[0.0][S02] Welcome to "Google DeepMind: The Podcast" with me,[2.97] [2.97][S02]your host, Professor Hannah Fry.[4.61] [4.61][S02]We are well into the era of generative AI now.[7.585] [7.585][S02]Just a few years ago, it was very[8.96] [8.96][S02]difficult to imagine the AI could successfully[12.11] [12.11][S02]produce stunning videos or compose symphonies,[15.05] [15.05][S02]or make art in the style of great Dutch masters,[17.94] [17.94][S02]or write a pacey thriller that has on the edge of your seat.[20.99] [20.99][S02]But today, all of this has come to pass.[24.09] [24.09][S02]And the limits of what machines can achieve[26.33] [26.33][S02]in the creative endeavors are being[28.67] [28.67][S02]nudged forwards all the time.[31.44] [31.44][S02]But is AI capable of true creativity?[35.13] [35.13][S02]Well, one person who has been dreaming about this moment[37.76] [37.76][S02]for decades is Doug Eck.[39.87] [39.87][S02]He is a pioneer in the world of generative AI,[42.53] [42.53][S02]and most recently oversaw the release[44.24] [44.24][S02]of Google's state-of-the-art video and image generation[46.94] [46.94][S02]models.[47.54] [47.54][S02]At Google DeepMind, Doug is a senior research director[50.03] [50.03][S02]who leads research across generative media.[52.83] [52.83][S02]He's also a musician.[53.75] [53.75][S02]And when he's not playing piano or guitar,[55.5] [55.5][S02]you might find him pondering some big questions[57.8] [57.8][S02]about the social purpose of art and whether an AI will ever[61.74] [61.74][S02]win an Oscar, both of which seem like excellent questions[64.935] [64.935][S02]for our conversation.[65.81] [65.81][THEME MUSIC][69.094] [73.32][S02]How long have you been working in this space.[75.657] [75.657][S01] I guess I can say with some pride,[77.49] [77.49][S01]I'm OG in this space.[79.24] [79.24][S01]Yeah, back in 2000, 2001, I was trying[84.12] [84.12][S01]to get recurrent neural networks to play jazz and blues music.[88.38] [88.38][S01]We didn't have data, we didn't have compute,[90.73] [90.73][S01]but we had passion.[92.02] [92.02][S01]That's what we had.[92.94] [92.94][S01]And in 2015 and 2016, I created a project[96.6] [96.6][S01]that I'm very proud of called Magenta, which stands[99.51] [99.51][S01]for Music and Art Generation.[100.72] [100.72][S01]And we were one of the early teams exploring[102.78] [102.78][S01]the creative aspects of generative AI.[106.98] [106.98][S01]And we were at the phase that the technology there[110.28] [110.28][S01]was quite young, and so I've been[112.41] [112.41][S01]thinking about these issues for a long time.[115.06] [115.06][S01]And I'm very, very excited to see where we are now.[118.377] [118.377][S01]It's like technology is starting to catch up[120.21] [120.21][S01]with my own personal aspirations, which is so cool.[123.85] [123.85][S01]If this happened 10 years or 15 years later and I were retired,[126.58] [126.58][S01]I would just be like, god, I missed it.[128.205] [128.205][S01]So I feel so excited to be here, able to do it[130.169] [130.169][S01]while it's happening.[130.69] [130.69][S02] Well, in terms of your personal aspirations, then,[132.56] [132.56][S02]was this the kind of thing that you wanted to happen back[135.04] [135.04][S02]when you were younger?[137.022] [137.022][S01] In some ways it outstrips,[138.65] [138.65][S01]I think we've done more than I thought was possible.[141.17] [141.17][S01]I really have to say that.[142.55] [142.55][S01]I think what we've seen with models like transformer[148.27] [148.27][S01]and diffusion and a bunch of other stuff[151.09] [151.09][S01]has really moved the field faster than I had imagined.[153.95] [153.95][S01]This hasn't been like a gradual, slow climb.[158.42] [158.42][S01]We've hit a step change in the last five years.[162.43] [162.43][S01]And then it's unblocked a bunch of potential.[165.02] [165.02][S01]And I think it's the combination of compute--[167.03] [167.03][S01]it's a combination of a lot of things,[168.613] [168.613][S01]but things didn't move very much and then they moved a lot.[171.69] [171.69][S01]And I think that's important to point out.[173.44] [173.44][S01]So now we're trying to figure out this technology.[175.945] [175.945][S01]It's really exciting.[176.82] [176.82][S02] I guess if we are asking[179.32] [179.32][S02]whether AI can be creative, it probably makes sense for us[181.81] [181.81][S02]to try and define what we mean by creativity[184.33] [184.33][S02]in the first place.[185.86] [185.86][S02]Can you?[187.16] [187.16][S01] You had to ask.[188.45] [188.45][S01]Yes, the creativity question.[190.95] [190.95][S01]I think a lot of people talk about creativity with a capital[193.45] [193.45][S01]C versus a lowercase c.[196.06] [196.06][S01]Clearly, AI models are capable of creating[199.45] [199.45][S01]new samples that look novel to us,[202.57] [202.57][S01]that surprise us, that delight us.[206.11] [206.11][S01]I think in that case, the question's[209.02] [209.02][S01]answered, and has been answered for probably a decade, yes.[213.37] [213.37][S01]On the other hand, we talk about genuinely new ideas, genuinely[218.62] [218.62][S01]new genre, for example.[221.45] [221.45][S01]I think, no, we're not there right now.[223.7] [223.7][S01]We're not seeing AI models really move the art field yet[227.5] [227.5][S01]like that.[228.64] [228.64][S01]Furthermore, I think there's a social component to creativity[232.12] [232.12][S01]with a capital C that matters a lot to me, which[234.25] [234.25][S01]is I care about the people that are creating the art that I[240.1] [240.1][S01]consume, whether it's a painting or whether it's music.[243.28] [243.28][S01]One of my favorite examples of this is, for a while,[246.76] [246.76][S01]AC/DC disappeared from the streaming services.[250.698] [250.698][S01]This was a long time ago.[251.74] [251.74][S01]This has been probably 15 years ago.[254.2] [254.2][S01]OK, so my son and I listened to "Back in Black" together.[256.575] [256.575][S01]It was one of these things, my son who[258.158] [258.158][S01]was like seven at the time.[259.31] [259.31][S01]And so AC/DC disappeared, and it was replaced in the streaming[262.9] [262.9][S01]services by this AC/DC cover band that[264.94] [264.94][S01]managed to game the system.[266.415] [266.415][S01]So if you searched for "Back in Black,"[268.04] [268.04][S01]their album came up and got played.[269.498] [269.498][S01]And it had a similar cover, and so I didn't notice.[271.72] [271.72][S01]And I heard it, and it was just horrible[276.19] [276.19][S01]because I know the voices of the actual singers that[279.85] [279.85][S01]made the music.[280.64] [280.64][S01]It's real to me.[281.6] [281.6][S01]And my son was like, this is the same thing.[282.92] [282.92][S01]What do you mean, Dad?[283.62] [283.62][S01]This is it.[284.02] [284.02][S01]This is the music.[284.69] [284.69][S01]And so for him, he didn't have the social connection[286.857] [286.857][S01]to the artist at his age.[288.08] [288.08][S01]It was almost like a bit perfect "Back in Black" cover.[291.62] [291.62][S01]But for me, it was utterly appalling.[294.11] [294.11][S01]And I think when we talk about art and creativity,[296.697] [296.697][S01]we talk about the people that made it,[298.28] [298.28][S01]because really what matters to us is a connection.[300.29] [300.29][S01]There's a connection to us.[301.415] [301.415][S01]If we had a that could make the 13th Beatles album,[306.79] [306.79][S01]then it could make the 14th Beatles album.[309.31] [309.31][S01]Then it could make the 114th Beatles album, et cetera,[311.87] [311.87][S01]et cetera.[312.452] [312.452][S01]And you have a kind of puzzle here.[313.91] [313.91][S01]It's like, no, that's not what the Beatles are.[316.48] [316.48][S01]They're a point in time.[318.22] [318.22][S01]They're the 1960s and 1970s.[320.72] [320.72][S01]They're my childhood.[321.838] [321.838][S01]They're John and Paul and George and Ringo.[323.63] [323.63][S01]And that matters a lot.[324.89] [324.89][S01]I think that matters tremendously.[326.39] [326.39][S01]I don't think that we should rule out[329.47] [329.47][S01]a future where there are AI agents that we grow[333.7] [333.7][S01]to appreciate and follow, and that move the field[336.7] [336.7][S01]and that appear on podcasts with professors.[340.69] [340.69][S01]But we're not there right now.[342.76] [342.76][S01]And I think if we talk too long about creativity,[345.1] [345.1][S01]and leave behind the social part of art,[348.148] [348.148][S01]we're really missing the point.[349.44] [349.44][S02] That's so interesting.[350.66] [350.66][S02]I mean, we've got quite deep quite quickly here, haven't we?[353.315] [353.315][S01] Sorry, I just had my coffee.[354.898] [354.898][LAUGHTER][355.435] [355.435][S02] But I like that idea that art[358.45] [358.45][S02]is something about communicating the human experience.[361.88] [361.88][S02]And actually, that cannot be replicated, at least for now.[365.45] [365.45][S01] Agreed.[366.8] [366.8][S01]And there's a ton of thought experiments.[369.93] [369.93][S01]One is, even if you talk about human-created art,[372.69] [372.69][S01]if you had a time machine--[373.89] [373.89][S01]by the way, if you have a time machine,[374.85] [374.85][S01]there are other things that you might want to do.[376.11] [376.11][S01]But let's say you have a time machine.[377.91] [377.91][S01]One thing you could do is take an Andy Warhol painting and ship[381.11] [381.11][S01]it back to 13th century France.[382.74] [382.74][S01]And I argue-- and most people would argue intuitively,[384.99] [384.99][S01]that would just make no sense.[386.69] [386.69][S01]What is this Campbell's Soup can that we're looking at?[389.158] [389.158][S01]Who the hell is Marilyn Monroe?[390.45] [390.45][S01]And so artists are constantly, like pointing to culture,[393.48] [393.48][S01]making comments about culture, moving culture.[396.96] [396.96][S01]And these cultural questions are all, right now,[400.38] [400.38][S01]created by, driven by responded to by us.[403.79] [403.79][S02] So the social side of art[405.98] [405.98][S02]and the importance of that, I want[407.44] [407.44][S02]to come back to that in a bit.[408.69] [408.69][S02]But it's almost like you're splitting it into two[410.66] [410.66][S02]different categories there.[411.785] [411.785][S02]There's the originality of just it hasn't existed before.[415.77] [415.77][S02]But then there's the quality and depth of the ideas[418.67] [418.67][S02]and the originality of that.[420.568] [420.568][S02]Is that the--[421.11] [421.11][S01] Yeah, I think so.[422.235] [422.235][S01]And if we go back to the move away from the culture question[425.49] [425.49][S01]and move to the science, we've been[428.01] [428.01][S01]able to generate great images with AI for quite some time.[432.1] [432.1][S01]What we haven't had is controllability.[434.22] [434.22][S01]So the ability for a user to type in a prompt,[437.02] [437.02][S01]for example, and actually get the image[438.96] [438.96][S01]that they asked for, that is one of the major parts[441.24] [441.24][S01]of the breakthrough that we've seen with transformer models[444.06] [444.06][S01]and with diffusion models, this idea of controllability.[446.393] [446.393][S02] Perfect.[447.25] [447.25][S01] But that ability to express ourselves,[450.0] [450.0][S01]coupled with the power of AI, is where we're[452.252] [452.252][S01]seeing something interesting.[453.46] [453.46][S01]So I want the AI to be really powerful[454.71] [454.71][S01]and to do really cool things for me,[456.31] [456.31][S01]but I've got to be able to control it somehow.[458.28] [458.28][S01]Or otherwise, frankly, it just becomes pretty boring[459.9] [459.9][S01]pretty fast.[460.395] [460.395][S02] Actually, in a lot of ways[461.978] [461.978][S02]there, the creativity remains in the human rather than in the AI[465.645] [465.645][S02]alone.[466.145] [466.145][S01] Yes, but yes/and.[468.18] [468.18][S01]That doesn't mean that the technology doesn't matter.[471.21] [471.21][S01]There's a marriage between the technology and the artist[476.77] [476.77][S01]that is incredibly important.[478.87] [478.87][S01]And for me, what I do is music.[482.682] [482.682][S01]I don't like claim to be a great musician, because I'm not.[485.14] [485.14][S01]So I'm well-aligned.[486.9] [486.9][S01]But really, I'm addicted to the flow[490.56] [490.56][S01]state that comes from improvising[492.48] [492.48][S01]on piano or on guitar.[494.31] [494.31][S01]And there's a point where cognitively, you[498.3] [498.3][S01]forget that this piano is not you, really.[501.27] [501.27][S01]Or the electric guitar, you forget that it's not you,[505.29] [505.29][S01]and it's this extension of you.[506.77] [506.77][S01]And that is beautiful.[508.84] [508.84][S01]That's technology giving rise to new forms of self-expression.[515.36] [515.36][S01]We're trying to get that with AI, at least I am.[517.97] [517.97][S01]That's my goal.[518.99] [518.99][S01]The goal is, can we build a new way[521.33] [521.33][S01]that you can take these ideas that are in your brain[523.752] [523.752][S01]and get them out there?[524.71] [524.71][S02] Do you reckon that's possible, though, to have it[527.66] [527.66][S02]where you are so connected with an AI, where you are[531.26] [531.26][S02]so in control of it, that you cease to realize that it's not[534.68] [534.68][S02]just an extension of your body?[536.695] [536.695][S01] I do think it's possible.[538.71] [538.71][S01]And I think it's even happening now with some of the stuff[541.513] [541.513][S01]that we're working on.[542.43] [542.43][S01]And I also don't want to mystify that.[545.738] [545.738][S01]I think one of the challenges with AI[547.28] [547.28][S01]is getting that fit right--[549.69] [549.69][S01]fitting the AI to the way that we as humans[554.24] [554.24][S01]understand the art that we're trying to create.[556.65] [556.65][S01]I think later, we may see entirely[558.62] [558.62][S01]new genre or entirely new forms of art come up.[561.99] [561.99][S01]There may be a new word that is not music and is not[564.89] [564.89][S01]painting and is not photography, and is not film or movie-making[569.48] [569.48][S01]And that AI will have helped us create it.[571.4] [571.4][S01]We're not there yet.[572.3] [572.3][S01]Where we are now is living in our current genre.[575.41] [575.41][S01]So we're using AI to make images.[577.36] [577.36][S01]We're using AI to make music, using AI to make video.[581.55] [581.55][S01]But even then, back to your question,[583.87] [583.87][S01]I think it's already possible now,[586.26] [586.26][S01]as we're making music models faster,[588.58] [588.58][S01]we're able to get closer and closer to real time.[591.33] [591.33][S01]And you can think about-- we're not quite there[594.3] [594.3][S01]yet, that you might be able to, as a musician,[596.26] [596.26][S01]play your guitar along with the AI.[598.08] [598.08][S01]Like, it's a kind of smart looper pedal,[600.96] [600.96][S01]a pedal that keeps looping.[602.422] [602.422][S01]And that's the simplest example.[603.755] [603.755][S02] Yeah, I mean, I really like[605.52] [605.52][S02]that as a view for the future, because I guess now[607.65] [607.65][S02]where we're at is that it's really[609.9] [609.9][S02]a case of a human curating, and then instructing and honing[614.58] [614.58][S02]and refining, rather than necessarily[616.14] [616.14][S02]that the whole process is seamlessly integrated[619.052] [619.052][S02]with one another.[619.76] [619.76][S01] I think that's right.[621.052] [621.052][S01]I think I would say transformation, curation,[623.85] [623.85][S01]and then creation would be the three[625.71] [625.71][S01]that I would think are out there right now.[627.93] [627.93][S01]An example of transformation is, I took a photograph,[630.48] [630.48][S01]I transform it into something else[632.31] [632.31][S01]that I love even more using AI.[634.29] [634.29][S01]That's transformation.[635.73] [635.73][S01]Curation--[636.54] [636.54][S02] Face filter on.[637.26] [637.26][S02]Make myself look better.[638.075] [638.075][S01] Exactly Yeah, that's right.[639.39] [639.39][S01]Good old-fashioned Instagram.[640.598] [640.598][S01]I'm a big fan.[642.23] [642.23][S01]Curation, the AI is allowing me to play around in a space[646.04] [646.04][S01]and suggest things to me that might surprise me and be useful,[649.11] [649.11][S01]and creation.[650.07] [650.07][S01]So just actually creating something new from a prompt.[652.44] [652.44][S01]So that's the hardest I think to do well.[655.405] [655.405][S02] But then, OK, that question of originality--[657.98] [657.98][S02]is AI actually creating things that are original?[662.39] [662.39][S02]Or is it in some ways just mimicking what exists already?[665.105] [665.105][S01] I think it's allowing us to create something original.[667.86] [667.86][S01]I think we have to admit that.[669.17] [669.17][S01]Even though these models are trained on data from our world--[675.29] [675.29][S01]I've often said they are reflecting our world.[678.72] [678.72][S01]But they're like funhouse mirrors,[680.64] [680.64][S01]so they're not reflecting our world perfectly[682.94] [682.94][S01]with a perfectly flat plane.[684.66] [684.66][S01]And in doing so, these funhouse mirrors[687.14] [687.14][S01]can actually give rise to some very, very interesting[689.63] [689.63][S01]new materials.[690.42] [690.42][S01]And I think we have to admit that those materials are[692.72] [692.72][S01]actually new and transformative and giving us[695.51] [695.51][S01]new things to work with.[696.845] [696.845][S02] The descriptions that we're using here[698.928] [698.928][S02]are quite like, I sort know sort creativity when I see it almost.[702.24] [702.24][S02]Is it, isn't it?[703.112] [703.112][S02]You can debate it, you can't.[704.32] [704.32][S02]Do you have ways that you measure creativity in AI.[707.22] [707.22][S01] I mean, we can certainly measure the--[711.1] [711.1][S01]yeah, we can use distance measures.[713.7] [713.7][S01]The basic idea is to take the--[717.28] [717.28][S01]I mean, you could do a pixel-by-pixel distance[719.2] [719.2][S01]of an image and another image and that might not--[721.0] [721.0][S02] See how similar they are to one another or how[722.44] [722.44][S02]far apart.[723.07] [723.07][S01] Then you realize the pixels[724.12] [724.12][S01]might be scaled differently and things like that.[725.75] [725.75][S01]So you move to more sophisticated measures.[727.79] [727.79][S01]We can measure the distance from the training set and things[730.93] [730.93][S01]like that.[731.92] [731.92][S01]We can measure something called recitation,[733.94] [733.94][S01]which is the model regurgitating chunks of the training set.[739.3] [739.3][S01]And so we have ways to do it.[741.53] [741.53][S01]I think we still really need to rely upon human evaluation[746.08] [746.08][S01]to understand this well.[748.13] [748.13][S01]I mean, we're quite good at this,[750.01] [750.01][S01]and machines end up being quite middling.[752.99] [752.99][S01]They're not perfect at this.[754.46] [754.46][S01]But yeah, I think we have ways to measure[756.58] [756.58][S01]the distance from how novel the outputs are, for sure.[762.91] [762.91][S02] OK, so I wanted to ask you, actually,[765.67] [765.67][S02]when AlphaGo happened, there was that very famous move[770.3] [770.3][S02]37 in the match with Lee Sedol, which really shocked all[774.25] [774.25][S02]the Go experts and the AI researchers, because it was,[777.8] [777.8][S02]I think, really outside of traditional Go strategies.[781.37] [781.37][S02]Has there been an equivalent like that[785.14] [785.14][S02]in your field of creativity?[787.37] [787.37][S02]Is there something where you're like, that is[789.25] [789.25][S02]a moment of creativity, which I'm really surprised by?[792.05] [792.05][S01] I haven't seen anything yet[793.75] [793.75][S01]where I feel like there was this perfect and brilliant marriage[796.69] [796.69][S01]of artist and AI.[798.5] [798.5][S01]I've seen suggestive work, certain visual artists[802.63] [802.63][S01]who were just generating stuff I really love.[804.91] [804.91][S01]I'm sure there are lots of artists out there[806.743] [806.743][S01]who are like, wait, I had my move 37.[808.33] [808.33][S01]Here it is.[808.83] [808.83][S01]I'd love to hear from them.[810.43] [810.43][S01]But no, I think we're just tantalizingly close.[813.17] [813.17][S01]I mean, my mind has been blown multiple times.[815.735] [815.735][S01]My mind was blown the first time that I[817.36] [817.36][S01]heard some of the music compositions sung[821.187] [821.187][S01]by Music Transformer, which was a project I was part of.[823.52] [823.52][S01]I felt like it was the first time[825.02] [825.02][S01]we've heard a neural network able to make[830.42] [830.42][S01]insanely interesting music.[831.885] [831.885][S02] We're not just talking about music here,[833.67] [833.67][S02]though, right?[834.26] [834.26][S02]I mean, this is in lots of different spaces[836.72] [836.72][S02]that you're hoping AI will be creative.[838.868] [838.868][S01] Of course, yes.[839.91] [839.91][S02] So tell me about some of the models.[841.91] [841.91][S01] Let's talk about the visual world.[844.38] [844.38][S01]Let's talk about image generation and video generation.[847.29] [847.29][S01]So I have my laptop here for you.[848.93] [848.93][S01]The first thing I wanted to show you[850.43] [850.43][S01]was a still, an image done by Imagen 3, which is our most[855.41] [855.41][S01]capable image generation model.[857.73] [857.73][S02] OK, so we have got a mixed landscape, some sort[860.97] [860.97][S02]of grass, it's a bit sandy in the background, a bit[864.3] [864.3][S02]rocky and mountainous.[865.51] [865.51][S02]And then there are three very beautifully composed[868.86] [868.86][S02]hot air balloons, which look extremely realistic.[873.22] [873.22][S02]The lighting is consistent across all three of them.[876.79] [876.79][S02]So the lights come from one particular direction[878.94] [878.94][S02]in the shot.[879.73] [879.73][S02]It looks at a glance very, very real.[883.64] [883.64][S02]I think when you look really, really carefully, you might say,[887.19] [887.19][S02]those rocks are perhaps a little bit pointier[889.56] [889.56][S02]than you might expect in reality.[891.28] [891.28][S02]But at a glance, it's extremely convincing as real.[894.335] [894.335][S01] And in fact, I would--[896.37] [896.37][S01]I'm glad you noticed the pointy rocks, because they're actually[899.5] [899.5][S01]in some sense real as well.[900.84] [900.84][S01]So this prompt is--[902.58] [902.58][S01]I'll read the entire prompt.[903.78] [903.78][S01]"Shot in the style of DLSR camera[906.9] [906.9][S01]with the polarizing filter.[910.18] [910.18][S01]A photo of three hot air balloons floating over[912.82] [912.82][S01]the unique rock formations in Cappadocia, Turkey."[915.98] [915.98][S01]It turns out those rock formations look pointy, just[919.36] [919.36][S01]like this.[919.92] [919.92][S02] No.[920.18] [920.18][S02]Amazing.[920.68] [920.68][S01] And "the colors and patterns on these balloons[923.89] [923.89][S01]contrast beautifully against the earthy tones of the landscape[927.62] [927.62][S01]below."[928.12] [928.12][S01]It's almost a poem.[928.94] [928.94][S02] Yeah, it is.[929.63] [929.63][S01] This was done by Irina Blok on our team.[931.91] [931.91][S01]She's a genius.[932.62] [932.62][S01]We call her a prompt whisperer.[933.912] [933.912][S02] Really.[935.206] [935.206][S01] "This shot captures the sense of adventure[938.2] [938.2][S01]that comes with enjoying such an experience.[941.74] [941.74][S01]It is poetic."[942.33] [942.33][S02] That is very poetic.[943.675] [943.675][S01] I don't know if you noticed,[945.258] [945.258][S01]but the balloons are not exactly round.[947.9] [947.9][S01]They're a little bit misshapen.[950.29] [950.29][S01]Someone else pointed this out, and we looked.[952.23] [952.23][S01]That happens when the wind blows on a hot air balloon.[955.12] [955.12][S01]So these pointy rocks are actually[957.55] [957.55][S01]what it looks like in Cappadocia, Turkey.[959.75] [959.75][S01]And even these little deformations that you're seeing,[962.3] [962.3][S01]the models where we are with image generation with Imagen 3[965.71] [965.71][S01]is truly astonishing.[966.6] [966.6][S02] So I wonder, how much work is that prompt doing[972.64] [972.64][S02]in terms of the way that the prompt is written,[974.93] [974.93][S02]in terms of the style of that prompt?[976.49] [976.49][S02]Because presumably, Irina is writing[979.21] [979.21][S02]prompts all day, every day.[980.54] [980.54][S02]She's really up close and personal with these models.[982.945] [982.945][S01] Yeah.[984.13] [984.13][S02] But she's not just writing "three hot air balloons[987.26] [987.26][S02]in Turkey."[988.15] [988.15][S01] So you hit on one of the most challenging,[992.51] [992.51][S01]and I think interesting, parts of getting these models to work[995.83] [995.83][S01]well, which is how well does the model respond[998.35] [998.35][S01]to very specific prompts?[1000.72] [1000.72][S01]Prompt coherence, we call it.[1002.63] [1002.63][S01]And getting prompt coherence right[1005.06] [1005.06][S01]is utterly critical to allow someone[1006.88] [1006.88][S01]like Irina, who has this idea in her head, to get it out.[1010.22] [1010.22][S01]And so if all the model did was respond to "three hot air[1013.45] [1013.45][S01]balloons floating over some mountains,"[1015.31] [1015.31][S01]we wouldn't be able to get the kind of detail that we're seeing[1017.29] [1017.29][S01]here.[1017.79] [1017.79][S01]And she has learned and figured out the model.[1021.77] [1021.77][S01]And you could say that we should do a better job of generating[1027.069] [1027.069][S01]images of exactly this high quality, even for someone[1029.89] [1029.89][S01]who's not Irina Blok.[1030.89] [1030.89][S01]If Irina Blok is the only one that can make these images,[1032.93] [1032.93][S01]we have a problem here.[1033.98] [1033.98][S01]And in fact, it's true.[1035.27] [1035.27][S01]You can pretty quickly come up to speed on these prompts.[1038.089] [1038.089][S01]She just happens to be an artiste.[1040.78] [1040.78][S01]Let's move to video.[1042.43] [1042.43][S01]So this is a one minute and five second video from v0.[1049.47] [1049.47][S02] And v0 is the new Google model.[1052.11] [1052.11][S01] v0 is our new Google video generation model.[1054.61] [1054.61][S01]So the first, there's four prompts here.[1056.29] [1056.29][S01]"A fast-tracking shot through a bustling dystopian sprawl with[1059.58] [1059.58][S01]bright neon signs, flying cars and mist, night, lens flare,[1064.24] [1064.24][S01]volumetric lighting."[1066.79] [1066.79][S01]And then it continues to, "A fast-tracking shot through[1070.24] [1070.24][S01]a futuristic dystopian sprawl with bright neon lights[1073.03] [1073.03][S01]starships in the sky, night, volumetric lighting,"[1076.148] [1076.148][S01]slightly different.[1076.94] [1076.94][S01]So minor changes.[1078.19] [1078.19][S01]And then we bring a car in.[1079.72] [1079.72][S01]"A neon hologram of a car driving at top speed,[1083.63] [1083.63][S01]speed of light, cinematic, incredible details,[1087.74] [1087.74][S01]volumetric lighting."[1088.775] [1088.775][S01]So if I were to describe what you should see,[1090.65] [1090.65][S01]we're going to come in from the top.[1092.15] [1092.15][S01]It's a tracking shot.[1093.94] [1093.94][S01]We're going to come down from the tracking shot.[1096.41] [1096.41][S01]We're going to have this neon hologram[1098.44] [1098.44][S01]of a car driving at the speed of light, cinematic.[1101.57] [1101.57][S01]And then, "The car leaves the tunnel back into the real-world[1105.97] [1105.97][S01]city of Hong Kong."[1107.42] [1107.42][S01]So we should expect a kind of transition back to Hong Kong.[1111.34] [1111.34][S02] All right, so we're starting off.[1113.69] [1113.69][S02]There's no other way to describe it than what the prompt said.[1116.3] [1116.3][S02]You've got these buildings covered in neon lights.[1118.568] [1118.568][S02]It's very smooth, this tracking shot.[1120.11] [1120.11][S02]And then you speed up and zoom in closer and closer.[1123.08] [1123.08][S02]You're in between the buildings now.[1124.64] [1124.64][S02]Oof.[1125.14] [1125.14][S02]And then we have this car racing through the streets.[1130.32] [1130.32][S02]You can see the neon lights reflected in the wet pavement[1134.24] [1134.24][S02]below.[1135.03] [1135.03][S02]There's other cars jostling for position around.[1137.72] [1137.72][S02]And it's almost like everything is blurred because you're just[1140.96] [1140.96][S02]going so fast.[1142.68] [1142.68][S02]But it's really consistent.[1143.9] [1143.9][S02]Now it's gone through a tunnel.[1145.5] [1145.5][S02]There are these big lights overhead.[1148.31] [1148.31][S02]And it's come out of the tunnel into an extremely realistic[1153.56] [1153.56][S02]modern scene.[1154.173] [1154.173][S01] It's a wow moment.[1155.34] [1155.34][S02] That's incredible.[1155.95] [1155.95][S01] That's a wow moment.[1156.89] [1156.89][S02] And it was seamless.[1158.25] [1158.25][S02]We were following the car the entire way.[1160.075] [1160.075][S01] If that's maybe--[1162.343] [1162.343][S01]it's not quite as good as the Lisa doll.[1164.01] [1164.01][S01]But when we come out of that tunnel, I get chills.[1167.29] [1167.29][S02] So this is not only that each individual scene[1171.86] [1171.86][S02]is following the prompt.[1173.22] [1173.22][S02]It's that it's blending between the scenes too.[1175.77] [1175.77][S01] So there's what's called[1177.187] [1177.187][S01]an autoregressive component that provides coherence over time.[1180.3] [1180.3][S02] How?[1181.935] [1181.935][LAUGHS][1183.11] [1183.11][S01] The magic of AI.[1184.462] [1184.462][S02] I mean, because video is much harder than image[1186.92] [1186.92][S02]anyway, right?[1188.78] [1188.78][S02]Tell us why.[1189.9] [1189.9][S01] Well, I mean, there's two reasons why.[1191.9] [1191.9][S01]One way to look at it is, videos are[1194.43] [1194.43][S01]lots of images, somewhere between 24 and 30 frames[1196.94] [1196.94][S01]a second.[1197.58] [1197.58][S01]And so there you are.[1199.502] [1199.502][S01]For a second of video, you have to generate more.[1203.998] [1203.998][S01]But there's also this temporal coherence problem.[1206.04] [1206.04][S01]And that's the massive thing.[1207.23] [1207.23][S02] Temporal coherence, meaning[1208.43] [1208.43][S02]it's got to make sense as you go forward in time.[1210.45] [1210.45][S01] Yeah, I mean, even simple things, like someone's[1212.867] [1212.867][S01]dribbling a basketball.[1214.23] [1214.23][S01]And the basketball can't change.[1216.86] [1216.86][S01]That basketballs dribbling away.[1218.34] [1218.34][S01]And so these physics issues, like how[1221.6] [1221.6][S01]do you simulate the physics of the world in a way that[1223.94] [1223.94][S01]is reflective of the real world we live in--[1226.515] [1226.515][S02] So that you believe it is a basketball.[1228.64] [1228.64][S01] That is correct.[1229.68] [1229.68][S01]That is correct.[1230.16] [1230.16][S02] Because if it's wobbling around,[1231.24] [1231.24][S02]the edges aren't quite right or there's[1232.46] [1232.46][S02]color changes or the pattern changes,[1234.08] [1234.08][S02]or it just doesn't move in a realistic way.[1236.25] [1236.25][S02]I mean, there's lots of layers to that, then,[1237.59] [1237.59][S02]that you have to get right.[1238.44] [1238.44][S01] Absolutely true.[1239.19] [1239.19][S01]And even if you look at--[1240.232] [1240.232][S01]I really like this.[1241.34] [1241.34][S02] This is a little puppy in a bathtub[1244.52] [1244.52][S02]and it's got some suds on it.[1245.78] [1245.78][S01] Look at the suds drop.[1246.77] [1246.77][S02] My goodness.[1247.345] [1247.345][S01] It's so nice.[1248.5] [1248.5][S02] Yeah.[1249.0] [1249.0][S01] So this is the kind of coherence problem[1250.85] [1250.85][S01]I'm talking about.[1251.6] [1251.6][S01]How does the model figure out the fact-- like the way[1255.81] [1255.81][S01]that the soap suds are falling from the chin of the puppy.[1259.81] [1259.81][S01]And then at some point in time, the puppy moves its head,[1262.95] [1262.95][S01]and then it drops.[1263.772] [1263.772][S02] At which point the suds drop.[1265.48] [1265.48][S02]Yeah, now, OK, my background is in fluid dynamics.[1268.18] [1268.18][S02]I know if I tried to do that using equations--[1273.28] [1273.28][INTERPOSING VOICES][1275.37] [1275.37][S01] That's right.[1277.11] [1277.11][S02] But this is doing it just based on the images.[1279.773] [1279.773][S01] No, it's doing more than that.[1281.44] [1281.44][S01]I think it's safe to say that these models are[1284.31] [1284.31][S01]learning about the physics of the world from the videos, yes.[1287.43] [1287.43][S01]So the input are annotated videos,[1290.372] [1290.372][S01]annotated with a description of what's happening in the video.[1292.955] [1292.955][S02] Mm-hmm.[1293.747] [1293.747][S01] And the model is being presented[1295.83] [1295.83][S01]with the frames of the video.[1297.212] [1297.212][S01]That's the input.[1297.92] [1300.57][S01]In order to reproduce these videos to do good generation,[1305.2] [1305.2][S01]it seems to be helpful for the model[1307.623] [1307.623][S01]to learn about the physics of the world.[1309.29] [1309.29][S01]And so we are as excited about these models[1313.08] [1313.08][S01]as world simulators, physics simulators[1316.23] [1316.23][S01]as we are about the ability to use them to make film.[1320.07] [1320.07][S01]We can talk about simulating environments for robotics.[1323.072] [1323.072][S01]We can talk about simulating environments[1324.78] [1324.78][S01]for almost anything.[1325.808] [1325.808][S01]And to the extent that these models capture[1327.6] [1327.6][S01]real-world physics, including fluid dynamics-- which[1329.94] [1329.94][S01]you above all in the room know is hard, really hard,[1334.39] [1334.39][S01]it's quite remarkable.[1336.67] [1336.67][S01]We are thinking about 3D.[1338.44] [1338.44][S01]We're thinking about ways to provide users, filmmakers,[1342.87] [1342.87][S01]or everybody with the ability to explicitly control camera.[1345.802] [1345.802][S01]We can do it with text prompts now,[1347.26] [1347.26][S01]so there's a lot of room for improvement.[1348.7] [1348.7][S01]This is just the beginning.[1349.825] [1349.825][S01]But I think v0 and Imagen 3 and the music work really[1354.21] [1354.21][S01]does mark a watershed moment for us[1356.297] [1356.297][S01]in terms of where we are with this research.[1358.13] [1358.13][S02] How can this be used, then?[1359.755] [1359.755][S02]Because this goes beyond just creating pretty images.[1363.79] [1363.79][S01] I mean, absolutely.[1365.14] [1365.14][S01]I think the major use case here does flow from the fact[1368.675] [1368.675][S01]that these models learn something[1370.05] [1370.05][S01]about the physical world.[1371.41] [1371.41][S01]Models that, in one way or another,[1373.858] [1373.858][S01]learn about the physics of the world,[1375.4] [1375.4][S01]can be incredibly useful for scientific exploration,[1378.1] [1378.1][S01]for simulation, et cetera.[1379.183] [1379.183][S01]So I think there's a whole scientific discovery[1381.142] [1381.142][S01]directionality around this work.[1382.66] [1382.66][S01]There's also the creative aspects,[1384.69] [1384.69][S01]which we spent a lot of time talking about.[1386.7] [1386.7][S01]Those are the two major areas that I see.[1389.49] [1389.49][S02] Because I think for a lot of people,[1391.93] [1391.93][S02]this image generation, the video generation, even the music[1394.5] [1394.5][S02]generation, feels like it's come very quickly from nowhere.[1398.47] [1398.47][S02]But you have been dreaming about this for a long time.[1401.625] [1401.625][S01] Yeah, actually, I have.[1403.0] [1403.0][S01]So some kids will talk about professional soccer players.[1405.838] [1405.838][S01]They'll be like, I just was kicking a soccer ball[1407.88] [1407.88][S01]from the time I was, like, two, or I've been on ice skates.[1411.12] [1411.12][S01]I was just a weird kid.[1412.66] [1412.66][S01]I was just, like, so fascinated by music.[1417.19] [1417.19][S01]I grew up in the town of South Bend, Indiana.[1421.47] [1421.47][S01]I grew up in a working-class family, very happy.[1423.765] [1423.765][S01]Very proud of growing up in a working-class family.[1425.89] [1425.89][S01]We actually didn't have a lot of money.[1427.515] [1427.515][S01]And I was asking for a piano, and I got a trumpet instead[1430.537] [1430.537][S01]to do trumpet lessons because it was easier to rent a trumpet.[1433.12] [1433.12][S01]It's true, it's true.[1434.14] [1434.14][S01]But I remember I was even younger--[1436.53] [1436.53][S01]five or six years old.[1437.74] [1437.74][S01]My next door neighbors had a player piano.[1442.57] [1442.57][S01]I mean, if you've never seen a player[1445.21] [1445.21][S01]piano from the turn of the century,[1447.31] [1447.31][S01]it has foot pedals that you pump back and forth.[1450.89] [1450.89][S01]And those control bellows send air[1453.79] [1453.79][S01]through the system that then moves the air through a paper[1459.16] [1459.16][S01]roll that has hole's cut in it.[1460.97] [1460.97][S01]And when the air is moved through the roll, when[1463.27] [1463.27][S01]the roll has a hole in it, that causes the corresponding key[1466.18] [1466.18][S01]to play.[1468.09] [1468.09][S01]And I just remember just asking my mom,[1471.43] [1471.43][S01]can we go to Ed and Jean's house I want to see the player piano.[1474.17] [1474.17][S01]I was fascinated beyond--[1476.55] [1476.55][S01]like kids, candy store.[1478.62] [1478.62][S01]Me, Doug, player piano next door neighbor.[1481.26] [1481.26][S01]It's almost bringing tears to my eyes to think about it now.[1483.76] [1483.76][S01]So I have been thinking about the marriage of--[1485.8] [1485.8][S01]I didn't even know it until people I brought this.[1488.38] [1488.38][S01]This thought came to mind when you asked this question.[1489.91] [1489.91][S01]It's like, yeah, I've been thinking[1491.368] [1491.368][S01]about the interplay between technology and music[1495.95] [1495.95][S01]since I was, like, five.[1496.95] [1496.95][S02] But there is something[1497.91] [1497.91][S02]quite magical about a machine playing music on its own.[1500.11] [1500.11][S01] It's so cool.[1500.74] [1500.74][S01]It's so cool.[1501.31] [1501.31][S01]Yeah.[1501.57] [1501.57][S02] Well, then, talk to me a bit about some of the stuff[1503.34] [1503.34][S02]that you've been playing around with in the last five years.[1505.84] [1505.84][S01] I have been spending a lot of time thinking[1508.8] [1508.8][S01]about the interplay between these technologies,[1513.637] [1513.637][S01]how do they work together.[1514.72] [1514.72][S01]I don't need to be in the room for v0[1516.87] [1516.87][S01]to get better or for Imagen to get better.[1520.54] [1520.54][S01]There's so many great people working on this.[1522.97] [1522.97][S01]But how do we actually put the pieces together?[1526.41] [1526.41][S01]For example, I've been thinking a lot about Pixar.[1532.21] [1532.21][S01]Pixar did something really beautiful.[1534.35] [1534.35][S01]They took this technology and they turned it[1538.99] [1538.99][S01]into a bunch of astonishing movies.[1541.16] [1541.16][S01]And I don't think that we can follow the Pixar playbook.[1544.28] [1544.28][S01]But I think we should be aware of what it really[1546.28] [1546.28][S01]takes to move technology into everybody's lives[1549.97] [1549.97][S01]with these movies.[1551.77] [1551.77][S01]And how do we do that with AI?[1555.01] [1555.01][S01]I think of heavier-than-air flight, right.[1557.83] [1557.83][S01]What is it, 65 years, 60-something years--[1561.91] [1561.91][S01]less than 70 years between the first flight and landing[1565.87] [1565.87][S01]on the moon.[1566.63] [1566.63][S01]And if you look at AI, we've been doing[1570.49] [1570.49][S01]this for at least 30 years.[1571.91] [1571.91][S01]And so what happens 30 years into this other industry?[1576.35] [1576.35][S01]You've already figured out flight.[1579.35] [1579.35][S01]What you're thinking about now are creating airlines.[1581.82] [1581.82][S01]You're thinking about how do you actually[1583.528] [1583.528][S01]move people around the world, how do you connect people?[1585.978] [1585.978][S01]Like, you're solving for something much different[1588.02] [1588.02][S01]than aeronautics?[1589.59] [1589.59][S01]You're still solving for aeronautics.[1591.36] [1591.36][S01]You have to keep solving for aeronautics.[1592.48] [1592.48][S02] But it's a level beyond.[1593.46] [1593.46][S01] But it's a level beyond that.[1595.085] [1595.085][S01]How can we do something that matters.[1597.48] [1597.48][S01]And to do something that matters is[1598.94] [1598.94][S01]going to require not just building[1600.38] [1600.38][S01]these models, but actually figuring out how they fit--[1603.45] [1603.45][S01]how they fit in society, how they fit for users.[1605.687] [1605.687][S01]What are we actually building here?[1607.145] [1607.145][S02] I think there's something really important[1609.395] [1609.395][S02]in that.[1610.168] [1610.168][S02]I think it's something that's the forefront[1611.96] [1611.96][S02]of a lot of people's minds, which is, what does this[1614.6] [1614.6][S02]mean for people?[1616.372] [1616.372][S02]Because there are some people, of course, who are welcoming[1618.83] [1618.83][S02]these changes with open arms.[1620.34] [1620.34][S02]But there are other people who are maybe[1622.64] [1622.64][S02]a bit more reluctant or concerned about it.[1627.245] [1627.245][S01] Agreed.[1629.08] [1629.08][S01]So first, I share these concerns.[1631.88] [1631.88][S01]I think we need to be very cautious.[1633.38] [1633.38][S01]We've been working on this for a long time.[1636.155] [1636.155][S01]We've been thinking about it for a long time.[1638.03] [1638.03][S01]In some ways, we've moved slowly in this space[1640.81] [1640.81][S01]because we realize how much impact we can have.[1645.407] [1645.407][S01]And we're trying to listen to the community.[1647.24] [1647.24][S01]So we have spent not months or weeks,[1650.93] [1650.93][S01]but years working with artists, listening to musicians,[1654.83] [1654.83][S01]listening to visual artists, thinking even about theater,[1657.62] [1657.62][S01]about filmmaking, trying to understand how this work fits[1660.24] [1660.24][S01]in.[1660.74] [1660.74][S01]And for me, it's some of the most rewarding.[1662.53] [1662.53][S01]Maybe the most rewarding part of what I'm doing[1664.488] [1664.488][S01]is working with these communities[1666.04] [1666.04][S01]and trying to understand what can we[1668.5] [1668.5][S01]do that's great for these communities and for everyone.[1673.06] [1673.06][S01]And so we take that responsibility very seriously.[1675.465] [1675.465][S02] So what can you do, then?[1677.18] [1677.18][S02]Because of course, the training of these models[1679.36] [1679.36][S02]is based on intellectual property[1681.19] [1681.19][S02]that's created by humans.[1683.21] [1683.21][S02]How can you negotiate that issue?[1687.16] [1687.16][S01] So we're thinking about ways[1690.1] [1690.1][S01]of identifying, watermarking, attributing[1694.27] [1694.27][S01]the outputs of our models back to the creators.[1697.542] [1697.542][S01]And this remains a very hard and open question.[1699.5] [1699.5][S01]But where we want to be, we want to help[1701.47] [1701.47][S01]build new ways to compensate artists for their work.[1706.28] [1706.28][S01]I think it's incredibly important to get this right.[1708.65] [1708.65][S01]And also, we're using technologies like SynthID[1713.74] [1713.74][S01]to watermark and make sure that we can identify this work as it[1716.74] [1716.74][S01]moves through the ecosystem.[1718.79] [1718.79][S01]I'm a musician.[1719.75] [1719.75][S01]I have work up on SoundCloud.[1721.7] [1721.7][S01]I won't tell you how to find it.[1723.22] [1723.22][S01]Even though it's, like, five songs, they're mine.[1725.44] [1725.44][S02] I think I found it already, actually.[1727.482] [1727.482][S02]I actually did, some of the AI music you did in lockdown.[1730.1] [1730.1][S01] Yeah yeah, yeah.[1730.9] [1730.9][S02] I found it.[1731.53] [1731.53][S01] Good for you.[1732.488] [1732.488][LAUGHS][1733.18] [1733.18][S01]I'm going to get 50-- or I might get 100 hits after this is over.[1736.69] [1736.69][S01]But I feel very strongly that we need[1738.25] [1738.25][S01]to protect that creative intellectual property[1742.78] [1742.78][S01]ownership of that work.[1744.235] [1744.235][S02] So what might that look like in future?[1746.57] [1746.57][S02]Is it where you give royalties to people whose[1750.23] [1750.23][S02]work is included in the models?[1751.62] [1751.62][S02]I mean, how does it work?[1752.455] [1752.455][S01] So I think there's a couple[1754.04] [1754.04][S01]of ways to think about it.[1755.123] [1757.98][S01]These are all-- so first, let me say,[1762.8] [1762.8][S01]we're adding building blocks towards a solution, which[1766.49] [1766.49][S01]is a protocol that allows a website to annotate what works[1771.83] [1771.83][S01]are OK to train on for AI.[1774.03] [1774.03][S01]And you know what?[1774.78] [1774.78][S01]I'm fine if someone uses that to completely say nothing[1777.38] [1777.38][S01]in my website is OK to trace.[1778.61] [1778.61][S01]Totally cool with me.[1779.49] [1779.49][S01]Other people are going to want to be part of that.[1781.573] [1781.573][S01]They're going to want to see their work show up there.[1784.55] [1784.55][S01]So that's one thing.[1785.82] [1785.82][S01]Then how do you take work and put it[1788.99] [1788.99][S01]in the system in a way that makes it really clean and easy[1793.31] [1793.31][S01]to do attribution?[1794.31] [1794.31][S01]And that's the hard problem.[1796.16] [1796.16][S01]So one technology that's out there[1799.01] [1799.01][S01]is called retrieval-based generation.[1802.95] [1802.95][S01]So imagine what you have is a system that[1805.46] [1805.46][S01]in general knows about the physics of the visual world.[1809.12] [1809.12][S01]It's trained on licensed data.[1810.99] [1810.99][S01]It's trained on generic data.[1815.43] [1815.43][S01]But now you're an artist with a very interesting and vivid[1818.85] [1818.85][S01]visual style.[1820.74] [1820.74][S01]Maybe we can retrieve your work.[1824.31] [1824.31][S01]By that, I mean your work is represented with your permission[1827.97] [1827.97][S01]in some database that has some vectors of numbers that would[1830.97] [1830.97][S01]allow us to use your style.[1832.72] [1832.72][S01]That retrieval yields some vectors that we're[1835.56] [1835.56][S01]attending to when we generate.[1837.18] [1837.18][S01]And now we generate an image that is very clearly reflecting[1841.68] [1841.68][S01]your style.[1842.91] [1842.91][S01]Well, the attribution issues become quite simple now.[1846.03] [1846.03][S01]We retrieved your work with your permission.[1849.81] [1849.81][S01]We can annotate that image as being generated with your style.[1854.41] [1854.41][S01]And then that opens the door for marketplaces.[1856.776] [1856.776][S02] So I guess my second follow-up question[1859.5] [1859.5][S02]to that is, has the horse not already bolted?[1864.3] [1864.3][S02]These models are already producing outputs[1867.15] [1867.15][S02]based on the work of artists.[1868.7] [1868.7][S01] I think that we already have lots of ways[1876.24] [1876.24][S01]that we could get this wrong.[1878.1] [1878.1][S01]I really think a lot about what happened with music[1883.83] [1883.83][S01]during the MP3 revolution.[1885.61] [1885.61][S01]And then we had LimeWire, and then we[1887.31] [1887.31][S01]had Napster, maybe not in that order, and BitTorrent.[1891.51] [1891.51][S01]And this was not good for music artists at all.[1895.68] [1895.68][S01]And one example of something that I[1898.41] [1898.41][S01]think worked well in at that point in time was YouTube.[1903.0] [1903.0][S01]And I'm actually I'm quite proud of what we did,[1907.68] [1907.68][S01]because I was a very small tangential part of it[1909.75] [1909.75][S01]as an audio researcher.[1910.75] [1910.75][S02] In the sense that it allowed artists[1912.75] [1912.75][S02]to connect with their audiences on their own terms.[1915.415] [1915.415][S01] Yeah, so what we did was[1917.487] [1917.487][S01]we created a user experience that was better[1919.32] [1919.32][S01]than BitTorrent, built an entire takedown[1921.45] [1921.45][S01]mechanism for infringing content,[1923.2] [1923.2][S01]built the Creator Network that not only connects creators[1926.07] [1926.07][S01]to their fans, but allows them to build their careers[1930.06] [1930.06][S01]and monetize their careers.[1931.47] [1931.47][S01]And now it's a quite thriving ecosystem,[1933.175] [1933.175][S01]and it's not the only ecosystem out there.[1935.21] [1935.21][S01]So we should hope to see ecosystems[1938.38] [1938.38][S01]that are like YouTube.[1941.81] [1941.81][S01]They're not YouTube maybe, but maybe they are.[1943.777] [1943.777][S01]My YouTube colleagues are like, wait, wait, maybe it could be.[1946.36] [1946.36][S01]They're like YouTube.[1947.72] [1947.72][S01]But they're extra moving parts that[1949.42] [1949.42][S01]account for the generative process,[1951.4] [1951.4][S01]but still create this flow of money-- frankly,[1955.04] [1955.04][S01]we all got to eat, this flow of money[1957.16] [1957.16][S01]and this flow of attribution, and also[1959.83] [1959.83][S01]the ability to market yourself as an artist[1961.93] [1961.93][S01]and show the world, and frankly, try to get famous.[1965.212] [1965.212][S01]That's cool, too.[1965.92] [1965.92][S01]A lot of what happens on the Creator Network[1967.72] [1967.72][S01]is you're trying to get famous.[1969.012] [1969.012][S01]That's great.[1969.64] [1969.64][S01]I want all of these things to be able to happen.[1972.08] [1972.08][S01]We don't have a solution, and I particularly personally[1974.403] [1974.403][S01]don't have a solution.[1975.32] [1975.32][S01]But I see tantalizing possibility[1978.01] [1978.01][S01]to see marketplaces and ecosystems,[1980.74] [1980.74][S01]like the one I just described, work in generative.[1983.61] [1983.61][S02] That's things from the perspective of the artist.[1986.39] [1986.39][S02]But there is another perspective here,[1987.53] [1987.53][S02]which is from the perspective of the audience.[1989.1] [1989.1][S01] Yeah.[1989.725] [1989.725][S02] I mean, how do you--[1991.21] [1991.21][S02]if it's suddenly you're lowering the barrier to entry[1993.43] [1993.43][S02]to create music, to create art, to create literature,[1995.638] [1995.638][S02]to whatever it might be, how do you[1997.84] [1997.84][S02]avoid the audience being flooded with junk?[2000.525] [2000.525][S01] Yeah I already mentioned that if you can[2002.83] [2002.83][S01]generate the 13th Beatles album, you can generate the millionth.[2008.17] [2008.17][S01]I mean, one of the connectors here[2010.03] [2010.03][S01]is back to people as artists connecting with people.[2013.0] [2013.0][S01]I think we will start to trust.[2014.3] [2014.3][S01]Even if it's a curation issue, we'll[2015.82] [2015.82][S01]start to trust certain curators in the same way[2018.31] [2018.31][S01]that we love certain DJs, who are doing more than curating,[2021.008] [2021.008][S01]don't get me wrong.[2021.8] [2021.8][S01]But we have that.[2023.36] [2023.36][S01]I think part of it is, like, we just[2025.33] [2025.33][S01]limit the true explosion of materials that are out there.[2028.79] [2028.79][S01]SynthID will give us ways to identify[2031.51] [2031.51][S01]these things as need be.[2033.13] [2033.13][S01]But yeah, it's still a challenge.[2034.9] [2034.9][S01]We're swimming in media.[2036.722] [2036.722][S02] But then I do wonder whether we end up in a situation[2039.43] [2039.43][S02]where, let's say that you're a book publisher[2041.5] [2041.5][S02]and you're reading submissions.[2043.3] [2043.3][S02]And suddenly you're inundated by generative AI novels.[2047.87] [2047.87][S02]I mean, I think at the moment, you can tell.[2049.79] [2049.79][S02]But let's imagine a future time where you can't.[2054.46] [2054.46][S02]The only solution there is to have[2055.989] [2055.989][S02]your own AI that's helping you with curation, surely.[2059.75] [2059.75][S01] Yeah.[2060.61] [2060.61][S01]That's not crazy.[2061.57] [2061.57][S01]I think you have this other question of trusting media.[2065.281] [2065.281][S01]The flip side of that is I'm not a book author.[2067.239] [2067.239][S01]I'm reading a news report that tells me that something happened[2069.864] [2069.864][S01]somewhere in the world.[2071.25] [2071.25][S01]There's two issues.[2072.06] [2072.06][S01]Is it AI-generated, and then is it actually true?[2076.26] [2076.26][S01]Those are very, very, very big questions.[2078.58] [2078.58][S01]And I think they're immensely important short-term questions[2081.08] [2081.08][S01]that we need to deal with.[2082.699] [2082.699][S01]We are trying to mitigate in certain ways this.[2086.0] [2086.0][S01]For example, Gemini will limit the number of political queries[2090.217] [2090.217][S01]that are possible during the election cycle, which[2092.3] [2092.3][S01]I think is great.[2093.179] [2093.179][S01]It just slow things down a little bit.[2095.19] [2095.19][S02] What, to stop AI generated articles from being--[2098.205] [2098.205][S02]I see.[2098.705] [2098.705][S01] Yeah, so this is one mitigation that you tie-in with[2102.92] [2102.92][S01]other mitigations to try to--[2105.44] [2105.44][S02] Just turn the volume down.[2108.05] [2108.05][S02]Just thinking ahead to the future then,[2110.21] [2110.21][S02]what do you imagine is going to happen[2113.48] [2113.48][S02]in generative AI in the next five years or so?[2115.52] [2115.52][S01] What I'm seeing happening[2117.38] [2117.38][S01]is a move towards multimodality.[2122.74] [2122.74][S02] Video, image, music.[2124.6] [2124.6][S01] Exactly, and I think in doing so,[2129.05] [2129.05][S01]I think what we're doing now is, we're[2130.69] [2130.69][S01]figuring out the AI for single-modality issues.[2134.81] [2134.81][S01]I showed you some really cool images.[2137.12] [2137.12][S01]I showed you some really cool--[2138.77] [2138.77][S01]I think they were cool, some cool videos.[2140.63] [2140.63][S01]But then you start adding audio.[2142.36] [2142.36][S01]And what you see is like, if we can truly[2144.94] [2144.94][S01]meld the visual and the audio in a generative way,[2149.83] [2149.83][S01]such that people can tell the stories that[2151.69] [2151.69][S01]are in their heads in different ways,[2153.26] [2153.26][S01]we really do change things.[2154.385] [2154.385][S01]And I think that's what will happen in the next five years.[2157.31] [2157.31][S01]We won't solve it in five years, I strongly believe.[2159.77] [2159.77][S01]But we will start to just really creatively bring together[2163.27] [2163.27][S01]these core chunks of technology.[2165.37] [2165.37][S01]This is also the dream of Gemini.[2168.17] [2168.17][S01]The mission of Gemini is explicitly and openly[2170.26] [2170.26][S01]multimodal.[2171.49] [2171.49][S01]We'll start to see the combinations of text and images[2175.3] [2175.3][S01]combine differently.[2176.27] [2176.27][S01]And that will open up a whole bunch[2177.76] [2177.76][S01]of new creative and expressive.[2180.56] [2180.56][S02] Well, I also wonder, though, if you--[2182.625] [2182.625][S02]I'm just going back to this point[2184.0] [2184.0][S02]that you made earlier about how these things now[2186.0] [2186.0][S02]understand physics.[2187.01] [2187.01][S02]Everything we've spoken about really about creativity[2189.95] [2189.95][S02]has been in the artistic space.[2191.72] [2191.72][S02]But if you understand physics, is there a sort of potential[2196.472] [2196.472][S02]there for a different type of creativity?[2198.18] [2198.18][S02]I mean, if you understand aerodynamics,[2200.27] [2200.27][S02]could you in future go in and say, design me an aeroplane?[2204.195] [2204.195][S01] Yes.[2205.94] [2205.94][S01]And I think if you had said more than five years,[2209.93] [2209.93][S01]I think if we're talking about scientific discovery[2212.48] [2212.48][S01]and having physics engines that will allow[2214.64] [2214.64][S01]us to do things build better airplanes,[2216.93] [2216.93][S01]I also believe that will come.[2219.86] [2219.86][S01]I actually hate making these predictions because I don't--[2222.705] [2222.705][S02] We all love it.[2223.83] [2223.83][S01] I'm putting that more on the decade.[2226.872] [2226.872][S02] A decade, do you think?[2228.33] [2228.33][S01] I think so, yeah.[2229.08] [2229.08][S02] Wow.[2229.762] [2229.762][S02]That feels short.[2230.47] [2230.47][S01] It does feel short.[2231.66] [2231.66][S01]But if you look at what's been happening in robotics,[2233.868] [2233.868][S01]like robotics has accelerated tremendously thanks[2237.05] [2237.05][S01]to the use of language models.[2238.38] [2238.38][S01]And now we're seeing diffusion being brought into this[2240.63] [2240.63][S01]as well in ways that are very, very surprising,[2244.37] [2244.37][S01]in terms of how quickly we can allow robots[2247.25] [2247.25][S01]to be able to basically dynamically exist[2250.01] [2250.01][S01]in a changing world in terms of grasp and things like that.[2255.0] [2255.0][S01]And so if we see that, and we start[2256.76] [2256.76][S01]to see that we can also provide much, much higher-resolution[2259.28] [2259.28][S01]physics, I definitely see the door opening[2261.89] [2261.89][S01]for the physics-related stuff.[2266.275] [2266.275][S02] Just to finish on a point about art,[2268.395] [2268.395][S02]because that has been so much of the discussion that we've had,[2271.02] [2271.02][S02]do you think there will be a point where an AI can win[2273.68] [2273.68][S02]an Oscar or a Pulitzer Prize or some great photography[2277.37] [2277.37][S02]competition?[2278.43] [2278.43][S01] Yeah, I wouldn't rule it out.[2281.3] [2281.3][S01]The era of agents--[2283.46] [2283.46][S01]that's the term we use to talk about an AI that[2286.37] [2286.37][S01]has an identity, I guess, is upon us.[2292.09] [2292.09][S01]The attribution problem is very interesting here.[2294.757] [2294.757][S01]You have people that are quite creative that[2296.59] [2296.59][S01]are doing the coding.[2297.47] [2297.47][S01]You have people that are trying to decide[2299.26] [2299.26][S01]what are the goals of the model, of the agent, et cetera.[2301.99] [2301.99][S01]If you're talking about winning an Academy Award,[2304.57] [2304.57][S01]so then the thought experiment is,[2306.28] [2306.28][S01]there's really no human involved.[2307.78] [2307.78][S01]I type into the prompt, OK, agent, go win me[2310.72] [2310.72][S01]an Academy Award.[2311.71] [2311.71][S01]And I get a cup of coffee.[2313.145] [2313.145][S02] Yeah, I think that's the idea.[2315.01] [2315.01][S01] That's really what you're saying, yeah.[2317.052] [2317.052][S01]I think we're very, very, very, very far from that.[2319.64] [2319.64][S01]And it could end up being like--[2321.31] [2321.31][S01]one answer is, if we solve that, we may have solved AGI.[2324.59] [2324.59][S01]Let's be real.[2325.88] [2325.88][S01]It could be AGI hard--[2328.27] [2328.27][S01]I mean, with zero--[2330.91] [2330.91][S02] External input, no editing.[2332.545] [2332.545][S01] Please go win me an Academy Award.[2334.3] [2334.3][S02] No curation.[2334.635] [2334.635][S01] That's right, zero.[2335.843] [2335.843][S01]I think that feels societally like an insanely, insanely[2340.21] [2340.21][S01]hard thing.[2341.262] [2341.262][S01]And it's going to be a pretty cool agent that pulls it off.[2343.72] [2343.72][S02] I think it also does bring us right back[2345.67] [2345.67][S02]to the very beginning of our conversation, which[2347.67] [2347.67][S02]is that perhaps one of the biggest[2350.77] [2350.77][S02]things about the reason why we consume art[2353.0] [2353.0][S02]is because it's something about connecting to other humans[2356.15] [2356.15][S02]and communicating the human experience.[2358.47] [2358.47][S02]So I guess, yeah, I mean, that's distinctly[2362.06] [2362.06][S02]lacking from an AGI-only-generated Oscar film.[2365.398] [2365.398][S01] I want to go in the opposite direction.[2367.44] [2367.44][S01]I think people are worried about AI taking them out[2371.48] [2371.48][S01]of the participatory process.[2373.46] [2373.46][S01]I want to bring more people in.[2375.66] [2375.66][S01]So I want to take us back to the time[2377.75] [2377.75][S01]before recorded music where we all stood around the piano[2380.72] [2380.72][S01]and sang.[2381.9] [2381.9][S01]And it might not be quite the same piano.[2385.05] [2385.05][S01]It might be something like a piano[2386.6] [2386.6][S01]that AI is helping us do that's lifting us all.[2388.83] [2388.83][S01]But that, I think, is a beautiful goal.[2391.08] [2391.08][S01]It's to bring more people into art-making and into creativity,[2395.82] [2395.82][S01]not fewer.[2396.965] [2396.965][S02] To use technology to increase human connection.[2399.835] [2399.835][S01] That's it.[2400.902] [2400.902][S02] I really like it.[2402.11] [2402.11][S01] Perfectly put.[2402.45] [2402.45][S02] I really like that.[2403.742] [2403.742][S02]Well, I think that's the perfect point to finish on.[2406.11] [2406.11][S02]So Doug Eck, thank you so much for joining us on "Google[2409.07] [2409.07][S02]DeepMind: The Podcast."[2410.4] [2410.4][S02]You're brilliant.[2411.36] [2411.36][S01] And it's been such a pleasure to be here.[2414.41] [2414.41][S02] I know things quickly get deeply philosophical[2416.93] [2416.93][S02]when you're talking about creativity[2418.43] [2418.43][S02]and what that means in an era of AI.[2421.17] [2421.17][S02]And from that conversation with Doug,[2422.79] [2422.79][S02]I think that we're in a different place[2424.76] [2424.76][S02]now than we were with the invention of the camera[2427.25] [2427.25][S02]or the piano, which gave humans just a different medium[2430.64] [2430.64][S02]to express themselves.[2432.12] [2432.12][S02]The difference here, I think in part is one of volume.[2435.95] [2435.95][S02]As Doug said, what value would the millionth Beatles album[2438.56] [2438.56][S02]have?[2439.29] [2439.29][S02]And while AI can, of course, do a brilliant job of curation,[2442.86] [2442.86][S02]I think that there are a lot of people[2444.98] [2444.98][S02]who work in creative industries who[2446.81] [2446.81][S02]are going to face substantial disruption[2448.76] [2448.76][S02]over the next few years.[2449.912] [2449.912][S02]But at the same time, I think there[2451.37] [2451.37][S02]is something interesting about Doug's definition of creativity,[2456.3] [2456.3][S02]the thing that needs to be preserved.[2458.15] [2458.15][S02]That AI can't be truly creative on its own[2461.54] [2461.54][S02]because the human component is missing.[2464.84] [2464.84][S02]And I think there's something quite hopeful in that,[2467.36] [2467.36][S02]actually, that even the people who are designing these tools,[2470.58] [2470.58][S02]they see art and literature and music as deeply human endeavors[2476.16] [2476.16][S02]for humans by humans.[2478.6] [2478.6][S02]And that is not going to change.[2481.243] [2481.243][S02]You have been listening to "Google DeepMind: The Podcast,"[2483.66] [2483.66][S02]presented by me, Professor Hannah Fry.[2485.398] [2485.398][S02]If you'd like to learn more about what we've discussed,[2487.69] [2487.69][S02]then you can check out the show notes[2488.94] [2488.94][S02]in the description of this episode.[2490.66] [2490.66][S02]We've got plenty more fascinating conversations[2493.32] [2493.32][S02]with some of the top leaders in AI[2494.88] [2494.88][S02]coming up on topics ranging from how AI is accelerating[2498.06] [2498.06][S02]the pace of scientific discoveries[2499.71] [2499.71][S02]to addressing some of the biggest[2501.12] [2501.12][S02]risks of this technology.[2502.78] [2502.78][S02]If you have enjoyed this episode,[2504.46] [2504.46][S02]then please make sure to subscribe to the podcast[2506.82] [2506.82][S02]and if you have any feedback or you[2508.62] [2508.62][S02]want to suggest a guest that you would like to hear from,[2511.24] [2511.24][S02]then why not leave us a comment on YouTube?[2513.28] [2513.28][S02]Until next time.[2514.02] [2514.02][THEME MUSIC][2517.37]