[0.423][S01] Welcome back to "Google DeepMind: The Podcast"[2.84] [2.84][S01]with me, your host, Professor Hannah Fry.[4.94] [4.94][S01]Now, those of you who've been following us from the beginning,[7.68] [7.68][S01]you will know that using artificial intelligence[10.43] [10.43][S01]to enhance a scientific understanding of the world[13.58] [13.58][S01]has always been a key goal of the team here.[17.06] [17.06][S01]You've got weather forecasting, that's advanced meteorology.[20.07] [20.07][S01]You've got nuclear fusion for physicists,[22.5] [22.5][S01]and perhaps most notably is AlphaFold,[25.2] [25.2][S01]which has been an absolute game changer for biology.[28.77] [28.77][S01]But if all of that seems a little bit academic,[31.2] [31.2][S01]a bit niche, perhaps, the kind of stuff that should only really[33.98] [33.98][S01]be exciting to a handful of scientists in their labs, well,[37.25] [37.25][S01]then hopefully, today's episode will persuade you[40.07] [40.07][S01]of the substantial impact that these ideas can[43.28] [43.28][S01]have on humanity.[45.24] [45.24][S01]Because I am joined, once again, by the guy who is in charge[48.2] [48.2][S01]of scientific research here at "Google DeepMind,"[50.39] [50.39][S01]Pushmeet Kohli.[51.46] [58.9][S01]Pushmeet, thank you so much for joining us back on the podcast.[62.14] [62.14][S01]Quite a lot has happened since we last spoke to you.[64.73] [64.73][S01]How many nature papers have you got now?[67.6] [67.6][S02] I'm not really sure.[69.1] [69.1][S02]We're not counting.[70.67] [70.67][S01] Too many to count.[71.92] [71.92][S02] Yeah.[72.9] [72.9][S01] So I want to talk to you about AlphaFold because it[75.525] [75.525][S01]feels as though this is one of the most exciting things that[79.32] [79.32][S01]has advanced even further since I last spoke to you.[83.22] [83.22][S01]And I should say, actually, that anybody[85.59] [85.59][S01]who wants to know more about how AlphaFold works[89.01] [89.01][S01]and what it does, can go back to episode[92.01] [92.01][S01]one of the previous series for more detail,[94.94] [94.94][S01]but I guess just briefly, can you summarize what AlphaFold is?[98.215] [98.215][S02] Yeah.[99.09] [99.09][S02]So AlphaFold is a system that, given a protein--[104.01] [104.01][S02]proteins make everything around us.[106.498] [106.498][S02]They are the building blocks of life.[108.04] [108.04][S02]And essentially, they can be represented[110.1] [110.1][S02]as a sequence of amino acids.[113.25] [113.25][S02]And it's a great mystery as to what[115.98] [115.98][S02]is the structure of any given protein.[118.21] [118.21][S02]So if you are given a sequence of these amino acids, a protein,[121.45] [121.45][S02]what is its structure?[123.49] [123.49][S02]And that's really important because that informs[126.24] [126.24][S02]the function of that protein.[128.06] [128.06][S02]What AlphaFold does, it takes a sequence of a protein[132.28] [132.28][S02]and predicts its structure.[134.33] [134.33][S02]And that's really important for scientists[136.93] [136.93][S02]to understand what is the function of that protein.[139.43] [139.43][S02]So that's what AlphaFold did.[141.14] [141.14][S02]It solved that 50-year-old grand challenge in science by making[147.94] [147.94][S02]scientists able to find the structure of any given protein[151.54] [151.54][S02]in a matter of seconds.[153.023] [153.023][S01] So I guess actually, a more accurate statement[155.44] [155.44][S01]would be it was a mystery but is no longer.[158.295] [158.295][S02] Yeah.[159.17] [159.17][S02]There's still a few things that are unknown about how proteins[164.08] [164.08][S02]behave, the dynamics of the proteins,[165.79] [165.79][S02]and so on, but the one standard big problem in protein[170.2] [170.2][S02]understanding, What is the structure of a protein?[174.355] [174.355][S02]is now-- we now know about it.[177.445] [177.445][S01] So what's changed since last time[179.32] [179.32][S01]then, since the last series when there was AlphaFold 2?[182.36] [182.36][S01]What can AlphaFold3 do that the previous versions couldn't?[184.997] [184.997][S02] That's a very good question.[186.83] [186.83][S02]Now, I said proteins are the building blocks of life,[189.86] [189.86][S02]but they are not the only molecules in our body.[192.86] [192.86][S02]Our body has many other biomolecules.[195.35] [195.35][S02]We have nucleic acids, RNA, DNA, which[199.27] [199.27][S02]are the recipe of life, what makes you and me, Hannah.[203.14] [203.14][S02]Then there are small molecules, drugs that[208.03] [208.03][S02]interact with these proteins.[210.1] [210.1][S02]There are antibodies.[211.31] [211.31][S02]There are so many different types[212.83] [212.83][S02]of molecules that are interacting in any living being.[218.21] [218.21][S02]And we want to understand the structure[220.09] [220.09][S02]of all these molecules.[222.14] [222.14][S02]And that's what AlphaFold 3 unlocks.[224.54] [224.54][S02]It not only gives you the structure of a protein,[227.81] [227.81][S02]but it also gives you the structure of,[229.48] [229.48][S02]how do these proteins form complexes[232.12] [232.12][S02]or they connect to each other?[234.38] [234.38][S02]Or how do they interact with small molecules or antibodies[237.8] [237.8][S02]and so on?[238.37] [238.37][S01] What's been the reaction then from scientists[241.21] [241.21][S01]now that this has had a bit of time to be[242.92] [242.92][S01]out there in the world?[244.48] [244.48][S02] So it was very hard to actually--[248.74] [248.74][S02]my background, I'm a computer scientist by training,[251.41] [251.41][S02]and it was very hard for me to actually understand[255.25] [255.25][S02]the impact of AlphaFold.[259.81] [259.81][S02]And the first time I realized, I went to a conference, a biology[263.68] [263.68][S02]conference, where a biologist spoke to me and said,[267.44] [267.44][S02]well, there was this protein that I've been trying[270.16] [270.16][S02]to study for the last 10 years.[272.05] [272.05][S02]And I have collected so much data about it,[275.921] [275.921][S02]but still the structure of that protein was a mystery.[279.73] [279.73][S01] Because it was so, so, so hard to work[281.95] [281.95][S01]out what the structure might be before AlphaFold.[284.32] [284.32][S02] Exactly.[286.052] [286.052][S02]And the scientists had used AlphaFold[288.31] [288.31][S02]and had the structure, and then the question[291.67] [291.67][S02]he had was, well, what do I do now?[295.79] [295.79][S02]I have the structure, and so--[300.0] [300.0][S02]like he had to completely rethink what biology requires[303.09] [303.09][S02]now in terms of the next steps.[305.64] [305.64][S01] A 10-year project, and he just put it[307.838] [307.838][S01]into AlphaFold, and it sorted it out for him in a few minutes[310.38] [310.38][S01]or maybe overnight.[311.59] [311.59][S01]Was he excited or was that in some ways a bit demoralizing?[316.21] [316.21][S02] No.[317.61] [317.61][S02]I think there was a mixture of both surprise[320.92] [320.92][S02]but also excitement, something that you wouldn't imagine.[325.912] [325.912][S02]It's like, imagine giving people an ability that did not exist.[335.67] [335.67][S02]Imagine the first time telephones came about,[339.25] [339.25][S02]and you could now talk to people who are miles away.[343.282] [343.282][S02]This was unimaginable.[345.215] [345.215][S02]And the fact that you can take any protein,[347.52] [347.52][S02]you can put in the sequence of that protein,[349.77] [349.77][S02]and visualize what the 3D structure is,[351.99] [351.99][S02]that just gave the scientists a superpower that they had not[356.2] [356.2][S02]imagined earlier.[357.5] [357.5][S02]And there was a lot of excitement[359.47] [359.47][S02]as to what we could do with that superpower.[361.98] [361.98][S01] And is that how people are feeling, like they've[364.48] [364.48][S01]got a superpower now?[365.66] [365.66][S02] Yeah.[366.535] [366.535][S02]So one of the things that we have seen is AlphaFold database.[370.94] [370.94][S02]So we created this AlphaFold database,[373.01] [373.01][S02]which is, we found the structures[375.04] [375.04][S02]of almost all known proteins.[377.23] [377.23][S02]And we put the structures for those in a database[381.25] [381.25][S02]hosted by the European microbiology lab, our partners,[384.77] [384.77][S02]EMBL-EBI, and this was available-- these 250 million[389.11] [389.11][S02]structures were available to anyone in the world for free.[392.89] [392.89][S02]And that particular database has been used across 140 countries[399.1] [399.1][S02]by 1.8 million scientists.[403.46] [403.46][S02]And the fact that there are 1.8 million scientists who[406.87] [406.87][S02]are looking for protein structures,[408.862] [408.862][S02]if that is not a positive statement[410.32] [410.32][S02]about the state of the world, then I don't know what is.[413.9] [413.9][S02]We think about the doom and gloom[417.27] [417.27][S02]in society, but if you look at how science has progressed--[421.12] [421.12][S02]there are 1.8 million people who are studying proteins.[424.96] [424.96][S01] So I think the really notable thing when[427.593] [427.593][S01]you talk to people about AlphaFold[429.01] [429.01][S01]is just how big of a difference there[430.66] [430.66][S01]is between the people who understand it,[432.55] [432.55][S01]that 1.8 million people, and everybody else.[436.43] [436.43][S01]Is this something that you notice as well?[439.27] [439.27][S01]AlphaFold doesn't-- it feels a bit technical to really[443.08] [443.08][S01]understand the magnitude of it.[444.92] [444.92][S01]What are the applications that you are excited[447.34] [447.34][S01]about that other people will understand?[449.74] [449.74][S02] So I think, for a large section[455.14] [455.14][S02]of the community, they see AlphaFold as an AI breakthrough[459.28] [459.28][S02]in the sciences, but the scientists[462.93] [462.93][S02]who work on this topic understand the implications[467.17] [467.17][S02]of it in a very deep way.[469.03] [469.03][S02]They know the implications of AlphaFold[472.99] [472.99][S02]for extremely important things like drug discovery,[476.99] [476.99][S02]designing a new vaccine, thinking[478.72] [478.72][S02]about antimicrobial resistance, thinking about new enzymes[482.02] [482.02][S02]to decompose plastics.[483.61] [483.61][S02]I can just go on and on and on.[486.47] [486.47][S01] But that's not the only project[489.2] [489.2][S01]that has been going on in here since we last spoke to you?[493.545] [493.545][S01]So tell us a little bit more about what[495.17] [495.17][S01]you've been working on.[496.25] [496.25][S02] So we have been working[498.11] [498.11][S02]on a whole spectrum of different topics,[500.25] [500.25][S02]from material science to fusion, to working[505.97] [505.97][S02]on new discoveries in computer science,[509.07] [509.07][S02]in mathematics, weather prediction, meteorology.[512.98] [512.98][S02]So there's a whole spectrum of areas that we are looking at.[517.03] [517.03][S01] And of those, where do you[519.14] [519.14][S01]think there's been really significant progress[521.48] [521.48][S01]in the last couple of years?[523.64] [523.64][S02] So one of the moments that we can talk about[526.55] [526.55][S02]is our work on weather prediction.[529.67] [529.67][S02]Weather and climate is something that we are all[533.18] [533.18][S02]thinking about at the moment, but if you[536.99] [536.99][S02]look at what DeepMind had done earlier,[539.97] [539.97][S02]we had our nowcasting model, which[541.88] [541.88][S02]was able to make very good prediction but at very[544.34] [544.34][S02]short timescales.[546.59] [546.59][S02]With our new model called GraphCast,[551.16] [551.16][S02]which we released last year, we can now[554.18] [554.18][S02]look at the problem of 10 day forecasts.[557.3] [557.3][S02]And what we have shown that this new model can make these 10 day[563.63] [563.63][S02]predictions more accurately than some of the models that[568.28] [568.28][S02]are being used, the classical models that[570.11] [570.11][S02]are being used by the Met Office.[571.798] [571.798][S01] The supercomputers.[573.09] [573.09][S02] Yes.[573.923] [573.923][S02]Which run on supercomputers for many hours,[576.98] [576.98][S02]and we can outperform them in terms of accuracy[581.72] [581.72][S02]and make predictions in a matter of a minute on a single chip.[585.14] [585.14][S02]I think in some sense, this really[588.35] [588.35][S02]opens up research in this area.[591.96] [591.96][S02]So a lot of other people, a lot of other entities[596.69] [596.69][S02]can now conduct research in weather prediction.[600.66] [600.66][S02]And the results are amazing.[602.732] [602.732][S02]One particular example that was fascinating[606.53] [606.53][S02]is that there was this cyclone, Hurricane Lee, last year, which[611.39] [611.39][S02]made landfall in Nova Scotia.[614.66] [614.66][S02]And our model, GraphCast, was able to make[619.46] [619.46][S02]the prediction of the landfall event nine days earlier--[623.683] [623.683][S01] Wow.[624.35] [624.35][S02] --while the classical models were only[626.6] [626.6][S02]able to do it six days earlier.[628.32] [628.32][S02]So they could give a three day additional heads up.[633.312] [633.312][S01] It's a superpower that doesn't just[635.27] [635.27][S01]apply to biology, then.[636.435] [636.435][S02] Absolutely.[637.56] [637.56][S02]The amount of impact that AI and machine learning[640.67] [640.67][S02]is having across all these disciplines is amazing.[644.94] [644.94][S02]And if you think about it, it feels natural[649.43] [649.43][S02]because, in any of these areas of science,[653.0] [653.0][S02]we are collecting a lot of data, and the complexity of models[657.08] [657.08][S02]that we are playing with and we are working with[659.12] [659.12][S02]is really expanding.[661.26] [661.26][S02]And it's just very natural that a single human mind[666.798] [666.798][S02]has difficulty in comprehending what are[670.36] [670.36][S02]the real patterns in this data.[672.22] [672.22][S02]And machine learning and AI just give you[675.19] [675.19][S02]that ability to figure out what is needed[681.025] [681.025][S02]in many of these problems.[683.56] [683.56][S01] You mentioned material science there.[685.69] [685.69][S01]Frame for us the general problem, as it were,[689.87] [689.87][S01]in material science pre AI?[691.42] [694.09][S02] So what is the problem of material discovery?[698.83] [698.83][S02]You want to discover materials which have certain properties.[701.62] [701.62][S02]We have gone through all these ages, from the Stone Age[706.24] [706.24][S02]to the Iron Age to the Bronze Age, and so on.[708.58] [708.58][S02]And at every age, we are working with a new material,[710.86] [710.86][S02]and new material gives us new abilities.[713.39] [713.39][S02]And the problem of material discovery[715.69] [715.69][S02]is to discover materials which have certain useful properties.[719.83] [719.83][S02]Now, how was that done till now?[723.02] [723.02][S02]It was done in a very experimental sort of way.[728.29] [728.29][S02]People try different things in the lab.[731.37] [731.37][S02]Sometimes the things worked out as expected.[734.41] [734.41][S02]Sometimes, things didn't work out as expected.[736.6] [736.6][S02]But there was no--[737.678] [737.678][S02]there were some rules of thumb.[738.97] [738.97][S02]There were some theory, but we did not[740.61] [740.61][S02]know the extent to what was possible in materials.[745.268] [745.268][S01] Often things get discovered by accident,[747.435] [747.435][S01]like vulcanized rubber or--[749.5] [749.5][S02] Exactly.[750.5] [750.5][S01] Just sort of a chance happening in a lab,[752.708] [752.708][S01]and then a new material that turns out to be really useful[755.125] [755.125][S01]pops up.[755.7] [755.7][S02] Exactly.[756.7] [756.7][S02]Like graphene.[757.875] [757.875][S02]We all know about this story about how[760.95] [760.95][S02]this magical material was isolated by sellotape, by taking[769.38] [769.38][S02]pieces of carbon and then repeatedly making it thinner[773.16] [773.16][S02]and thinner by a sticky tape.[776.38] [776.38][S02]And it has amazing properties.[779.448] [779.448][S01] Ending up being the thinnest substance that[781.74] [781.74][S01]can be manufactured.[783.22] [783.22][S02] Exactly.[783.51] [783.51][S01] A single atom thick.[784.45] [784.45][S02] Yes.[785.283] [785.283][S02]And it has a very interesting properties[787.51] [787.51][S02]in terms of conductivity and so on.[790.01] [790.01][S02]So before AI and before even computational methods,[793.93] [793.93][S02]that essentially was the norm.[795.94] [795.94][S02]That people-- and even today, in some sense,[799.12] [799.12][S02]material science is a very experimental science,[801.94] [801.94][S02]where people are trying to discover new materials[805.27] [805.27][S02]for whether it's for constructing a battery[809.05] [809.05][S02]or for a photovoltaic cell or for a superconductor and so on.[814.36] [814.36][S02]Now, how computational systems are used today[818.71] [818.71][S02]is to then post-hoc rationalize, why[822.07] [822.07][S02]is that material behaving in a way that it's behaving?[826.63] [826.63][S02]But we are very far from the place[829.72] [829.72][S02]where we could rationally design a material.[832.03] [832.03][S02]Given a property, you say, find me the material,[835.77] [835.77][S02]invent a new material which can maximize these properties.[839.535] [839.535][S02]And that's what the problem is for AI.[842.29] [842.29][S02]Can we somehow in silico de novo from scratch[848.74] [848.74][S02]start to invent any given material with some property?[855.25] [855.25][S01] So that you can go in and say,[857.72] [857.72][S01]I want something that is extraordinarily flexible,[860.27] [860.27][S01]extraordinarily light, whatever, easy to mine, something,[864.25] [864.25][S01]something, something.[865.36] [865.36][S01]And then it's just like, here's the physical structure of the--[869.29] [869.29][S01]atomic structure that will result in that material.[872.42] [872.42][S02] Exactly.[873.42] [873.42][S02]That's the vision.[874.657] [874.657][S01] Let's anchor this to an example, then.[876.74] [876.74][S01]So you mentioned batteries there.[879.47] [879.47][S01]We've got batteries.[880.93] [880.93][S01]What's wrong with the batteries we have?[882.76] [882.76][S02] So I think they're doing well.[884.78] [884.78][S02]They're lithium ion batteries.[886.96] [886.96][S02]There are a number of issues with that.[889.31] [889.31][S02]First, they are based on certain resources which are difficult.[893.75] [893.75][S02]For example, the lithium ion batteries, the batteries[898.12] [898.12][S02]used today use cobalt, which is difficult to mine.[903.01] [903.01][S02]We might want to increase the energy[905.08] [905.08][S02]density of these batteries in the future.[907.19] [907.19][S02]We might want to make them more thermally stable.[909.86] [909.86][S02]We might want to make them cheaper.[911.54] [911.54][S02]And if you could somehow have a magic sort of material, which[914.98] [914.98][S02]can have higher energy density, is more stable,[918.53] [918.53][S02]is easy to manufacture, then, of course,[920.8] [920.8][S02]you would want to transition to it.[923.02] [923.02][S01] And find it without just waiting for an accident[926.02] [926.02][S01]to happen in a lab.[926.91] [926.91][S02] Exactly.[927.91] [927.91][S01] Are you sure there is one, though?[929.827] [929.827][S01]How do you know that lithium isn't just the best[931.99] [931.99][S01]that the universe has to offer?[934.55] [934.55][S02] It could be that lithium is the best[936.82] [936.82][S02]and we were extremely lucky, and we found it,[940.31] [940.31][S02]but that seems very remote.[944.03] [944.03][S02]There is, the number of materials--[946.04] [946.04][S02]just to give you an example, there were around 20,000[949.99] [949.99][S02]inorganic materials that people play with.[952.75] [952.75][S02]Now, using computational methods,[956.11] [956.11][S02]28,000 have been found out.[960.8] [960.8][S02]So they are roughly 40,000, 50,000 known materials that were[965.47] [965.47][S02]known to be stable, in the sense that,[967.39] [967.39][S02]at 0 Kelvin or under some theoretical situations,[975.47] [975.47][S02]they will not decompose to other materials.[977.84] [977.84][S01] So if you freeze them[979.215] [979.215][S01]right down to absolute zero, they don't split apart.[981.39] [981.39][S02] Exactly.[982.39] [982.39][S02]So they are stable materials.[984.38] [984.38][S02]So that was what was known in the material science community.[988.93] [988.93][S02]Our new AI model known last year expanded[992.74] [992.74][S02]that set and said there are 2.2 million new inorganic materials[999.31] [999.31][S02]that are stable.[1002.22] [1002.22][S01] From 50,000?[1003.61] [1003.61][S02] Yes.[1004.786] [1004.786][S01] Wow.[1005.453] [1008.08][S01]Wow.[1008.74] [1008.74][S01]That's massive.[1010.813] [1010.813][S02] Right.[1011.73] [1011.73][S02]And you're talking about graphene,[1013.147] [1013.147][S02]there are 52,000 single chain layered materials in that set.[1020.28] [1020.28][S02]So the number of possibilities, the number of things that now we[1023.96] [1023.96][S02]can search over is immense.[1026.919] [1026.919][S02]And you asked the question whether[1030.92] [1030.92][S02]the lithium-cobalt batteries are the optimal batteries, well,[1036.589] [1036.589][S02]there are so many things in that 2.2 million set that one of them[1041.99] [1041.99][S02]could be much, much better.[1043.89] [1043.89][S01] The chances that lithium is actually quite--[1047.262] [1047.262][S01]what's it called?[1047.97] [1047.97][S01]GNNME?[1048.41] [1048.41][S02] Yes.[1048.86] [1048.86][S01] What does it stand for?[1049.92] [1049.92][S02] Graph neural networks[1051.462] [1051.462][S02]for material exploration.[1053.185] [1053.185][S01] And how does it work?[1055.67] [1055.67][S01]How are you deciding new material structures that[1059.27] [1059.27][S01]haven't yet been discovered?[1061.38] [1061.38][S02] So it essentially tries[1065.24] [1065.24][S02]to start with existing structures and compositions.[1070.467] [1070.467][S01] 50,000 that we know.[1071.8] [1071.8][S02] Yeah.[1072.675] [1072.675][S02]And says, OK, let me change some of those,[1075.15] [1075.15][S02]and then, I'll learn a model to say which[1079.95] [1079.95][S02]of those changes, which of those modified materials[1084.21] [1084.21][S02]are stable or not stable.[1086.345] [1086.345][S02]And it is able to do those calculations very efficiently,[1089.562] [1089.562][S02]and that's where the machine learning model comes in.[1091.77] [1091.77][S02]It's able to make predictions about the stability[1096.57] [1096.57][S02]and the free energy of these new compositions[1100.47] [1100.47][S02]and these new crystal structures in a much more accurate way.[1104.89] [1104.89][S01] So in one sense, then,[1106.32] [1106.32][S01]taking the 50,000 and then doing some kind of atomic shuffling,[1110.38] [1110.38][S01]if you like, just trying different combinations,[1112.77] [1112.77][S01]but then, the clever bit, as you're describing,[1114.96] [1114.96][S01]is that you can calculate without actually having made[1118.89] [1118.89][S01]ever this sort of fantasy material which has been[1122.22] [1122.22][S01]constructed by atomic shuffling.[1124.15] [1124.15][S01]You can tell whether or not it will be stable at 0 degree[1128.07] [1128.07][S01]Kelvin if you made it.[1129.247] [1129.247][S02] Yes.[1130.08] [1130.08][S01] How?[1131.15] [1131.15][S02] You can do that theoretically,[1133.17] [1133.17][S02]but you will need to do a lot of very complex calculations.[1136.95] [1136.95][S02]And what this model is able to do[1138.56] [1138.56][S02]is basically approximate those calculations[1141.47] [1141.47][S02]and do it much, much computationally inexpensively.[1144.813] [1144.813][S01] So in many ways, actually, I[1146.48] [1146.48][S01]can see the similarities between this and AlphaFold.[1150.02] [1150.02][S01]You're talking about the atomic structure of something,[1152.6] [1152.6][S01]right down at the level, I mean amino acids and atoms,[1155.585] [1155.585][S01]and then predicting larger structural properties[1159.89] [1159.89][S01]as a result.[1160.988] [1160.988][S02] So in this particular case,[1162.78] [1162.78][S02]you are given a new crystal structure or a new composition,[1166.97] [1166.97][S02]and you are told, is it going to be stable or not?[1169.74] [1169.74][S02]And the model is able to say that.[1171.908] [1171.908][S01] How do you validate it, though?[1173.7] [1173.7][S02] So we have validated these in two ways.[1176.34] [1176.34][S02]One form of validation is by doing calculations.[1180.63] [1180.63][S02]So when I said, these are 2.2 million stable structures,[1184.95] [1184.95][S02]what do I mean?[1185.69] [1185.69][S02]How do I say that these are stable?[1188.155] [1191.16][S02]There are theories in quantum chemistry[1194.64] [1194.64][S02]which give us approaches to figure out whether something[1199.11] [1199.11][S02]is going to be stable.[1200.31] [1200.31][S02]These are very difficult calculations.[1202.15] [1202.15][S02]So we can run those calculations on the predictions[1204.87] [1204.87][S02]that GNNME has made and see whether the theory says[1209.4] [1209.4][S02]that those will be stable.[1211.31] [1211.31][S02]So that's one way.[1212.41] [1212.41][S02]And then we have also taken a subset of the most stable[1218.37] [1218.37][S02]predictions and then experimentally try[1220.35] [1220.35][S02]to validate them in the lab.[1221.617] [1221.617][S01] Like build it?[1222.7] [1222.7][S02] Yeah.[1223.2] [1223.2][S01] Oh, wow.[1224.24] [1224.24][S01]But then, hold on.[1225.3] [1225.3][S01]If it's giving you some fantasy atomic structure,[1230.76] [1230.76][S01]does it tell you how to make it?[1232.27] [1232.27][S02] No, it doesn't.[1233.562] [1233.562][S02]So then we have to figure out how do you make it.[1236.918] [1236.918][S01] And you have managed to do this[1238.71] [1238.71][S01]with a small number of them?[1239.74] [1239.74][S02] Yes.[1240.21] [1240.21][S01] Right.[1241.3] [1241.3][S01]And do they hold up?[1242.53] [1242.53][S01]Are they stable?[1243.37] [1243.37][S02] Yeah.[1243.56] [1243.56][S02]Yeah.[1244.06] [1244.06][S02]And large proportion of them are stable.[1245.743] [1245.743][S01] Give me numbers.[1246.91] [1246.91][S01]Give me numbers.[1248.915] [1248.915][S02] This is constantly changing--[1250.79] [1250.79][S01] Of course.[1251.707] [1251.707][S02] --but the success rates are quite high.[1254.79] [1254.79][S02]Between 70% to 90% of those that we try in the lab[1259.64] [1259.64][S02]end up being stable.[1261.62] [1261.62][S02]And then now once we have those valid materials,[1265.495] [1265.495][S02]then we also have to think about,[1266.87] [1266.87][S02]what are the properties of those materials?[1269.01] [1269.01][S02]Now, which of them would be better batteries[1272.09] [1272.09][S02]or would be useful in photovoltaics[1274.61] [1274.61][S02]or would be good in superconductivity, and so on?[1277.59] [1277.59][S01] So can it also predict the properties, then?[1280.11] [1280.11][S02] So no, not our current generation.[1282.29] [1282.29][S02]It just makes the predictions about the stability[1285.05] [1285.05][S02]of those structures, but we are working[1287.27] [1287.27][S02]on new models for these other types of problems.[1293.23] [1293.23][S01] What kind of materials are you hoping for?[1296.17] [1296.17][S01]Battery materials is one example.[1298.79] [1298.79][S01]What other kind of things are you[1300.22] [1300.22][S01]hoping that GNNME one will help develop?[1302.05] [1302.05][S02] So one of the key things[1304.63] [1304.63][S02]that people have been thinking about[1306.49] [1306.49][S02]is materials that can exhibit superconductivity.[1309.782] [1309.782][S01] By which we mean?[1310.99] [1310.99][S02] Which means, basically,[1312.615] [1312.615][S02]they exhibit zero resistance.[1315.59] [1315.59][S02]And why is that important?[1317.17] [1317.17][S02]That's important because, if you have such a material,[1320.9] [1320.9][S02]you can create very strong magnetic fields.[1323.99] [1323.99][S02]You can store a lot of energy using those materials.[1327.64] [1327.64][S02]Why are magnetic fields important?[1329.87] [1329.87][S02]They're important from everything[1332.71] [1332.71][S02]to do with MRI scanners to creating fusion reactors.[1338.415] [1338.415][S02]The magnets that are used in fusion reactors[1341.65] [1341.65][S02]require very high magnetic fields.[1343.97] [1343.97][S02]So superconductivity is an extremely important property.[1347.703] [1347.703][S01] And at the moment, you[1349.12] [1349.12][S01]have to super cool things in order[1350.71] [1350.71][S01]to be able to get up to those high levels?[1352.483] [1352.483][S01]Because otherwise, it just gets too hot.[1354.15] [1354.15][S02] Exactly.[1355.15] [1355.15][S02]And so what is the Holy Grail in material science[1358.61] [1358.61][S02]is to discover a room temperature superconductor.[1362.31] [1362.31][S02]And there have been many attempts at it[1364.73] [1364.73][S02]and some false starts, but--[1367.55] [1367.55][S01] And some quite cheeky pretending.[1369.66] [1369.66][S02] Yes.[1369.92] [1369.92][S01] This happen too.[1370.79] [1370.79][S02] Yeah, that too.[1372.082] [1372.082][S02]But I think one day, if we discover it,[1376.2] [1376.2][S02]it will be transformational.[1377.67] [1377.67][S01] And do you think AI will[1379.4] [1379.4][S01]be involved in that discovery?[1380.76] [1380.76][S02] Yeah, I'm absolutely[1382.26] [1382.26][S02]sure that AI will aid in the search for superconductors,[1388.18] [1388.18][S02]definitely.[1388.68] [1388.68][S01] At the very least, accelerate the process.[1390.66] [1390.66][S02] Exactly.[1391.92] [1391.92][S01] So that part of making it, though.[1395.805] [1395.805][S01]the way that you described it was like, yeah,[1397.68] [1397.68][S01]we've got to figure out how to make it.[1398.8] [1398.8][S01]So it feels like we're skipping over quite a bit tricky part[1401.73] [1401.73][S01]of the process.[1402.61] [1402.61][S01]Is there any way that you can automate that bit too?[1405.04] [1405.04][S02] Yeah.[1405.915] [1405.915][S02]Once you know what the material is, then it's a whole--[1410.76] [1410.76][S02]there is another sort of stream of material science work, which[1414.54] [1414.54][S02]is, how do you make something cheaply?[1418.605] [1418.605][S02]And there is a lot of work happening in that area as well,[1422.88] [1422.88][S02]but at the moment, our team is looking more[1425.97] [1425.97][S02]on the discovery side.[1427.72] [1427.72][S02]And just to say, we have to be also be[1431.31] [1431.31][S02]cautious in saying that a lot of it--[1434.89] [1434.89][S02]yes, there are these 2.2 million materials, but how many of them,[1439.45] [1439.45][S02]as you said, are valid materials,[1442.03] [1442.03][S02]which actually will prove out when synthesized?[1445.2] [1445.2][S02]How many of them are useful and will have interesting material[1449.7] [1449.7][S02]properties?[1450.22] [1450.22][S02]All of that are open questions.[1454.746] [1454.746][S02]But I think the fact that there is this new place that we[1460.05] [1460.05][S02]find ourselves in in that--[1461.94] [1461.94][S02]from 20,000 to 2.2 million, it's a different ball game.[1466.84] [1466.84][S01] Absolutely.[1468.43] [1468.43][S01]Here's another open question.[1469.68] [1469.68][S01]How long do you think it will be before the first AI developed--[1475.83] [1475.83][S01]or material developed with the assistance of AI[1478.65] [1478.65][S01]makes a big difference?[1480.63] [1480.63][S02] So I think, unlike biology, materials[1484.95] [1484.95][S02]science is much more experimental at the moment.[1488.67] [1488.67][S02]Drug discovery is still--[1490.2] [1490.2][S02]and biology still has used computational methods,[1494.92] [1494.92][S02]but material science is much more experimental.[1498.73] [1498.73][S02]So it will take time, but I think in the next 5 to 10 years,[1502.51] [1502.51][S02]we will see really, as I say, in silico material design[1509.81] [1509.81][S02]starting to make a big impact.[1511.06] [1511.06][S01] So I just want to zoom out a little bit further,[1513.56] [1513.56][S01]because--[1514.16] [1514.16][S01]so you're sitting here talking to us[1516.16] [1516.16][S01]very confidently about material science but also about biology.[1518.99] [1518.99][S01]And you're a computer scientist.[1521.17] [1521.17][S01]So how come you got into this?[1524.835] [1524.835][S02] The last time I studied science was in school,[1527.42] [1527.42][S02]and I came to DeepMind hoping to work[1530.23] [1530.23][S02]on general intelligent systems and make them safe and reliable.[1535.9] [1535.9][S02]But I was always fascinated about,[1538.75] [1538.75][S02]how can intelligent systems have real-world impact?[1541.97] [1541.97][S02]And that's what I used to keep on having discussions with Demis[1548.14] [1548.14][S02]with.[1548.69] [1548.69][S02]And one day he comes to my office[1550.21] [1550.21][S02]and says, oh, Pushmeet, I think we have a good role for you.[1553.49] [1553.49][S02]And I said, OK, what the role?[1555.64] [1555.64][S02]And I was assuming some product area[1561.4] [1561.4][S02]that I'm going to finally use machine learning for.[1564.72] [1564.72][S02]And he said, we want to start a science team,[1566.99] [1566.99][S02]and you should lead it.[1569.09] [1569.09][S02]And I said, why me?[1571.25] [1571.25][S02]Science, I have no background.[1573.06] [1573.06][S02]I studied science in school.[1575.01] [1575.01][S02]I loved it at school, but then I became a computer scientist,[1578.3] [1578.3][S02]and then that's what I did.[1580.11] [1580.11][S02]But he said, well, see you're into multidisciplinary research.[1583.8] [1583.8][S02]You want to understand the problems[1585.29] [1585.29][S02]and you want to have real-world impact.[1587.01] [1587.01][S02]And what better place to have impact[1589.25] [1589.25][S02]in pushing the boundaries of knowledge[1591.86] [1591.86][S02]forward in the natural sciences?[1594.29] [1594.29][S02]So I said, OK.[1595.97] [1595.97][S02]I agreed.[1597.15] [1597.15][S02]And then over the next couple of months,[1600.84] [1600.84][S02]I found what I've got myself into,[1603.75] [1603.75][S02]because, if somebody was designing a job[1606.41] [1606.41][S02]to experience imposter syndrome, this is the job.[1609.96] [1609.96][S02]Because you will be working with Nobel Prize winners[1613.413] [1613.413][S02]and trying to understand those problems,[1615.08] [1615.08][S02]and you don't have no clue.[1616.82] [1616.82][S02]And you're trying to make a difference,[1619.13] [1619.13][S02]and you start from Wikipedia.[1622.88] [1622.88][S01] You start from Wikipedia?[1624.63] [1624.63][S02] Exactly.[1625.63] [1625.63][S02]You start from Wikipedia and try to get a crash[1628.43] [1628.43][S02]course in the area, but--[1630.32] [1630.32][S02]I mean the scientific community is amazing.[1635.3] [1635.3][S02]People are there who are willing to sit down with you,[1640.19] [1640.19][S02]are willing to teach you or go on that journey with you.[1644.818] [1644.818][S01] I guess the really key point, though,[1646.86] [1646.86][S01]is that you really, really, really understand[1649.43] [1649.43][S01]the heart of these algorithms.[1651.0] [1651.0][S01]You really understand the type of systems[1653.0] [1653.0][S01]that they can perform well in and where they can really excel.[1656.73] [1656.73][S01]And so in some ways, the fact that you're[1659.72] [1659.72][S01]talking about lots of different areas,[1661.43] [1661.43][S01]meteorology on the one hand, material science on another,[1663.84] [1663.84][S01]then biology, actually, I can see[1666.23] [1666.23][S01]how there are these, behind the scenes,[1668.76] [1668.76][S01]these actual technical similarities between the systems[1672.53] [1672.53][S01]that you're designing.[1673.53] [1673.53][S01]So in many ways, being a generalist,[1675.84] [1675.84][S01]it requires a generalist to be able to do this kind of stuff?[1679.01] [1679.01][S02] Yeah, absolutely.[1680.385] [1680.385][S02]If you think about intelligence, what is intelligence?[1684.63] [1684.63][S02]How do you create intelligence?[1686.04] [1686.04][S02]Intelligence does not happen in a vacuum.[1687.96] [1687.96][S02]Suddenly, you have a system which gets the intuitions[1692.63] [1692.63][S02]and is able to make these new discoveries.[1694.95] [1694.95][S02]It happens through experience.[1697.43] [1697.43][S02]And what form of experience?[1700.38] [1700.38][S02]How rich is your experience?[1701.88] [1701.88][S02]And this holds true for humans as well.[1704.81] [1704.81][S02]The amount of things that we see in our life,[1708.21] [1708.21][S02]it gives us a different perspective.[1710.37] [1710.37][S02]And so, with these models, the richness of the data[1714.92] [1714.92][S02]that they see, whether that data is collected[1717.86] [1717.86][S02]by a scientist in a lab or whether that data is generated[1722.0] [1722.0][S02]by simulations, if it is rich enough--[1725.01] [1725.01][S02]you want the data to be rich enough so[1726.62] [1726.62][S02]that the model is forced to learn general concepts[1731.51] [1731.51][S02]to explain that data.[1733.35] [1733.35][S02]And then the other sort of element[1734.87] [1734.87][S02]is to have good evaluation metrics.[1739.07] [1739.07][S02]Machine learning methods are very good at cheating.[1743.78] [1743.78][S02]They are amazing at memorization.[1746.58] [1746.58][S02]They are like the student that can memorize anything.[1749.66] [1749.66][S02]So if you just give them a lot of data, they will memorize it.[1752.64] [1752.64][S02]And you ask them any question from the-- which is close[1755.72] [1755.72][S02]to the training set, and they will say, ah, yeah,[1758.495] [1758.495][S02]this is the answer, but they actually don't--[1760.37] [1760.37][S01] Understand what's going on.[1761.52] [1761.52][S02] Exactly.[1762.17] [1762.17][S01] I guess that must have[1762.77] [1762.77][S01]happened quite a lot with proteins[1764.187] [1764.187][S01]at some point or another.[1765.458] [1765.458][S01]They've just seen they've peaked at the back of the textbook.[1768.0] [1768.0][S02] Exactly.[1769.0] [1769.0][S02]And so what we had to do is make sure[1771.74] [1771.74][S02]that we made that test extremely hard,[1775.65] [1775.65][S02]made it impossible to cheat.[1777.63] [1777.63][S02]And the true test of a good machine learning system[1782.27] [1782.27][S02]is its ability to generalize, to unseen examples, to have those--[1789.43] [1789.43][S02]to make those predictions in things[1791.24] [1791.24][S02]which it had never seen before.[1795.29] [1795.29][S02]So understanding what is the right data[1797.87] [1797.87][S02]and what is the right evaluation metric[1800.27] [1800.27][S02]for a different scientific discipline[1804.13] [1804.13][S02]is one part of the problem.[1806.39] [1806.39][S02]It was one of the things that I needed to work with our teams.[1810.35] [1810.35][S02]And then the other part of the solution[1814.99] [1814.99][S02]is, in the design of these machine[1818.23] [1818.23][S02]learning models and these AI models,[1819.95] [1819.95][S02]you want to learn from data, but you also[1821.68] [1821.68][S02]want to bake in any information that you[1824.17] [1824.17][S02]can about the problem into the design of the model itself.[1828.25] [1828.25][S02]And that is where domain knowledge makes a lot of sense.[1833.32] [1833.32][S02]You don't want to start from scratch if you don't have to.[1835.74] [1835.74][S02]So in all our projects, we started[1838.96] [1838.96][S02]with a very multidisciplinary viewpoint.[1841.88] [1841.88][S02]We got in experts, we asked them what was working,[1845.48] [1845.48][S02]what was not working, and tried to inject everything[1849.22] [1849.22][S02]that was currently known into the design of the model itself.[1853.222] [1853.222][S01] But it's noticeable that the common theme[1855.43] [1855.43][S01]of everything that you've described there[1857.138] [1857.138][S01]is where there is a version of success.[1861.19] [1861.19][S01]There's a right answer that you are looking for,[1863.41] [1863.41][S01]like the true underlying structure of the protein,[1866.79] [1866.79][S01]whether it's truly stable or not,[1868.41] [1868.41][S01]whether you really are predicting the weather.[1870.47] [1870.47][S01]Is that absolutely crucial, then, for any of these projects?[1873.885] [1873.885][S02] Absolutely.[1875.01] [1875.01][S02]You really need to have a good sense[1877.19] [1877.19][S02]of what does success look like.[1880.05] [1880.05][S01] So then when it comes to choosing projects--[1882.65] [1882.65][S01]because, I mean, if you are, as you say, the generalist who[1885.95] [1885.95][S01]can, with domain-specific knowledge,[1889.28] [1889.28][S01]adapt these algorithms to anything,[1892.922] [1892.922][S01]how do you decide what's worth your time?[1897.297] [1897.297][S02] That's a very good question.[1899.13] [1899.13][S02]And there are a number of dimensions that we look at.[1903.63] [1903.63][S02]One dimension is the data.[1906.96] [1906.96][S02]If there is no data and there is no experience,[1911.33] [1911.33][S02]the machine learning model will not learn by itself.[1913.986] [1913.986][S02]So that's one of the important considerations.[1917.94] [1917.94][S02]The second thing-- and this is something[1920.93] [1920.93][S02]that we have also always sort of considered from the very start--[1924.41] [1924.41][S02]is a focus on root node problems,[1927.95] [1927.95][S02]problems that are so fundamental that once you unlock them,[1931.77] [1931.77][S02]they have implications for a number of different applications[1934.67] [1934.67][S02]downstream.[1935.25] [1935.25][S02]And protein folding, as I said, had implications[1938.66] [1938.66][S02]for drug discovery to design new enzymes[1943.85] [1943.85][S02]for plastic decomposition and so on.[1945.63] [1945.63][S02]And the same thing is true for materials.[1948.29] [1948.29][S02]If you can understand material discovery[1951.941] [1951.941][S02]in a transformational way, that has[1953.87] [1953.87][S02]so many different applications.[1955.162] [1955.162][S01] It's much more effective[1956.662] [1956.662][S01]than only looking at, say, one particular drug for Alzheimer's[1959.48] [1959.48][S01]or whatever it might be.[1962.09] [1962.09][S01]So then, do you have a list?[1964.86] [1964.86][S01]Is there a whiteboard somewhere in this building[1967.37] [1967.37][S01]where you have a list of potential projects[1970.94] [1970.94][S01]and then you're evaluating them on that basis?[1973.08] [1973.08][S01]Which ones have got good data?[1974.4] [1974.4][S01]Which ones are more important problems than others?[1978.225] [1978.225][S02] Absolutely.[1979.35] [1979.35][S02]We are constantly doing that.[1982.64] [1982.64][S02]And there is a lot of uncertainty[1986.45] [1986.45][S02]that we have to deal with.[1988.28] [1988.28][S02]So we approach the problem in a scientific manner.[1992.22] [1992.22][S02]We conduct experiments.[1994.41] [1994.41][S02]We do exploration studies and see, oh, yes, we[1997.67] [1997.67][S02]are making progress.[1999.06] [1999.06][S02]Some of our assumptions were correct.[2001.67] [2001.67][S02]So that means that we can now make that big conviction[2007.57] [2007.57][S02]commitment to that problem.[2009.61] [2009.61][S02]And the other difference in how we are operating is--[2014.11] [2014.11][S02]most of the topics that I've mentioned to you--[2016.28] [2016.28][S02]we have very focused teams that pursue these problems.[2020.41] [2020.41][S02]And they are not operating at the level[2022.84] [2022.84][S02]of six months or 12 months.[2026.0] [2026.0][S02]They are operating at the level of many years.[2028.55] [2028.55][S02]They are dedicating researchers and engineers[2033.376] [2033.376][S02]in those teams, are dedicating their whole careers[2036.61] [2036.61][S02]to that topic.[2038.57] [2038.57][S02]So we take that responsibility very seriously,[2041.2] [2041.2][S02]as to which are the right problems that we[2043.75] [2043.75][S02]should be working on.[2045.52] [2045.52][S02]Like if we believe that a problem can[2049.81] [2049.81][S02]be done at Oxford or MIT or Harvard or Berkeley or Imperial,[2055.28] [2055.28][S02]we wouldn't want to work on it, because we[2058.389] [2058.389][S02]want to work on the problems that really require[2064.969] [2064.969][S02]a scale and the type of multidisciplinary team[2069.19] [2069.19][S02]that we have to come together to solve them.[2072.21] [2072.21][S01] Well, let's pick another topic then,[2074.21] [2074.21][S01]because I think the big thing that the world's got very[2076.389] [2076.389][S01]excited about in the last couple of years[2078.097] [2078.097][S01]is generative AI and in particular large language[2081.19] [2081.19][S01]models.[2081.94] [2081.94][S01]Have you started to incorporate large language models[2084.61] [2084.61][S01]into your research for science?[2086.675] [2086.675][S02] Yeah.[2087.55] [2087.55][S02]So we are looking at it across the board.[2090.83] [2090.83][S02]I mean, there are two main themes that we are exploring.[2095.06] [2095.06][S02]One is that, till now, most of the work that we have[2101.08] [2101.08][S02]been doing in the scientific areas,[2105.32] [2105.32][S02]we were using structured data.[2107.65] [2107.65][S02]So this is data, whether it's genomic data or protein[2110.62] [2110.62][S02]structure prediction data, where you[2112.27] [2112.27][S02]have sequences and structures--[2113.642] [2113.642][S01] They're directly connected to each other.[2115.85] [2115.85][S02] Exactly.[2116.85] [2116.85][S02]The tables, right?[2118.33] [2118.33][S02]But there's a lot of scientific intuition and knowledge[2121.94] [2121.94][S02]which is embedded in scientific publications in free form text.[2127.15] [2127.15][S02]And how do you learn from that experience,[2131.71] [2131.71][S02]from the diary entries of key scientists[2138.01] [2138.01][S02]that have worked in that area over the last many centuries?[2142.79] [2142.79][S02]So large language models give us the ability[2146.17] [2146.17][S02]to now ingest all that data and to learn from all of it.[2150.65] [2150.65][S02]So that's one key idea.[2153.81] [2153.81][S02]The other way is basically where you[2155.92] [2155.92][S02]use the large language model to generate answers[2160.15] [2160.15][S02]in certain domains.[2161.72] [2161.72][S02]And one example of that is our project[2165.47] [2165.47][S02]on algorithmic discovery, aptly named FunSearch, which[2172.28] [2172.28][S02]stands for function search.[2174.98] [2174.98][S01] Is much better named FunSearch.[2177.21] [2177.21][S01]I'm much prefer that as well.[2179.57] [2179.57][S02] I think our team was very proud of--[2182.36] [2182.36][S01] Of that.[2183.32] [2183.32][S02] --of the name.[2184.85] [2184.85][S01] The fun team.[2185.45] [2185.45][S02] Yeah, exactly.[2186.7] [2186.7][S02]The fun team working on FunSearch.[2189.32] [2189.32][S02]They are trying to discover new algorithms[2191.39] [2191.39][S02]for important problems in computer science.[2193.73] [2193.73][S02]And the model is asked, here is a problem, an important problem[2199.94] [2199.94][S02]in computer science, whether it's[2202.95] [2202.95][S02]a conceptual problem like the bin packing problem,[2208.13] [2208.13][S02]given a set of boxes and some items,[2210.63] [2210.63][S02]how do you compactly pack those items in those boxes?[2214.32] [2214.32][S02]And you might think, well, I do it at home.[2217.01] [2217.01][S02]What's the relevance of it?[2218.3] [2218.3][S02]That conceptual problem is everywhere.[2221.45] [2221.45][S02]It's in how delivery companies go and deliver your groceries[2227.78] [2227.78][S02]to your homes or how cloud providers schedule[2236.12] [2236.12][S02]computational jobs on different computers that they have.[2239.61] [2239.61][S02]So it's a very, very important problem in the real world.[2244.52] [2244.52][S02]And so what this model does, it then[2248.63] [2248.63][S02]tries to make new-- propose new algorithms.[2253.14] [2253.14][S02]And many of those algorithms are maybe well known algorithms[2257.42] [2257.42][S02]that it had seen before.[2259.8] [2259.8][S02]And we tell it, well, that's fine,[2262.44] [2262.44][S02]but try to improve this particular part[2264.2] [2264.2][S02]and try to refine it.[2265.86] [2265.86][S02]And it goes on trying to refine it.[2269.743] [2269.743][S02]And sometimes, it makes a mistake,[2271.16] [2271.16][S02]and we tell it, oh, here's the mistake that you've made.[2273.493] [2273.493][S02]And it keeps on doing that.[2275.7] [2275.7][S02]We try to get the right solutions and feed it back.[2280.67] [2280.67][S02]And in that process, sometimes, it[2283.22] [2283.22][S02]discovers something completely new that was not known before[2287.3] [2287.3][S02]and ends up improving the performance[2291.38] [2291.38][S02]or coming up with a new heuristic or a new algorithm,[2294.69] [2294.69][S02]which solves the problem in a remarkably different and much[2297.95] [2297.95][S02]more efficient manner.[2299.162] [2299.162][S01] So the reason it's able to do that, then,[2301.37] [2301.37][S01]is because there's this deeper network of connections[2306.08] [2306.08][S01]between knowledge that isn't necessarily visible to us.[2308.88] [2308.88][S01]So is that why sometimes large language[2311.96] [2311.96][S01]models will hallucinate in a way that sort of makes sense?[2316.29] [2316.29][S01]So I don't know.[2316.97] [2316.97][S01]Maybe one would say Marie Curie invented penicillin.[2321.92] [2321.92][S01]And of course, she didn't, but she did come up[2324.23] [2324.23][S01]with a discovery that was really important around the same time.[2327.15] [2327.15][S01]So it can swap over pieces of information,[2330.36] [2330.36][S01]as it were, because it's seeing deeper connections that[2333.38] [2333.38][S01]aren't visible to us.[2334.535] [2334.535][S02] Absolutely.[2335.66] [2335.66][S02]It is basically, because it's operating in that latent space,[2339.24] [2339.24][S02]it feels that there are certain things which[2342.02] [2342.02][S02]are related to each other.[2343.8] [2343.8][S02]And in the case of the Marie Curie example,[2347.2] [2347.2][S02]that was a mistake, and that's a problematic mistake[2350.06] [2350.06][S02]if somebody did not know about it.[2353.001] [2353.001][S02]It's incorrect information, but in our case, that's[2358.52] [2358.52][S02]a fine mistake to have, because what[2361.05] [2361.05][S02]we have is the evaluation function, which[2363.84] [2363.84][S02]is coupled with FunSearch, which is[2365.94] [2365.94][S02]coupled with the large language model, which can call bullshit.[2369.963] [2369.963][S01] Like a truth detector.[2371.38] [2371.38][S02] Yeah, exactly.[2372.63] [2372.63][S02]Which can call bullshit and say, oh, this doesn't make sense,[2375.7] [2375.7][S02]very, very quickly.[2376.93] [2376.93][S02]So then, what matters is creativity.[2381.57] [2381.57][S02]So if it comes up with something completely creative,[2385.03] [2385.03][S02]we pick that apart and say, oh, yeah, great.[2387.82] [2387.82][S02]And the bad stuff that it says, we[2390.84] [2390.84][S02]are able to filter it out very easily.[2392.95] [2392.95][S01] But every now and then, the creative stuff it says[2398.57] [2398.57][S01]is revealing something more about the underlying[2404.13] [2404.13][S01]network of knowledge.[2405.335] [2405.335][S02] Exactly.[2406.42] [2406.42][S01] It's basically like you've harnessed hallucinations[2408.0] [2408.0][S01]for good.[2408.61] [2408.61][S02] Yes.[2409.62] [2409.62][S02]Hallucinations are good in this particular case[2411.58] [2411.58][S02]if you can somehow leverage creativity[2413.97] [2413.97][S02]and keep the good part and filter out all the invalid part.[2419.53] [2419.53][S01] So don't let the fun team know that.[2423.54] [2423.54][S01]They can call themselves the hallucinations for good team.[2426.66] [2426.66][S01]No, might get ahead of themselves.[2430.05] [2430.05][S01]So is it finding stuff that wasn't known to computer[2432.98] [2432.98][S01]scientists and mathematicians?[2434.23] [2434.23][S02] Yes, absolutely.[2435.563] [2435.563][S02]So in fact, with this method, we were[2441.3] [2441.3][S02]able to get a new result in computer science.[2447.7] [2447.7][S02]So there is a very interesting problem called the cap set[2450.36] [2450.36][S02]problem, which is trying to find independent sets[2453.87] [2453.87][S02]in a structured graph.[2456.58] [2456.58][S02]It's like a computational problem where you have a graph[2459.48] [2459.48][S02]and you are trying to find certain sort of nodes and edges,[2464.19] [2464.19][S02]which certain properties.[2465.73] [2465.73][S02]And it has been studied for a long time.[2469.21] [2469.21][S02]It's a very interesting problem in computer science.[2472.27] [2472.27][S02]And what FunSearch was able to do is was, for the first time,[2476.64] [2476.64][S02]produce a result which nobody had been able to produce.[2480.6] [2480.6][S02]And not only was it able to produce that result,[2484.92] [2484.92][S02]but the algorithm, the program that it had generated[2490.64] [2490.64][S02]to find that result had extremely[2493.43] [2493.43][S02]interesting substructure and intuitions[2496.25] [2496.25][S02]that, when mathematicians, who were working with us saw it,[2501.512] [2501.512][S02]they felt that the program had extracted[2504.65] [2504.65][S02]a new symmetry in the problem.[2506.996] [2506.996][S01] Oh.[2507.77] [2507.77][S01]So in this hallucination, it had really[2511.58] [2511.58][S01]stumbled upon something that had been unexplored[2514.69] [2514.69][S01]but turned out to be true?[2516.18] [2516.18][S02] Yeah.[2517.285] [2517.285][S02]And it was leveraging some very interesting property[2521.3] [2521.3][S02]about the problem that we had not told it about.[2524.93] [2524.93][S01] Wow.[2526.95] [2526.95][S01]I can see how that might be useful.[2529.73] [2529.73][S01]Tell me about the Olympiad.[2531.59] [2531.59][S02] So there is the International Maths Olympiad,[2536.16] [2536.16][S02]which is a competition that students take part in.[2542.85] [2542.85][S02]Some of the best students around the world[2545.58] [2545.58][S02]come to this competition.[2547.18] [2547.18][S02]It has very hard math problems.[2550.51] [2550.51][S02]And if you perform well, then you[2554.49] [2554.49][S02]can get a bronze, silver, or gold medal.[2560.355] [2560.355][S02]The level of problems that are asked at the International Maths[2565.08] [2565.08][S02]Olympiad are extremely hard.[2567.04] [2567.04][S01] Yes, they are.[2568.17] [2568.17][S02] The current generation of AI systems,[2570.67] [2570.67][S02]for example, even, they are not able to tackle those problems,[2577.44] [2577.44][S02]but--[2578.07] [2578.07][S01] They require an incredible level[2579.903] [2579.903][S01]of lateral thinking, of logic, of deep understanding[2583.038] [2583.038][S01]of mathematical concepts, despite the fact that they're[2585.33] [2585.33][S01]aimed at schoolchildren.[2587.02] [2587.02][S02] Absolutely.[2590.52] [2590.52][S02]They're not your normal--[2592.14] [2592.14][S01] They are not your normal kids.[2593.89] [2593.89][S02] Yes, exactly.[2594.52] [2594.52][S01] They're not the Demis's of the world.[2595.68] [2595.68][S01]You know what I mean?[2596.35] [2596.35][S02] Exactly.[2596.95] [2596.95][S02]Exactly.[2597.45] [2597.45][S02]I'm sure.[2598.047] [2598.047][S02]I don't know whether Demis has participated in one but--[2600.38] [2600.38][S01] I bet he did.[2602.18] [2602.18][S02] --but they are exceptional.[2604.61] [2604.61][S02]And it has been a long-standing challenge[2608.3] [2608.3][S02]for a computational system to be able to solve[2611.96] [2611.96][S02]any of these problems, because these are extremely[2614.33] [2614.33][S02]hard mathematics problems.[2616.71] [2616.71][S02]And mathematics, unlike the game of chess or Go,[2621.57] [2621.57][S02]is an open-ended environment in the sense[2625.64] [2625.64][S02]that it does not have a specific number of moves.[2629.6] [2629.6][S02]The moves are infinite.[2631.465] [2631.465][S01] And in any direction.[2632.84] [2632.84][S02] And in any direction.[2634.382] [2634.382][S02]So it's an extremely large space that you are reasoning over.[2638.55] [2638.55][S02]And so the more sophisticated AI systems[2642.44] [2642.44][S02]are not able to tackle any of these sort of problems.[2646.74] [2646.74][S02]So we had a system called AlphaGeometry,[2652.11] [2652.11][S02]which, for the first time, showed that an AI system can[2659.0] [2659.0][S02]solve geometry problems that are at the International Math[2664.61] [2664.61][S02]Olympiad level.[2665.96] [2665.96][S01] So this is like--[2667.4] [2667.4][S01]I mean, how can we describe them?[2669.45] [2669.45][S01]So you get a picture of some circles and triangles[2673.205] [2673.205][S01]and squares and things, and then you'll[2675.47] [2675.47][S01]be asked something about--[2677.88] [2677.88][S01]I don't know-- how to calculate some seemingly impossible thing[2682.01] [2682.01][S01]from this image based only on what[2683.852] [2683.852][S01]you understand about the rules of circles and squares[2686.06] [2686.06][S01]and triangles.[2686.85] [2686.85][S02] Yes, something like that.[2689.01] [2689.01][S01] It's really hard.[2690.59] [2690.59][S02] It's quite hard.[2691.923] [2691.923][S02]And you really have to understand not only geometry[2695.3] [2695.3][S02]very well, but you also need to be able to plan ahead[2699.65] [2699.65][S02]and think about what kind of solution can make sense,[2705.32] [2705.32][S02]can lead you to the final answer.[2706.888] [2706.888][S01] So how does AlphaGeometry work, then?[2708.93] [2708.93][S02] So AlphaGeometry, how it works,[2710.888] [2710.888][S02]it transforms this problem, which is given sometimes[2717.26] [2717.26][S02]in text, sometimes in image, into a formal language,[2721.06] [2721.06][S02]into its own domain-specific language in which it can reason[2725.1] [2725.1][S02]about the problem, and then it tries[2729.09] [2729.09][S02]to solve the problem in that language.[2731.47] [2731.47][S02]Now, what the AlphaGeometry did was very smart.[2735.88] [2735.88][S02]They generated a very, very large number of problems,[2741.01] [2741.01][S02]synthetically generated problems,[2743.1] [2743.1][S02]in that language and the corresponding solutions.[2747.7] [2747.7][S02]So with this, they could train the machine learning model[2751.17] [2751.17][S02]on the hundreds of thousands of these problems.[2753.82] [2753.82][S02]And then that made it extremely effective[2757.23] [2757.23][S02]in, given a new problem of that form, it could solve it.[2760.527] [2760.527][S01] It just knows from its own experience.[2762.61] [2762.61][S02] Yes.[2763.21] [2763.21][S01] Amazing.[2764.043] [2764.043][S01]And actually, I can see the similarities there,[2766.002] [2766.002][S01]again, between that and the material science thing[2768.09] [2768.09][S01]is like, OK, let's just use combinatorics.[2770.94] [2770.94][S02] Exactly.[2771.94] [2771.94][S01] It's just like, roll the dice,[2773.26] [2773.26][S01]come up with loads of random things in the beginning,[2774.99] [2774.99][S01]and then narrow it down and narrow it down, narrow it down[2776.88] [2776.88][S01]until eventually it can look at a single problem and say,[2779.05] [2779.05][S01]I know how to solve it.[2780.008] [2780.008][S02] Exactly.[2781.515] [2781.515][S01] I'm quite jealous of your job.[2783.265] [2786.39][S01]We have gone on a wild list of topics here,[2790.86] [2790.86][S01]but what are you hoping to tackle next?[2793.538] [2793.538][S02] So I think there are many problems that[2795.83] [2795.83][S02]remain unsolved.[2798.68] [2798.68][S02]In any area that we're working on,[2800.73] [2800.73][S02]whether it's understanding proteins,[2804.93] [2804.93][S02]whether it's understanding the genome, whether it's[2807.47] [2807.47][S02]understanding the weather, whether it's understanding[2810.47] [2810.47][S02]a material science, there is just so much work[2813.11] [2813.11][S02]that still remains to be done.[2815.16] [2815.16][S02]We were talking about materials.[2816.6] [2816.6][S02]We are only making predictions about stability[2819.38] [2819.38][S02]of these materials, but how do you[2821.36] [2821.36][S02]extend that to making the prediction about properties[2826.76] [2826.76][S02]of these materials and then synthesizing them?[2828.867] [2828.867][S01] Have you got a favorite, though?[2830.7] [2830.7][S01]Is there one that you're like, that's the one I really want?[2833.85] [2833.85][S02] No, you can't ask me that question.[2835.02] [2835.02][S01] I can.[2835.77] [2835.77][S01]I did.[2836.858] [2836.858][S02] No.[2837.65] [2837.65][S02]I think all--[2839.58] [2839.58][S01] Not all your babies are equal.[2841.62] [2841.62][S01]You got to pick a favorite.[2843.282] [2843.282][S02] I'm a computer scientist,[2844.99] [2844.99][S02]I love the computer science work, but--[2848.99] [2848.99][S02]all of them have different elements which are on the day,[2852.36] [2852.36][S02]they are like, wow, this is so amazing.[2854.88] [2854.88][S02]When you are trying to learn a policy that is going to control[2860.6] [2860.6][S02]the magnets of a fusion reactor, the day that that experiment[2864.41] [2864.41][S02]happens, regardless of what's happening, I'm--[2866.808] [2866.808][S01] You care most about that.[2868.35] [2868.35][S02] Yeah, exactly.[2869.4] [2869.4][S01] Is there one that you think,[2871.067] [2871.067][S01]though, will have the biggest impact?[2875.45] [2875.45][S02] I think the work that we[2878.3] [2878.3][S02]are doing in understanding biology[2881.57] [2881.57][S02]and in understanding chemistry and materials, I think,[2884.81] [2884.81][S02]it's so fundamental.[2886.35] [2886.35][S01] Such root node problems.[2887.85] [2887.85][S02] Yeah.[2888.03] [2888.03][S02]There are such root node problems[2889.46] [2889.46][S02]that it's difficult to even limit what the impact would be.[2895.19] [2895.19][S02]The effects are difficult to predict.[2899.19] [2899.19][S01] Absolutely.[2900.17] [2900.17][S01]I'm very much looking forward to coming back and talking to you[2902.93] [2902.93][S01]again to see what other massive things have been happening[2906.65] [2906.65][S01]and push me.[2907.152] [2907.152][S01]Thank you very much for joining me.[2908.61] [2908.61][S02] Thank you, Hannah.[2910.027] [2910.027][S01] What I'm struck by after meeting Pushmeet is that,[2913.79] [2913.79][S01]the thing about science, it moves really slowly, and then,[2918.41] [2918.41][S01]every now and then, maybe once in a generation,[2921.51] [2921.51][S01]you get this seismic shift.[2923.79] [2923.79][S01]You get a big step forward that fundamentally[2926.9] [2926.9][S01]changes our understanding.[2928.82] [2928.82][S01]But the thing about this shift of using artificial intelligence[2932.36] [2932.36][S01]for science is that this isn't just[2934.52] [2934.52][S01]going to make a difference to physics or one equation[2937.5] [2937.5][S01]in cosmology.[2938.68] [2938.68][S01]It's that this is going to transform[2942.93] [2942.93][S01]all of science and in the most fundamental way.[2947.38] [2947.38][S01]And OK, I know that I sound like I've just[2950.1] [2950.1][S01]swallowed the hype here, but don't just[2951.96] [2951.96][S01]take my word for this.[2952.96] [2952.96][S01]Go and ask your friendly neighborhood scientist[2956.25] [2956.25][S01]what they think of what is happening here.[2959.02] [2959.02][S01]Because the people who really understand[2961.59] [2961.59][S01]what this means for the world behind the scenes,[2966.21] [2966.21][S01]they don't have words for how big this shift is[2970.26] [2970.26][S01]or is going to be.[2973.43] [2973.43][S01]You have been listening to "Google DeepMind, The Podcast"[2976.43] [2976.43][S01]with me, Professor Hannah Fry.[2978.02] [2978.02][S01]If you enjoyed that episode, do subscribe to our YouTube[2980.9] [2980.9][S01]channel, and you can also find us[2982.7] [2982.7][S01]on your podcast platform of choice.[2985.35] [2985.35][S01]Now, we have lots more to explore[2987.05] [2987.05][S01]on this podcast, including a deep dive[2989.18] [2989.18][S01]into how AI could enhance education.[2992.26] [2992.26][S01]Plus, should AI assistants be given human traits?[2996.09] [2996.09][S01]That is coming up soon on "Google DeepMind, The Podcast."[2999.14] [2999.14][MUSIC PLAYING][3002.19]