[0.0][MUSIC PLAYING][3.311] [6.127][S02] Welcome to "Google DeepMind, the Podcast" with me,[8.71] [8.71][S02]your host, Professor Hannah Fry.[10.38] [10.38][S02]Now, when we first started thinking[12.18] [12.18][S02]about making this podcast way back in 2017,[15.82] [15.82][S02]DeepMind was this relatively small, focused AI research lab.[20.35] [20.35][S02]They'd just been bought by Google[21.9] [21.9][S02]and given the freedom to do their own quirky research[24.93] [24.93][S02]projects from the safe distance of London.[28.02] [28.02][S02]How things have changed.[29.65] [29.65][S02]Because since the last season, Google[31.26] [31.26][S02]has reconfigured its entire structure,[33.61] [33.61][S02]putting AI and the team at DeepMind[35.85] [35.85][S02]at the core of its strategy.[37.95] [37.95][S02]Google DeepMind has continued its quest[40.5] [40.5][S02]to endow AI with human-level intelligence,[44.16] [44.16][S02]known as artificial general intelligence, or AGI.[47.34] [47.34][S02]It has introduced a family of powerful new AI models called[51.15] [51.15][S02]Gemini, as well as an AI agent called[53.76] [53.76][S02]Project Astra that can process audio, video, image, and code.[58.39] [58.39][S02]The lab is also making huge leaps[61.74] [61.74][S02]in applying AI to a host of scientific domains,[64.76] [64.76][S02]including a brand new third version of AlphaFold,[67.73] [67.73][S02]which can predict the structures of all of the molecules[70.33] [70.33][S02]that you will find in the human body, not just proteins.[73.39] [73.39][S02]And in 2021, they spun off a new company, Isomorphic Labs,[77.84] [77.84][S02]to get down to the business of discovering[80.05] [80.05][S02]new drugs to treat diseases.[82.37] [82.37][S02]Google DeepMind is also working on powerful AI agents that[85.84] [85.84][S02]can learn to perform tasks by themselves using reinforcement[89.8] [89.8][S02]learning, and continuing that legacy of AlphaGo's[93.19] [93.19][S02]famous victory over a human in the game of Go.[96.59] [96.59][S02]Now, of course, you'll all have been following this podcast[99.927] [99.927][S02]since the beginning.[100.76] [100.76][S02]You'll all be familiar with the stories[102.94] [102.94][S02]behind all of those changes.[104.72] [104.72][S02]But just in case you are coming to us fresh, welcome.[108.2] [108.2][S02]You can find our first award-winning previous seasons[111.778] [111.778][S02]on Google DeepMind's YouTube channel,[113.32] [113.32][S02]or wherever you get your podcasts.[115.13] [115.13][S02]They also, those episodes go into detail[117.58] [117.58][S02]about a lot of the themes that we're[119.53] [119.53][S02]going to hear come up over and over again from the people[122.2] [122.2][S02]here, like reinforcement learning, deep learning,[125.03] [125.03][S02]large language models and, so on.[126.59] [126.59][S02]So have a listen.[128.229] [128.229][S02]They are really good, even if we do say so ourselves.[131.63] [131.63][S02]Now, all of the newfound attention[134.53] [134.53][S02]on AI since the last series does mean that there are quite a few[138.85] [138.85][S02]more podcasts out there for you to choose from.[141.35] [141.35][S02]But on this podcast, in just the same way as we always have,[144.86] [144.86][S02]we want to offer you something a little bit different.[147.11] [147.11][S02]We want to take you right to the heart of where these ideas are[151.12] [151.12][S02]coming from to introduce you to the people who[154.57] [154.57][S02]are leading the design of our collective future--[158.18] [158.18][S02]no hype, no spin, just compelling discussions[161.62] [161.62][S02]and grand scientific ambition.[164.3] [164.3][S02]So with all of that in mind, I am here[166.81] [166.81][S02]with the DeepMind co-founder and now CEO[169.66] [169.66][S02]of Google DeepMind, Demis Hassabis.[172.21] [172.21][S02]So with all of that in mind, do I have to call you Sir Demis[176.15] [176.15][S02]now?[176.65] [176.65][S01] No, absolutely not.[178.575] [178.575][S02] OK.[179.2] [179.2][S02]Well, Demis, welcome to the podcast.[180.59] [180.59][S01] Thank you.[181.39] [181.39][S02] Thank you very much for being here.[183.348] [183.348][S02]OK, I want to know, is your job easier or harder[186.37] [186.37][S02]now that there has been this explosion in public interest?[191.14] [191.14][S01] I think it's double edged.[193.04] [193.04][S01]I think it's harder because there's[195.13] [195.13][S01]just so much scrutiny, focus, and actually quite[197.38] [197.38][S01]a lot of noise in the whole field.[200.09] [200.09][S01]I actually preferred it when it was less people,[202.63] [202.63][S01]and maybe a little bit more focused on the science.[206.23] [206.23][S01]But it's also good because it shows that the technology is[209.86] [209.86][S01]ready to impact the real world in many different ways,[213.16] [213.16][S01]and impact people's everyday lives in positive ways.[216.29] [216.29][S01]So I think it's exciting, too.[217.882] [217.882][S02] Have you been surprised[219.34] [219.34][S02]by how quickly this has caught the public's imagination?[221.673] [221.673][S02]I mean, I guess you would have expected that eventually people[224.68] [224.68][S02]would have got on board.[225.68] [225.68][S01] Yes, exactly.[226.888] [226.888][S01]So at some point, those of us who've[230.47] [230.47][S01]been working on it like us for many years now, even decades,[234.56] [234.56][S01]so I guess at some point the general public[237.19] [237.19][S01]would wake up to that fact.[238.58] [238.58][S01]And effectively, everyone's starting[240.76] [240.76][S01]to realize how important AI is going to be.[242.99] [242.99][S01]But it's been quite surreal still[244.54] [244.54][S01]to see that actually come to fruition, and for that[247.45] [247.45][S01]to happen.[248.47] [248.47][S01]And I guess it is the advent of the chat bots and language[251.32] [251.32][S01]models because everyone, of course, uses language.[254.24] [254.24][S01]Everyone can understand language.[255.8] [255.8][S01]So it's an easy way for the general public[258.04] [258.04][S01]to understand and maybe measure where AI has got to.[262.24] [262.24][S02] I heard you describe these chat bots[264.34] [264.34][S02]as though they were unreasonably effective, which I really like.[267.903] [267.903][S02]And actually, later in the podcast[269.32] [269.32][S02]we are going to be discussing transformers, which[271.39] [271.39][S02]was the big breakthrough, I guess-- the big advance[274.87] [274.87][S02]that gave us those tools.[277.07] [277.07][S02]But tell me first, what do you mean by unreasonably effective?[281.145] [281.145][S01] What I mean by it[282.52] [282.52][S01]is I suppose if one were to wind back 5, 10 years ago,[286.27] [286.27][S01]and you were to say the way we're[289.27] [289.27][S01]going to go about this is build these amazing architectures,[292.48] [292.48][S01]and then scale from there, and not necessarily crack[295.81] [295.81][S01]specific things like concepts or abstractions.[299.878] [299.878][S01]These are a lot of debates we would have 5,[301.67] [301.67][S01]10 years ago is do you need a special way of doing[304.09] [304.09][S01]abstractions?[305.19] [305.19][S01]The brain certainly seems to do that.[307.35] [307.35][S01]But yet somehow, the systems, if you give them enough data--[310.39] [310.39][S01]i.e.[310.89] [310.89][S01]The whole internet-- then they do[313.47] [313.47][S01]seem to learn this and generalize[315.3] [315.3][S01]from those examples-- not just rote memorize,[318.31] [318.31][S01]but actually somewhat understand what they're processing.[323.76] [323.76][S01]And it's a little bit unreasonably effective[325.95] [325.95][S01]in the sense that I don't think anyone[327.84] [327.84][S01]would have thought that it would work as well as it has done,[330.96] [330.96][S01]say, five years ago.[332.02] [332.02][S02] Yeah.[332.728] [332.728][S02]I suppose it is a surprise that things[334.47] [334.47][S02]like conceptual understanding and abstraction[337.29] [337.29][S02]have emerged rather than been--[338.715] [338.715][S01] Yes, and we would have been--[340.59] [340.59][S01]probably we discussed last time things like concepts[343.41] [343.41][S01]and grounding--[345.374] [345.374][S01]grounding language in real world experience,[348.1] [348.1][S01]maybe in simulations or as robots embodied intelligence,[352.08] [352.08][S01]would have been necessary to really understand[355.44] [355.44][S01]the world around us.[356.92] [356.92][S01]And of course, these systems are not there yet.[360.13] [360.13][S01]They make lots of mistakes.[361.33] [361.33][S01]They don't really have a proper model of the world,[364.41] [364.41][S01]but they've got a lot further than one might expect just[368.26] [368.26][S01]by learning from language.[370.03] [370.03][S02] I guess we probably should actually[371.23] [371.23][S02]say what grounding is for those who haven't listened[373.397] [373.397][S02]to series 1 and series 2.[374.53] [374.53][S02]Because this was a big thing.[375.74] [375.74][S02]I mean, we were talking about this a lot.[377.81] [377.81][S02]So do you want to just give us an overview[379.18] [379.18][S02]of what grounding is?[380.055] [380.055][S01] Grounding is when--[383.08] [383.08][S01]one of the reasons the systems that were built[385.24] [385.24][S01]in the '80s and '90s, the classical AI systems built[388.09] [388.09][S01]at places like MIT, they were big logic systems.[391.04] [391.04][S01]So you can imagine them as huge databases of words[393.37] [393.37][S01]connected to other words.[394.76] [394.76][S01]And the problem was you could say something, a dog has legs,[398.99] [398.99][S01]and that would be in the database.[400.46] [400.46][S01]But the problem was, as soon as you showed it[402.335] [402.335][S01]a picture of a dog, it had no idea that collection of pixels[405.07] [405.07][S01]was referring to that symbol.[406.735] [406.735][S01]And that's the grounding problem.[408.11] [408.11][S01]So you have this symbolic representation,[410.09] [410.09][S01]this abstract representation, but what does it really[412.3] [412.3][S01]mean in the real world--[413.93] [413.93][S01]in the messy real world?[415.34] [415.34][S01]And then, of course, they tried to fix that,[417.62] [417.62][S01]but you never get that quite right.[419.77] [419.77][S01]And instead of that, of course, today's systems,[422.03] [422.03][S01]they're directly learning from the data.[424.31] [424.31][S01]So in a way, they're forming that connection[426.46] [426.46][S01]from the beginning.[427.4] [427.4][S01]But the interesting thing was that if you learn just[429.79] [429.79][S01]from language, in theory, there should[432.16] [432.16][S01]be missing a lot of the grounding that you need.[434.53] [434.53][S01]But it turns out that a lot of it is inferrable somehow.[437.3] [437.3][S02] Why, in theory?[438.49] [438.49][S01] Well, because where[439.33] [439.33][S01]is that grounding coming from?[440.39] [440.39][S01]These systems, at least the first large language models--[442.81] [442.81][S02] Don't exist in the real world.[443.56] [443.56][S01] --don't exist in the real world.[444.74] [444.74][S01]They're not connected to simulators.[446.36] [446.36][S01]They're not connected to robots.[447.98] [447.98][S01]They don't have any access to even--[450.323] [450.323][S01]they weren't multimodal to begin with, either.[452.24] [452.24][S01]They don't have access to the visuals or anything else.[455.78] [455.78][S01]It's just purely they live in language space.[457.91] [457.91][S01]So they're learning in an abstract domain,[461.27] [461.27][S01]so it's pretty surprising they can then infer some things[464.17] [464.17][S01]about the real world from that.[465.673] [465.673][S02] Which makes sense if the grounding gets in by people[468.34] [468.34][S02]interacting with the system and saying[469.75] [469.75][S02]that's a rubbish answer, that's a good answer.[471.5] [471.5][S01] Yes.[472.333] [472.333][S01]So for sure, part of that, if the[473.95] [473.95][S01]question that they're getting wrong,[475.52] [475.52][S01]the early versions of this, was due to grounding missing--[479.35] [479.35][S01]actually, the real world dogs bark in this way[481.57] [481.57][S01]or whatever it is-- and it's answering it incorrectly,[484.28] [484.28][S01]then that feedback will correct it.[486.2] [486.2][S01]And part of that feedback is from our own grounded knowledge.[489.77] [489.77][S01]So some grounding is seeping in like that for sure.[492.4] [492.4][S02] I remember seeing a really nice example[494.525] [494.525][S02]about crossing the English Channel versus walking[496.87] [496.87][S02]across the English Channel.[498.22] [498.22][S01] Exactly, those kinds of things.[499.49] [499.49][S01]And if it answered wrong, you would tell it it's wrong.[501.782] [501.782][S01]And then it would have to slightly figure out[503.77] [503.77][S01]that you can't walk across the Channel.[506.285] [506.285][S02] So some of these properties[507.91] [507.91][S02]that have emerged that weren't necessarily[510.1] [510.1][S02]expected to be, I want to ask you a little bit about hype.[513.44] [513.44][S02]Do you think that where we are right now,[516.77] [516.77][S02]how things are at this moment, is overhyped or underhyped?[521.679] [521.679][S02]Or is it just hyped, perhaps, in the wrong direction?[524.125] [524.125][S01] Yeah, I think it's more the latter.[526.25] [526.25][S01]So I would say that in the near term, it's hyped too much.[531.87] [531.87][S01]So I think people are claiming can do all sorts of things it[534.37] [534.37][S01]can't.[534.87] [534.87][S01]There's all sorts of startups and VC money chasing crazy ideas[539.05] [539.05][S01]that are just not ready.[541.48] [541.48][S01]On the other hand, I think it's still underhyped.[543.522] [543.522][S02] Coming from you, Demis--[545.022] [545.022][S01] Yes, I know, I know, I know.[547.01] [547.01][S02] AI in 2010.[548.11] [548.11][S01] Exactly, exactly.[549.485] [549.485][S01]But I think it's still underhyped or perhaps[552.96] [552.96][S01]underappreciated still even now what's[555.63] [555.63][S01]going to happen when we get to AGI and post-AGI.[558.66] [558.66][S01]I still don't feel like that's people[561.342] [561.342][S01]are quite understood how enormous that's going to be,[563.55] [563.55][S01]and therefore, the responsibility of that.[566.35] [566.35][S01]So it's both, really.[568.0] [568.0][S01]I think it's a little bit overhyped[569.58] [569.58][S01]in the near term at the moment.[572.56] [572.56][S01]We're going through that cycle.[574.207] [574.207][S02] I guess, though, so in terms of all of these[576.54] [576.54][S02]potential startups, and VC funding,[578.22] [578.22][S02]and so on, you who have lived and breathed this stuff[580.68] [580.68][S02]for, as you say, decades, are very well placed to spot which[584.88] [584.88][S02]ones are realistic goals and which ones aren't.[587.53] [587.53][S02]But for other people, how can they distinguish between[591.45] [591.45][S02]what's real and what isn't?[593.71] [593.71][S01] Yeah, well look, I think you need to look at--[597.63] [597.63][S01]obviously you've got to do your technical due diligence, have[601.29] [601.29][S01]some understanding of the technology,[602.91] [602.91][S01]and the latest trends.[605.24] [605.24][S01]I think also look at, perhaps, the background of the people[608.33] [608.33][S01]saying it, how technical they are.[610.08] [610.08][S01]Have they just arrived in AI last year from somewhere else?[612.898] [612.898][S01]I don't know.[613.44] [613.44][S01]They were doing crypto last year.[614.87] [614.87][S01]These might be some clues that perhaps they're[618.41] [618.41][S01]jumping on a bandwagon.[619.73] [619.73][S01]And it doesn't mean to say, of course, they[621.56] [621.56][S01]could still have some good ideas, and many will do.[624.06] [624.06][S01]But it's a bit more lottery ticket like, shall we say.[627.75] [627.75][S01]And I think that always happens when there's a ton of attention[632.077] [632.077][S01]suddenly on a place, and obviously, then the money[634.16] [634.16][S01]follows that.[635.73] [635.73][S01]And everyone feels like they're missing out.[637.83] [637.83][S01]And that creates a kind of opportunistic,[641.79] [641.79][S01]shall we say, environment, which is a little bit[643.79] [643.79][S01]opposite to those of us who've been in for decades[646.49] [646.49][S01]in a deep technology, deep science way, which is ideally[650.21] [650.21][S01]the way I think we need to carry on going as we get closer[653.21] [653.21][S01]to AGI.[653.94] [653.94][S02] Yeah.[654.36] [654.36][S02]And I guess one of the big things that we're[655.64] [655.64][S02]going to talk about in this series[657.057] [657.057][S02]is Gemini, which really comes from that very deep science[660.47] [660.47][S02]approach, I guess.[662.945] [662.945][S02]In what ways is Gemini different from the other large language[666.15] [666.15][S02]models that are released by other labs?[668.02] [668.02][S01] So from the beginning with Gemini,[670.12] [670.12][S01]we wanted it to be multi-modal from the start so it could[674.01] [674.01][S01]process not just language, but also audio, video, image, code--[679.09] [679.09][S01]any modality, really.[680.91] [680.91][S01]And the reason we wanted to do that was firstly,[683.8] [683.8][S01]we think that's the way to get these systems to actually[686.19] [686.19][S01]understand the world around them and build better world models.[689.68] [689.68][S01]So actually still going back to our grounding[691.8] [691.8][S01]question earlier, still building grounding in,[695.79] [695.79][S01]but piggybacking on top of language this time.[699.06] [699.06][S01]And so that's important.[700.597] [700.597][S01]And we also had this vision in the end[702.18] [702.18][S01]of having a universal assistant, and we[705.21] [705.21][S01]prototyped something called Astro,[706.66] [706.66][S01]which I'm sure we'll talk about, which[708.54] [708.54][S01]understands not just what you're typing, but actually the context[711.9] [711.9][S01]you're in.[712.45] [712.45][S01]And if you think about something like a personal assistant[714.87] [714.87][S01]or digital assistant, it will be much more useful the more[718.17] [718.17][S01]context it understood about what you're asking it for[720.84] [720.84][S01]or the situation that you're in.[722.92] [722.92][S01]So we always thought that would be a much more useful type[727.29] [727.29][S01]of system, and so we built multi-modality[729.69] [729.69][S01]in from the start.[730.54] [730.54][S01]So that was one thing, natively multi-modal.[732.67] [732.67][S01]And then at the time, that was the only model doing that.[735.88] [735.88][S01]So now the other models are trying to catch up.[738.27] [738.27][S01]And then the other big innovations[739.71] [739.71][S01]we had are on memory.[740.8] [740.8][S01]So long context.[742.21] [742.21][S01]So actually holding in mind 1 million-- or 2 million now--[746.43] [746.43][S01]tokens, you can think of them as more or less like words,[749.19] [749.19][S01]in mind.[749.89] [749.89][S01]So you can give it "War and Peace," or even a whole--[753.21] [753.21][S01]because it's multi-modal-- a whole video now, a whole film,[756.396] [756.396][S01]or a lecture, and then get it to answer questions or find you[759.33] [759.33][S01]things within that video stream.[761.17] [761.17][S02] OK Project Astra, that's[762.67] [762.67][S02]the new universal AI agent, the one that can[766.26] [766.26][S02]take in video and audio data.[769.53] [769.53][S02]At Google.[770.14] [770.14][S02]I/O, I think you used the example of how Astra[772.89] [772.89][S02]could help you remember where you left[774.72] [774.72][S02]your glasses, for instance.[776.79] [776.79][S02]So I wonder, though, about the lineage[780.03] [780.03][S02]of this stuff because is this just a fancy, advanced version[786.12] [786.12][S02]of those old Google glasses?[787.405] [787.405][S01] So, of course, Google have a long history[789.78] [789.78][S01]of developing glass-type devices actually back to,[793.23] [793.23][S01]I think, 2012 or something.[794.53] [794.53][S01]So they were way ahead of the curve.[796.03] [796.03][S01]But maybe it was just missing this kind of technology[799.42] [799.42][S01]So you could actually understand-- a smart agent,[801.583] [801.583][S01]or a smart assistant that could actually[803.25] [803.25][S01]understand what it's seeing.[804.79] [804.79][S01]And so we're very excited about that digital assistant[808.99] [808.99][S01]to go around with you and understand the world around you.[811.78] [811.78][S01]So it seems a really-- when you use it,[814.3] [814.3][S01]it feels a really natural use case.[816.605] [816.605][S02] OK.[817.23] [817.23][S02]I want to rewind a tiny bit to the start of Gemini[822.06] [822.06][S02]because it came from two separate parts[825.03] [825.03][S02]of the organization.[826.0] [826.0][S01] Yes.[826.833] [826.833][S01]So we-- actually, last year we combined our two research[830.88] [830.88][S01]divisions at Alphabet.[832.69] [832.69][S01]So obviously, the old DeepMind, and then Google Brain into one[837.04] [837.04][S01]we call it super unit, bringing all the talent together--[840.3] [840.3][S01]that amazing talent we have across the company,[842.59] [842.59][S01]across the whole of Google, into one unified unit.[846.42] [846.42][S01]And what it meant was that we combined all the best[850.56] [850.56][S01]knowledge that we had from all the research[853.89] [853.89][S01]we were doing, but especially on language models.[855.94] [855.94][S01]So we had Chinchilla, and Gopher, and things like that,[858.73] [858.73][S01]and they were building things like PaLM, and LaMDA,[861.12] [861.12][S01]and early language models.[862.28] [862.28][S01]And they had different strengths and weaknesses,[864.28] [864.28][S01]and we pulled them all together into what became Gemini[867.24] [867.24][S01]as the first Lighthouse project that the combined group would[871.62] [871.62][S01]output.[872.193] [872.193][S01]And then the other important thing, of course,[874.11] [874.11][S01]was bringing together all the compute,[876.51] [876.51][S01]as well, so that we could, do these really massive training[879.72] [879.72][S01]runs and actually pull the compute resources together.[882.79] [882.79][S01]So it's been great.[883.787] [883.787][S02] I guess, in a lot of ways,[885.37] [885.37][S02]the focus of Google Brain and DeepMind was slightly different.[888.63] [888.63][S02]Is that fair to say?[889.57] [889.57][S01] Yeah.[890.445] [890.445][S01]So I think it was.[892.47] [892.47][S01]I mean, we were obviously focused, both of us,[894.63] [894.63][S01]on the frontiers of AI, and there[896.43] [896.43][S01]was a lot of collaborations already on a individual research[899.55] [899.55][S01]level, but maybe not on a strategic level.[902.17] [902.17][S01]Obviously, now the combined group, Google DeepMind,[904.6] [904.6][S01]I describe it as we're the engine room of Google now.[907.91] [907.91][S01]But it's worked really well.[909.2] [909.2][S01]I think there were a lot more similarities, actually,[911.408] [911.408][S01]in the way we were working than there were differences,[914.17] [914.17][S01]and we've continued to keep and double down our strengths[918.34] [918.34][S01]on things fundamental research.[920.24] [920.24][S01]So where does the next transformer architecture[922.83] [922.83][S01]come from?[923.33] [923.33][S01]We want to invent that.[924.288] [924.288][S01]Obviously, Google Brain invented the previous one.[926.927] [926.927][S01]We combined it with deep reinforcement[928.51] [928.51][S01]learning that we pioneered, and I still[931.21] [931.21][S01]think more innovations are going to be needed.[933.29] [933.29][S01]And I would back us to do that just as we've[935.38] [935.38][S01]done in the past 10 years collectively,[938.09] [938.09][S01]both Brain and DeepMind.[939.59] [939.59][S01]So it's been exciting.[941.82] [941.82][S02] I want to come back to that merge in a moment.[944.29] [944.29][S02]But I think just sticking on Gemini[946.21] [946.21][S02]for a second, how good is it?[948.29] [948.29][S02]How does it compare to other models?[950.21] [950.21][S01] Yeah, well, I think some of the benchmarks[952.9] [952.9][S01]are not--[953.743] [953.743][S01]the problem is that we need more--[955.16] [955.16][S01]I think this is one thing the whole field needs is[956.89] [956.89][S01]much better benchmarks.[957.805] [957.805][S02] Yeah.[958.513] [958.513][S02]How do you decide?[959.302] [959.302][S01] Well, there are some well-known benchmarks,[961.76] [961.76][S01]academic ones.[962.87] [962.87][S01]But they're getting saturated now,[964.682] [964.682][S01]and they don't really differentiate[966.14] [966.14][S01]between the nuances, between the different top models.[969.93] [969.93][S01]I would say there's three models that[971.63] [971.63][S01]are at the top, the frontier.[974.28] [974.28][S01]So it's Gemini from us, OpenAI's, GPT, of course,[978.717] [978.717][S01]and then Anthropic with their Claude models.[980.55] [980.55][S01]And then obviously, there's a bunch of other good models,[983.0] [983.0][S01]too, that people like Meta, and Mistral, and others built,[986.368] [986.368][S01]and they're differently good at different things.[988.41] [988.41][S01]It depends what you want--[990.05] [990.05][S01]coding, perhaps that's Claude.[991.55] [991.55][S01]And reasoning, maybe that's GPT.[993.35] [993.35][S01]And then memory stuff, long context,[995.51] [995.51][S01]and multimodal understanding, that would be Gemini.[998.09] [998.09][S01]Of course, we're continuing to-- all[999.62] [999.62][S01]of us are improving our models all the time.[1001.7] [1001.7][S01]So given where we started from, which Gemini as a project[1006.76] [1006.76][S01]only existed for a year, obviously,[1009.28] [1009.28][S01]based on some of our other projects,[1011.06] [1011.06][S01]I think our trajectory is very good.[1013.64] [1013.64][S01]So when we talk next time, we should hopefully[1016.57] [1016.57][S01]be right at the forefront.[1018.672] [1018.672][S02] Because there is still a way to go.[1020.63] [1020.63][S02]I mean, there are still some things[1022.088] [1022.088][S02]that these models aren't very good at.[1023.72] [1023.72][S01] Yes, for sure.[1024.68] [1024.68][S01]And actually, that's the big debate right now.[1026.75] [1026.75][S01]So this last set of things emerged[1029.829] [1029.829][S01]from the technologies that were invented five, six years ago.[1034.16] [1034.16][S01]The question is, they're still missing a ton of things--[1036.589] [1036.589][S01]so their factuality, they hallucinate, as we know.[1040.089] [1040.089][S01]They also not good at planning yet.[1042.579] [1042.579][S02] Planning in what sense?[1044.27] [1044.27][S01] Well, long term planning.[1046.099] [1046.099][S01]So they can't problem solve.[1048.55] [1048.55][S01]Something long term, you give it an objective,[1051.05] [1051.05][S01]they can't really do actions in the world for you.[1053.66] [1053.66][S01]So they're very much like passive Q&A systems.[1056.75] [1056.75][S01]You put the energy in by asking the question,[1058.84] [1058.84][S01]and then they give you some kind of response.[1061.99] [1061.99][S01]But they're not able to solve a problem for you.[1066.823] [1066.823][S01]You can't say something like, if you wanted it[1068.74] [1068.74][S01]as a digital assistant, you might want to say something[1071.298] [1071.298][S01]like, book me that holiday in Italy, and all the restaurants,[1073.84] [1073.84][S01]and the museums, and whatever, and it knows what you like,[1077.07] [1077.07][S01]but then it goes out and books the flights[1078.82] [1078.82][S01]and all of that for you.[1080.0] [1080.0][S01]So it can't do any of that.[1082.07] [1082.07][S01]But I think that's the next era--[1084.07] [1084.07][S01]these more agent-based systems, we[1086.02] [1086.02][S01]would call them, or agentic systems[1087.79] [1087.79][S01]that have agent-like behavior.[1090.288] [1090.288][S01]But of course, that's what we're expert in.[1092.08] [1092.08][S01]That's what we used to build with all our game agents--[1094.522] [1094.522][S01]AlphaGo and all of the other things[1095.98] [1095.98][S01]we've talked in about in the past.[1097.73] [1097.73][S01]So a lot of what we're doing is marrying[1100.66] [1100.66][S01]that work that we're, I guess, famous[1103.33] [1103.33][S01]for with the new large multimodal models.[1108.26] [1108.26][S01]And I think that's going to be the next generation of systems.[1112.52] [1112.52][S01]You can think of it as combining AlphaGo with Gemini.[1114.767] [1114.767][S02] Yeah, because I guess AlphaGo was very, very[1117.1] [1117.1][S02]good at planning.[1117.74] [1117.74][S01] Yes, it was very good at planning.[1118.82] [1118.82][S01]Of course, only in the domain, though, of games.[1121.07] [1121.07][S01]And so we need to generalize that[1123.49] [1123.49][S01]into the general domain of everyday workloads and language.[1128.897] [1128.897][S02] You mentioned a minute ago[1130.48] [1130.48][S02]how Google DeepMind is now the engine room of Google.[1134.27] [1134.27][S02]I mean, that is quite a big shift[1136.09] [1136.09][S02]since I was last here in the last couple of years ago.[1138.34] [1138.34][S02]Is Google taking quite a big gamble on you?[1140.455] [1140.455][S01] Well, I guess so.[1141.83] [1141.83][S01]I mean, I think Google have always understood[1145.0] [1145.0][S01]the importance of AI.[1147.01] [1147.01][S01]Sundar, when he took over as CEO,[1148.9] [1148.9][S01]said that Google was an AI-first company.[1152.45] [1152.45][S01]And we discussed that very early on in his tenure,[1155.12] [1155.12][S01]and he saw the potential in AI as the next big paradigm[1157.99] [1157.99][S01]shift after mobile and internet, but bigger than those things.[1160.992] [1160.992][S01]But then I think maybe in the last year or two,[1162.95] [1162.95][S01]we've really started living what that means--[1165.77] [1165.77][S01]not just from a research perspective,[1167.36] [1167.36][S01]but also from products and other things.[1169.97] [1169.97][S01]So it's very exciting, but I think[1172.87] [1172.87][S01]it's the right bet for us to coordinate all of our talents[1176.71] [1176.71][S01]together, and then push as hard as possible.[1179.983] [1179.983][S02] And then how about the other way around?[1182.15] [1182.15][S02]Because I guess from DeepMind, having that very strong research[1185.71] [1185.71][S02]and science focus, does becoming the engine room for Google now[1190.33] [1190.33][S02]mean that you have to care much more about commercial interests[1193.78] [1193.78][S02]rather than the purer stuff that--[1195.367] [1195.367][S01] Yeah, well, we do definitely[1197.2] [1197.2][S01]have to worry more about, and it's[1200.35] [1200.35][S01]in our remit now, the commercial interests.[1202.45] [1202.45][S01]But actually, there's a couple things say about that.[1205.4] [1205.4][S01]First of all, we're continuing on with our science work[1208.27] [1208.27][S01]in AlphaFolds, and you just saw AlphaFold 3 come out.[1211.45] [1211.45][S01]And we're doubling down on our investments there.[1214.42] [1214.42][S01]That's, I think, a unique thing that we do at Google DeepMind[1217.9] [1217.9][S01]now.[1219.61] [1219.61][S01]And even our competitors point at those things[1221.68] [1221.68][S01]as universal goods, if you like, that come out of AI.[1225.942] [1225.942][S01]And that's going really well.[1227.15] [1227.15][S01]And we spun out isomorphic to do drug discovery.[1229.82] [1229.82][S01]So it's very exciting, and that's all going really well.[1232.88] [1232.88][S01]And so we're going to continue to do that.[1234.76] [1234.76][S01]And then was all our work on climate and all of these things.[1238.022] [1238.022][S01]But then, we're quite a large team,[1239.48] [1239.48][S01]so we can do more than one things at once.[1241.23] [1241.23][S01]We're also building our large models, Gemini and et cetera,[1244.13] [1244.13][S01]and then we have a product team that we're building out[1246.82] [1246.82][S01]that is going to bring all this amazing technology to all[1251.86] [1251.86][S01]of the surfaces that Google has.[1253.64] [1253.64][S01]So it's an incredible privilege, in a way,[1256.6] [1256.6][S01]to have that there to plug in all of our stuff.[1259.94] [1259.94][S01]And we invent something, it immediately[1261.67] [1261.67][S01]can become useful to a billion people.[1263.84] [1263.84][S01]And so that's really motivating.[1266.33] [1266.33][S01]And actually, the other thing is there's a lot more convergence[1271.16] [1271.16][S01]now between the technology we need to develop for a product[1274.67] [1274.67][S01]to have AI in it and what you would do for pure AGI research[1278.33] [1278.33][S01]purposes.[1279.06] [1279.06][S01]So there's not really-- five years ago, you'd[1281.57] [1281.57][S01]have had to build some special case AI for a product.[1284.93] [1284.93][S01]Now, you can branch off your main research[1287.412] [1287.412][S01]and, of course, you still need to do some things that[1289.62] [1289.62][S01]are product specific, but maybe it's only 10% of the work.[1292.38] [1292.38][S01]So there's actually not that tension[1294.14] [1294.14][S01]anymore between what you would develop for an AI product[1298.23] [1298.23][S01]and what you would develop for trying to build AGI.[1300.75] [1300.75][S01]It's 90%, I would say, the same research program.[1305.88] [1305.88][S01]And then finally, of course, if you do products,[1308.4] [1308.4][S01]and you get them out into the world,[1309.9] [1309.9][S01]you learn a lot from that.[1311.66] [1311.66][S01]And people using it, and you learn[1313.4] [1313.4][S01]a lot about, oh, your internal metrics[1315.32] [1315.32][S01]don't quite match what people are saying,[1317.423] [1317.423][S01]so then you can update that.[1318.59] [1318.59][S01]And that's really helpful for your research.[1320.872] [1320.872][S02] Absolutely.[1321.83] [1321.83][S02]Well, OK, we are going to talk a lot more in this podcast[1325.34] [1325.34][S02]about those breakthroughs that have come from applying AI[1328.64] [1328.64][S02]to science, but I want to ask you about that tension[1331.73] [1331.73][S02]that there is between knowing when the right moment is[1335.33] [1335.33][S02]to release something to the public.[1337.11] [1337.11][S02]Because internally at DeepMind, those tools[1340.86] [1340.86][S02]like large language models were being used for research[1344.09] [1344.09][S02]rather than being seen as a potentially commercial thing.[1347.442] [1347.442][S01] Yeah, that's right.[1348.9] [1348.9][S01]So as you know, we've always taken responsibility incredibly[1352.43] [1352.43][S01]seriously here, and safety, right from the beginning,[1356.15] [1356.15][S01]way back when we started in 2010 and before that.[1358.95] [1358.95][S01]And Google then adopted some of our, basically, ethics[1362.745] [1362.745][S01]charter effectively into their AI principles.[1364.62] [1364.62][S01]So we've always been well aligned with the whole of Google[1367.037] [1367.037][S01]and wanting to be responsible about deploying this as one[1369.74] [1369.74][S01]of the leaders in this space.[1371.81] [1371.81][S01]And so it's been interesting now starting[1374.45] [1374.45][S01]to ship real products with Gen AI in them.[1376.98] [1376.98][S01]Actually there's a lot of learning that is going on,[1380.097] [1380.097][S01]and we're learning fast, which is good[1381.68] [1381.68][S01]because we're at relatively low stakes[1383.263] [1383.263][S01]here with the current technology.[1384.9] [1384.9][S01]So it's not that powerful yet.[1386.49] [1386.49][S01]But as it gets more powerful, we have to be more careful.[1389.63] [1389.63][S01]And that's just learning about the product teams[1392.95] [1392.95][S01]and other groups learning about how to test Gen AI technologies.[1397.5] [1397.5][S01]It's different from a normal piece of technology[1399.5] [1399.5][S01]because it doesn't always do the same thing.[1402.36] [1402.36][S01]It's almost like testing an open world game.[1404.922] [1404.922][S01]It's almost infinite what you can try and do with it,[1407.13] [1407.13][S01]so it's interesting to figure out how do you[1409.73] [1409.73][S01]do the red teaming on it.[1411.0] [1411.0][S02] So red teaming, in this case,[1412.708] [1412.708][S02]being where you're competing against yourselves?[1415.005] [1415.005][S01] Yeah.[1415.88] [1415.88][S01]So red teaming is when you set up a specific separate team[1419.27] [1419.27][S01]from the team that's developed the technology[1421.88] [1421.88][S01]to stress test it and try and break it in any way possible.[1425.4] [1425.4][S01]You actually need to use tools to automate that because nobody[1429.05] [1429.05][S01]can red team-- even if you had thousands of people doing it,[1431.902] [1431.902][S01]that's not enough compared to billions of users[1433.86] [1433.86][S01]when you put it out there.[1434.88] [1434.88][S01]They're going to try all sorts of things.[1436.588] [1436.588][S01]So it's kind of interesting to take that learning,[1440.03] [1440.03][S01]and then improve our processes so that our future launches will[1444.98] [1444.98][S01]be as smooth as possible.[1446.37] [1446.37][S01]And I think we got to do it in stages where there's[1448.82] [1448.82][S01]an experimental phase, then a closed beta, and then launch--[1453.805] [1453.805][S01]a little bit, again, like we used[1455.18] [1455.18][S01]to launch our games back in the day,[1458.09] [1458.09][S01]and learn at each step of the way.[1460.73] [1460.73][S01]And then the other thing we've got to do,[1462.74] [1462.74][S01]and I think we need to do more on, is use AI itself to help us[1467.15] [1467.15][S01]internally with red teaming and actually[1470.33] [1470.33][S01]spotting some errors automatically or triaging[1473.51] [1473.51][S01]that so that, then, our developers and human testers[1477.83] [1477.83][S01]can actually focus on those hard cases.[1480.078] [1480.078][S02] You said something really interesting[1482.12] [1482.12][S02]there about how you're just in a much more probabilistic space[1486.07] [1486.07][S02]here.[1486.57] [1486.57][S02]And then, if there's even a very small chance of something[1489.86] [1489.86][S02]happening, if you have enough tries,[1491.73] [1491.73][S02]eventually, something will go wrong.[1493.23] [1493.23][S02]And I guess there have been a couple of mistakes[1495.23] [1495.23][S02]that-- public mistakes.[1497.04] [1497.04][S01] Yeah, so that's why I think that,[1500.4] [1500.4][S01]as I mentioned, that product teams are just getting[1502.88] [1502.88][S01]used to the sorts of testing.[1504.39] [1504.39][S01]They tested these things, but they[1506.93] [1506.93][S01]have this stochastic nature, probabilistic nature.[1509.37] [1509.37][S01]So in fact, a lot of cases where if it[1512.107] [1512.107][S01]was a normal piece of software, you could say I've[1514.19] [1514.19][S01]tested 99.999% of things, so then extrapolates.[1517.88] [1517.88][S01]So then it's enough because there's[1519.62] [1519.62][S01]no way of exposing the flaw that it has if it has one.[1523.07] [1523.07][S01]But that's not the case with these generative systems.[1525.6] [1525.6][S01]They can do all sorts of things that[1527.36] [1527.36][S01]are a little bit left field, or out of the box,[1530.37] [1530.37][S01]out of distribution, in a way, from what you've seen before[1532.95] [1532.95][S01]if someone clever or adversarial decides to-- it's almost[1536.15] [1536.15][S01]like a hacker decides to test push it in some way.[1539.913] [1539.913][S01]And it could even be--[1540.83] [1540.83][S01]I mean, it's so combinatorial, it could even[1542.48] [1542.48][S01]be with all the things that you've happened[1543.89] [1543.89][S01]to have said before to it.[1545.1] [1545.1][S01]And then it's in some kind of peculiar state[1548.25] [1548.25][S01]which then-- or it's got its memories filled up[1550.25] [1550.25][S01]with this particular thing, and then[1551.75] [1551.75][S01]that's why it outputs something.[1553.59] [1553.59][S01]So there's a lot of complexity there, but it's not infinite.[1558.87] [1558.87][S01]So there's ways to deal with it.[1561.21] [1561.21][S01]But it's just a lot more nuanced than launching[1564.63] [1564.63][S01]normal technology.[1565.383] [1565.383][S02] I remember you saying,[1566.8] [1566.8][S02]I think it was in the first time I interviewed you[1569.82] [1569.82][S02]about how, actually, you have to think that this is a completely[1573.66] [1573.66][S02]different way of computing.[1575.95] [1575.95][S02]You have to move away from the things that we completely[1578.73] [1578.73][S02]understand-- the deterministic stuff--[1580.68] [1580.68][S02]into this much more messy, probabilistic error-ridden[1587.04] [1587.04][S02]place, as well as your testers.[1588.57] [1588.57][S02]Do you think the public slightly has[1590.07] [1590.07][S02]to shift its mindset on the type of computing that we're doing?[1593.23] [1593.23][S01] Yeah, I think so,[1594.75] [1594.75][S01]and maybe that's another thing, interestingly,[1597.51] [1597.51][S01]that we're thinking about is actually putting out[1602.16] [1602.16][S01]a kind of principles document or something before you release[1605.22] [1605.22][S01]something to show what is the expectation from this system.[1609.03] [1609.03][S01]What's it designed for?[1610.465] [1610.465][S01]What's it useful for?[1611.34] [1611.34][S01]What can't it do?[1613.05] [1613.05][S01]And I think there is some sort of education there needed of,[1618.42] [1618.42][S01]you'll be able to find it useful if you do these things with it,[1621.412] [1621.412][S01]but don't try and use it for these other things[1623.37] [1623.37][S01]because it won't work.[1624.43] [1624.43][S01]And I think that that's something[1628.2] [1628.2][S01]that we need to get better at clarifying as a field,[1632.01] [1632.01][S01]and then probably users need to get more experienced on.[1635.61] [1635.61][S01]And actually, this interesting.[1637.36] [1637.36][S01]This is probably why chatbots themselves came a little bit out[1640.08] [1640.08][S01]of the blue.[1641.11] [1641.11][S01]Even obviously ChatGPT, but even to OpenAI, it surprised them.[1644.25] [1644.25][S01]And we had our own chat bots, and Google had theirs.[1646.907] [1646.907][S01]And one of the things was we were looking at them,[1648.99] [1648.99][S01]and we were looking at all the flaws they still had,[1651.76] [1651.76][S01]and they still do.[1652.63] [1652.63][S01]And it's like, well, it's getting these things wrong,[1654.838] [1654.838][S01]and it sometimes hallucinates, and blah, blah, blah.[1657.25] [1657.25][S01]And there's so many things.[1658.81] [1658.81][S01]But then what we didn't realize is, actually,[1660.93] [1660.93][S01]there's still a lot of very good use cases for that even now that[1664.44] [1664.44][S01]people find very valuable-- summarizing documents,[1667.2] [1667.2][S01]and really long things, or writing--[1668.715] [1668.715][S02] Awkward emails?[1669.84] [1669.84][S01] --awkward emails, or mundane forms[1672.45] [1672.45][S01]to be filled in.[1673.98] [1673.98][S01]And there's all these use cases which, actually,[1676.44] [1676.44][S01]people don't mind if there's some small errors.[1680.05] [1680.05][S01]They can fix them easily, and saves a huge amount of time.[1682.9] [1682.9][S01]And I guess that was the surprising thing.[1684.79] [1684.79][S01]They discovered-- people discovered when you put it[1687.21] [1687.21][S01]in the hands of everyone, there were actually these valuable use[1690.6] [1690.6][S01]cases, even though the systems were flawed in all of these ways[1694.84] [1694.84][S01]we know.[1695.64] [1695.64][S02] Well, OK, so I think that sort of takes me[1697.47] [1697.47][S02]on to the next question I want to ask,[1699.053] [1699.053][S02]which is about open source.[1700.42] [1700.42][S02]Because when things are in the hands of people,[1703.24] [1703.24][S02]as you mentioned, really extraordinary things can happen.[1706.36] [1706.36][S02]And I know that DeepMind in the past[1707.94] [1707.94][S02]has open sourced lots of its research projects,[1710.5] [1710.5][S02]but it feels like that's slightly[1713.64] [1713.64][S02]changing now as we go forward.[1714.915] [1714.915][S02]So just tell me what your stance is on open source.[1717.04] [1717.04][S01] Yeah.[1717.28] [1717.28][S01]Well, look, we're huge supporters[1718.77] [1718.77][S01]of open source and open science, as you know.[1720.96] [1720.96][S01]I mean, we've given away and published[1723.24] [1723.24][S01]almost everything we've done, collectively, including like,[1727.53] [1727.53][S01]things like transformers, and AlphaGo.[1729.46] [1729.46][S01]We published all these things in "Nature" and "Science."[1731.793] [1731.793][S01]AlphaFold was open source, as we covered last time.[1735.18] [1735.18][S01]And these are all good choices, and you're absolutely right.[1737.68] [1737.68][S01]That's the reason that all works is[1739.17] [1739.17][S01]because that's the way technology and science[1742.74] [1742.74][S01]advances as quickly as possible, by sharing information.[1746.31] [1746.31][S01]So almost always, that's a universal good[1748.85] [1748.85][S01]to do it like that, and that's how science works.[1751.73] [1751.73][S01]The only exception is when you--[1754.82] [1754.82][S01]and AGI and powerful AI does fall[1758.63] [1758.63][S01]into this-- is when you have a dual purpose technology.[1762.6] [1762.6][S01]And so then, the problem is that you[1765.08] [1765.08][S01]want to enable all the good use cases and all[1768.17] [1768.17][S01]the genuine scientists who are acting in good faith and so on,[1770.91] [1770.91][S01]technologists, to build on the ideas, critique[1773.6] [1773.6][S01]the ideas, and so on.[1774.63] [1774.63][S01]That's the way society advances the quickest.[1778.11] [1778.11][S01]But the problem is how do you restrict access[1781.4] [1781.4][S01]at the same time for bad actors who would take the same systems,[1785.22] [1785.22][S01]repurpose them for bad ends, misuse them--[1787.85] [1787.85][S01]weapon systems, who knows what?[1789.69] [1789.69][S01]And those general purpose systems[1792.65] [1792.65][S01]can be repurposed like that.[1794.4] [1794.4][S01]And it's OK today because I don't think[1797.33] [1797.33][S01]the systems are that powerful.[1798.81] [1798.81][S01]But in two, three, four years time,[1801.18] [1801.18][S01]especially when you start getting agent-like systems[1803.96] [1803.96][S01]or agentic behaviors, then, I think,[1806.96] [1806.96][S01]if something's misused by someone, or perhaps even[1810.65] [1810.65][S01]a rogue nation, state, there could be serious harm.[1814.44] [1814.44][S01]So then, I don't have a solution to that.[1817.8] [1817.8][S01]But as a community, we need to think about what does that[1820.22] [1820.22][S01]mean for open source?[1822.62] [1822.62][S01]Perhaps the frontier models need to have more checks on them,[1825.83] [1825.83][S01]and then only after they've been out for a year or two years,[1828.9] [1828.9][S01]then they can get open sourced.[1830.49] [1830.49][S01]That's the model we're following because we[1832.76] [1832.76][S01]have our own open models of Gemini called Gemma[1835.4] [1835.4][S01]because they're smaller.[1836.49] [1836.49][S01]So they're not frontier models.[1839.41] [1839.41][S01]So their capabilities are very useful still to the developer[1841.91] [1841.91][S01]because they're also easy to run on a laptop[1843.987] [1843.987][S01]because they're small numbers of parameters.[1845.82] [1845.82][S01]But the capabilities they have are well[1848.84] [1848.84][S01]understood at this point.[1850.65] [1850.65][S01]Because they're not frontier models.[1852.18] [1852.18][S01]So it's just not as powerful as the latest, say,[1854.73] [1854.73][S01]Gemini 1.5 models.[1856.92] [1856.92][S01]So I think that's probably the approach[1859.73] [1859.73][S01]that we'll end up taking is we'll have open source models,[1863.74] [1863.74][S01]but they'll be lagging maybe one year behind the most[1867.81] [1867.81][S01]cutting edge models just so that we can really assess out[1871.89] [1871.89][S01]in the open by users what those models can do-- the frontier[1875.61] [1875.61][S01]ones can do.[1876.11] [1876.11][S02] And you can really, I guess,[1877.777] [1877.777][S02]test those boundaries of the stochastic--[1879.51] [1879.51][S01] Yeah, and we'll see what those are.[1880.63] [1880.63][S01]The problem with open source is if something goes wrong,[1883.21] [1883.21][S01]you can't recall it.[1884.76] [1884.76][S01]With a proprietary model, if your bad actor[1887.25] [1887.25][S01]starts using it in a bad way, you can just close the tap off.[1892.29] [1892.29][S01]In the limit, you could switch it off.[1894.52] [1894.52][S01]But once you open source something,[1896.95] [1896.95][S01]there's no pulling it back.[1898.54] [1898.54][S01]So it's a one way door, so you should be very, very[1900.99] [1900.99][S01]sure when you do that.[1902.088] [1902.088][S02] Is it definitely possible[1903.63] [1903.63][S02]to contain an AGI, though, within the walls[1908.33] [1908.33][S02]of an organization?[1909.122] [1909.122][S01] Well, that's a whole separate question.[1911.413] [1911.413][S01]I don't think we know how to do that right now.[1913.66] [1913.66][S01]So when you start talking about AGI level powerful,[1916.84] [1916.84][S01]like human level AI--[1917.74] [1917.74][S02] Well, what about intermediary?[1919.49] [1919.49][S01] Well, intermediary, I think,[1921.323] [1921.323][S01]we have good ideas of how to do that.[1923.56] [1923.56][S01]So one would be things like secure sandboxing.[1928.543] [1928.543][S01]So you test-- that's what I'd want[1929.96] [1929.96][S01]to test the agent behaviors in is in a game environment,[1933.23] [1933.23][S01]or a version of the internet that's[1935.39] [1935.39][S01]not quite fully connected.[1937.32] [1937.32][S01]So there's a lot of security work[1939.98] [1939.98][S01]that's done and known in this space, and in fintech,[1942.86] [1942.86][S01]and other places.[1943.59] [1943.59][S01]So we'd probably borrow those ideas,[1945.41] [1945.41][S01]and then build those kinds of systems.[1947.73] [1947.73][S01]And that's how we would test the early prototype systems.[1951.39] [1951.39][S01]But we also know that's not going[1953.6] [1953.6][S01]to be good enough to contain an AGI, something that's[1956.09] [1956.09][S01]potentially smarter than us.[1957.66] [1957.66][S01]So I think we got to understand those systems better so that we[1961.16] [1961.16][S01]can design the protocols for an AGI.[1965.33] [1965.33][S01]When that time comes, we'll have better ideas[1968.0] [1968.0][S01]for how to contain that, potentially[1969.75] [1969.75][S01]also using AI systems and tools to monitor the next versions[1973.88] [1973.88][S01]of the AI system.[1974.793] [1974.793][S02] So on the subject of safety,[1976.46] [1976.46][S02]because I know that you are a very big part of the AI Safety[1979.94] [1979.94][S02]Summit at Bletchley Park in 2023, which was, of course,[1982.82] [1982.82][S02]hosted by the UK government.[1984.44] [1984.44][S02]And from the outside, I think a lot of people[1988.11] [1988.11][S02]just say the word regulation as though it's just going[1990.9] [1990.9][S02]to come in and fix everything.[1992.68] [1992.68][S02]But what is your view on how regulation should be structured?[1996.763] [1996.763][S01] Well, I think it's great[1998.43] [1998.43][S01]that governments are getting up to speed on it and involved.[2001.35] [2001.35][S01]I think that's one of the good things[2002.96] [2002.96][S01]about the recent explosion of interest is that, of course,[2005.79] [2005.79][S01]governments are paying attention.[2007.92] [2007.92][S01]And I think it's been great.[2009.212] [2009.212][S01]The UK government specifically, who[2010.67] [2010.67][S01]I've talked to a lot, and US, as well,[2012.9] [2012.9][S01]they've got very smart people in the civil service[2015.56] [2015.56][S01]staff that understand the technology now to a good degree.[2020.45] [2020.45][S01]And it's been great to see the AI safety institutes[2023.42] [2023.42][S01]being set up in the UK and US, and I[2024.98] [2024.98][S01]think many other countries are going to follow.[2026.938] [2026.938][S01]So I think these are all good precedents and protocols[2029.72] [2029.72][S01]to settle into, again, before the stakes get really high.[2033.87] [2033.87][S01]So this is a proving stage, again, as well.[2037.76] [2037.76][S01]And I do think international cooperation is going[2040.07] [2040.07][S01]to be needed, ideally around things like regulation,[2043.13] [2043.13][S01]and guardrails, and deployment norms.[2046.11] [2046.11][S01]So because AI is a digital technology, very much so,[2051.17] [2051.17][S01]it's hard to contain it within national boundaries.[2054.03] [2054.03][S01]So if the UK or Europe does something, or even the US,[2057.389] [2057.389][S01]but China doesn't, does that really help the world?[2060.17] [2060.17][S01]When we start getting closer to AGI, not really.[2062.79] [2062.79][S01]So I think my view on it is you've[2064.88] [2064.88][S01]got to be, because the technology is changing so fast,[2069.17] [2069.17][S01]we've got to be very nimble and light-footed with regulation so[2073.4] [2073.4][S01]that it's easy to adapt it to where the latest technology is[2077.09] [2077.09][S01]going.[2077.67] [2077.67][S01]If you'd regulated AI five years ago,[2079.732] [2079.732][S01]you'd have regulated something completely different to what[2082.19] [2082.19][S01]we see today, which is Gen AI.[2083.69] [2083.69][S01]But it might be different again in five years.[2085.927] [2085.927][S01]It might be these agent-based systems[2087.469] [2087.469][S01]that are the ones that carry the highest risk.[2090.12] [2090.12][S01]So right now, I would recommend to beef up existing regulations[2095.12] [2095.12][S01]in domains that already have them-- health, transport, so on.[2098.4] [2098.4][S01]I think you can update them for an AI world[2101.57] [2101.57][S01]just like they were updated for mobile and internet.[2103.83] [2103.83][S01]That's probably the first thing I'd[2105.288] [2105.288][S01]do, while doing a watching brief on making sure you understand[2108.75] [2108.75][S01]and test the frontier systems.[2110.86] [2110.86][S01]And then as things become clear and more clearly obvious,[2115.72] [2115.72][S01]then start regulating around that.[2118.59] [2118.59][S01]Maybe in a couple of years time would make sense.[2121.45] [2121.45][S01]One of the things we're missing is, again, the benchmarks--[2124.66] [2124.66][S01]the right tests for capabilities that--[2127.338] [2127.338][S01]what we'd all want to know, including[2128.88] [2128.88][S01]the industry in the field, is at what point are capabilities[2132.78] [2132.78][S01]posing some big risk?[2135.1] [2135.1][S01]And there's no answer to that at the moment beyond what I've just[2138.42] [2138.42][S01]said, which is agent-based capabilities is probably[2140.73] [2140.73][S01]our next threshold.[2141.82] [2141.82][S01]But there's no agreed-upon test for that.[2144.52] [2144.52][S01]One thing you might imagine is testing for deception,[2147.19] [2147.19][S01]for example, as a capability.[2148.84] [2148.84][S01]You really don't want that in the system[2150.75] [2150.75][S01]because then you can't rely on anything else[2152.583] [2152.583][S01]that it's reporting.[2153.79] [2153.79][S01]So that would be my number one emerging capability that I think[2158.67] [2158.67][S01]would be good to test for.[2160.06] [2160.06][S01]But there's many-- ability to achieve certain goals,[2163.67] [2163.67][S01]the ability to replicate.[2165.152] [2165.152][S01]And there's quite a lot of work going on on this now.[2167.36] [2167.36][S01]And I think the safety institutes, which are basically[2170.41] [2170.41][S01]government agencies, I think it would be great for them[2174.76] [2174.76][S01]to push on that, as well.[2176.42] [2176.42][S01]As well as the labs, of course, contributing what we know.[2179.14] [2179.14][S02] I wonder, in this picture of the world[2181.3] [2181.3][S02]that you're describing, what's the place for institutions[2185.73] [2185.73][S02]in this?[2186.23] [2186.23][S02]I mean, if we get to the stage where[2187.73] [2187.73][S02]we have AGI that's supporting all scientific research,[2191.0] [2191.0][S02]is there still a place for great institutions?[2194.09] [2194.09][S01] Yeah, I think so.[2195.6] [2195.6][S01]There's the stage up to AGI, and I[2198.1] [2198.1][S01]think that's got to be a cooperation[2199.6] [2199.6][S01]between civil society, academia, government,[2203.44] [2203.44][S01]and the industrial labs.[2205.82] [2205.82][S01]So I really believe that's the only way[2207.73] [2207.73][S01]we're going to get to the final stages of this.[2211.04] [2211.04][S01]Now, if you're asking after AGI happens, maybe[2214.18] [2214.18][S01]that is what you're asking, then AGI, of course,[2216.24] [2216.24][S01]one of the reasons I've always wanted to build it is then we[2218.74] [2218.74][S01]can use it to start answering some[2220.302] [2220.302][S01]of the biggest, most fundamental questions about the nature[2222.76] [2222.76][S01]of reality, and physics, and all of these things,[2225.37] [2225.37][S01]and consciousness, and so on.[2227.06] [2227.06][S01]It depends what form that takes, whether that[2229.84] [2229.84][S01]will be a human-expert combination with AI.[2234.04] [2234.04][S01]I think that will be the case for a while in terms[2236.56] [2236.56][S01]of discovering the next frontier.[2238.28] [2238.28][S01]So like right now, these systems can't come up[2241.03] [2241.03][S01]with their own conjectures or hypotheses.[2242.923] [2242.923][S01]They can help you prove something,[2244.34] [2244.34][S01]and I think we'll be able to prove[2246.89] [2246.89][S01]get gold medals on international maths[2248.71] [2248.71][S01]Olympiad, things like that.[2249.98] [2249.98][S01]But maybe even solve a famous conjecture.[2252.44] [2252.44][S01]I think that's within reach now.[2254.27] [2254.27][S01]But they don't have the ability to come up[2256.51] [2256.51][S01]with Riemann hypothesis in the first place,[2258.83] [2258.83][S01]or general relativity.[2260.81] [2260.81][S01]So that, really, was always my test[2263.11] [2263.11][S01]for maybe a true artificial general intelligence[2266.41] [2266.41][S01]is it will be able to do that, or invent Go.[2269.44] [2269.44][S01]And so we don't have any systems.[2271.58] [2271.58][S01]We don't really know how we would design, in theory, even,[2276.31] [2276.31][S01]a system that could do that.[2277.517] [2277.517][S02] You know the computer scientist,[2279.35] [2279.35][S02]Stuart Russell?[2279.975] [2279.975][S02]So he told me that he was a bit worried that once we get to AGI,[2283.3] [2283.3][S02]it might be that we all become like the royal princes[2287.28] [2287.28][S02]of the past--[2288.63] [2288.63][S02]the ones who never had to ascend the throne or do any work,[2291.52] [2291.52][S02]but just got to live this life of unbridled luxury[2295.41] [2295.41][S02]and have no purpose.[2297.152] [2297.152][S01] Yeah, so that is the interesting question, is it?[2299.86] [2299.86][S01]Maybe it's beyond AGI.[2300.91] [2300.91][S01]It's more like artificial superintelligence or something--[2303.52] [2303.52][S01]sometimes people call it ASI.[2305.1] [2305.1][S01]But then we should have radical abundance.[2307.56] [2307.56][S01]And assuming we make sure we distribute that fairly[2310.89] [2310.89][S01]and equitably, then we will be in this position[2313.32] [2313.32][S01]where we'll have more freedom to choose what to do.[2319.26] [2319.26][S01]And then meaning will be a big philosophical question.[2322.18] [2322.18][S01]And I think we'll need philosophers,[2325.48] [2325.48][S01]perhaps theologians even, to start[2327.84] [2327.84][S01]thinking as social scientists.[2329.28] [2329.28][S01]They should be thinking about that now.[2331.06] [2331.06][S01]What brings meaning?[2332.59] [2332.59][S01]I mean, I still think there's, of course, self-actualization,[2336.96] [2336.96][S01]and I don't think we'll all just be sitting there meditating.[2339.63] [2339.63][S01]But maybe we'll be playing computer games.[2341.38] [2341.38][S01]I don't know.[2341.922] [2341.922][S01]But is that a bad thing even, or not?[2343.99] [2343.99][S01]Who knows?[2344.83] [2344.83][S02] I don't think the princes of the past[2346.18] [2346.18][S02]came off particularly well.[2347.305] [2347.305][S01] No.[2348.097] [2348.097][S01]Traveling the stars.[2349.07] [2349.07][S01]But then there's also extreme sports people do.[2351.92] [2351.92][S01]Why do they do them?[2352.91] [2352.91][S01]I mean, climb Everest, all these.[2354.675] [2354.675][S01]I mean, there'll be-- but I think[2356.05] [2356.05][S01]it's going be very interesting.[2357.342] [2357.342][S01]And that I don't know, but that's[2359.14] [2359.14][S01]what I was saying earlier about it's[2361.48] [2361.48][S01]underappreciated what's going to happen going back to the hype[2365.41] [2365.41][S01]near-term versus far-term.[2367.045] [2367.045][S01]So if you want to call that hype,[2368.42] [2368.42][S01]even, it's definitely under hyped,[2370.22] [2370.22][S01]I think, the amount of transformation that will happen.[2373.25] [2373.25][S01]I think it will be very good in the limit.[2376.43] [2376.43][S01]We'll cure lots of diseases, and/or all diseases,[2379.18] [2379.18][S01]solve our energy problems, climate problems.[2382.03] [2382.03][S01]But then the next question comes is, is there meaning?[2385.31] [2385.31][S02] So bring us back slightly closer to AGI[2388.42] [2388.42][S02]rather than superintelligence.[2389.9] [2389.9][S02]I know that your big mission is to build artificial intelligence[2393.47] [2393.47][S02]to benefit everybody, but how do you make sure[2396.01] [2396.01][S02]that it does benefit everybody?[2397.67] [2397.67][S02]How do you include all people's preferences[2401.53] [2401.53][S02]rather than just the designers?[2403.16] [2403.16][S01] Yeah, I think what's[2404.873] [2404.873][S01]going to have to happen is--[2406.04] [2406.04][S01]I mean, it's impossible to include all preferences[2408.26] [2408.26][S01]in one system.[2409.01] [2409.01][S01]Because by definition, people don't agree.[2411.03] [2411.03][S01]We can see that in, unfortunately,[2412.62] [2412.62][S01]in the current state of the world.[2414.037] [2414.037][S01]Countries don't agree.[2415.37] [2415.37][S01]Governments don't agree.[2417.185] [2417.185][S01]We can't even get agreement on obvious things[2419.06] [2419.06][S01]like dealing with the climate situation.[2422.1] [2422.1][S01]So I think that's very hard.[2424.41] [2424.41][S01]What I imagine will happen is that we'll[2426.95] [2426.95][S01]have a set of safe architectures,[2428.54] [2428.54][S01]hopefully, that personalized AIs can be built on top of.[2433.4] [2433.4][S01]And then everyone will have, or different countries[2437.27] [2437.27][S01]will have their own preferences about what they use it for,[2440.12] [2440.12][S01]what they deploy it for, what can and can't be done with them.[2444.15] [2444.15][S01]But overall-- and that's fine.[2445.83] [2445.83][S01]That's for everyone to individually decide[2447.62] [2447.62][S01]or countries to decide themselves,[2449.04] [2449.04][S01]just like they do today.[2450.18] [2450.18][S01]But as a society, we know that there's[2453.83] [2453.83][S01]some provably safe things about those architectures.[2456.96] [2456.96][S01]And then you can let them proliferate, and so on.[2459.42] [2459.42][S01]So I think that we've got to get through the eye of a needle[2462.89] [2462.89][S01]in a way where as we get closer to AGI,[2466.57] [2466.57][S01]we've probably got to cooperate more, ideally internationally,[2470.82] [2470.82][S01]and then make sure we build AGIs in a safe architecture way.[2476.225] [2476.225][S01]Because I'm sure there are unsafe ways,[2477.85] [2477.85][S01]and I'm sure there are safe ways of building AGI.[2480.84] [2480.84][S01]And then once we get through that,[2482.56] [2482.56][S01]then we can open the funnel again,[2484.83] [2484.83][S01]and everyone can have their own personalized pocket AGIs,[2489.06] [2489.06][S01]if they want.[2489.81] [2489.81][S02] What a version of the future.[2491.53] [2491.53][S02]But then, in terms of the safe way to build it,[2494.17] [2494.17][S02]I mean, are we talking about undesirable behaviors[2496.38] [2496.38][S02]here that might emerge?[2497.62] [2497.62][S01] Yes, undesirable emergent behaviors, capabilities[2502.6] [2502.6][S01]that--[2503.1] [2503.1][S02] Deception.[2504.017] [2504.017][S01] Deception is one example that you don't want.[2507.51] [2507.51][S01]Value systems.[2508.81] [2508.81][S01]We got to understand all of these things[2510.72] [2510.72][S01]better-- what kind of guardrails work, not circumventable.[2514.828] [2514.828][S01]And there's two cases to worry about.[2516.37] [2516.37][S01]There's bad uses by bad individuals or nations, so[2521.85] [2521.85][S01]human misuse, and then there's the AI itself[2524.95] [2524.95][S01]as it gets closer to AGI going off the rails.[2527.68] [2527.68][S01]And I think you need different solutions for those two[2530.13] [2530.13][S01]problems.[2531.27] [2531.27][S01]And so, yeah, that's what we're going[2533.55] [2533.55][S01]to have to contend with as we get closer[2535.35] [2535.35][S01]to building these technologies.[2538.11] [2538.11][S01]And also, just going back to your benefiting everyone point,[2540.61] [2540.61][S01]of course, we're showing the way with things[2543.03] [2543.03][S01]like AlphaFold and isomorphic.[2544.93] [2544.93][S01]I think we could cure most diseases within the next decade[2549.18] [2549.18][S01]or two if AI drug design works.[2552.135] [2552.135][S01]And then they could be personalized medicines[2554.01] [2554.01][S01]where it minimizes the side effects on the individual[2556.95] [2556.95][S01]because it's mapped to the person's[2559.02] [2559.02][S01]individual illness, and their individual metabolism,[2561.46] [2561.46][S01]and so on.[2562.12] [2562.12][S01]So these are amazing things--[2565.56] [2565.56][S01]clean energy, renewable energy sources,[2567.63] [2567.63][S01]fusion, or better solar power, all of these types of things.[2571.335] [2571.335][S01]I think they're all within reach.[2572.71] [2572.71][S01]And then that would sort out water access[2575.13] [2575.13][S01]because you could do desalination everywhere.[2577.18] [2577.18][S01]So I just feel like an enormous good[2580.92] [2580.92][S01]is going to come from these technologies,[2584.17] [2584.17][S01]but we have to mitigate the risks, too.[2585.978] [2585.978][S02] And one way that you said[2587.52] [2587.52][S02]that you would want to mitigate the risks[2589.26] [2589.26][S02]was that there would be a moment where you would basically[2592.05] [2592.05][S02]do the scientific version of Avengers assemble.[2594.31] [2594.31][S01] Yes, sure.[2595.413] [2595.413][S02] Terence Tao, get him on the phone.[2597.33] [2597.33][S01] Exactly.[2597.87] [2597.87][S02] Bring him on down.[2599.12] [2599.12][S01] Yeah, exactly.[2600.51] [2600.51][S02] Is that still your plan?[2602.01] [2602.01][S01] Yeah, well, I think so.[2603.94] [2603.94][S01]I think if we can get the international cooperation,[2606.69] [2606.69][S01]I'd love there to be a international CERN, basically,[2610.2] [2610.2][S01]for AI.[2610.95] [2610.95][S01]Where you get the top researchers in the world,[2613.05] [2613.05][S01]and you go, look, let's focus on the final few years of this AGI[2617.28] [2617.28][S01]project, and get it really right,[2619.23] [2619.23][S01]and do it scientifically, and carefully,[2621.6] [2621.6][S01]and thoughtfully at every step--[2623.88] [2623.88][S01]the final steps.[2625.002] [2625.002][S01]I still think that would be the best way.[2626.71] [2626.71][S02] How do you know when is the time to press the button?[2629.2] [2629.2][S01] Well, that's the big question.[2630.43] [2630.43][S01]Because you can't do it too early because you[2632.46] [2632.46][S01]would never be able to get the buy-in to do that.[2634.598] [2634.598][S01]A lot of people would disagree.[2635.89] [2635.89][S01]I mean, today people disagree with the risks.[2637.765] [2637.765][S01]You see very famous people saying there's no risks,[2640.37] [2640.37][S01]and then you have people like Jeff Hinton saying[2642.37] [2642.37][S01]there's lots of risks.[2643.51] [2643.51][S01]And I'm in the middle of that.[2645.245] [2645.245][S02] I want to talk to you[2646.62] [2646.62][S02]a bit more about neuroscience.[2648.54] [2648.54][S02]How much does it still inspire what you're doing?[2652.69] [2652.69][S02]Because I noticed the other day that DeepMind had unveiled[2655.65] [2655.65][S02]this computerized rat with an artificial brain[2659.87] [2659.87][S02]that helps to change our understanding of how[2663.57] [2663.57][S02]the brain controls movement.[2665.17] [2665.17][S02]But in the first season of the podcast,[2667.03] [2667.03][S02]I remember we talked a lot about how[2669.36] [2669.36][S02]DeepMind takes direct inspiration[2671.73] [2671.73][S02]from biological systems.[2673.36] [2673.36][S02]Is that still the core of your approach?[2675.027] [2675.027][S01] No, it's evolved now[2676.527] [2676.527][S01]because I think we've got to a stage now.[2678.355] [2678.355][S01]In the last, I would say, two to three years,[2680.23] [2680.23][S01]we've gone more into an engineering phase--[2682.5] [2682.5][S01]large scale systems, massive training architectures.[2687.96] [2687.96][S01]So I would say that the influence of neuroscience[2691.59] [2691.59][S01]on that is a little bit less.[2693.39] [2693.39][S01]It may come back in.[2694.63] [2694.63][S01]So any time where you need more invention,[2697.118] [2697.118][S01]then you want to get as many sources as possible.[2699.16] [2699.16][S01]And neuroscience would be one of those sources of ideas.[2702.42] [2702.42][S01]But when it's more engineering heavy,[2704.28] [2704.28][S01]then I think that takes a little bit more of a backseat.[2707.5] [2707.5][S01]So it may be more applying AI to neuroscience now[2710.94] [2710.94][S01]like you saw with the virtual rat brain.[2712.98] [2712.98][S01]And I think we'll see that as we get closer to AGI[2715.59] [2715.59][S01]using that to understand the brain.[2717.8] [2717.8][S01]I think it would be one of the coolest use[2719.55] [2719.55][S01]cases for AGI and science.[2720.882] [2720.882][S02] I guess this stuff goes[2722.34] [2722.34][S02]through phases of the engineering challenge,[2724.2] [2724.2][S02]the intervention challenge.[2725.325] [2725.325][S01] So it's done its part for now,[2727.32] [2727.32][S01]and it's been great.[2728.65] [2728.65][S01]And we still obviously keep a close track of it[2731.04] [2731.04][S01]and take any other ideas, too.[2733.59] [2733.59][S02] OK.[2734.55] [2734.55][S02]All of the pictures of the future that you've painted[2738.24] [2738.24][S02]are still anchored quite in reality.[2740.74] [2740.74][S02]But I know that you've said that you really[2743.1] [2743.1][S02]want AGI to be able to peer into the mysteries of the universe[2746.85] [2746.85][S02]down at the Planck scale.[2748.24] [2748.24][S01] Yes![2749.073] [2749.073][S02] Like subatomic, quantum worlds.[2752.79] [2752.79][S02]Do you think that there are things that we have not even[2755.4] [2755.4][S02]yet conceived of that might end up being possible?[2759.345] [2759.345][S02]I'm talking wormholes here.[2760.47] [2760.47][S01] Completely, yes.[2761.803] [2761.803][S01]I'd love wormholes to be possible.[2763.82] [2763.82][S01]I think there is a lot of probably misunderstanding,[2767.592] [2767.592][S01]I would say, still things we don't understand about physics[2770.05] [2770.05][S01]and the nature of reality.[2771.73] [2771.73][S01]And obviously, the quantum mechanics, and unifying[2774.76] [2774.76][S01]that with gravity, and all of these things,[2776.98] [2776.98][S01]and there's all these problems with the standard model.[2779.48] [2779.48][S01]So I think there's--[2781.09] [2781.09][S01]and string theory, I mean, I just think--[2784.18] [2784.18][S02] There's giant gaping holes in physics.[2786.4] [2786.4][S01] Yes, in physics, all over the place.[2788.567] [2788.567][S01]And I talk to my physics friends about this,[2790.6] [2790.6][S01]and there's a lot of things that don't fit together.[2793.15] [2793.15][S01]I don't really like the multiverse explanation.[2795.32] [2795.32][S01]So I think that it would be great to come up[2799.3] [2799.3][S01]with new theories and then test those on massive apparatus,[2803.09] [2803.09][S01]perhaps out in space, at these tiny--[2806.847] [2806.847][S01]the reason I'm obsessed with Planck scale things--[2808.93] [2808.93][S01]Planck time, Planck space--[2810.4] [2810.4][S01]is because that seems to be the resolution of reality in a way.[2816.82] [2816.82][S01]That's the smallest quanta you can break anything into.[2820.01] [2820.01][S01]So that feels like the level you want[2822.7] [2822.7][S01]to experiment on if you had powerful apparatus perhaps[2827.83] [2827.83][S01]designed or enabled by having AGI and radical abundance.[2831.08] [2831.08][S01]You would need both to be able to afford to build[2833.47] [2833.47][S01]those types of experiments.[2834.86] [2834.86][S02] The resolution of reality.[2836.56] [2836.56][S02]What a phrase.[2837.55] [2837.55][S02]What, so as in the resolution that we're at the moment?[2840.61] [2840.61][S02]Human level is just an approximation of reality.[2843.01] [2843.01][S01] Yes, that's right.[2843.5] [2843.5][S01]And then we know there's the atomic level,[2844.87] [2844.87][S01]and below that is the Planck level, which as far as we know[2847.328] [2847.328][S01]is the smallest resolution one can even talk about things.[2850.58] [2850.58][S01]And so that, to me, would be the resolution[2853.09] [2853.09][S01]one wants to experiment on to really understand[2855.31] [2855.31][S01]what's going on here.[2856.22] [2856.22][S02] I wonder whether you're also[2857.92] [2857.92][S02]envisaging that there will be things that[2859.81] [2859.81][S02]are beyond the limits of human understanding[2862.46] [2862.46][S02]AGI will help us to uncover--[2864.37] [2864.37][S02]that actually, we're just not really capable of understanding.[2867.89] [2867.89][S02]And then I wonder if things are unexplainable or[2871.6] [2871.6][S02]ununderstandable, are they still falsifiable?[2874.383] [2874.383][S01] Yeah well, look, I mean,[2876.05] [2876.05][S01]these are great questions.[2877.133] [2877.133][S01]I think there will be a potential for an AGI system[2879.79] [2879.79][S01]to understand higher level abstractions than we can.[2882.68] [2882.68][S01]So again, going back to neuroscience,[2884.96] [2884.96][S01]we know that it's your prefrontal cortex that[2887.17] [2887.17][S01]does that.[2887.93] [2887.93][S01]And there's up to about six or seven layers of indirection one[2892.3] [2892.3][S01]could take.[2892.99] [2892.99][S01]This person's thinking this, and I'm[2894.615] [2894.615][S01]thinking this about that person thinking this, and so on.[2896.99] [2896.99][S01]And then we lose track.[2898.46] [2898.46][S01]But I think an AI system could have an arbitrarily large[2901.99] [2901.99][S01]prefrontal cortex effectively.[2903.83] [2903.83][S01]So you could imagine higher levels[2905.41] [2905.41][S01]of abstraction and patterns that it[2907.037] [2907.037][S01]will be able to see about the universe[2908.62] [2908.62][S01]that we can't really comprehend or hold in mind at once.[2912.91] [2912.91][S01]And then I think in terms of explainability point of view,[2916.52] [2916.52][S01]the way I think that is a little bit[2918.04] [2918.04][S01]different to other philosophers who've thought about this, which[2920.71] [2920.71][S01]is we'll be closer to an ant and then the AGI, in terms of IQ.[2924.57] [2924.57][S01]But I don't think that's the way to think of it.[2926.57] [2926.57][S01]I think it's we are Turing complete,[2930.89] [2930.89][S01]so we're a full general intelligence as ourselves,[2934.61] [2934.61][S01]albeit a bit slow because we run on slow machinery.[2938.0] [2938.0][S01]And we can't infinitely expand our own brains.[2941.15] [2941.15][S01]But we can, in theory, given enough time and memory,[2946.4] [2946.4][S01]understand anything that's computable.[2949.4] [2949.4][S01]And so I think it will be more like Garry Kasparov or Magnus[2954.7] [2954.7][S01]Carlsen playing an amazing chess move.[2956.54] [2956.54][S01]I couldn't have come up with it, but they can explain it to me[2959.14] [2959.14][S01]why it's a good move.[2960.59] [2960.59][S01]So I think that's what an AGI system will be able to do.[2964.25] [2964.25][S02] You said that DeepMind was a 20-year project.[2967.78] [2967.78][S02]How far through are we?[2968.87] [2968.87][S02]Are you on track?[2969.64] [2969.64][S01] I think we're on track, yeah, crazily.[2971.89] [2971.89][S01]Because usually 20-year projects stay 20 years away.[2974.56] [2974.56][S01]But yeah, we're a good way in now, and I think we're--[2977.77] [2977.77][S02] 20 years is 2030 for AGI.[2979.312] [2979.312][S01] 2030, yeah.[2980.437] [2980.437][S01]So I think the way I say is I wouldn't be surprised[2982.6] [2982.6][S01]if it comes in the next decade.[2984.37] [2984.37][S01]So I think we're on track.[2985.84] [2985.84][S02] That matches what you said last time.[2987.05] [2987.05][S02]You haven't updated your priors.[2988.383] [2988.383][LAUGHTER][2990.01] [2990.01][S01] Exactly[2991.217] [2991.217][S02] Amazing.[2992.05] [2992.05][S02]Demis, thank you so much.[2993.4] [2993.4][S02]Absolute delight.[2994.313] [2994.313][S02]Absolute delight, as always.[2995.48] [2995.48][S01] Very fun to talk, as always, as well.[2997.688] [2997.688][S01]Thank you.[2998.41] [2998.41][S02] OK, I think there are a few really important[3000.743] [3000.743][S02]things that came out of that conversation, especially[3003.15] [3003.15][S02]when you compare it to what Demis was saying last time we[3006.38] [3006.38][S02]spoke to him in 2022.[3008.36] [3008.36][S02]Because there have definitely been a few surprises[3011.39] [3011.39][S02]in the last couple of years.[3013.04] [3013.04][S02]The way that these models have demonstrated[3015.26] [3015.26][S02]a genuine conceptual understanding is one--[3018.38] [3018.38][S02]this real world grounding that came in from language[3021.8] [3021.8][S02]and human feedback alone.[3023.76] [3023.76][S02]We did not think that would be enough.[3026.72] [3026.72][S02]And then how interesting and useful, imperfect AI[3030.53] [3030.53][S02]has been to the everyday person.[3032.79] [3032.79][S02]Demis himself there admitted that he had not[3035.12] [3035.12][S02]seen that one coming.[3036.62] [3036.62][S02]And that makes me wonder about the other challenges[3039.02] [3039.02][S02]that we don't yet know how to solve[3041.305] [3041.305][S02]like long-term planning, and agency, and robust, unbreakable[3046.64] [3046.64][S02]safeguards.[3047.85] [3047.85][S02]How many of those--[3049.55] [3049.55][S02]which we're going to cover in detail in this podcast,[3051.78] [3051.78][S02]by the way--[3052.56] [3052.56][S02]are we going to come back to in a couple of years[3055.1] [3055.1][S02]and realize that they were easier than we thought?[3058.91] [3058.91][S02]And how many of them are going to be harder?[3062.0] [3062.0][S02]And then as for the big predictions[3063.87] [3063.87][S02]that Demis made, like cures for most diseases in 10 or 20 years,[3067.62] [3067.62][S02]or AGI by the end of the decade, or how[3070.32] [3070.32][S02]we're about to enter into an era of abundance,[3073.6] [3073.6][S02]I mean, they all sound like Demis is being a bit overly[3076.95] [3076.95][S02]optimistic, doesn't it?[3078.96] [3078.96][S02]But then again, he hasn't exactly been wrong so far.[3083.49] [3083.49][S02]You've been listening to "Google DeepMind, the Podcast" with me,[3086.38] [3086.38][S02]Professor Hannah Fry.[3087.28] [3087.28][S02]If you have enjoyed this episode, hey, why not subscribe?[3090.63] [3090.63][S02]We've got plenty more fascinating conversations[3092.97] [3092.97][S02]with the people at the cutting edge of AI[3095.76] [3095.76][S02]coming up on topics ranging from how AI is accelerating[3099.18] [3099.18][S02]the pace of scientific discoveries[3100.98] [3100.98][S02]to addressing some of the biggest[3102.72] [3102.72][S02]risks of this technology.[3104.53] [3104.53][S02]If you have any feedback, or you want to suggest a future guest,[3107.59] [3107.59][S02]then do leave us a comment on YouTube.[3109.56] [3109.56][S02]Until next time.[3110.76] [3110.76][MUSIC PLAYING][3113.81]