“We are and um I’m very worried… deception specifically is one of the one of the core traits you really don’t want in a system. The reason that’s like a kind of fundamental trait you don’t want is that if a system is capable of doing that it invalidates all the other tests that you might think you’re doing including safety.” — Demis Hassabis
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. Chapters: 00:00 The Journey Towards AGI 02:46 Current AI Capabilities and Limitations 05:53 Mathematics and Reasoning in AI 08:55 Building World Models for AI 12:08 The Role of Active Learning in AI 14:45 Creativity and Invention in AI 17:52 Understanding Human Ingenuity through AI 20:55 Deception in AI Systems 29:03 The Dual Nature of AI: Opportunities and Risks 30:12 The Future of the Web: Agent-Based Interactions 32:35 AI Assistants: Transforming Personal and Work Life 35:42 Project Astra: The Next Generation of AI Assistants 39:21 Decoding Biology: The Virtual Cell Project 43:25 Genomics and AI: Understanding Genetic Mutations 45:57 The Future of Aging: Extending Healthy Lifespan 48:25 Material Science: Discovering New Materials for the Future 53:21 Visions of Superintelligence: A Philosophical Perspective
The Journey Towards AGI Google Deep Mind CEO and Noble laurate Demis aabis 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 nuance 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 aabis Demis great to see you again welcome welcome to the show thanks for having me on the show definitely it’s great to be here so every research house right now is working toward building AI that mirrors human intelligence human level intelligence they call it AGI where are we right now in the progression and how long is it going to take to get there well look I mean of course the last uh few years has been an incredible amount of progress um actually you know maybe over the last decade plus um this is what’s on everyone’s lips right now and the debate is is how CL are we to AGI what’s the correct definition of AGI um we’ve been working on this for more than 20 plus years um we’ve sort of had a consistent view about AGI being a system that’s capable of exhibiting all the cognitive capabilities humans can um and I think we’re getting you know closer and closer but I think we’re still probably a handful of years away okay and so what is it going to take to get there so the models today are pretty capable of course we’ve all interacted with the language models and and now they’re becoming multimod model I think there are still some missing attributes things like reasoning um hierarchical planning uh long-term memory um there’s quite a few capabilities that uh the current systems uh I would say don’t have they’re also not consistent across the board you know they’re very very strong in some things but they’re still surprisingly weak and flawed in in other areas so you’d want a an AGI to have pretty consistent robust Behavior across the board all the cognitive tasks and I think one thing that’s clearly missing I always always had as a benchmark for for AGI was the ability for these systems to invent their own hypotheses or conjectures about science not just prove existing ones so of course that’s extremely useful already to prove an existing maths conjecture or something like that or or play a game of go to a world champion level but could a system invento could it come up with a new rean hypothesis uh or could it come up with relativity um back in the days that Einstein did it with the information that he had and I think today’s systems are still pretty far away from having that kind of creative uh inventive capability okay so a couple years away till we hit AGI I think um you know I I would say probably like three to five years away so if someone were to declare that they’ve reached AGI in 2025 probably marketing I think so I mean I think there’s a lot of um uh hype in the Current AI Capabilities and Limitations era of course uh I mean some of it’s very Justified I mean I would say that um AI research today is um over estimated in the short term um I think probably a bit overhyped at this point um but still underappreciated and um and some and very underrated about what it’s going to do in the medium to long term um so it’s sort of we’re still in that weird kind of space uh and I think part of that is you know there’s a lot of people that need to do fundraising a lot of startups and other things and so I think we’re going to have quite a few sort of Fairly outlandish and and and slightly exaggerated claims um and you know I think that’s a bit of a actually yeah in the AI in the AI products what’s it going to look like on the path there I mean you’ve talked about memory again planning um being better at some of the tasks that it’s not excelling at at the moment so when we’re using these AI products let’s say we’re using Gemini what are some of the things that we should look for in these domains that will make us say oh okay it seems like it’s that’s a step closer and that’s a step closer yeah so I think um uh today’s systems you know obviously we’re very proud of Gemini 2.0 I’m sure we’re going to talk about that but I feel like um they’re very useful for still quite Niche tasks right if you’re doing some research perhaps you’re summarizing some area of research incredible you know I use notebook LM and deep research all the time to kind of especially like um break the ice on a new area of research that I want to get into or summarize some you know maybe a fairly mundane set of documents or something like that so they’re extremely good for certain tasks and then people are getting a lot of value out of them um but they’re still not pervasive in my opinion in everyday life like helping me every day with my research my work my day-to-day um my daily life too and I think that’s where we’re going with our products with building things like um as project Astro our vision for Universal assistant is it should be uh involved in all aspects of your life and be enriching helpful and and making that more efficient and I think part of the reason is these systems are still fairly brittle partly because they are quite flawed still and they’re not agis and and you have to be quite specific for example with your prompts or you need a lot of there’s quite a lot of skill there in in coaching or guiding these systems to be useful and to stick to uh uh the areas they’re good at and and a true AGI system shouldn’t be that difficult to uh to coax it should be much more straightforward you know just like talking to another human yeah and then on the reasoning front you said that’s another thing that’s missing I mean that’s everybody’s talking about reasoning right now so how does that end up getting us closer to artificial general intelligence so reasoning and and Mathematics and other things and there’s a lot of progress on maths and coding and on but let’s take math for example you have systems uh uh 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 and for for various reasons um like the classic you know counting the number of RS in strawberries and so and and the word strawberry and so on and and um is 9.11 Mathematics and Reasoning in AI bigger than 9.9 and so and things like that and and and of course of course you can fix those things and we are and everyone’s improving on those systems but we shouldn’t really be seeing those kinds of flaws in a system that is that capable in other domains in more narrow domains of doing you know Olympiad level mathematics so there’s something still a little bit missing in my opinion about the robustness of uh these systems and then that’s I think that speaks to the generality of these systems a truly General system would not have those sorts of weaknesses it would be very very strong maybe even better than the best humans in some things like playing go or doing mathematics but it it would be overall consistently good now can you talk a little bit about how these systems are attacking math problems because you know I think the general understanding of these systems is the llms is they Encompass all the world’s knowledge and then they predict what you know as somebody might answer if they were asked a question but it’s kind of different different when you’re working step by- step through an algorithm or through a math problem yes that’s not enough of course you know just understanding the world’s information and and then trying to sort of almost compress that into your memory that’s not enough for solving a novel math problem or novel novel conjecture um so there you know we start needing to bring in I think we talked about this last time more kind of like alphao planning ideas into the mix with these large Foundation models which are now Beyond just language they’re multimodel of course um and there that what what you need to do is you need to have um your system uh uh uh uh not just pattern matching roughly what it’s seeing which is the model but also planning and being able to kind of go over uh uh that plan re you know re re re re revisit that branch and then go into a different direction until you find the right uh uh criteria or the right match to the criteria that you’re looking for and that’s very much like the the kind of games playing AI agents that we used to build for go chess and so on they had those um aspects and I think we got to bring them back in but now working in a more General way on these General models not just a narrow domain like games um and I think that also that approach of a model gu in a search or planning process so it’s efficient uh works very well with mathematics as well you can sort of turn maths into kind of game like search right and I want to ask about math like once these models get math right is that generalizable because I think there was like a whole hubub uh when people first learned about reasoning systems and they’re like oh this is like this is going to be a problem these these models are getting smarter than we can control because if they can do math then they can do X Y and Z so is that generalizable or is it like we’re going to teach them how to do math they can just do math I think for now uh uh the jury’s out on that I mean I feel like it’s a clearly a capability you want of a general AGI system uh it can be very powerful in itself obviously mathematics is is extremely General in itself um but it’s not clear you know maths and even coding and games these are areas they’re quite special uh uh uh areas of of knowledge because you can verify if the answer is correct right in all of those uh domains right the math you know the final answer the AI system puts out you Building World Models for AI can check whether that maths uh that solves the the the the conjecture or the problem so but most things in in the general World which is messy and IL defined um do not have easy ways to verify whether you’ve done something correct so that that puts a limit on these self-improving systems if they want to go beyond these areas of high you know maybe very highly defined spaces like mathematics coding or or games so how are you trying to solve that problem well you you you know you got to first of all you’ve got to build um General models World models we call them uh to understand the world around you the physics of the world um the Dynamics of the world the spa spatial temporal dynamics of the world and so on and the structure of the real world we live in and of course um you need that for a universal assistance so project Astro is our project built on Gemini to do that to understand you know objects and the and and the context around us I think that’s important if you want to have an assistant but also robotics requires too of course robots are physically embodied AIS and they need to understand their environment the physical environment the physics of the world so we’re building those uh types of models um and also you can you can also use them in simulation to understand game environment so that’s another way to bootstrap more data for to to understand uh you know the physics of a world um but the issue at the moment is that those models are not 100% accurate right so they you know maybe they’re accurate 90% of the time or even 99% of the time um but the problem is if you start using those models to plan maybe you’re planning a 100 steps in the future with that model even if you only have a 1% error in what the model’s telling you that’s going to compound over 100 steps to the point where you’ll be in a you know you’ll kind of get almost a random answer and so that makes the planning very difficult whereas with maths with gaming with C coding you can verify each step are you still grounded uh to reality um and is the final answer mapped to what you’re expecting and and so um I think part of the answer is to is to make the the world models more more sophisticated uh and more more accurate and and um and not hallucinate and all of those kinds of things so you get you know the errors are are really minimal another approach is to um plan not at each sort of uh uh uh linear time step but actually do what’s called hierarchical planning another thing we used to we’ve done a lot of research on in the past and I think it’s going to come back into Vogue where you plan at different levels of tempal abstraction so instead of that that could that could also alleviate the need for your model to be super super accurate because you’re not planning over hundreds of time steps you’re planning over only a handful of time steps but at different levels of abstraction how do you build a world model because you know I always thought it was going to be like our send robots out into the world and have them figure out how the world works but one thing that surprised me is with these video generation tools yes you would think that if the AI didn’t have a good World model then nothing would really fit together when they try to figure out how the world works as they show you these videos like V2 for instance but they actually get the physics pretty right yeah so can you get a world model just by showing an AI video do you have to be out in the world how’s this going to work it’s interesting and actually been pretty surprising I think to the extent of how far these models can go The Role of Active Learning in AI without being out in the world right as you say so VO2 our latest video model which is actually surprisingly uh uh accurate on things like physics you know uh there’s this uh this great demo that someone created of like uh chopping a tomato with a knife right and and and getting the slices of the Tomato just right and the fingers and all of that and vo is the first model that can do that you know if you look at other competing models they often the Tomato sort of randomly comes back together or yeah exactly splits from the knife um so those things are if you think that really hard you’ve got to understand consistency across frames all of these things and it turns out that you you know you can do that by using enough data and and viewing that um I think these systems will get even better if they’re some imped by some real world data like collected by an acting robot or even potentially in very realistic simulations where you have avatars that um act in the world too so I think that’s the next big step actually for agent based systems is to go beyond World models can you collect enough data where the agents are also acting in the world and making plans and achieving tasks um and I think for that you will need uh uh not just passive observation you will need actions active participation I think you just answered my next question which is if I if you develop AI that can reasonably plan and have and reason about the world and has a model of how the world works it can and it seems like that’s the answer it can be an agent that could go out and do things for you yes exactly and I think that’s that’s that’s what will unlock robotics I think that’s also what will then allow uh this notion of a universal assistant that can help you in your daily life across both the digital world and the real world um that’s what that’s that’s the thing we’re missing um and I think that’s going to be incredibly powerful and useful uh tool you can’t get there then by just scaling up the current models and building you know hundreds of thousand or million GPU clusters like elon’s doing right now and that’s not going to be the path to AGI um well look I actually think it so my view is a bit more nuanced than that is like that that the scaling approach is absolutely working of course that’s where we’ve why we got to where we have now um one can argue about are we getting diminishing returns or we what my view is that we are getting substantial returns but not but it’s slowing but but it would would have to I mean it’s it’s not just continuing to be exponential but that doesn’t mean the scaling is not working it’s absolutely working and we’re still getting you know you see Gemini 2 over Gemini 1.5 and by Creativity and Invention in AI the way the other thing that was working with the scaling is also making efficiency gains on the smaller size models so the the cost or the size per performance is is is is radically improving under the hood as well which which is very important for for scaling you know the adoption of these systems um but yeah so so you know you’ve got you’ve got the scaling part um and that’s absolutely needed to build more more sophisticated World models um but then I think we are missing or we need to reintroduce some ideas on the planning side uh memory side the searching side uh uh uh the reasoning to build on top of the model the model itself is not enough to be in AGI you need uh uh this other capability for it to to act in the world and solve problems for you and and then there’s still the additional question mark of the of the invention piece and the creativity piece true creativity be you know beyond uh mashing together uh what’s already known right so uh and that’s also unknown yet if if something new is required or again if existing techniques will eventually scale to that I can see both arguments and I think from my perspective it’s an empirical question we just got to push both the scaling and the invention part to the Limit and and fortunately at at Google deep mind we have you know a big enough group we we can invest in both those things so Sam Alman recently said uh something that caught people’s eye he said we are now confident we know how to build AGI as we have tra 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 what it was meaning I would agree with it is that we we roughly know the zones of techniques that required what’s probably missing which bits need to be put together but um that’s still incredible amount of research in my opinion that needs to be done to get that all to work even if that was the case and that’s and I think there’s a 50% chance we are U missing some new techniques you know maybe we need one or two more Transformer like breakthroughs and I and I think I’m genuinely uncertain about that so that’s why I say 50% so I mean I wouldn’t be surprised either way if we got there with existing techniques and things we already knew but put them together in the right way and scaled that up or if it turned out one or two things were missing 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 to call AGI in my opinion which is where everybody’s trying to go um I was rewatching the alphao documentary yeah and the algorithms make a creative move they do uh move 37 3 yes I just had it okay thank you uh that’s interesting because it was a couple years ago they the algorithms were already being creative yes why have we not really seen creativity from large language models I mean this is to me I think the greatest disappointment that people have with these tools is like they say this is very impressive work but it’s just limited to the training set will mix and match what it knows but it can’t come up with anything new yeah well look so what and I should probably write this up but what I sometimes talk Understanding Human Ingenuity through AI about in talks ever since the alphao match which is now you know plus years ago amazingly right that happened that was probably the reason that was such a water shared moment for AI was first of all there was the Everest of of of of you know um cracking go right which was always considered to be one of the Holy Grails of AI um so we did that um second thing was the way we did it which was these learning systems that were generalizable right eventually they became Alpha zero and and so on even play any two-player game and so on uh and then the third thing was this move 37 so not only did It win 41 it beat Lisa doal the great ladal 41 it also played original moves but so I I have three categories of of of of originality or creativity the most basic kind of mundane form is just interpolation which is like averaging of what you see so if I said to a system you know come up with a new picture of a cat and it’s seen a million cats and it produces just some kind of average of all the ones it’s seen in theory that’s an original cat because you won’t find the average in the the specific examples but it’s a pretty boring you know it’s not really very creative I would’t call that creativity that’s the lowest level next level is what alphao 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 um and now it comes up with a new strategy Ino that um 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 alphaa because clearly extrapolation beyond what we already know what’s in the training set um could be extremely useful so that’s already very valuable and and I think truly creative but there’s one level above that that humans can do which is inventg go can you invent me a game if I that you know if I specify to an abstract level you know takes five minutes to learn the rules U but a lifetime to many lifetimes to master it’s beautiful aesthetically encompasses some sort of mystical part of the universe in it that it’s beautiful to look at uh it but you can play a game in a human afternoon in 2 hours right that’s the that’s 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 and perfect as go now we can’t do that now the the question is why is it that we don’t know how to specify that type of goal to our systems at the moment um what’s the objective function is very amorphous it’s very abstract so I’m not sure if it’s just we need higher level uh more abstracted uh uh layers in our system systems um building more and more abstract models so we can talk to it in this way give it those kind of amorphous goals or is there a missing capability actually about that that we still have human intelligence has that are still missing from our systems and again I’m unsure about that which which way that is I can see arguments both ways and we’ll try both but I think the thing that people are upset or or not upset but people are disappointed by is they don’t even see a move 37 in today’s llms well and because Deception in AI Systems okay so well that’s because I don’t think we we have so if you look at alphao and I’ll give you an example of there which which maps to today’s llms so um you can run alpago 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 most the most pattern matched most likely good move okay and it can do that it’ll play reasonable game but it will only be around uh Master Level or possibly grandmas level it won’t be world champion level and it certainly won’t come up with um original moves that for that I think you need um the search component to get you beyond where the model knows about which is mostly summarizing existing knowledge to some new part of the tree of knowledge right so you can use the search to get beyond what the model currently understands and that’s where I think you can get uh new ideas like you know move 37 what’s it searching the web no so well it it depends on uh what the domain is searching that that Knowledge Tree so obviously in go it was searching go moves beyond what the model KN knew um I think for language models it would be searching the world model for new parts configurations in the world um that are useful so of course that’s so much more complicated which is why we haven’t seen it yet um but I think the agent based systems that are coming will be capable of move 37 type things so are we setting too high of a bar for AI because I’m curious if you’ve learned anything about Humanity doing this work yeah it seems like we almost give too much of a premium on Humanity or individual people’s Ingenuity where like a lot of us like we kind of take in stuff we spit it out like our society really works in memes like we have a cultural thing and it gets translated so um what do you what have you learned about like the nature of humans from doing the work with the AIS well look I I think humans are incredible and and and especially the best humans in the best domains I love watching any sports or or or talented musician or games player at the top of their game the absolute Pinnacle of human performance it’s always incredible no matter what it is um so I think as a species we’re amazing uh individual individually we’re also kind of amazing what everyone can do with their brains so generally right deal with new technologies I mean I’m always fascinated by how we just adapt to these things sort of almost effortlessly as a society and as individuals um so that speaks to the power and the generality of our minds um now the reason I had set the bar like that and I don’t think it’s a question of like can we get economic worth out of these systems I think that’s already coming very soon but that’s not what AGI shouldn’t be uh uh I think we should treat AGI with scientific Integrity not just move goal poost for commercial reasons or whatever it is hype and so on and there the the the definition of that was always having a system that was you know if we think about it theoretically that was capable of being as powerful as a touring machine so Alan churing one of my alltime scientific Heroes you know he described a touring machine which underpins all modern Computing right uh as a system that can simulate any other comp can compute anything that’s computable so we know we have the theory there that if an AI system is chewing powerful it’s called if it can simulate a cheing machine then it’s able to calculate anything in theory that is is is computable and the human brain is probably some sort of cheing machine at least that’s what I believe uh and so um in order for our to now and that I think that what AGI is is a system that’s truly General and in theory could be applied to anything and and the only way we’ll know that is if we um it exhibits all the cognitive capabilities that humans have assuming that human the human mind is a type of touring machine or is at least as powerful as a touring machine so that’s my always been my sort of bar um it seems like people are trying to re badge things as that as being what’s called ASI artificial super intelligence but I think that’s beyond that that’s after you have that system and then it starts going Beyond in certain domains what humans are capable of uh potentially inventing themselves okay so when I see everybody making the same joke on the same topic on Twitter it’s and I say oh that’s just us being llms I think I’m selling Humanity a little short uh well we’ll yes I guess so I guess so okay yeah I want to ask you about deceptiveness I mean one of the most interesting things I saw at the end of last year was that these AI Bots are starting to try to fool their evaluators and they don’t want their initial training uh uh rules to be thrown out the window so they’ll like take an action that’s against their values in order to be able to remain the way that they were built yes that’s just incredible stuff to me I mean I know it’s scary to researchers but it blows my mind that it’s able to do this uh are you seeing similar things and what and the stuff that you’re testing within Deep Mind and what are we supposed to think about all this yeah we are and um I’m very worried about uh I think deception specifically is one of the one of the core traits you really don’t want in a system the reason that’s like a kind of fundamental trait you don’t want is that if a system is capable of doing that it invalidates all the other tests that you you might think you’re doing including safety ones it’s testing and it’s like right it’s five year yeah it’s it’s playing some meta game right and then and that’s incredibly dangerous if you think about then invalidates all the all of the the results of your other tests that you might you know safety tests and other things you might be doing with it so I think there’s a handful of abilities like deception which are uh uh fundamental and you don’t want and you want to test early for and I’ve been encouraging the safety institutes and evaluation Benchmark Builders including and also obviously all the internal work we’re doing to to look at uh at Deception as a kind of class a thing that we need to prevent and monitor uh as important as tracking the performance and intelligence of the systems um the answer to this as well and one way to there’s many answers to the SA sa 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 worldclass here at security at Google and at deepmind and also we are worldclass at games environments and we can combine those two things together to kind of create digital sandboxes with guard rails around them sort of the kind of guard rails you’d have for for cyber security but internal as well as blocking external actors and um and then test these agent systems in those kind of secure sandboxes that would probably be a good advisable next step for things like deception Y what sort what sort of deception have you seen because I just read a paper from anthropic where they gave it a a sketch a sketch pad yeah and it’s like oh I better not tell them this then you see it like give a result after thinking it through so what type of deception have you seen from the B well look we we’ve seen similar types of things where it’s trying to um resist sort of re revealing its it’s it’s it some of its training or you know I think there was an example recently of um one of the chat Bots being told to play against stockfish and it just sort of hacks its way around playing stockfish at all at chess because it knews it would lose so but you know you had an AI that knew it was going to lose a game and decided to I think we’re anthropomorphizing these things quite a lot at the moment because I feel like these systems are still pretty basic I get too alarmed about them right now but I think it it it it it shows the type of issue we’re going to have to deal with maybe in 2 three years time when these agent systems become quite powerful and quite General so and that’s exactly what AI safety uh 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 rep report that back reliably but for whatever reason it’s uh interpreted the goal it’s been given in a way where it causes it to do these undesirable behaviors I know I’m having a weird reaction to this but in on one hand this scares The Living Daylights out of me on the other hand it makes me respect these models more than anything it’s like go well look of course you know these are it’s impressive capabilities and and and The Dual Nature of AI: Opportunities and Risks and the the the the you know the the negatives are things like deception but the positives would be things like inventing you know new materials accelerating science you need that kind of uh ability to problem solve and get around you know uh issues that are blocking progress um but of course you want that only in the positive direction right so those exactly the kinds of capabilities I mean they are you know uh it’s kind of mind-blowing we’re talking about those those possibilities but also at the same time uh there’s risk and it’s scary so I think both the things are true wild yeah all right let’s talk about product quickly sure um 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 uh what do you think is going to happen with the web because obviously the web is so important to Google I had an editor that told me he was like oh you’re going to speak speak with Demus asking what happens when we stop clicking right we’re clicking through the web at all times the they Rich uh Corpus of websites that we use um if we’re all just dialoguing with AI then maybe we don’t click anymore so what do you what is your scenario plan for what happens to the web well look I think there’s um The Future of the Web: Agent-Based Interactions it’s going to be there’s going to be a very interesting phase in the next few years on the web and and the way we we interact with websites and apps and so on um you know if everything becomes more agent based then I think we’re going to want our assistants and our agents to do a lot of the work and a lot of the mundane work um that we currently do right um you know fill in forms make payments uh you know book tables this kind of thing so you know I think that we’re going to end up with uh probably a sit a kind of Economics model where agents talk to other agents and negotiate things between themselves and then give you back the results right and you’ll have the service providers with agents as well that are offering services and maybe there’s some uh uh uh bding and cost and things like that involved uh and efficiency and then I hope from the user perspective you know you have this assistant that’s super capable that you can just like a brilliant uh 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 uh the structure of of the web and the way we currently use lot of middleman yeah sure but there will be many other I think there’ll be incredible other opportunities that will appear economic and otherwise uh based on this this change but I I think it’s going to be a big disruption and what about information well uh I mean finding information I think uh you’ll still need the reliable sources I think you’ll have assistance that um able to synthesize and and and and help you kind of understand that information um I think education is going to be revolutionized by AI um so uh again I I hope that uh uh these assistants will will be able to uh more efficiently gather information for you and perhaps you know what I dream of is again assistants like take care of a lot of the mundane things perhaps replying to you know everyday emails and other things so that you have you protect your own mind and brain space from this bombardment we’re getting today from social media and emails and so on and texts and so on so it actually blocks deep work and and being in flow and things like that which I I value very much so I would quite like these assistants to take away uh a lot of the uh the mundane aspects of of admin that we do every day what’s your best guest as to what type of relationships we’re AI Assistants: Transforming Personal and Work Life going to have with our AI agents or AI assistants so there’s on one hand you could have a dispassionate agent that’s just like really good at getting stuff done for you on the other hand like it’s already clear that people are like falling in love with these Bots there was a New York Times article last week about someone who’s falling in love with hat PT like for real falling in love and I had uh the CEO of replica on the show a couple weeks ago and she said that they are regularly invited to marriages uh of people who are marrying their replicas and they’re moving into this more assistive space so do you think that when we when we start interacting with something that knows us so well uh that helps us with everything we need yeah is it going to be like a third type of relationship where it’s not necessarily a friend not a lover but it’s going to be a deep relationship don’t you think yeah it’s going to be really interesting I think the way I’m modeling that first of all is uh two at least two domains first of all which is your your personal life and then your work life right so I think you’ll have this notion of virtual workers or something maybe we’ll have a set of them or managed by a you know a lead assistant that does a lot of the uh uh helps us be way more productive at work you know or or whether that’s email across workspace or whatever that is so we’re really thinking about that then there’s a personal side where you know we’re talking about earlier about all these um uh booking holidays for you avenging things mundane things for you sorting things out um and then uh that makes your life more efficient I think it can also enrich your life so recommend you things that amazing things that it knows you as well as you know yourself um so those two I think are definitely going to happen and then I think there is a a philosophical discussion to be had about is there a third space where these things start becoming so integral to your life they become more like companions I think that’s possible too we seen that a little bit in gaming so you may have seen we had a little prototypes of Astro working in and Gemini working with like being almost a game companion commenting in you almost 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 it’s it’s very fun um so I I haven’t you know quite through thought through all the implications of that but they’re going to be big uh and I’m sure there is going to be demand for companionship and other things maybe the good side of that is or help with loneliness and these sorts of things but there’s also you know I think it’s going to be it’s going to have to be really carefully thought through by Society whether uh you know what directions we want to take that in I mean my personal opinion is that that it’s the most underappreciated part of AI right now and that people are just going to form such deep relationships with these Bots as they get better because like I know as a meme in AI that this is the worst it’s ever going to be yeah and uh it’s going to be crazy Happ I think I think it’s going to be pretty crazy this is what I meant about the under under appreciating what’s to come I still don’t think this this kind of thing I’m talking about right I think that it’s going to be really crazy it’s going to be very disruptive um I think there’s going to be lots of positives out of it too and lots of things will be amazing and better but there are also risks with this new Brave New World we’re going into so you brought up Astra a couple times uh let’s just talk about it’s project Astra as you call it yeah it is almost an always Project Astra: The Next Generation of AI Assistants 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 um you can say okay where am I and it’ll be like oh you’re in a podcast Studio anything okay so it could have this contextual awareness yes uh can that work without smart glasses because it’s really annoying to hold my phone up so like when are when are we going to see Google smart glasses with this technology embedded they’re coming so we we teased it in some of our early prototype so that we’re mostly prototyping on on on phones currently because they have more processing power but we’re of course Google’s always been a leader in in glasses yeah and exactly just a little too early yeah maybe a little too early and now I think and with they’re super excited that team is that you know maybe this assistant is the killer use case that glasses has always been looking for and I think it’s quite obvious when you when you start using Astro in your daily life which we have with truster testers at the moment and in kind of beta form um there are many use cases where it would be so useful to use it but it’s a bit it’s it’s inconvenient that you’re holding the phone so one example is while you’re cooking for example right and and it can advise you what to do next the menu you know how to whether you’ve chopped the thing correctly or or or or fried the thing correctly but you want it to just be handsfree right so I think that um uh glasses and maybe other form factors uh that are handsfree will uh come into their own in the next few years and and we we we we you know we plan to be at the Forefront of that other form factors well you could imagine earbuds with cameras and you know glasses is obvious next stage but is that the optimal form prob probably not either um but partly we’ve also got to see we’re still very early in this journey of seeing what are the are the um the the regular user Journeys and killer sort of use Journeys that everyone uses a bread and butter uses every day and that’s what the the trust ATT tester program is for at the moment who’re kind of collecting uh 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 um agentic agents AI agents this has been the buzzword in AI for more than a year now yeah there aren’t really any AI agents out there no what’s going on yeah well again you know I think the hype train can potentially is ahead of where the the the the actual science and research is but I do believe that this year will be the year of Agents um the beginnings of it I think you’ll start seeing that uh you know uh La maybe second half of this year uh but there’ll be the early versions and then um you know I think they’ll rapidly improve and mature so um but I think you’re right I think the the the technology at the moment it’s still in the research lab the agent Technologies um but things like Astra robotics I think it’s coming you think people are going to trust them I mean it’s like go use the internet for me here’s my credit card I don’t know well so I think to begin with you would probably my my view at least would be to not allow have human in the loop for the final steps like like don’t pay for anything use your credit card unless the the the human user operator authorizes it so that would to me be a sensible First Step also perhaps um certain types of activities or websites or whatever kind of off limit you know banking websites and other things in the first phase uh while we continue to test out in the world that that how robust these systems are I propose we’ve really reached AGI when they say don’t worry I won’t spend your money and then they do the deceptiveness thing and then next thing you know you’re on a flight somewhere yes yeah that would be that would be that would be getting closer for sure for sure yeah all right science so um you worked on basically uh Decoding Biology: The Virtual Cell Project decoding all protein folding without fold you won the Nobel Prize for that not to skip over the thing that you won the Nobel Prize for but I want to talk about what’s on the road map which is that you have um an interest in mapping of virtual cell yes uh what is that and what does it get us yeah well so if you think about what we did with Alpha fold was essentially solve the problem of the the the the finding the structure of a protein and proteins everything in life depends on proteins right everything in your body um so that’s the kind of static picture of a protein but the thing about biology is really it’s you only understand what’s going on in biology if you understand the Dynamics and the interactions between the different things in in a cell and so a virtual cell project is about building a simulation an AI simulation of a full working cell I probably start with something like a yeast cell because of the Simplicity of of the yeast organism and um and you have to build up there so the next step is with Alpha 3 for example we started doing pairwise interactions between protein and lians 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’ be helpful for solving a disease and then finally a whole cell and the reason that’s important is you would be able to hypoth make hypotheses and test those hypotheses about making some change some nutrient change or injecting a drug into the cell and then seeing what happens to the how the cell responds um and at the moment of course you have to do that pain mistakingly in a wet lab but imagine if you could do it a thousand a million times faster in silico first and only at the last step do you do a validation in the wet lab so instead of doing the search in in in the wet lab which is millions of times more expensive and timec consuming than the validation step you just do the search part um in silico so it’s again it’s sort of translating again what we did in the games environments uh but here in the sciences and the biology so you you build a model and then you use that to do the reasoning and the search over and then the predictions are you know at least better than not maybe they’re not perfect but they’re useful enough to um to be useful for experimentalists to to validate against and the wet lab is within people yeah so the wet lab uh you you you’d still need a final step with the with the wet lab to prove that uh what the predictions were actually valid so you know you but you wouldn’t have to do all of the work to get to that prediction in in in the wet lab so you just get here’s the prediction if you put this chemical in this should be the change right and then you just do that one experiment so and then after that of course you still have to have clinical trials if you’re talking about a drug you would still need to test that properly through the clinical trials and so on and test it on humans for efficacy and so on that I also think could be improved with AI that whole clinical TR that’s also takes many many years but this that would be a different technology from the virtual cell the virtual cell would be would be helping the discovery phase for drug Discovery just like I have idea for a drug throw it in the virtual cell see what it does yeah and maybe eventually it’s a liver cell or or a brain cell or something like that so you have different cell models and then you know at least 90% of the time it g is giving you back what would really happen that’ be incredible how how long do you think that’s going to take toig I think that’ll be like um maybe 5 years from now yeah yeah so I have a kind of fiveyear project and a lot of the alpha fold the old Alpha Team are working on that yeah I was asking your team here so you you figured out yeah I speaking with him I was like you figur out uh protein folding what’s next and this is like it’s just very cool to hear about these new challenges because yeah the H developing drugs is a mess yeah right now we have so many promising ideas they never get out the door because just the process is absurd it’s process too slow and Discovery phase too slow I mean look how long we’ve been working on Alzheimer’s and and I mean in this tragic way to for someone to go and for the families and and and you know we should be a lot further it’s 40 years of work on that yeah yeah I’ve seen it a Genomics and AI: Understanding Genetic Mutations couple times in my family if we can ensure that doesn’t happen it’s just one of the best things we could use AI in my opinion yeah it’s ter terrible way to see somebody uh decline so yeah it’s important work um in addition to that there’s the genome yes and so the Human Genome Project sort of I was like okay so they decoded the whole genome there’s no more work to do there like just same way that you decoded proteins with fold but it turns out that actually we just have like a bunch of letters when it’s decoded and so now you’re working to use it to translate what those letters mean yes so yeah we have lots of cool work uh on on genomics and um uh uh uh trying to figure out if mutations are going to be harmful or or benign right most mutations to your DNA are are are harmless U but of course some are pathogenic and you want to know which ones there are so our first uh systems um are have are the best in the world at predicting that um and then um uh uh the next step is to to look at uh uh sit situations where the disease isn’t caused just by one genetic mutation but maybe a series of them in concert and obviously that’s a lot harder like and a lot of more complex diseases that we haven’t made progress with are probably not due to a single mutation right that’s more like rare childhood diseases things like that um so there you know we need to I think AI is the Perfect Tool uh to to to sort of um uh uh try and figure out what these weak interactions uh alike right how they maybe um uh uh uh kind of compound on top of each other um and so maybe the statistics are not very obvious but an AI system that’s able to kind of spot patterns would be able to figure out there is some connection here and so we talk about this a lot in terms of disease but also I wonder what happens in terms of uh making people superhuman I mean if you’re really able to Tinker with the genetic code right the possibilities seem endless so what do you think about that is that something that we’re going to be able to do through AI I think one day I mean we’re focusing much more on on the on the disease profile and fixing yeah that’s the first step and and I’ve always felt that that’s the most important if you ask me what’s the number one thing I wanted to use AI for and the most important thing we use AI for is for helping human health um but then of course beyond that one could imagine uh aging things like that you know is of course there’s a whole field in itself is aging a disease is it a combination of diseases can we extend um our healthy lifespan um these are all important questions and I think very interesting and I’m I’m pretty sure AI will be extremely useful in helping us The Future of Aging: Extending Healthy Lifespan find answers to those questions too you I see Memes come across my Twitter feed and maybe I need to change the stuff I’m recommended but it’s often like if you will live to 2050 you’re not going to die yeah uh what do you think the potential Max lifespan is for a person well look I know those a lot of those folks in aging research very well I think it’s very interesting the pioneering work they they do um I think there’s nothing good about getting old and your body decaying I think it’s you know if anyone who’s seen that up close with their relatives it’s a pretty hard thing to go through right as a family or or the or the person of course and um and and so I think anything we can alleviate human suffering and and extend healthy lifespan is a good thing um you know the natural limit seems to be about 120 years old but um from what we know you know if you look at the oldest people that that that are lucky enough to to to live to that age so there’s you know it’s it’s it’s it’s a it’s an area I follow quite closely I don’t have any I I guess new insights that are not already known in that um but I do I I would be surprised if there if that’s if that’s the limit right because there’s a sort of two steps to this one is curing all diseases one day which I think we’re going to do with isomorphic and the work we’re doing there our spin out our drug Discovery spin out um but then that’s not enough to probably get you past 120 because there’s some sort of then there’s the question of just natural systemic Decay right Aging in other words so not specific disease right uh often those people that live to 120 they don’t seem to die from a specific disease it’s just sort of just general atrophy um so then you’re going to need something more like Rejuvenation where you you you rejuvenate your cells or you you know maybe stem cell research you know companies like Altos are are are working on these things resetting the the cell clocks um seems like that could be possible but again I feel like it’s so complex because biology is such a comple licated emerg system you need a in my view you need AI to help to to be able to crack anything anything close to that very quickly on Material Science I don’t want to leave here without talking about the fact that you’ve discovered many new materials or potential materials uh this stat I have here is known to humanity uh recently were 30,000 stable uh materials you’ve discovered 2.2 million with a new AI program yeah um just dream a little bit Material Science: Discovering New Materials for the Future because we don’t know what all those materials can do we don’t know what you know whether they’ll be able to handle being out of like a frozen box or whatever uh dream materials for you to find in that set of new materials well I mean we’re working really hard on materials to me it’s like the next uh one of the next sort of uh big uh impacts we can have like the level of alpha fold really in biology but this time in chemistry in materials you know I dream of uh one day discovering a room temperature superconductor so what will that do that’s another big meme that people talk about well it would help with the energy crisis and climate crisis because um if you had sort of cheap uh superconductors you know then you can transport energy from one place to another without any loss of that energy right so you could potentially put solar panels in the Sahara Desert and then just have a the the the the superconductor you know uh funneling that into Europe where it’s needed at the moment you would just lose a ton of the power to heat and other things on the way so then you need other technology like batteries and other things to store that because you can’t you can’t just pipe it to the place that you want without without without being incredibly inefficient so uh but also materials could help with things like batteries too like but come up with the optimal battery I don’t think we have the optimal battery designs um that maybe we can do things like uh combination of materials and and and proteins we can do things like carbon capture you know modify uh uh algae or other things to to do carbon capture uh better than um uh our artificial system um I mean even the one of the most famous and most important chemic chemical processes the harbor process to make fertilizer and ammonia you know to take nitrogen out of the air um was was was something that allows modern civilization uh but there might be many other uh chemical processes that could be catalyzed in that way if we knew what the right Catalyst and the right material was um so uh I think it’s going to be would be one of the most impactful Technologies ever is to to to basically have in silico design of materials so we’ve done step one of that where we showed we can come up with new stable materials but we need a way of testing the properties of those materials because no lab can test 200,000 you know tens of thousands of materials or millions of materials at the moment so we have to that’s that’s the hard part is to is to do the testing you think it’s in there the room temperature superconductor think um well I heard that we we actually think there are some super conducted materials I I I I doubt their room temperature ones though but uh I think think at some point if if it’s possible with physics um an AI system will one day find it so that’s one use uh 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 Manu I mean look I think there is incredible one I mean big part of my early career was in game design yeah theme park and simulations that’s what got me into simulations and AI in the first place and why I’ve always loved both of those things and if in in many respects of the work I do today is just an extension of that um and and I I just dream about like what could I have done what kinds of amazing game experiences could have been made if IID 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 starting to see some crazy stuff with NPCs that like are starting but but of course that be like intelligent you know dynamic story lines uh but also just new types of AI first games with learning consist with with characters and and agents that can learn um and uh you know well once worked on a game called black and white where you had a creature that you were nurturing was a bit like a pet dog that that that leared what you wanted right and but we were we were using very basic reinforcement learning this was like in the late ’90s you know imagine what could be done today uh and I think the same for for maybe smart toys as well right um and then course on the militaries you know uh unfortunately AI is a du Dual Purpose technology so one has to confront the reality that um especially in today’s geopolitical world uh people are using some of these general purpose Technologies to apply to drones and other things and um it’s not surprising that that works are you impressed with what China’s up to I mean deep seek is this new model impressive um it’s a little bit unclear how much they relied on on Western systems to do that you know both training data there’s some rumors about that uh uh and and and also maybe using some of the open source models to as a starting point um um but look it’s for sure it’s impressive what they’ve been able to do um and um you know I think that’s something we’re going to have to think about how to keep uh the Western frontier models in in the lead I think they still are at the moment but um you know for sure China is very very capable engineering and and scaling let me ask you one final question um just give us your vision of what a world looks like when there’s super intelligence so let’s move past we started with AGI let’s know super Visions of Superintelligence: A Philosophical Perspective intelligence yeah well look I think for there you you you two things there one is um I think a lot of the best sci-fi can we can look at as as interesting models to debate about what kind of uh uh Galaxy or or or Universe do we want to a world do we want to to to to move towards and the one I’ve always liked most is actually the culture series by Ian Banks um I started reading that back in the ’90s and and I think that is a picture it’s it’s like a thousand years into the future but it’s in a post AGI world where there are AGI systems coexisting with Human Society and also alien society and we we’ve Humanity’s basically maximally flourished and spread to the Galaxy and um I 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 I think there is a need for great philosophers to you know where are they the great next philosophers that the equivalence of K or Vicken Stein or even Aristotle um uh I think we’re going to need that to to to help navigate Society to that next step because I think the you know a AGI and artificial superintelligence is going to change um uh humanity and The Human Condition Demis thank you so much for doing this great to see you in person and hope to do it again soon thank you thank you very much all right everybody thank you for listening and we’ll see you next time on big technology podcast