we don't know what all those materials can do. We don't know whether they'll be able to handle being out of like a frozen box or whatever. 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 a room temperature superconductor.

49:38So 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, 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 the superconductor 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.

50:17But also materials could help with things like batteries too, like 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 harbor 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.

51:01So 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, tens of thousands of materials or millions of materials at the moment. So that's the hard part is to do the testing. You think it's in there, the room temperature superconductor? I think, well, I heard that we actually think there are some superconductor materials. I doubt they're room temperature ones, though.

51:37But 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 manifest. I mean, look, I think there is incredible one. I mean, a big part of my early career was in game design. Games, right, theme park. Yeah, theme park and simulations. That's what got me into simulations and AI in the first place. And I've always loved both of those things. And in many respects, the work I do today is just an extension of that.

52:12And 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 are starting to... 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.

52:48It was a bit like a pet dog that learned what you wanted, right? But we were using very basic reinforcement learning. This was like in the late 90s. Imagine what could be done today. And I think the same for maybe smart toys as well. And then, of course, on the militaries, 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, DeepSeek is this new model getting?

53:24Yeah, 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. 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 for sure China is very, very capable at 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.

54:01Let's move past. We started with AGI. Let's head on super intelligence. Yeah. Well, look, I think for there, two things there. One is I think a lot of the best sci-fi we 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 1000 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 we've humanity's basically maximally flourished and spread to the galaxy and um i i that that i think is a great vision of um how the thing 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.

55:06I think there is a need for some great philosophers to, you know, where are they? The great next philosophers, the equivalence 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 that, you know, AGI and artificial superintelligence is going to change humanity and the human condition. Demis, thank you so much for doing this. Great to see you in person. I hope to do it again soon. Thank you. Thank you for having me. everybody thank you for listening and we'll see you next time on big technology podcast