[0.0][S04] Hello, and welcome to the Google DeepMind Podcast[2.6] [2.6][S04]with me, Professor Hannah Fry.[3.99] [3.99][S04]Now, those of you who are watching[5.42] [5.42][S04]will notice that we are not in the studio today,[7.68] [7.68][S04]and that is because we are backstage at the AI[10.49] [10.49][S04]for Science Forum, a very special event that[12.98] [12.98][S04]has been co-hosted between the Royal Society and Google[16.37] [16.37][S04]DeepMind.[17.07] [17.07][S04]And as part of the day, I had the opportunity[19.58] [19.58][S04]to speak to Demis Hassabis on stage, to interview him,[22.97] [22.97][S04]and we thought that you might like to hear that conversation.[26.1] [26.1][S04]So we have included it here as a special episode.[29.01] [29.01][S04]And then even more special than that,[31.29] [31.29][S04]as though one Nobel laureate wasn't enough,[34.47] [34.47][S04]we have three further Nobel laureates[37.31] [37.31][S04]joining me on stage for an extended panel for you to enjoy.[41.19] [41.19][MUSIC PLAYING][42.16] [47.99][S04]OK, now for the next session, it is always[52.25] [52.25][S04]an absolute treat for me and often a mind boggling[54.98] [54.98][S04]experience to get to interview your co-host for today,[60.3] [60.3][S04]sir Demis Hassabis.[61.81] [61.81][S04]Now, Demis is a computer scientist.[63.79] [63.79][S04]He's an Artificial Intelligence researcher.[65.74] [65.74][S04]He's an entrepreneur who co-founded DeepMind[68.13] [68.13][S04]in 2010, where he is still CEO.[72.03] [72.03][S04]Now, I don't know how much you know about Demis' background.[74.53] [74.53][S04]Because as well as the Nobel Prize, and the knighthood,[76.74] [76.74][S04]and being a fellow of the Royal Society, and AlphaFold,[79.11] [79.11][S04]and AlphaGo, and having 150,000 citations,[82.23] [82.23][S04]he also had a previous life as a very successful neuroscientist,[86.32] [86.32][S04]chess champion, and video games designer.[89.04] [89.04][S04]I have to say, I have a theory that, actually, Demis[91.98] [91.98][S04]worked out the secrets to AGI a little while ago[94.92] [94.92][S04]and has been hiding it in his basement and then just, like,[98.37] [98.37][S04]slowly eking out all of these massive breakthroughs[100.74] [100.74][S04]one-by-one to the rest of us.[102.057] [102.057][S04]But one thing is for sure, our conversations[103.89] [103.89][S04]are always genuinely fascinating.[105.55] [105.55][S04]So please, welcome Demis Hassabis.[106.98] [106.98][S04]Thank you.[107.65] [107.65][APPLAUSE][109.111] [111.06][S04]Thank you, Demis.[111.9] [111.9][S04]Thank you so much.[113.64] [113.64][S04]OK, I heard that you didn't know that the Nobel Prize was coming.[117.71] [117.71][S04]Is that true?[118.63] [118.63][S03] Yes, no--[119.23] [119.23][S04] Really, really?[119.98] [119.98][S03] Yeah.[120.255] [120.255][S04] How did you find out?[121.63] [121.63][S03] Well, it was quite a funny story, actually,[124.088] [124.088][S03]because we had heard some rumor that our fold had[126.43] [126.43][S03]been sort of nominated, but you never[129.19] [129.19][S03]expect something like that.[130.4] [130.4][S03]And in the morning, I was just getting on with my normal work.[132.8] [132.8][S03]Actually, my wife was working at home, as well,[134.758] [134.758][S03]and it got to about 10:30.[136.1] [136.1][S03]And we thought, oh, obviously, it's[137.56] [137.56][S03]not happened this year, because we hadn't heard.[139.56] [139.56][S03]And then, suddenly, my wife's computer[141.79] [141.79][S03]started ringing on her Skype, I think it was.[144.56] [144.56][S03]And I said, what's that annoying noise?[146.43] [146.43][S03]And then it turned out to be a call from Sweden,[148.43] [148.43][S03]and they were desperately trying to get hold of my number,[151.04] [151.04][S03]and they didn't have John's number either.[152.79] [152.79][S03]So everyone was in a kind of panic,[154.25] [154.25][S03]but it all added to the drama of the moment.[156.55] [156.55][S03]Just 10 minutes before--[157.78] [157.78][S04] You had john's number?[158.21] [158.21][S03] Yes, exactly.[158.81] [158.81][S03]Just before it was all announced.[159.825] [159.825][S04] Absolutely amazing, and I also[161.71] [161.71][S04]saw that you celebrated by having a poker[164.11] [164.11][S04]night with some chess champions, including Magnus[167.38] [167.38][S04]Carlsen and Hikaru, as well.[169.31] [169.31][S04]I want to know, who won?[170.635] [170.635][S04]What's your bluffing strategy?[171.885] [171.885][LAUGHING][172.588] [172.588][S03] Well, it turned out-- yeah, so that happened.[175.13] [175.13][S03]I think it was on the Wednesday, and it turned out[177.213] [177.213][S03]that there was a big, huge chess tournament going on in London.[182.77] [182.77][S03]And one of my chess friends, old chess friends[184.78] [184.78][S03]from when I was young, was hosting a poker and chess[187.39] [187.39][S03]evening the day after.[189.29] [189.29][S03]So I thought it was the perfect way to celebrate,[191.63] [191.63][S03]but I don't recommend a home poker game[195.94] [195.94][S03]with a couple of world poker champions[197.56] [197.56][S03]and a couple of ex-world chess champions[199.67] [199.67][S03]if you want to win some money.[201.95] [201.95][S03]But that's my idea of fun, and it was actually[204.902] [204.902][S03]an amazing night.[205.61] [205.61][S03]I know it sounds pretty nerdy, but it was actually my idea[209.83] [209.83][S03]of heaven to celebrate it.[211.64] [211.64][S04] Not at all.[211.81] [211.81][S04]It sounds like--[212.33] [212.33][S03] Yeah, I can't tell you the bluffing strategy.[213.7] [213.7][S03]Otherwise, I'll never beat Magnus again.[215.025] [215.025][LAUGHING][215.525] [215.525][S03]Yeah.[216.048] [216.048][S04] Well, this is true.[217.34] [217.34][S04]This is true.[218.59] [218.59][S04]I mean, aside from the Nobel Prize,[220.81] [220.81][S04]a sentence that no one has ever said, before that,[225.71] [225.71][S04]in advance of that, you also were named as a citation[229.21] [229.21][S04]laureates, because the AlphaFold work has now been cited over[232.72] [232.72][S04]28,000 times, which is incredible.[235.11] [235.11][S04]I mean, this is really not very long ago that it came through.[238.76] [238.76][S04]Are there standout applications for you that really resonate?[242.473] [242.473][S03] Well, I mean, it is mind boggling, really,[244.89] [244.89][S03]and it's everything we hoped would happen[247.34] [247.34][S03]by putting AlphaFold out there, and open sourcing[249.77] [249.77][S03]it, and putting it out with the community,[251.52] [251.52][S03]with our fantastic partners at EMBL-EBI, like Janet, and Ewan,[255.56] [255.56][S03]and the audience as well, to get it out there to everyone.[259.73] [259.73][S03]I mean, there's so many applications to mention,[263.43] [263.43][S03]but I think maybe I can mention two or three of my favorites[266.54] [266.54][S03]were kind of determining the structure of the nuclear pore[271.31] [271.31][S03]complex, one of the most important and largest proteins[274.04] [274.04][S03]in the human body.[275.228] [275.228][S03]And it's really important, because it[276.77] [276.77][S03]governs the molecules and nutrients that go in[279.17] [279.17][S03]and out of your cell nucleus.[280.74] [280.74][S03]And, actually, several teams used AlphaFold predictions,[284.51] [284.51][S03]as well as their own experimental data,[286.61] [286.61][S03]to finally piece that very, very complex protein[291.08] [291.08][S03]structure together.[292.48] [292.48][S03]So that was amazing.[293.89] [293.89][S03]Another piece of work that I really like[295.74] [295.74][S03]is [INAUDIBLE] work from the [? Broad, ?][298.32] [298.32][S03]developing a molecular syringe to deliver drug payloads[301.8] [301.8][S03]to hard parts of the body to reach,[304.2] [304.2][S03]and he also used AlphaFold to help[305.88] [305.88][S03]modify how that molecular syringe was designed.[309.58] [309.58][S03]And then maybe, finally, my other favorite project[311.94] [311.94][S03]is from the University of Portsmouth, John McKeon's group,[316.77] [316.77][S03]designing plastic eating enzymes using AlphaFold.[321.91] [321.91][S03]But I think it's just a small sample[324.21] [324.21][S03]of all the incredible work researchers are doing with it.[326.77] [326.77][S04] But these kind of projects, I[327.74] [327.74][S04]mean, these are your favorites, right?[329.323] [329.323][S04]The ones where it's not just sort of the technical idea[332.1] [332.1][S04]itself, it's the potential impact[333.54] [333.54][S04]that it can have further downstream?[334.55] [334.55][S03] Yeah, that's right.[336.008] [336.008][S03]I mean, that's why I was always intrigued by the protein folding[341.28] [341.28][S03]problem or protein structure prediction problem was because I[345.33] [345.33][S03]felt-- we sometimes call it a deepmind, a root node problem.[348.468] [348.468][S03]What we mean by that is, if you think[350.01] [350.01][S03]of the whole tree of knowledge, there[351.88] [351.88][S03]are certain problems, where if there are root node problems,[354.74] [354.74][S03]if you unlock them, if you discover a solution to them,[357.05] [357.05][S03]it would unlock a whole new branch or avenue of discovery.[361.01] [361.01][S03]And I always felt that determining a protein structure[364.57] [364.57][S03]in this way would do that, lead to disease understanding,[368.23] [368.23][S03]drug design, and much more, and that[370.383] [370.383][S03]seems to be what's happened.[371.55] [371.55][S04] OK, so not to be greedy, but what's next?[375.43] [375.43][S04]Is there another AlphaFold?[376.67] [376.67][S04]I mean, GNoME is a personal favorite of mine.[378.77] [378.77][S04]Maybe tell us a little bit about that project.[379.94] [379.94][S03] Yes, so GNoME, look, there's so many areas,[382.19] [382.19][S03]and actually, James mentioned it in the morning,[383.86] [383.86][S03]and Pushmeet talked about it as well,[385.402] [385.402][S03]that we're touching on nearly every area of science.[389.38] [389.38][S03]And GNoME is one of my favorite projects, which[391.42] [391.42][S03]is on material design, and I think[393.49] [393.49][S03]material design has some of the same characteristics that we[396.97] [396.97][S03]look for in a problem that's suitable for AI.[398.99] [398.99][S03]It's massive combinatorial space.[401.38] [401.38][S03]You need to try and build a model that[403.06] [403.06][S03]understands the physics and the chemistry[405.16] [405.16][S03]of the natural phenomena.[408.26] [408.26][S03]And then, if you have a model like that,[409.97] [409.97][S03]you can then maybe use it to do a very efficient search[413.27] [413.27][S03]through that search space, combinatorial space,[416.07] [416.07][S03]and then find an optimal solution.[417.81] [417.81][S03]And in materials, I think that would[419.63] [419.63][S03]be also just as groundbreaking.[421.17] [421.17][S03]You could imagine designing new batteries or maybe, one day,[424.208] [424.208][S03]discovering a room temperature superconductor.[426.125] [426.125][S03]It has always been one of my dreams.[428.99] [428.99][S03]So I think we're, obviously, at the early stages of that.[432.42] [432.42][S03]I would characterize it as sort of AlphaFold one level maybe,[435.865] [435.865][S03]and we've got to get to AlphaFold[437.24] [437.24][S03]two level of predictions.[439.89] [439.89][S03]But we can see a clear path from there,[441.66] [441.66][S03]and GNoME is the beginning of that work.[443.49] [443.49][S03]We published it in--[444.57] [444.57][S03]I think it was in Science, last year,[446.52] [446.52][S03]and we discovered 200,000 new crystals that no one had ever[451.55] [451.55][S03]seen before.[452.43] [452.43][S03]So I think that shows some of the potential of AI in things,[456.95] [456.95][S03]like material design.[457.86] [457.86][S03]And then maybe the other thing I'm excited about[459.86] [459.86][S03]is AI applied to mathematics and, perhaps,[462.98] [462.98][S03]solving one of the great conjectures, maybe[465.8] [465.8][S03]one of the Millennium prize problems,[468.11] [468.11][S03]and using AI as a big part of that solution.[474.03] [474.03][S04] So that idea, though, about GNoME,[476.12] [476.12][S04]also about AlphaFold, is you're sort of shortcutting,[479.87] [479.87][S04]or you're making the sort of synthetic world[483.8] [483.8][S04]really serve your advantage.[485.678] [485.678][S04]So you're not having to do everything[487.22] [487.22][S04]through experimentation.[488.51] [488.51][S04]I know that you and Paul Nurse have[490.04] [490.04][S04]been talking about a virtual cell for a long while.[493.697] [493.697][S04]Tell us a little bit about that.[495.03] [495.03][S03] Well, Paul has been one of my mentors.[499.07] [499.07][S03]He's very generously mentored me in the life sciences[501.5] [501.5][S03]for more than 25 years now or something,[504.09] [504.09][S03]and it's been incredible, sort of inspiring,[507.45] [507.45][S03]talking to Paul very regularly about these topics.[510.44] [510.44][S03]And, unusually, I think Paul for a life scientist, a biologist,[514.47] [514.47][S03]he's thought a lot about biology as an information system.[517.02] [517.02][S03]He's written a lot of interesting research papers[519.62] [519.62][S03]on that.[520.23] [520.23][S03]So we've always had this discussion in the background,[523.74] [523.74][S03]and I've always wondered-- every five years or so, I've thought,[526.73] [526.73][S03]do we have enough technologies, enough[528.7] [528.7][S03]know how to actually really attempt this Mount[532.565] [532.565][S03]Everest of a problem or trying to build[534.19] [534.19][S03]a virtual cell, a simulation of a cell, basically,[536.57] [536.57][S03]that would be predictive of something that's[538.96] [538.96][S03]really going to happen?[541.09] [541.09][S03]And every five years, I thought, we don't really[543.22] [543.22][S03]have enough technologies yet.[544.7] [544.7][S03]But, finally, now, I think the answer is, yes, we probably[548.35] [548.35][S03]do have enough know how and techniques that we could attempt[552.61] [552.61][S03]this seriously now.[553.94] [553.94][S03]And maybe, in the next five to 10 years,[555.92] [555.92][S03]we could eventually build up a picture[558.52] [558.52][S03]of a virtual cell, perhaps, a yeast cell to start off with,[561.62] [561.62][S03]as Paul's always been working on as the model organism for this.[567.34] [567.34][S03]And my way of thinking about it is you[569.32] [569.32][S03]can think of AlphaFold2 as essentially[571.69] [571.69][S03]solving the static picture of what a protein looks like.[574.52] [574.52][S03]But of course, we know biology is a dynamic system.[576.993] [576.993][S03]That's where all the interesting things happen in biology,[579.41] [579.41][S03]and AlphaFold3 is our first step towards trying[583.18] [583.18][S03]to model those interactions.[584.75] [584.75][S03]So AlphaFold3 can model pairwise interactions[587.59] [587.59][S03]between proteins and proteins, proteins and RNA, proteins,[590.09] [590.09][S03]and DNA.[591.28] [591.28][S03]And then maybe the next step up from that[593.08] [593.08][S03]would be to model a whole pathway,[594.86] [594.86][S03]and then, eventually, maybe we can make it to an entire cell.[599.26] [599.26][S04] Amazing.[600.37] [600.37][S04]I mean, I imagine that the advent of quantum computing[603.94] [603.94][S04]will make a difference now that it's possible to do simulations[606.673] [606.673][S04]down at the molecular level.[607.84] [607.84][S03] Yeah, so look, quantum computing[610.3] [610.3][S03]is very exciting.[611.23] [611.23][S03]It itself is accelerating, and James also[615.1] [615.1][S03]mentioned earlier this morning about[616.6] [616.6][S03]the interesting cross-pollination[620.62] [620.62][S03]that's happening between AI and quantum computing.[622.82] [622.82][S03]Actually, we collaborate a lot with our quantum computing[625.715] [625.715][S03]group at Google, which is one of the world's best quantum[628.09] [628.09][S03]computing teams, on things, like error correction codes[631.0] [631.0][S03]and things like that.[632.59] [632.59][S03]And of course, one of the uses of a quantum computer[635.74] [635.74][S03]would be to simulate quantum systems, like molecules,[640.33] [640.33][S03]and atoms, and things like that, and compounds,[643.14] [643.14][S03]and then produce a lot of synthetic data potentially.[646.97] [646.97][S03]But, interestingly, I also have a slightly controversial take,[650.43] [650.43][S03]which is--[651.32] [651.32][S03]and I've talked to some of the world's top quantum computer[655.22] [655.22][S03]scientists on this, is that, actually, I[657.47] [657.47][S03]believe that classical Turing machines, classical computers,[661.59] [661.59][S03]are capable of a lot more than we previously thought.[665.58] [665.58][S03]And I think that's what the work that we've been doing[668.12] [668.12][S03]has shown, both with AlphaFold, but also[670.43] [670.43][S03]previously with AlphaGo, our program that[673.28] [673.28][S03]beat the world champion at the game of Go, which[675.62] [675.62][S03]just as an indication of the complexity[677.69] [677.69][S03]Go, much more complicated than chess[679.58] [679.58][S03]and has more possible board positions[682.82] [682.82][S03]than there are atoms in the universe, 10 to the power 170.[685.71] [685.71][S03]So what that means is you can't possibly[687.56] [687.56][S03]brute force a solution to find the best[689.23] [689.23][S03]move in a particular position.[690.48] [690.48][S03]You have to do something much cleverer.[692.24] [692.24][S03]So both protein folding also has enormous numbers[695.57] [695.57][S03]of possibilities, if you were to do it naively and just try[698.51] [698.51][S03]every combination, which is the naive way of doing it.[701.65] [701.65][S03]But yeah, if you do things, like you[703.66] [703.66][S03]do a huge amount of pre-compute to build a model of the system[707.86] [707.86][S03]before you ask it the question that you're interested in,[711.19] [711.19][S03]what move should I play in this go position,[713.48] [713.48][S03]how does this novel protein fold, it turns out[716.92] [716.92][S03]you can actually come back with a near optimal move in Go[719.98] [719.98][S03]in a few seconds or fold a protein in a matter of minutes,[723.88] [723.88][S03]which you might have expected in protein space,[727.09] [727.09][S03]at least, that you might need a quantum computer for or quantum[730.21] [730.21][S03]algorithm for.[731.12] [731.12][S03]And it turns out, you don't.[732.32] [732.32][S03]So I actually think we should take this very seriously,[735.28] [735.28][S03]that classical systems, if you use them in the right way,[739.13] [739.13][S03]may be capable of modeling a lot more complex systems,[743.03] [743.03][S03]perhaps, even counterintuitively,[744.98] [744.98][S03]quantum systems.[745.7] [745.7][S03]Because, normally, you talk about needing quantum computers[748.24] [748.24][S03]to model any kind of classical system,[751.523] [751.523][S03]but it may be that classical systems can[753.19] [753.19][S03]model quantum systems.[754.49] [754.49][S03]And I've tested this out with some of the people,[756.94] [756.94][S03]like Professor Zeilinger, who won the Nobel Prize in physics[759.81] [759.81][S03]recently for his pioneering quantum computing work,[762.64] [762.64][S03]and he thought it was very interesting.[764.41] [764.41][S03]And David Deutsch, also, one of my scientific heroes,[767.97] [767.97][S03]who's basically invented quantum computing, said it was crazy,[771.97] [771.97][S03]but the right sort of crazy, which coming from him,[774.64] [774.64][S03]I take as a compliment and as also a sign[777.93] [777.93][S03]to maybe pursue it further.[779.055] [779.055][S04] So let me make sure I understand this then.[781.347] [781.347][S04]So, I guess, the classical computers[783.24] [783.24][S04]are thought of as deterministic machines,[785.53] [785.53][S04]and you're talking here about probabilistic questions.[788.305] [788.305][S04]But you can use the deterministic ones.[789.93] [789.93][S03] Yeah, and the idea[791.37] [791.37][S03]is that quantum systems are anything you try[793.74] [793.74][S03]to mold, any natural phenomena.[795.07] [795.07][S03]If you start trying to model it by every possibility[798.12] [798.12][S03]that it could take, that system, then you quickly[800.76] [800.76][S03]run out of computation in a classical system.[803.23] [803.23][S03]You need too many bits to model it,[805.29] [805.29][S03]but that's just not the way that you would attempt to do it[807.79] [807.79][S03]with a classical system.[808.79] [808.79][S03]You would build a model, first, that learns--[812.22] [812.22][S03]my conjecture on it would be that any natural phenomena[817.3] [817.3][S03]tends to have structure.[818.72] [818.72][S03]And if it has structure, you could potentially[821.17] [821.17][S03]learn it with a classical machine learning system,[824.17] [824.17][S03]learn an efficient model of that,[825.8] [825.8][S03]and then use that to search the possibilities[829.51] [829.51][S03]in an efficient way.[830.75] [830.75][S03]So I think that might get around some of the inefficiencies[834.94] [834.94][S03]from a naive way of doing this.[837.16] [837.16][S03]I mean, it's a pretty big claim, so I'm making it in a soft way.[841.31] [841.31][S03]But it's a kind of hobby of mine to look at this area,[844.782] [844.782][S03]and it's something, I think, that could be quite promising.[847.24] [847.24][S04] Another hobby of yours[848.68] [848.68][S04]that has now spun off into its own business, Isomorphic,[853.84] [853.84][S04]because, I mean, this is work using AlphaFold[856.15] [856.15][S04]to apply to drug discovery.[857.467] [857.467][S03] Yes.[858.3] [858.3][S04] You've got some pretty prestigious partnerships[861.1] [861.1][S04]now as well.[862.3] [862.3][S04]Can you tell us what Isomorphic is focusing on at the moment?[865.17] [865.17][S03] Yeah, so Isomorphic is our spin out[867.94] [867.94][S03]to try and revolutionize drug discovery from first principles[871.73] [871.73][S03]using AI from the ground up, trying to re-imagine the drug[876.74] [876.74][S03]discovery process.[878.07] [878.07][S03]And I had this in mind when we were doing AlphaFold.[880.53] [880.53][S03]So I think it's one of the most, obviously, good use cases of AI[886.46] [886.46][S03]is to cure diseases.[887.94] [887.94][S03]I mean, what better use of AI could there be?[890.72] [890.72][S03]So that has always been one of the number one things[893.48] [893.48][S03]I wanted to do with AI once it got mature enough.[896.69] [896.69][S03]So AlphaFold, of course, is a great tool[899.57] [899.57][S03]for fundamental research and fundamental biology research.[902.34] [902.34][S03]Over two million researchers around the world[904.88] [904.88][S03]have used AlphaFold and the structures we put out there,[907.67] [907.67][S03]but it's also a practical use to help with drug discovery.[911.37] [911.37][S03]Of course, knowing the structure of a protein[913.25] [913.25][S03]is only one small part of the whole drug discovery process.[917.36] [917.36][S03]So we spun out isomorphic after we did AlphaFold two[919.85] [919.85][S03]to build on that work and extend and build new machine learning[924.23] [924.23][S03]systems into adjacent areas, things,[926.87] [926.87][S03]like designing chemical compounds and drug compounds,[931.97] [931.97][S03]testing for toxicity and predicting things,[934.2] [934.2][S03]like ADME properties, important properties[936.24] [936.24][S03]that you need for drugs to work in the body[938.31] [938.31][S03]and minimize side effects and things like that.[940.57] [940.57][S03]And we're building-- you can think of it as building up[942.9] [942.9][S03]further AlphaFold-like models in these adjacent areas.[946.473] [946.473][S03]And then, eventually, we'll stick them all together,[948.64] [948.64][S03]and we hope that one day, in the near future, actually,[952.24] [952.24][S03]we'll reduce the time down from years, maybe even decade,[955.66] [955.66][S03]to design a drug down to months or perhaps even weeks.[959.46] [959.46][S03]And that would revolutionize, I think,[961.06] [961.06][S03]the drug discovery process.[962.015] [962.015][S04] This really does seem[963.21] [963.21][S04]like one of those endeavors, where you're not[965.085] [965.085][S04]having to make a choice between doing something that benefits[967.68] [967.68][S04]humanity and doing something that can be[969.81] [969.81][S04]profitable and sustain itself.[971.8] [971.8][S04]How much do you look for those kind of projects at the outset,[975.34] [975.34][S04]or how much of it is that you do sort of blue sky research[977.865] [977.865][S04]and then hope that it'll turn out that way in the end?[980.115] [980.115][S03] I think we do both.[983.49] [983.49][S03]We're research led as a group, always have been,[986.83] [986.83][S03]so we try to do what's the right next step for researching AGI,[992.69] [992.69][S03]Artificial General Intelligence, or something, like AlphaFold,[996.64] [996.64][S03]where you really want to solve the scientific challenge.[1000.04] [1000.04][S03]But then, in the back of my mind, I'm also pretty practical.[1003.49] [1003.49][S03]So I want to solve things that will also[1005.85] [1005.85][S03]have a practical, positive impact on the world[1008.46] [1008.46][S03]pretty directly.[1010.38] [1010.38][S03]So if you can find projects that do both,[1012.88] [1012.88][S03]then that's the holy grail, really,[1014.8] [1014.8][S03]and something that really worthwhile,[1016.86] [1016.86][S03]investing a lot of time and effort.[1018.54] [1018.54][S03]And AlphaFold was exactly like that.[1020.92] [1020.92][S03]So I was fascinated by the problem itself.[1023.64] [1023.64][S03]As Janet mentioned earlier, the more you get into proteins,[1027.16] [1027.16][S03]they're exquisite bionanomachines.[1030.25] [1030.25][S03]I mean, they're unbelievably beautiful.[1032.572] [1032.572][S03]I totally agree with Janet on that,[1034.03] [1034.03][S03]and you sort of fall in love with them[1035.613] [1035.613][S03]when you start working on them, and it's just unbelievable[1039.66] [1039.66][S03]what nature's engineered.[1041.55] [1041.55][S03]But then I always had in mind that would be a social good,[1047.079] [1047.079][S03]but then Isomorphic can do both, right?[1049.06] [1049.06][S03]I think we can cure many diseases with the help of AI,[1054.09] [1054.09][S03]but also, it should be an incredibly valuable company.[1057.34] [1057.34][S03]And that, of course, if that turns out to be the case,[1059.92] [1059.92][S03]that will give us more money to invest in fundamental research.[1063.85] [1063.85][S03]So it's all a virtuous cycle.[1065.68] [1065.68][S04] With all of these different projects[1067.68] [1067.68][S04]going on in fundamental research,[1068.95] [1068.95][S04]I mean, do you think that we are, at this moment, where we're[1071.492] [1071.492][S04]going to have sort of a vertical takeoff, as it were,[1074.02] [1074.02][S04]on progress?[1075.01] [1075.01][S03] I think we are on the cusp of that.[1077.41] [1077.41][S03]I really do feel like we're on the brink of a new golden era[1081.3] [1081.3][S03]of discovery, like the whole of today's symposium is called.[1086.82] [1086.82][S03]And I think what we need is a lot more[1089.61] [1089.61][S03]interdisciplinary science, so using AI, bringing[1093.0] [1093.0][S03]in the right way.[1093.92] [1093.92][S03]We're asking the right questions with domain experts,[1096.81] [1096.81][S03]and I think it's almost limitless,[1099.18] [1099.18][S03]what its applications could be.[1100.54] [1100.54][S03]And of course, AI itself is a scientific discipline,[1102.97] [1102.97][S03]is improving all the time.[1104.29] [1104.29][S03]So there's applying today's technologies directly[1108.4] [1108.4][S03]to the other fields, and then there's also[1110.98] [1110.98][S03]continuing to improve AI itself, and that as well[1114.79] [1114.79][S03]is a sort of exponential improvement.[1116.84] [1116.84][S03]So there's a lot of, I think, progress[1120.34] [1120.34][S03]to be made in just the next few years.[1122.33] [1122.33][S04] I think interdisciplinarity[1124.03] [1124.03][S04]has definitely been one of the big themes of today,[1126.41] [1126.41][S04]but I think that the other themes are about what science[1129.16] [1129.16][S04]looks like in the future.[1130.46] [1130.46][S04]What does it mean to do science in an era of AI?[1133.16] [1133.16][S04]And I know that you're a sort of big advocate[1135.34] [1135.34][S04]for the scientific method, but just talk[1138.24] [1138.24][S04]to me a little bit about that.[1139.49] [1139.49][S04]What does that look like as we kind of move forward?[1141.657] [1141.657][S03] Well, look, I think the scientific method[1144.22] [1144.22][S03]is arguably maybe the greatest idea humans have ever had[1147.83] [1147.83][S03]and, I think, underpins, obviously, all[1150.03] [1150.03][S03]of science, but then also because of that,[1152.41] [1152.41][S03]science and technology, modern civilization.[1155.36] [1155.36][S03]And I think more than ever, we need to anchor around[1159.94] [1159.94][S03]that method in today's world, and I think, especially[1164.68] [1164.68][S03]with something as powerful and potentially transformative[1167.96] [1167.96][S03]as AI, I think it's important we use more the scientific method[1172.22] [1172.22][S03]than, perhaps, lean on normal sort of technology,[1175.95] [1175.95][S03]A/B testing out in the wild.[1177.57] [1177.57][S03]That is often the case with new technologies,[1180.9] [1180.9][S03]and I feel we should treat this more as a scientific endeavor,[1184.1] [1184.1][S03]if possible, although, it obviously[1185.57] [1185.57][S03]has all the implications that breakthrough[1188.51] [1188.51][S03]technologies normally have in terms of the speed of adoption[1191.36] [1191.36][S03]and the speed of change.[1192.9] [1192.9][S03]So it's an interesting situation that we're in,[1196.22] [1196.22][S03]and I think we need to use the scientific method to do things,[1199.19] [1199.19][S03]like better understand what these systems do, build[1202.61] [1202.61][S03]benchmarks and rigorous evaluations[1204.86] [1204.86][S03]for understanding the limits of the capabilities,[1207.9] [1207.9][S03]interpretability.[1209.36] [1209.36][S03]Actually, I think we should be using neuroscience techniques,[1212.24] [1212.24][S03]which are, obviously, built for understanding real brains,[1215.06] [1215.06][S03]to model virtual brains.[1217.91] [1217.91][S03]I sometimes call it virtual brain analysis.[1220.05] [1220.05][S03]What's the equivalent of an fMRI of these neural networks, right?[1224.01] [1224.01][S03]And I think there's a lot we can learn, and bring across,[1227.53] [1227.53][S03]and translate from the natural sciences[1231.01] [1231.01][S03]to what is basically an engineering science.[1233.055] [1233.055][S03]I call it an engineering science,[1234.43] [1234.43][S03]because unlike the natural sciences,[1237.41] [1237.41][S03]you have to build the artifact of interest, first,[1240.413] [1240.413][S03]and then, once you have it, you can then[1242.08] [1242.08][S03]use the scientific method to reduce it down and understand[1245.32] [1245.32][S03]its components.[1247.18] [1247.18][S03]So it's a whole challenge.[1248.38] [1248.38][S03]That's for sure, and these systems, in my opinion,[1251.175] [1251.175][S03]are as complex as the natural phenomena we normally[1253.3] [1253.3][S03]want to study.[1253.94] [1253.94][S03]So no one should think that it would be easier[1256.24] [1256.24][S03]to understand one of these artificial systems[1258.28] [1258.28][S03]than it would be to understand a natural system.[1260.36] [1260.36][S03]I think they're just as complex in many ways.[1263.02] [1263.02][S03]So we've got a lot of work in front of us,[1264.77] [1264.77][S03]and I think this is something that industry, academia,[1268.7] [1268.7][S03]and civil society needs to come together to understand better,[1272.29] [1272.29][S03]including how to deploy these technologies.[1274.34] [1274.34][S04] Where does a scientist's intuition[1276.257] [1276.257][S04]fit in all of this?[1277.095] [1277.095][S03] Well, I think scientist's intuition[1279.94] [1279.94][S03]and creativity is critical.[1282.49] [1282.49][S03]I think, right now, the AI systems are just tools.[1285.38] [1285.38][S03]I think they're great for finding correlations,[1287.83] [1287.83][S03]and patterns, and structures in data, but for the moment,[1291.38] [1291.38][S03]they're not able to come up with their own hypotheses[1294.1] [1294.1][S03]or their own questions.[1295.503] [1295.503][S03]And I think, as all the scientists in the room know,[1297.67] [1297.67][S03]I think that's the hardest thing about science[1299.587] [1299.587][S03]is asking the right question, and that[1303.16] [1303.16][S03]has to still come from human scientists.[1305.327] [1305.327][S03]And for the foreseeable future, I think that will be the case.[1307.91] [1307.91][S03]So I think it could be-- that's why[1309.97] [1309.97][S03]I'm very excited about the use of these AI systems[1312.97] [1312.97][S03]as maybe the ultimate tools to help us accelerate[1315.79] [1315.79][S03]scientific discovery.[1316.665] [1316.665][S04] I guess on that topic of humans, I mean,[1318.832] [1318.832][S04]there has been some recent research saying[1321.13] [1321.13][S04]that the progress of scientific discovery[1324.46] [1324.46][S04]has slowed down over recent years.[1326.39] [1326.39][S04]What's your take on that, and what[1328.36] [1328.36][S04]could be done to change that?[1330.74] [1330.74][S03] Well, look, I think things--[1333.55] [1333.55][S03]I've seen studies like that, too,[1335.06] [1335.06][S03]and I think there's interesting conjectures as to why that is.[1337.99] [1337.99][S03]Science has become a bigger endeavor.[1340.017] [1340.017][S03]You need to have bigger teams, more expensive equipment,[1342.35] [1342.35][S03]and so on.[1343.23] [1343.23][S03]So that leads to some slowdown, and perhaps, the questions[1348.38] [1348.38][S03]we're now tackling are ever more complex.[1351.23] [1351.23][S03]So that's also tricky.[1353.6] [1353.6][S03]I think what I would recommend is,[1356.06] [1356.06][S03]again, I think, a lot of the advances in the next 10 years[1358.61] [1358.61][S03]are going to be interdisciplinary work, sort[1361.78] [1361.78][S03]of bringing together experts from two or more fields,[1365.63] [1365.63][S03]and then that making the big advances in between those areas.[1370.223] [1370.223][S03]And, actually, that's the story of DeepMind, which originally, I[1372.89] [1372.89][S03]think, was a combination of neuroscience ideas[1375.68] [1375.68][S03]with machine learning ideas.[1377.1] [1377.1][S03]It's also the story of AlphaFold,[1378.51] [1378.51][S03]which is the combination of biologists and chemists[1381.68] [1381.68][S03]on our team, along with machine learning experts and engineers.[1385.68] [1385.68][S03]So I've always found that's the place[1387.26] [1387.26][S03]to get the most advances most quickly,[1391.35] [1391.35][S03]and actually, we're announcing today, a $20 million[1395.6] [1395.6][S03]fund from Google.org to fund this kind[1398.45] [1398.45][S03]of interdisciplinary work in academia.[1400.74] [1400.74][S03]And I hope that other funders join that effort,[1403.873] [1403.873][S03]and I think that's what we need is[1405.29] [1405.29][S03]to train a new generation of PhDs and postdocs[1408.74] [1408.74][S03]in these kind of combination of these different areas.[1411.25] [1411.25][S04] What an extraordinary piece of news[1413.208] [1413.208][S04]to finish on, $20 million for research[1415.85] [1415.85][S04]in interdisciplinary teams.[1417.09] [1417.09][S04]Demis, thank you so much, indeed.[1420.157] [1420.157][S04]Thank you very much.[1420.99] [1420.99][APPLAUSE][1421.58] [1421.58][S04]OK, I think you go down to join the other laureates,[1425.84] [1425.84][S04]while we out the stage.[1428.63] [1428.63][S04]Now, I should tell you, actually,[1431.3] [1431.3][S04]the laureates, the word, laureates, by the way,[1434.01] [1434.01][S04]comes from the laurel wreath, which[1436.85] [1436.85][S04]was given to victors in ancient Greece as a sign of honor.[1439.79] [1439.79][S04]And every year, there are five separate prizes[1442.4] [1442.4][S04]that are awarded to those who are considered to have conferred[1446.84] [1446.84][S04]the greatest benefit to humankind courtesy of Alfred[1450.23] [1450.23][S04]Nobel.[1451.25] [1451.25][S04]There is a rumor that there is no Nobel Prize for mathematics,[1455.96] [1455.96][S04]because Alfred Nobel's great love ran away[1458.565] [1458.565][S04]with a mathematician.[1459.44] [1459.44][S04]Have we heard this?[1461.13] [1461.13][S04]I mean, we are charming.[1462.46] [1462.46][S04]What can I say?[1463.83] [1463.83][S04]Now, so joining me on the stage, we[1466.53] [1466.53][S04]have three further recipients of this esteemed prize,[1470.23] [1470.23][S04]John Jumper.[1470.73] [1470.73][S04]We've not yet had the pleasure of having John[1472.605] [1472.605][S04]Jumper on the stage thus far.[1474.07] [1474.07][S04]He is a director at Google DeepMind, where[1476.52] [1476.52][S04]he led the team that built AlphaFold and continues[1480.09] [1480.09][S04]to work on new methods to apply machine learning[1482.61] [1482.61][S04]to protein biology.[1483.82] [1483.82][S04]John has won numerous awards for his work,[1486.76] [1486.76][S04]including the Lasker Award, the Breakthrough[1489.63] [1489.63][S04]Prize in Life Sciences, the Canada International Award,[1493.08] [1493.08][S04]and of course, this year's Nobel Prize in chemistry.[1495.69] [1495.69][S04]We are also joined by Sir Paul Nurse,[1497.89] [1497.89][S04]who is CEO of the Francis Crick Institute[1499.86] [1499.86][S04]and winner of the 2001 prize in physiology of medicine[1503.7] [1503.7][S04]for his work also on proteins, specifically the protein[1506.22] [1506.22][S04]molecules that control the division of cells in the cell[1508.95] [1508.95][S04]cycle.[1509.82] [1509.82][S04]And, finally, Jennifer Doudna, who earlier shared her lessons[1513.9] [1513.9][S04]from CRISPR with James, she won the Nobel Prize in chemistry[1517.66] [1517.66][S04]in 2020.[1518.9] [1518.9][S04]Please, join me in welcoming our four Nobel laureates[1522.13] [1522.13][S04]to the stage.[1522.77] [1522.77][S04]Thank you.[1523.84] [1523.84][S04]OK, so I know that, often, you get[1526.45] [1526.45][S04]asked about where were when you heard,[1528.212] [1528.212][S04]what were you doing when you heard.[1529.67] [1529.67][S04]I want to ask you a slightly different question.[1531.94] [1531.94][S04]I want to ask you about whether there was a moment when[1534.88] [1534.88][S04]you realized that the work that you were doing[1537.16] [1537.16][S04]was genuinely groundbreaking.[1539.167] [1539.167][S04]Was there sort of a moment when you realized[1541.0] [1541.0][S04]the significance of it?[1542.27] [1542.27][S04]John?[1542.99] [1542.99][S06] I think there were kind of two moments for me.[1548.87] [1548.87][S06]One was actually watching Twitter[1551.41] [1551.41][S06]after-- whenever you release some work into the world,[1553.97] [1553.97][S06]you anxiously refresh Twitter, and we[1556.99] [1556.99][S06]stick to the word AlphaFold, and just see what tweets popped up.[1559.82] [1559.82][S06]And I remember seeing, when the database[1563.92] [1563.92][S06]became available, so many astounded grad students saying--[1567.95] [1567.95][S06]I think one was, how did they get my structure,[1570.68] [1570.68][S06]it hasn't been published, how in the world,[1574.1] [1574.1][S06]and just had absolute astounding.[1576.335] [1576.335][S06]Like, the number of people that said either[1580.327] [1580.327][S06]we predicted their structure, this person down the hall,[1582.66] [1582.66][S06]we predicted their structure, so I thought they were absolutely[1584.72] [1584.72][S06]blown away.[1585.22] [1585.22][S06]And I think the second moment was[1587.09] [1587.09][S06]there was a special issue of Science related[1588.978] [1588.978][S06]to the structure of the nuclear pore, the largest[1591.02] [1591.02][S06]collection of proteins in the human cell, protein chains,[1594.6] [1594.6][S06]and there had been, like, four papers in a special issue[1598.94] [1598.94][S06]of Science, and three out of the four[1600.71] [1600.71][S06]had made huge usage of AlphaFold.[1602.738] [1602.738][S06]Something like more than 100 mentions[1604.28] [1604.28][S06]of the word AlphaFold in Science,[1606.06] [1606.06][S06]and we had nothing to do with it.[1607.547] [1607.547][S06]We didn't know it was happening.[1608.88] [1608.88][S06]It just appeared one day, and that[1610.297] [1610.297][S06]was the moment when I really knew that people were doing[1613.49] [1613.49][S06]science worthy of appearing in Science, one of the most[1616.25] [1616.25][S06]prestigious journals in the world, on top of our tools[1619.495] [1619.495][S06]and without us.[1620.12] [1620.12][S06]And the moment at which people start making these discoveries[1623.48] [1623.48][S06]on top of what we've built, that's[1625.04] [1625.04][S06]when you really make it as someone who makes tools.[1627.267] [1627.267][S04] Jennifer, did you have a moment?[1629.1] [1629.1][S05] Well, I think for us,[1630.683] [1630.683][S05]yeah, maybe also two moments.[1632.16] [1632.16][S05]One was in the Fall of probably 2011, when[1638.36] [1638.36][S05]we had started a collaboration with Emmanuelle Charpentier[1641.09] [1641.09][S05]to work on CRISPR, which is a bacterial immune system,[1644.94] [1644.94][S05]and we wondered, how does it work?[1646.68] [1646.68][S05]And we had together figured out that it's an RNA guided[1652.1] [1652.1][S05]system that targets DNA for cutting,[1655.68] [1655.68][S05]for cleavage, and furthermore, that we could actually program[1659.57] [1659.57][S05]the system, because we understood[1661.13] [1661.13][S05]the chemistry of how it worked.[1662.97] [1662.97][S05]And it was really one of those aha moments.[1667.46] [1667.46][S05]Personally, I was just astounded that bacteria had figured out[1670.94] [1670.94][S05]how to do this and that, furthermore, we now understood[1674.63] [1674.63][S05]how to harness that system to manipulate DNA in new ways.[1679.65] [1679.65][S05]So that was one, and then the second one[1681.95] [1681.95][S05]was sort of akin to what you said,[1683.58] [1683.58][S05]John, where I think it was then fast forwarding about a year[1687.28] [1687.28][S05]or so.[1687.78] [1687.78][S05]We published that work in the Summer of 2012, and by the Fall,[1691.9] [1691.9][S05]I was starting to get emails from people[1693.9] [1693.9][S05]all over the world who had read the paper[1696.39] [1696.39][S05]and said, "Oh, my gosh, this is so exciting.[1698.92] [1698.92][S05]I'm now using it to test genes in Drosophila.[1702.49] [1702.49][S05]I'm starting to test genes in zebrafish.[1705.66] [1705.66][S05]I'm excited about what I'm doing in human cells."[1708.55] [1708.55][S05]I was getting messages from people that I often barely knew[1711.6] [1711.6][S05]who were just very, very excited,[1713.08] [1713.08][S05]and you could start to feel the momentum that[1715.08] [1715.08][S05]was building in the field.[1716.945] [1716.945][S09] Cool.[1718.02] [1718.02][S09]Well, I'm the oldie here, so it's 1985, I'm afraid,[1722.97] [1722.97][S09]and I work on yeast, as you've already been told,[1726.79] [1726.79][S09]and my lab had worked out the genes that[1730.89] [1730.89][S09]controlled the cell cycle.[1733.39] [1733.39][S09]That is the process that leads to the reproduction of one cell[1736.38] [1736.38][S09]into two fundamental to growth and development in all living[1740.76] [1740.76][S09]organisms, and we'd identified that gene.[1744.01] [1744.01][S09]But the blunt thing is that, who cares about yeast?[1747.13] [1747.13][S09]I mean, being perfectly honest, I care about yeast,[1750.17] [1750.17][S09]but most of the world does not care about yeast.[1752.89] [1752.89][S09]So I found our work wasn't really being[1755.68] [1755.68][S09]taken much notice of.[1757.3] [1757.3][S09]So I thought, do humans have the same gene?[1760.82] [1760.82][S09]Now, this is long before the human genome sequence and so on.[1764.39] [1764.39][S09]So we did a crazy experiment.[1767.09] [1767.09][S09]I want to just tell you how crazy it was.[1769.01] [1769.01][S09]We had a mutant in yeast that couldn't grow,[1771.46] [1771.46][S09]because it was defective in this gene.[1773.81] [1773.81][S09]We took the first human cDNA library.[1777.05] [1777.05][S09]It's genES that had ever been made.[1780.13] [1780.13][S09]We didn't make it.[1781.13] [1781.13][S09]We got it a few months afterwards and fundamentally[1784.63] [1784.63][S09]sprinkled the genes onto the defective yeast,[1788.62] [1788.62][S09]arguing that, if there was a gene in humans that[1791.98] [1791.98][S09]could do the same job as the defective gene in yeast,[1795.71] [1795.71][S09]if the yeast cell took it up, if it could be expressed,[1799.55] [1799.55][S09]if it worked, then those cells would grow and divide,[1803.15] [1803.15][S09]and we could get the gene back and show that humans had it.[1808.32] [1808.32][S09]That experiment had no right to work.[1810.6] [1810.6][S09]Yeast, humans probably diverged 1,500 million years ago,[1817.11] [1817.11][S09]and we were demanding that it still worked after 1,500 million[1821.42] [1821.42][S09]years, and you know, it did work.[1824.54] [1824.54][S09]When we got that result--[1825.812] [1825.812][S09]and by the way, there were a couple[1827.27] [1827.27][S09]of months when we had to sequence the genes.[1829.103] [1829.103][S09]It took that long.[1830.01] [1830.01][S09]Then I used to think, I'm just going[1832.43] [1832.43][S09]to go home and believe this worked.[1834.86] [1834.86][S09]Tomorrow, I will go into work, and we[1837.59] [1837.59][S09]will have shown it didn't work, but it did work.[1840.9] [1840.9][S09]And that's when I thought, perhaps, it[1842.57] [1842.57][S09]might be recognized, because it wasn't yeast.[1844.56] [1844.56][S09]It was humans.[1845.34] [1845.34][S04] What an amazing collection of stories.[1847.423] [1847.423][S04]Now, I know that all of you must be bursting with questions[1851.54] [1851.54][S04]for our panel, so I might take some questions[1855.08] [1855.08][S04]from the audience.[1855.93] [1855.93][S04]Do we have mics running around?[1857.28] [1857.28][S04]Oh, there's one there.[1857.97] [1857.97][S04]I might take them in a little cluster.[1859.29] [1859.29][S04]Is there anyone else who would like to pop them in?[1861.48] [1861.48][S04]OK, so we have one here and another one here as well.[1864.03] [1864.03][S04]Roger?[1864.53] [1864.53][S01] This has been such an impressive day,[1868.18] [1868.18][S01]and Thank you for closing it out like this.[1871.09] [1871.09][S01]I've got a very playful question, which is,[1874.08] [1874.08][S01]if you could take yourself back in time and met your 18-year-old[1879.03] [1879.03][S01]or your 21-year-old self, and you could say something that[1883.38] [1883.38][S01]would reassure that person that you were making good life[1887.07] [1887.07][S01]choices, what would you say?[1890.26] [1890.26][S01]And you're not allowed to say, you're[1892.65] [1892.65][S01]going to win a Nobel Prize or anything to do with that.[1896.56] [1896.56][S01]So what would you say to your 18-year-old,[1901.66] [1901.66][S01]20-year-old former self?[1903.852] [1903.852][S04] OK, well, we'll collect another question.[1906.06] [1906.06][S04]I think Roger Highfield just here had a question too.[1909.63] [1909.63][S08] Roger Highfield, the Science Museum.[1911.86] [1911.86][S08]Just a comment, we've seen images[1914.73] [1914.73][S08]of Sycamore, the very cool looking Google quantum computer.[1918.37] [1918.37][S08]Can we, please, get some sexy looking AI hardware[1921.69] [1921.69][S08]that we can collect at the Science Museum, please?[1923.81] [1923.81][LAUGHING][1925.147] [1925.147][S08]Actually, a more serious point is-- and you've kind of alluded[1927.73] [1927.73][S08]to it, Demis, is this thing about how[1931.18] [1931.18][S08]AI is great at giving you answers,[1932.83] [1932.83][S08]but it doesn't give-- it's not very good at giving you[1935.08] [1935.08][S08]mechanistic insights.[1936.26] [1936.26][S08]You've got billions of parameters.[1937.97] [1937.97][S08]They've got no physical meaning.[1940.22] [1940.22][S08]How much of a barrier is this to the public trusting AI?[1945.59] [1945.59][S08]And I've talked to John about this as well.[1947.87] [1947.87][S08]Where are we in actually getting an AI[1950.68] [1950.68][S08]that can give us the laws of biology,[1953.18] [1953.18][S08]just like we've got the laws of physics,[1955.07] [1955.07][S08]so we get true mechanistic insights?[1957.18] [1957.18][S04] Fantastic.[1958.36] [1958.36][S04]Two very good questions there.[1959.66] [1959.66][S04]We'll start with the first one.[1960.952] [1960.952][S04]Demis, we go to you first.[1962.24] [1962.24][S04]What would you what would you say to your 18-year-old self?[1964.22] [1964.22][S03] Well, it's a little bit complicated for me.[1966.8] [1966.8][S03]I mean, I did actually have this plan when I was 18.[1969.91] [1969.91][LAUGHING][1971.02] [1971.02][S03]What is amazing is it worked out.[1973.31] [1973.31][S03]But I would have told myself--[1975.222] [1975.222][S03]I mean, it was a chess player in me is I always plan, like, many,[1977.93] [1977.93][S03]many years ahead.[1979.072] [1979.072][S03]That's what happens if you play chess from the age of four.[1981.53] [1981.53][S03]You end up like this.[1982.49] [1982.49][S03]But probably what I would have said to myself[1985.27] [1985.27][S03]is just enjoy the journey a bit more, because it will work out.[1989.86] [1989.86][S03]Because at the time, I was like, how[1991.36] [1991.36][S03]is this ever going to work out?[1992.652] [1992.652][S03]I had all these dreams, and then that's probably what[1995.05] [1995.05][S03]I'd recommend to my 18-year-old self.[1996.832] [1996.832][S04] John?[1997.54] [1997.54][S06] I think two things, actually.[1999.665] [1999.665][S06]Amusingly, I was thinking, don't worry, Carolyn will marry you.[2002.29] [2002.29][S06]But--[2002.79] [2002.79][LAUGHING][2003.99] [2006.85][S06]It's the gradient descent of life.[2008.77] [2008.77][S06]Do the right thing right now has worked out really well,[2012.06] [2012.06][S06]and be open to the interesting things that will open up[2014.945] [2014.945][S06]for you, and then, I think, we're[2016.32] [2016.32][S06]in one of the golden ages of biology, and AI, and biology.[2020.702] [2020.702][S06]And it's really fun to live through that.[2022.41] [2022.41][S06]So I think, like, don't be afraid to be[2024.78] [2024.78][S06]locally optimal, basically, the opposite of Demis' advice.[2027.605] [2027.605][LAUGHING][2028.806] [2030.193][S04] How about Jennifer?[2031.485] [2031.485][S04]Are you gradient descent, or are you[2033.81] [2033.81][S04]planning strategically in advance?[2035.445] [2035.445][S05] Probably closer to John.[2038.37] [2038.37][S05]Yeah, I think I would tell my 18-year-old self to follow[2042.85] [2042.85][S05]my passion and never, ever give up,[2045.74] [2045.74][S05]and don't listen to people that are naysayers.[2048.35] [2048.35][S05]That's really important.[2051.53] [2051.53][S09] I came from a non-academic background.[2054.59] [2054.59][S09]I couldn't believe that you could get paid for just[2059.17] [2059.17][S09]following your curiosity.[2061.33] [2061.33][S09]I still don't believe it now, 40 years later, 50 years later,[2067.48] [2067.48][S09]actually, more than that, 55 years later.[2070.78] [2070.78][S04] Just hold on to believing that we can,[2073.245] [2073.245][S04]OK, as president of the Royal Society?[2076.03] [2076.03][S04]You're good.[2077.08] [2077.08][S04]The second question from Roger there[2079.06] [2079.06][S04]about intuition from AI systems?[2081.135] [2081.135][S03] Yeah, look, I think, actually, this[2083.26] [2083.26][S03]is pretty interesting.[2084.23] [2084.23][S03]I'm not so worried about it as other people[2086.022] [2086.022][S03]are, because I think we're in a moment in time.[2088.58] [2088.58][S03]So what I said earlier, actually, my talk[2090.85] [2090.85][S03]was what I believe, which is that AI is an engineering[2095.25] [2095.25][S03]science.[2095.75] [2095.75][S03]So what it means is you have to build the artifact, first,[2098.67] [2098.67][S03]worthy of study, and then you can break it down[2101.28] [2101.28][S03]with a scientific method.[2102.87] [2102.87][S03]So what you've seen in the last five,[2104.92] [2104.92][S03]10 years is the building of the artifacts that are even worth[2108.42] [2108.42][S03]putting any effort into study, until you have things,[2111.24] [2111.24][S03]like today's transformer models and AlphaFold's, AlphaGo's.[2115.355] [2115.355][S03]The earlier systems were probably[2116.73] [2116.73][S03]not worth really putting the effort in to study,[2118.53] [2118.53][S03]because they were not sophisticated enough.[2120.49] [2120.49][S03]Now, they are.[2121.39] [2121.39][S03]So now, people are seriously studying the current systems.[2124.57] [2124.57][S03]On top of that, you've got the additional benefit[2127.11] [2127.11][S03]of these systems improving themselves, and at the minimum,[2133.15] [2133.15][S03]I think, we'll be in a situation,[2134.58] [2134.58][S03]where, firstly, the systems might[2136.29] [2136.29][S03]be able to explain themselves in language, or mathematics,[2139.41] [2139.41][S03]or code.[2140.17] [2140.17][S03]So we're getting close to that as you could say to a system,[2142.785] [2142.785][S03]OK, you've understood this.[2144.61] [2144.61][S03]Now, explain that in a mathematical equation[2147.39] [2147.39][S03]to the extent that it can be.[2148.76] [2148.76][S03]And I'm not sure biology can be explained,[2150.51] [2150.51][S03]like the laws of physics, by the way.[2152.08] [2152.08][S03]I think it is a lot messier.[2153.285] [2153.285][S03]It's more to do with interaction.[2154.66] [2154.66][S03]So I think a simulation would be more appropriate that you probe[2157.62] [2157.62][S03]than, like, Newton's laws of motion or something like that,[2160.695] [2160.695][S03]and I don't think biology could be reduced to that.[2162.82] [2162.82][S03]It's too complex.[2164.4] [2164.4][S03]And then the other thing is what I also said earlier[2166.98] [2166.98][S03]about applying neuroscience techniques, analysis[2169.05] [2169.05][S03]techniques to these artificial neural networks,[2173.59] [2173.59][S03]and we should be able to get, at least,[2175.47] [2175.47][S03]the same level of insights into them[2177.3] [2177.3][S03]as we do with natural brains.[2179.53] [2179.53][S03]So if you combine that together, we[2181.53] [2181.53][S03]should get pretty far already, let alone[2183.78] [2183.78][S03]with further engineering efforts that we're[2185.61] [2185.61][S03]going to put on top to decompose these systems.[2188.17] [2188.17][S03]So I think, in the next five years, we'll be out of this era[2190.89] [2190.89][S03]that we're currently in of kind of black boxes.[2193.95] [2193.95][S04] Fascinating.[2194.95] [2194.95][S04]OK, further questions?[2195.867] [2195.867][S04]All right, we've got a couple here.[2197.325] [2197.325][S04]So let's go one, two.[2198.287] [2198.287][S04]And then, if you can keep your hands up, actually,[2200.37] [2200.37][S04]so I can see, we'll go we'll go one, two,[2202.68] [2202.68][S04]and then we'll go over there.[2204.48] [2204.48][S04]Read them out in chunks, if that's OK.[2206.35] [2206.35][S04]Go for it.[2207.893] [2207.893][S02] Thank you.[2209.31] [2209.31][S02]Danny Newman Griffiths from University of Sheffield.[2211.477] [2211.477][S02]Just following up on the topic earlier[2213.66] [2213.66][S02]that came up about the social sciences in AI,[2217.39] [2217.39][S02]the idea was raised about potential for AI[2219.91] [2219.91][S02]to transform social sciences.[2221.3] [2221.3][S02]I want to ask you about flipping that around and thinking about,[2224.74] [2224.74][S02]in your understanding, how can you[2226.75] [2226.75][S02]see the work of social science as helping[2228.94] [2228.94][S02]to transform these next features of AI[2231.82] [2231.82][S02]and building the ways to transform that engineering[2235.33] [2235.33][S02]science into the interdisciplinarity[2237.34] [2237.34][S02]that we've been talking about, where AI tools are being[2239.95] [2239.95][S02]combined with really deep understanding of application[2243.13] [2243.13][S02]areas and contexts?[2244.79] [2244.79][S04] Fascinating question.[2246.19] [2246.19][S04]Your neighbor next to you.[2247.95] [2247.95][S01] A very simple question,[2250.6] [2250.6][S01]is attention still all we need?[2254.572] [2254.572][S04] Go for it.[2255.489] [2255.489][S07] Michael Chang from the National Eye Institute[2257.989] [2257.989][S07]in the US.[2258.53] [2258.53][S07]Thank you very much.[2259.58] [2259.58][S07]This was an awesome conference.[2261.25] [2261.25][S07]I have a-- the theme here is AI to do better science,[2264.86] [2264.86][S07]and I think one of the common things that we do is,[2268.34] [2268.34][S07]well, we can just teach scientists more about AI,[2271.15] [2271.15][S07]and they can use it.[2272.68] [2272.68][S07]My question is flipping that around.[2274.43] [2274.43][S07]Do you think there are things that AIs do not do,[2278.11] [2278.11][S07]that scientists should be getting more training on[2281.05] [2281.05][S07]to maximize their added value and what those would be?[2283.785] [2283.785][S07]Just love your perspectives on that.[2285.285] [2285.285][S04] Fascinating.[2286.4] [2286.4][S04]OK, all right, so we'll start off with--[2288.34] [2288.34][S04]I might start with you, Paul, actually,[2289.55] [2289.55][S04]because you were the one who was talking[2290.26] [2290.26][S04]about social sciences earlier.[2291.51] [2291.51][S04]So yeah, AI and social sciences, how much[2293.89] [2293.89][S04]do we have to think about that sort of interface between humans[2296.547] [2296.547][S04]and the machines?[2297.255] [2297.255][S09] Well, first of all,[2299.57] [2299.57][S09]I think we need to focus more on the social sciences[2302.68] [2302.68][S09]to help us scientists.[2306.97] [2306.97][S09]We have to appreciate, though, that this is pretty complicated,[2311.18] [2311.18][S09]the human interactions.[2313.21] [2313.21][S09]So I'm not sure how much it's going[2316.99] [2316.99][S09]to help us at the beginning, not sure about that,[2320.78] [2320.78][S09]but we really ought to be accommodating it,[2323.56] [2323.56][S09]ought to be thinking about how it might.[2326.47] [2326.47][S09]I could imagine certain social sciences problems,[2329.72] [2329.72][S09]and I suspect you're a social scientist,[2332.57] [2332.57][S09]to do with transport and things of this sort.[2335.52] [2335.52][S09]So I think there are some mechanical things, trying[2338.09] [2338.09][S09]to understand how human beings interact and work.[2341.42] [2341.42][S09]Well, you have to ask Carol, was it?[2344.751] [2344.751][S06] Yes, sure.[2347.17] [2347.17][S09] John, yes, exactly.[2348.63] [2348.63][S06] Oh, Caroline?[2349.78] [2349.78][S09] Yes, that Carol.[2351.113] [2351.113][LAUGHING][2352.04] [2352.04][S09]In other words-- sorry, I thought it was straightforward.[2354.78] [2354.78][S09]Getting emotion, understanding emotion--[2356.87] [2356.87][S06] You got my wife's name wrong.[2357.08] [2357.08][S09] --is going to be difficult.[2357.67] [2357.67][LAUGHING][2358.435] [2358.435][S03] And he was looking at me.[2360.143] [2360.143][S03]It was doubly confusing.[2362.365] [2362.365][S09] Over to you, John.[2364.99] [2364.99][S06] I mean, should I answer this,[2366.74] [2366.74][S06]or should I go to the--[2367.71] [2367.71][S04] Yeah, go to the attention, go to the attention.[2370.168] [2370.168][S06] I think attention is all you[2372.14] [2372.14][S06]need is an oversimplification.[2374.095] [2374.095][S06]I think one of the things that's really interesting[2376.22] [2376.22][S06]is AlphaFold isn't just, like, grab a transformer[2378.65] [2378.65][S06]off the shelf from the transformer store[2380.48] [2380.48][S06]and apply it to protein structure prediction.[2382.695] [2382.695][S06]It took a couple of years, because there's a lot of work,[2385.07] [2385.07][S06]and there's a lot of new ideas, where attention is a component,[2389.118] [2389.118][S06]but we have what we would call evil former.[2390.91] [2390.91][S06]We have new ideas on top.[2392.53] [2392.53][S06]We have all sorts of--[2394.995] [2394.995][S06]Demis runs an incredible AI research[2396.93] [2396.93][S06]org, which goes into work every day and doesn't just say, well,[2399.653] [2399.653][S06]we've got all we need.[2400.57] [2400.57][S06]There's a tension, right?[2401.612] [2401.612][S06]We all are doing this work, and it's really,[2403.78] [2403.78][S06]I think, people underestimate how much[2405.99] [2405.99][S06]really novel and exciting work is going on in AI[2409.11] [2409.11][S06]right now that are making these systems transformatively better,[2412.3] [2412.3][S06]both unlocking new data sources and learning a lot more[2414.84] [2414.84][S06]from the existing data sources.[2416.26] [2416.26][S06]AlphaFold is a story of having the same data as everyone else[2419.13] [2419.13][S06]and learning a tremendously more about protein structure from it.[2423.81] [2423.81][S06]So I think we'll continue to see these dividends from AI[2426.42] [2426.42][S06]research.[2427.005] [2427.005][S06]We're going to continue to see exciting new things,[2429.13] [2429.13][S06]and we may always call something a tension.[2432.04] [2432.04][S06]But it reminds me of this old computer scientist joke.[2434.65] [2434.65][S06]I don't know what the scientific computing language of the future[2436.95] [2436.95][S06]will look like, but it will be called Fortran, right?[2439.158] [2439.158][LAUGHING][2439.71] [2439.71][S06]So we tend to keep the labels of ideas and then update within it.[2445.317] [2445.317][S06]And I think we shouldn't underestimate[2446.9] [2446.9][S06]how much really wonderful, and exciting, and clever research is[2449.72] [2449.72][S06]going on these days.[2450.665] [2450.665][S03] Maybe I can just add quickly to that.[2452.873] [2452.873][S03]I agree with that.[2453.86] [2453.86][S03]And, actually, the transformer architecture,[2458.36] [2458.36][S03]which is what was invented with that paper, is being amazing[2462.93] [2462.93][S03]and, I think, will underpin, be one of the main components[2465.68] [2465.68][S03]of a future AGI system.[2467.61] [2467.61][S03]But my prediction is it's not going to be enough on its own.[2470.42] [2470.42][S03]I think we're going to need a couple[2471.92] [2471.92][S03]of other big breakthroughs like that in addition,[2475.16] [2475.16][S03]and they're still to come.[2476.67] [2476.67][S04] Jennifer, I might come to you just[2478.97] [2478.97][S04]to pick up on our third question there about the gaps in what AI[2483.71] [2483.71][S04]can do, and we need to really focus[2485.93] [2485.93][S04]on the skill set from humans.[2487.523] [2487.523][S05] Yeah, and thanks for that question,[2489.69] [2489.69][S05]Michael.[2490.19] [2490.19][S05]So I guess what I've been thinking about with regard[2493.13] [2493.13][S05]to that is there's been a lot of mention today about data[2496.64] [2496.64][S05]and the kind of data that are necessary for training models[2500.09] [2500.09][S05]like this.[2501.0] [2501.0][S05]And one of the challenges in biology, of course,[2503.858] [2503.858][S05]is the quality of data, but it's also[2505.4] [2505.4][S05]the quantity of data and the fact[2508.1] [2508.1][S05]that, typically, one needs a lot of data[2510.62] [2510.62][S05]that's of high quality to train models, at least, currently.[2514.4] [2514.4][S05]So what I'd like to see AI do for us scientists is to educate[2519.5] [2519.5][S05]us about how to collect data, perhaps, sparsely, but smartly,[2525.12] [2525.12][S05]so that your sparse data is broad enough[2528.65] [2528.65][S05]that it actually does provide the right platform for training.[2532.362] [2532.362][S05]And I think that's something we don't, at least[2534.32] [2534.32][S05]as an experimentalist, right now, we[2535.97] [2535.97][S05]don't think about that when we design experiments,[2538.08] [2538.08][S05]but we could.[2538.64] [2538.64][S04] I guess that kind of comes back to the asking[2540.65] [2540.65][S04]the right questions thing that we were talking about.[2542.19] [2542.19][S05] It does.[2542.6] [2542.6][S04] What is the [INAUDIBLE]?[2543.92] [2543.92][S06] That's one of the key questions.[2544.8] [2544.8][S04] Yeah, OK, we probably have time for another.[2547.68] [2547.68][S04]All right, we've got red jumper at the back.[2549.81] [2549.81][S04]You can go be number one, and then I[2551.75] [2551.75][S04]need to think strategically about microphones.[2553.888] [2553.888][S04]OK, if we go, there's a little cluster over here.[2555.93] [2555.93][S04]So we'll go 2, 3, 4 further back.[2558.56] [2558.56][S04]Yes, just-- there we go.[2561.21] [2561.21][S04]We found it.[2561.71] [2561.71][S04]OK, go for it, number one.[2562.89] [2562.89][S11] Hi, it's Wendy Hall, University of Southampton.[2565.728] [2565.728][S11]Someone who's been working with social scientists[2567.77] [2567.77][S11]for a long time, the big issue there,[2569.61] [2569.61][S11]collecting the data and the issues around privacy.[2573.47] [2573.47][S11]It makes it so much harder than the science you do.[2576.812] [2576.812][S11]The more we can do with that, the better.[2578.52] [2578.52][S11]My question, though, as someone who's[2580.91] [2580.91][S11]just been suffering from a cold, as many people, please,[2584.52] [2584.52][S11]can AI sort out the common cold?[2586.54] [2586.54][LAUGHING][2587.73] [2588.23][S04] Guys, what have you been doing?[2590.78] [2590.78][S04]OK, we've got three very closely together here.[2593.645] [2593.645][S04]Yeah, go ahead.[2594.27] [2594.27][S10] Hi, Thomas Crampton.[2596.372] [2596.372][S10]There's been a lot of talk, obviously, about great science,[2598.83] [2598.83][S10]a lot of fantastic science discussed.[2601.745] [2601.745][S10]What about the people outside of this room, though,[2603.87] [2603.87][S10]who don't understand science and might be suspicious of it?[2607.95] [2607.95][S10]How much concern do you have that society might reject[2610.755] [2610.755][S10]a lot of these great breakthroughs that[2612.38] [2612.38][S10]are coming to the world, and what[2613.755] [2613.755][S10]do you think we should do to address that?[2615.77] [2615.77][S04] Great question.[2617.12] [2617.12][S04]Next to you?[2618.12] [2618.12][S01] Hi, I'm [INAUDIBLE] from the African Institute[2621.35] [2621.35][S01]for Mathematical Sciences.[2623.76] [2623.76][S01]Thanks very much for a wonderful discussion.[2627.0] [2627.0][S01]I have two questions, if you permit.[2629.46] [2629.46][S01]The first is that, when people talk about AGI,[2633.1] [2633.1][S01]I think the common assumption is that the target is[2637.56] [2637.56][S01]human intelligence.[2639.07] [2639.07][S01]But human intelligence may be suboptimal,[2641.85] [2641.85][S01]because this is the result of historically contingent[2645.81] [2645.81][S01]evolutionary process.[2647.23] [2647.23][S01]So to what extent do you think AGI[2650.19] [2650.19][S01]is going to be something supra optimal with respect[2654.06] [2654.06][S01]to human intelligence?[2656.13] [2656.13][S01]The second is about inclusion.[2660.33] [2660.33][S01]Africa, by 2050, would have the largest population[2666.75] [2666.75][S01]of young adults in the world.[2668.52] [2668.52][S01]This population would be serving the world.[2671.29] [2671.29][S01]It will have to in order for humanity to continue its ascent.[2677.37] [2677.37][S01]To what extent is the community ensuring[2679.72] [2679.72][S01]that Africans are included?[2681.94] [2681.94][S01]I should mention that Google has been amazing,[2685.82] [2685.82][S01]and Google DeepMind has been an amazing supporter[2689.35] [2689.35][S01]of capacity building for young Africans in AI.[2692.77] [2692.77][S01]As a whole, what else is the community doing?[2696.32] [2696.32][S01]Thank you.[2697.07] [2697.07][S04] Great, and then, I think,[2698.02] [2698.02][S04]there's one bit immediately behind you as well.[2699.978] [2699.978][S04]Thank you.[2702.797] [2702.797][S01] Thank you.[2703.63] [2703.63][S01][INAUDIBLE] Digital Science.[2705.65] [2705.65][S01]I'm a recovering academic, a nanochemist by training,[2709.57] [2709.57][S01]and in chemistry, technological developments in things,[2713.26] [2713.26][S01]like microscopy, have always opened doors[2715.51] [2715.51][S01]to many, many other areas of research.[2718.28] [2718.28][S01]AI is very much the same, but I think[2720.49] [2720.49][S01]we've seen from a lot of the speakers and the discussions[2723.22] [2723.22][S01]today that a lot of the developments[2725.2] [2725.2][S01]come about through industry rather than academia.[2729.08] [2729.08][S01]So, Paul, I know you talked about research cultures[2731.62] [2731.62][S01]and how maybe-- some of our other speakers[2733.48] [2733.48][S01]talked about how maybe the way that we reward[2736.18] [2736.18][S01]successful research, at the moment,[2737.89] [2737.89][S01]isn't conducive to making space for innovations in the same way.[2742.65] [2742.65][S01]How do we change a global culture[2745.22] [2745.22][S01]when nobody wants to be the first to do that,[2749.46] [2749.46][S01]because they may be losing out?[2750.85] [2750.85][S04] OK, right.[2752.37] [2752.37][S04]Four extremely good questions, we[2754.43] [2754.43][S04]have precisely four minutes to answer them, everybody, OK?[2757.49] [2757.49][S04]All right, so Paul, we'll start with you.[2759.72] [2759.72][S04]Why isn't there a cure for the common cold yet?[2762.552] [2762.552][S09] Well, it's difficult, isn't it?[2766.37] [2766.37][S09]I'll go to the--[2769.43] [2769.43][S04] Of course, you absolutely can.[2771.64] [2771.64][S09] Keeping the public on board,[2774.18] [2774.18][S09]this is really critical.[2775.38] [2775.38][S09]It's crucial.[2776.04] [2776.04][S09]We really need to concentrate on it.[2777.66] [2777.66][S09]It is, of course, not the first time.[2779.85] [2779.85][S09]Nearly always, when there's been a new technology and changes,[2783.48] [2783.48][S09]there has been concern, and I think I mentioned it earlier.[2788.4] [2788.4][S09]We have to talk to the right people.[2790.17] [2790.17][S09]We have to talk to the public, and too often, these discussions[2793.88] [2793.88][S09]get hijacked by interest groups and people who[2797.88] [2797.88][S09]say they talk for the public when they talk often[2803.04] [2803.04][S09]for their own particular interests[2805.56] [2805.56][S09]or their own particular passion that they have.[2808.9] [2808.9][S09]So we have to work out ways in which[2810.9] [2810.9][S09]we can discuss with the public and discuss in a sensible way,[2815.65] [2815.65][S09]and I mentioned deliberative democracy.[2817.78] [2817.78][S09]It's expensive to do, but I think it's[2819.6] [2819.6][S09]a really important thing to do.[2822.18] [2822.18][S09]Because, bluntly, if you don't take the public with you,[2826.39] [2826.39][S09]we won't be able to see all the merits that can come out[2830.01] [2830.01][S09]of this, merits in terms of understanding[2832.68] [2832.68][S09]the world better around us, merits[2834.78] [2834.78][S09]in terms of actually using those discoveries for the public good.[2839.05] [2839.05][S09]We have to engage, and we have to convince the public[2841.913] [2841.913][S09]that these things are right.[2843.08] [2843.08][S04] I think I want to actually just add[2845.37] [2845.37][S04]on a little bit to Susie's question[2847.537] [2847.537][S04]there, which I think also incorporates[2849.12] [2849.12][S04]part of your question.[2850.98] [2850.98][S04]Because for all of you, we were talking[2852.9] [2852.9][S04]a lot today about measures of success and incentives[2855.42] [2855.42][S04]for scientists, and of course, all of you[2857.34] [2857.34][S04]have the ultimate prize of being Nobel Laureates.[2861.37] [2861.37][S04]How has your view of success changed,[2864.54] [2864.54][S04]and how should we kind of influence future generations[2869.07] [2869.07][S04]across the world of scientists to make sure that we're[2871.95] [2871.95][S04]getting the right outcomes?[2873.53] [2873.53][S04]John, do you want to go first?[2874.78] [2874.78][S06] I think that's--[2876.951] [2876.951][S06]I think one of the things that's really been informing me[2880.44] [2880.44][S06]over my scientific career is just[2882.15] [2882.15][S06]the power and the fun of working on a team, doing science,[2885.58] [2885.58][S06]and I was able to do it at Google DeepMind.[2887.8] [2887.8][S06]I did it for a time before my PhD,[2891.3] [2891.3][S06]and then the PhDs are a little bit lonelier, right?[2893.64] [2893.64][S06]You're working on a particular individual thing.[2895.81] [2895.81][S06]And I think, really, the power of working within a team[2899.503] [2899.503][S06]is really, really important.[2900.67] [2900.67][S06]I think that provides also its own motivation.[2902.89] [2902.89][S06]Science is about loads of failure[2905.01] [2905.01][S06]and occasional dramatic success, and we're all up here[2908.34] [2908.34][S06]from those dramatic successes.[2910.12] [2910.12][S06]But I think that--[2911.28] [2911.28][S06]and maybe Demis has it down to a repeatable science, but--[2914.16] [2914.16][LAUGHING][2914.92] [2914.92][S06]I think that, really, encouraging people[2919.96] [2919.96][S06]to work in teams, work together, and I[2921.633] [2921.633][S06]think that helps provide the kind of motivation that[2923.8] [2923.8][S06]leads to better science, that leads to more fun science.[2926.78] [2926.78][S06]If you don't have fun doing it, you won't do it.[2928.84] [2928.84][S06]All the great scientists seem to be having fun at it.[2930.91] [2930.91][S04] Jennifer?[2931.525] [2931.525][S05] Couldn't agree more.[2933.067] [2933.067][S05]And I have to say that, when I think back on my career so far,[2937.31] [2937.31][S05]I really feel great joy and pride in the students that I've[2942.43] [2942.43][S05]trained and the work that they're now doing,[2945.17] [2945.17][S05]and it's just there's something incredibly[2947.74] [2947.74][S05]satisfying about that.[2948.74] [2948.74][S05]And, actually, can I speak to one of the other questions?[2950.895] [2950.895][S04] Please.[2951.687] [2951.687][S05] You asked about the question about Africa[2954.16] [2954.16][S05]and involving African scientists and young people who[2958.36] [2958.36][S05]want to do science.[2959.63] [2959.63][S05]So I'm really proud that the Innovative Genomics Institute[2962.11] [2962.11][S05]has an ongoing effort right now in Kenya, where now, I think,[2966.108] [2966.108][S05]we've done three years running.[2967.4] [2967.4][S05]We've sent a team to different parts of Kenya,[2971.66] [2971.66][S05]where they've been working with scientists there[2974.36] [2974.36][S05]to work with them and really help them understand CRISPR.[2979.56] [2979.56][S05]It's really been really motivating[2981.86] [2981.86][S05]to see some of the videos that come back,[2983.9] [2983.9][S05]where these local scientists then go back[2986.45] [2986.45][S05]to their communities, and they work with students,[2988.55] [2988.55][S05]and they get excited, and they start[2990.05] [2990.05][S05]doing interesting, creative science[2993.59] [2993.59][S05]in their own laboratories.[2995.46] [2995.46][S05]So I'd like to see more of that.[2997.32] [2997.32][S05]I think there's an extraordinary opportunity there,[2999.93] [2999.93][S05]and I'm also very excited about the things[3001.93] [3001.93][S05]that I hear about Google doing.[3003.68] [3003.68][S05]So I think there's a big opportunity for all of us[3006.4] [3006.4][S05]to work together on that.[3007.62] [3007.62][S04] More and more people are[3009.12] [3009.12][S04]being paid to follow their curiosity, Paul.[3011.667] [3011.667][S09] You almost said what I was going to say.[3014.0] [3014.0][S04] Oh, sorry.[3014.355] [3014.355][LAUGHING][3014.89] [3014.89][S09] Yeah, but I'll amplify it slightly.[3017.54] [3017.54][S09]We live in this world of big data,[3020.08] [3020.08][S09]and sometimes, we have low standards,[3022.72] [3022.72][S09]that it's enough just to report lots of big data.[3026.74] [3026.74][S09]There are certain journals, very high profile journals,[3029.94] [3029.94][S09]that seem to do nothing, but actually[3032.07] [3032.07][S09]report lots and lots of data.[3033.97] [3033.97][S09]And I think it's worth giving some attention to creativity[3038.19] [3038.19][S09]in the world of big data, because creativity[3042.15] [3042.15][S09]can get lost in this scenario, when in fact, there[3046.56] [3046.56][S09]is enormous opportunities with big data[3049.75] [3049.75][S09]if we actually take a creative approach.[3052.96] [3052.96][S09]And we need to think a bit--[3054.55] [3054.55][S09]one, what is exactly creativity?[3057.1] [3057.1][S09]And I could go on about that, but I won't,[3059.28] [3059.28][S09]because it's near the end.[3060.7] [3060.7][S09]But we need to encourage in our colleagues,[3064.24] [3064.24][S09]in our students, creative thinking,[3067.29] [3067.29][S09]and that is a bit different from collecting[3069.6] [3069.6][S09]lots and lots of data.[3071.26] [3071.26][S09]But lots and lots of data really will[3073.62] [3073.62][S09]deliver if they take a creative approach to it.[3076.21] [3076.21][S04] Demis?[3077.43] [3077.43][S03] Well, so many things to pick up on,[3078.93] [3078.93][S03]and there's not much time.[3080.013] [3080.013][S03]But, I mean, yeah, creativity would[3081.568] [3081.568][S03]be a very interesting thing to have a whole panel[3083.61] [3083.61][S03]discussion on it.[3084.27] [3084.27][S03]Maybe the next time, we do this.[3085.603] [3085.603][S03]But maybe just to pick up on a couple of questions,[3088.22] [3088.22][S03]I think in terms of encouraging the next generation, for me,[3095.17] [3095.17][S03]one of my heroes was Feynman.[3096.578] [3096.578][S03]But it wasn't just his physics books,[3098.12] [3098.12][S03]which were very famous, right?[3099.37] [3099.37][S03]But, actually, it was his layperson's books[3101.62] [3101.62][S03]that inspired me to get into science,[3103.19] [3103.19][S03]and I really think all school kids should read them,[3105.65] [3105.65][S03]so Surely You're Joking, Mr. Feynman,[3107.56] [3107.56][S03]and The Great Joy In Finding Things Out.[3110.093] [3110.093][S03]Because I think those books more than any other I've read,[3112.51] [3112.51][S03]and maybe there are others like that,[3114.052] [3114.052][S03]show how exhilarating it is to be at the frontier of knowledge[3118.03] [3118.03][S03]and what that means.[3118.9] [3118.9][S03]I'm feeling getting goosebumps even just talking about that,[3121.4] [3121.4][S03]and it instilled that in me when I was--[3123.325] [3123.325][S03]I can't remember what age, like 10, or 11, or something.[3125.87] [3125.87][S03]And I think it would be great for school kids[3127.745] [3127.745][S03]to be exposed to that, how incredible and fun doing science[3131.74] [3131.74][S03]should be, and I think Feynman was[3133.193] [3133.193][S03]one of those people who had a lot of fun[3134.86] [3134.86][S03]doing his incredible science and as a great role model for that.[3138.203] [3138.203][S04] Thank you very much to all of you, very much indeed.[3140.87] [3140.87][S04]Thank you.[3141.37] [3141.37][S04]Well, as always with Demis, there[3143.38] [3143.38][S04]was a dizzying array of topics that were covered there.[3147.4] [3147.4][S04]We had room temperature superconductors.[3150.25] [3150.25][S04]We had nuclear pore complex.[3152.2] [3152.2][S04]We had molecular syringes.[3154.18] [3154.18][S04]There was plastic eating enzymes, drug design,[3157.69] [3157.69][S04]even poker bluffing strategies.[3160.5] [3160.5][S04]But I think for me, the most standout[3163.23] [3163.23][S04]moment from the conversation today[3165.81] [3165.81][S04]is about what it means to be a scientist in the era[3169.89] [3169.89][S04]of artificial intelligence, because this event was co-hosted[3174.15] [3174.15][S04]by the Royal Society, the Royal Society, which was founded[3177.3] [3177.3][S04]during the era of great lone geniuses,[3181.08] [3181.08][S04]standing on the shoulders of giants, as the phrase goes.[3184.45] [3184.45][S04]But I think that maybe we need to start[3186.99] [3186.99][S04]accepting that those days of lone geniuses[3189.84] [3189.84][S04]really are behind us.[3192.13] [3192.13][S04]Because I think, if we are going to address the biggest[3194.85] [3194.85][S04]challenges that society faces, climate change, energy, disease,[3199.93] [3199.93][S04]our understanding of the universe,[3201.7] [3201.7][S04]then what we really need is big, talented teams[3205.9] [3205.9][S04]of scientists who are working across traditional discipline[3209.14] [3209.14][S04]boundaries.[3210.17] [3210.17][S04]Politics has never needed science more,[3212.99] [3212.99][S04]but I think neither has the world.[3215.78] [3215.78][S04]And I think we need this collaboration[3217.72] [3217.72][S04]between our public institutions, between governments, our health[3221.26] [3221.26][S04]providers, and also the private sector,[3224.3] [3224.3][S04]because innovation is a pursuit for people, but also by people.[3231.43] [3231.43][S04]You've been listening to Google DeepMind, The Podcast,[3234.22] [3234.22][S04]with me, Professor Hannah Fry.[3235.96] [3235.96][S04]If you enjoyed that episode, do subscribe[3238.21] [3238.21][S04]to our YouTube channel.[3239.48] [3239.48][S04]You can also find us on your favorite podcast platform.[3242.99] [3242.99][S04]And we have got plenty more episodes[3245.08] [3245.08][S04]on a whole range of topics to come, so do check those out too.[3248.81] [3248.81][S04]See you next time.[3249.91] [3249.91][MUSIC PLAYING][3252.36]