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The following is a conversation with Aurel Vinales, his second time in the podcast.
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Aurel is the research director and deep learning lead at DeepMind, and one of the most brilliant thinkers and researchers in the history of artificial intelligence.
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As a fan of a bunch of podcasts, I find merch to be pretty cool.
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And now, dear friends, here s Ariel Vinales.
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You are one of the most brilliant researchers in the history of AI, working across all kinds of modalities.
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Probably the one common theme is it s always sequences of data.
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So we re talking about languages, images, even biology and games as we talked about last time.
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So you re a good person to ask this.
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In your lifetime, will we be able to build an AI system that s able to replace me as the interviewer in this conversation in terms of ability to ask questions that are compelling to somebody listening?
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And then further question is, are we close, will we be able to build a system that replaces you as the interviewee in order to create a compelling conversation?
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How far away are we, do you think?
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It s a good question.
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I think partly I would say, do we want that?
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I really like when we start now with very powerful models, interacting with them and thinking of them more closer to us.
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The question is, if you remove the human side of the conversation, is that an interesting
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You know, is that an interesting artifact?
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And I would say probably not.
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I ve seen, for instance, last time we spoke, we were talking about StarCraft and creating, you know, agents that play games involves self-play, but ultimately what people care about was
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how does this agent behave when the opposite side is a human?
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So without a doubt, we will probably be more empowered by AI.
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Maybe you can source some questions from an AI system.
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I mean, that even today, I would say it s quite plausible that with your creativity, you might actually find very interesting questions that you can filter.
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We call this cherry picking sometimes in the field of language.
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And likewise, if I had now the tools on my side, I could say, look, you re asking this interesting question.
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From this answer, I like the words chosen by this particular system that created a few words.
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Completely replacing it feels not exactly exciting to me.
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Although in my lifetime, I think way, I mean, given the trajectory, I think it s possible that perhaps
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there could be interesting, maybe self-play interviews, as you re suggesting, that would look or sound quite interesting and probably would educate or you could learn a topic through listening to one of these interviews at a basic level, at least.
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So you said it doesn t seem exciting to you, but what if exciting is part of the objective function the thing is optimized over?
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So there s probably a huge amount of data
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of humans, if you look correctly, of humans communicating online, and there s probably ways to measure the degree of, you know, as they talk about engagement.
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So you can probably optimize the question that s most created an engaging conversation in the past.
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So actually, if you strictly use the word exciting, there is probably
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a way to create a optimally exciting conversations that involve AI systems.
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At least one side is AI.
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Yeah, that makes sense.
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I think maybe looping back a bit to games and the game industry, when you design algorithms, you re thinking about winning as the objective, right, or the reward function.
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But in fact, when we discussed this with Blizzard, the creators of StarCraft in this case, I think
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what s exciting, fun, if you could measure that and optimize for that.
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That s probably why we play video games or why we interact or listen or look at cat videos or whatever on the internet.
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So it s true that modeling reward beyond the obvious reward functions we re used to in reinforcement learning is definitely very exciting.
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And again, there is some progress actually into
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a particular aspect of AI, which is quite critical, which is, for instance, is a conversation or is the information truthful, right?
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So you could start trying to evaluate these from, except from the internet, right, that has lots of information.
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And then if you can learn a function automated, ideally, so you can also optimize it more easily,
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then you could actually have conversations that optimize for non-obvious things such as excitement.
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So yeah, that s quite possible.
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And then I would say in that case, it would definitely be a fun exercise and quite unique to have at least one side that is fully driven by an excitement reward function.
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But obviously there would be still quite a lot of humanity in the system, both from who are,
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who is building the system, of course, and also, ultimately, we think of labeling for excitement, that those labels must come from us because it s just hard to have a computational measure of excitement.
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As far as I understand, there s no such thing.
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Well, oof.
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You mentioned truth also.
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I would actually venture to say that excitement is easier to label than truth.
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or perhaps has lower consequences of failure.
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But there is perhaps the humanness that you mentioned, that s perhaps part of a thing that could be labeled, and that could mean
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An AI system that s doing dialogue, that s doing conversations, should be flawed, for example.
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That s the thing you optimize for, which is have inherent contradictions by design, have flaws by design.
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Maybe it also needs to have a strong sense of identity, so it has a backstory it told itself that it sticks to.
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It has memories.
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not in terms of how the system is designed, but it s able to tell stories about its past.
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It s able to have mortality and fear of mortality in the following way, that it has an identity, and if it says something stupid and gets cancelled on Twitter, that s the end of that system.
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So it s not like you get to rebrand yourself.
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That system is, that s it.
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So maybe the high stakes nature of it, because you can t say anything stupid now, because you d be canceled on Twitter.
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And there s stakes to that.
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And that I think part of the reason that makes it interesting.
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And then you have a perspective, like you ve built up over time, that you stick with, and then people can disagree with you.
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So holding that perspective strongly,
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holding sort of maybe a controversial, at least a strong opinion.
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All of those elements, it feels like they can be learned because it feels like there s a lot of data on the internet of people having an opinion.
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And then combine that with a metric of excitement, you can start to create something that, as opposed to trying to optimize for,
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sort of grammatical clarity and truthfulness, the factual consistency over many sentences, you re optimized for the humanness.
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And there s obviously data for humanness on the internet.
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So I wonder if there s a future where that s part
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Or, I mean, I sometimes wonder that about myself.
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I m a huge fan of podcasts, and I listen to some podcasts, and I think, like, what is interesting about this?
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What is compelling?
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The same way you watch other games.
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Like you said, watch, play StarCraft, or have Magnus Carlsen play chess.
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So I m not a chess player, but it s still interesting to me.
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What is that?
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That s the...
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the stakes of it, maybe the end of a domination of a series of wins.
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I don t know, there s all those elements somehow connect to a compelling conversation, and I wonder how hard is that to replace, because ultimately all of that connects to the initial proposition of how to test whether an AI is intelligent or not with the Turing test, which I guess my question comes from a place of the spirit of that test.
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Yes, I actually recall I was just listening to our first podcast where we discussed Turing test.
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So I would say from a neural network, you know, AI builder perspective, there s
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Usually, you try to map many of these interesting topics you discuss to benchmarks, and then also to actual architectures on how these systems are currently built, how they learn, what data they learn from, what are they learning.
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We re talking about weights of a mathematical function.
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And then looking at the current state of the game, maybe
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what do we need leaps forward to get to the ultimate stage of all these experiences, lifetime experience, fears, like words that currently barely we re seeing progress just because what s happening today is you take all these human interactions.
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It s a large vast variety of human interactions online.
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And then you re distilling these
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sequences, right?
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Going back to my passion, like sequences of words, letters, images, sound.
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There s more modalities here to be to be at play.
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And then you re trying to
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just learn a function that will be happy, that maximizes the likelihood of seeing all these through a neural network.
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Now, I think there s a few places where the way currently we train these models would clearly like to be able to develop the kinds of capabilities you say.
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I ll tell you maybe a couple.
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One is
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the lifetime of an agent or a model.
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So you learn from this data offline, right?
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So you re just passively observing and maximizing this, you know, it s almost like a mountains, like a landscape of mountains.
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And then everywhere there s data that humans interacted in this way, you re trying to make that higher and then, you know, lower where there s no data.
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And then these models generally
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don t then experience themselves.
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They just are observers, right?
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They re passive observers of the data.
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And then we re putting them to then generate data when we interact with them.
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But that s very limiting.
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The experience they actually experience when they could maybe be optimizing or further optimizing the weights, we re not even doing that.
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So to be clear, and again, mapping to
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AlphaGo, AlphaStar, we train the model.
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And when we deploy it to play against humans, or in this case, interact with humans, like language models, they don t even keep training, right?
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They re not learning in the sense of the weights that you ve learned from the data.
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They don t keep changing.
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Now, there s something a bit more feels magical, but it s understandable if you re into neural net, which is, well, they might not learn in the strict sense of the words, the way it s changing.
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Maybe that s mapping to how neurons interconnect and how we learn over our lifetime.
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But it s true that
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the context of the conversation that takes place when you talk to these systems, it s held in their working memory, right?
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It s almost like you start a computer, it has a hard drive that has a lot of information, you have access to the internet, which has probably all the information, but there s also a working memory where these agents, as we call them, or start calling them, build upon.
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Now, this memory is very limited.
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I mean, right now, we re talking, to be concrete, about 2000 words that we hold.
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And then beyond that, we start forgetting what we ve seen.
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So you can see that there s some short term coherence already, right?
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With when you said, I mean, it s a very interesting topic, having sort of a mapping, an agent to like have consistency, then, you know, if
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If you say, oh, what s your name?
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It could remember that, but then it might forget beyond 2000 words, which is not that long of context, if we think even of this podcast, books are much longer.
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So technically speaking, there s a limitation there.
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Super exciting from people that work on deep learning to be working on, but
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I would say we lack maybe benchmarks and the technology to have this lifetime-like experience of memory that keeps building up.
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However, the way it learns offline is clearly very powerful, right?
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So, you know, you asked me three years ago, I would say, oh, we re very far.
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I think we ve seen the power of this imitation again on the internet scale that has enabled this to
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feel like at least the knowledge, the basic knowledge about the world now is incorporated into the weights.
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But then this experience is lacking.
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And in fact, as I said, we don t even train them when, you know, when we re talking to them, other than their working memory, of course, is affected.
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So that s the dynamic part.
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But they don t learn in the same way that you and I have learned, right?
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When
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from basically when we were born and probably before.
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So lots of fascinating, interesting questions you asked there.
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I think the one I mentioned is this idea of memory and experience versus just kind of observe the world and learn its knowledge, which I think for that, I would argue lots of recent advancements that make me very excited about the field.
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And then the second maybe issue that I see is
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all these models, we train them from scratch.
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That s something I would have complained three years ago, or six years ago, or 10 years ago.
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And it feels, if we take inspiration from how we got here, how the universe evolved us, and we keep evolving, it feels that is a missing piece, that we should not be training models from scratch every few months, that there should be some sort of
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way in which we can grow models, much like as a species and many other elements in the universe is building from the previous sort of iterations.
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And that from a just purely neural network perspective, even though we would like to make it work, it s proven very hard to not
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throw away the previous weights, this landscape we learned from the data, and refresh it with a brand new set of weights, given maybe a recent snapshot of this dataset we trained on, etc., or even a new game we re learning.
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So that feels like something is missing, fundamentally.
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We might find it, but it s not very clear how it will look like.
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There s many ideas, and it s super exciting as well.
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That s just for people who don t know.
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When you approach a new problem in machine learning, you re going to come up with an architecture that has a bunch of weights, and then you initialize them somehow, which in most cases is some version of random.
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So that s what you mean by start it from scratch, and it seems like it s a waste every time you solve a problem.
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The Game of Go and chess, StarCraft, protein folding, like surely there s some way to reuse the weights as we grow this giant database of neural networks that have solved some of the toughest problems in the world.
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And so some of that is, what is that, methods, how to reuse weights.
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how to learn extract was generalizable, or at least has a chance to be, and throw away the other stuff.
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And maybe the neural network itself should be able to tell you that.
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What ideas do you have for better initialization of weights?
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Maybe stepping back, if we look at the field of machine learning, but especially deep learning, right?
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At the core of deep learning, there s this beautiful idea that is a single algorithm can solve any task, right?
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So it s been proven over and over with more increasing set of benchmarks and things that were thought impossible that are being cracked by this basic principle.
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That is, you take a neural network of uninitialized weights, so like a blank,
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computational brain, then you give it, in the case of supervised learning, a lot, ideally, of examples of, hey, here is what the input looks like and the desired output should look like this.
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I mean, image classification is a very clear example, images to maybe one of a thousand categories.
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That s what ImageNet is like.
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But many, many, if not all problems can be mapped this way.
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And then
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There s a generic recipe, right, that you can use.
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And this recipe, with very little change, and I think that s the core of deep learning research, right, that what is the recipe that is universal, that for any new given task, I ll be able to use without thinking, without having to work very hard on the problem at stake.
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We have not found this recipe, but I think the field is
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excited to find less tweaks or tricks that people find when they work on important problems specific to those and more of a general algorithm, right?
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So at an algorithmic level, I would say we have something general already, which is this formula of training a very powerful model and neural network on a lot of data.
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And in many cases,
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you need some specificity to the actual problem you re solving.
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Protein folding being such an important problem has some basic recipe that is learned from before, right?
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Like transformer models, graph neural networks, ideas coming from NLP, like, you know,
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something called BERT that is a kind of loss that you can emplace to help the model.
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Knowledge distillation is another technique, right?
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So this is the formula.
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We still had to find some particular things that were specific to alpha fold, right?
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That s very important because protein folding is such a high value problem that as humans, we should solve it no matter if we need to be a bit specific.
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And it s possible that some of these learnings will apply then to the next iteration of this recipe that deep learners are about.
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But it is true that so far, the recipe is what s common, but the weights you generally throw away, which feels very sad.
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Although maybe in the last, especially in the last two, three years, and when we last spoke, I mentioned this area of meta-learning, which is the idea of learning to learn.
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That idea and some progress has been had starting, I would say, mostly from GPT-3 on the language domain only, in which you could conceive a model that is trained once, and then this model is not narrow in that it only knows how to translate a pair of languages, or it only knows how to assign sentiment to a sentence.
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These actually,
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you could teach it via prompting, it s called.
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And this prompting is essentially just showing it a few more examples, almost like you do show examples, input-output examples, algorithmically speaking, to the process of creating this model.
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But now you re doing it through language, which is a very natural way for us to learn from one another.
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I tell you, hey, you should do this new task.
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I ll tell you a bit more.
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Maybe you ask me some questions.
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And now you know the task.
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You didn t need to retrain it from scratch.
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And we ve seen these magical moments almost in this way to do few-shot prompting through language on language-only domain.
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And then in the last...
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two years, we ve seen this expanded to beyond language, adding vision, adding actions and games, lots of progress to be had.
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But this is maybe, if you ask me like about how are we going to crack this problem, this is perhaps one way in which you have a single model.
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The problem of this model is it s hard to grow.
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in weights or capacity, but the model is certainly so powerful that you can teach it some tasks, right?
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In this way that I teach you, I could teach you a new task now, if we were all at a text-based task or a classification, a vision-style task.
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But it still feels like more breakthroughs should be had.
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But it s a great beginning, right?
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We have a good baseline.
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We have an idea that this maybe is the way we want to benchmark progress towards AGI.
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And I think in my view, that s critical to always have a way to benchmark the community sort of converging to this overall, which is good to see.
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And then this is actually
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What excites me in terms of also next steps for deep learning is how to make these models more powerful.
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How do you train them?
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How to grow them if they must grow?
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SPEAKER_00
Should they change their weights as you teach it, task or not?
1
SPEAKER_00
There s some interesting questions, many to be answered.
1
SPEAKER_01
Yeah, you ve opened a door.
1
SPEAKER_01
to a bunch of questions I want to ask, but let s first return to your tweet and read it like it s Shakespeare.
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SPEAKER_01
You wrote, Gato is not the end, it s the beginning.
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SPEAKER_01
And then you wrote, meow, and then an emoji of a cat.
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SPEAKER_01
So first, two questions.
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SPEAKER_01
First, can you explain the meow and the cat emoji?
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SPEAKER_01
And second, can you explain what Gatto is and how it works?
1
SPEAKER_00
Right, indeed.
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SPEAKER_00
I mean, thanks for reminding me that we re all exposing on Twitter and- Permanently there.
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SPEAKER_00
Yes, permanently there.
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SPEAKER_01
One of the greatest AI researchers of all time, meow and cat emoji.
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SPEAKER_01
Yes.
1
SPEAKER_01
There you go.
1
SPEAKER_01
Right.
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SPEAKER_01
So can you imagine like touring, tweeting meow and cat?
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SPEAKER_01
Probably he would probably would probably.
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SPEAKER_00
So, yeah, the tweet is important, actually.
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SPEAKER_00
You know, I put thought on the tweets.
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SPEAKER_00
I hope people which part do you think?
1
SPEAKER_01
OK, so there s three sentences.
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SPEAKER_01
Gatto is not the end.
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SPEAKER_01
Gatto is the beginning.
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SPEAKER_01
Meow cat emoji.
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SPEAKER_01
Okay, which is the important part?
1
SPEAKER_00
The meow?
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SPEAKER_00
No, no.
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SPEAKER_00
Definitely that it is the beginning.
1
SPEAKER_00
I mean, I probably was just explaining a bit where the field is going, but let me tell you about Gato.
1
SPEAKER_00
So first, the name Gato
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SPEAKER_00
It comes from maybe a sequence of releases that DeepMind had that used animal names to name some of their models that are based on this idea of large sequence models.
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SPEAKER_00
Initially, their only language, but we are expanding to other modalities.
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SPEAKER_00
So we had Gopher, Chinchilla, these were language only.
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SPEAKER_00
And then more recently, we released Flamingo, which adds vision to the equation, and then Gato, which adds vision and then also actions in the mix, right?
1
SPEAKER_00
As we discuss, actually, actions, especially discrete actions like up, down, left, right,
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SPEAKER_00
I just told you the actions, but they re words.
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SPEAKER_00
So you can kind of see how actions naturally map to sequence modeling of words, which these models are very powerful.
1
SPEAKER_00
Gato was named after, I believe, I can only, from memory, right?
1
SPEAKER_00
These things always happen with an amazing team of researchers behind.
1
SPEAKER_00
So before the release, we had a discussion about which animal would we pick, right?
1
SPEAKER_00
And I think because of the word general agent, right?
1
SPEAKER_00
And this is a property quite unique to Gato.
1
SPEAKER_00
We kind of were playing with the GA words, and then, you know, Gato- Kind of rhymes with cat.
1
SPEAKER_00
Yes, and gato is obviously a Spanish version of cat.
1
SPEAKER_00
I had nothing to do with it, although I m from Spain.
1
SPEAKER_01
Oh, how do you, wait, sorry, how do you say cat in Spanish?
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SPEAKER_00
Gato.
1
SPEAKER_01
Oh, gato, okay.
1
SPEAKER_01
Yeah.
1
SPEAKER_00
Now it all makes sense.
1
SPEAKER_00
Okay, okay, I see, I see, I see.
1
SPEAKER_00
Now it all makes sense.
1
SPEAKER_00
Okay, so.
1
SPEAKER_00
How do you say meow in Spanish?
1
SPEAKER_00
No, that s probably the same.
1
SPEAKER_00
I think you say it the same way, but you write it as M-I-A-U.
1
SPEAKER_01
Okay, it s universal.
1
SPEAKER_01
Yes.
1
SPEAKER_01
All right, so then how does the thing work?
1
SPEAKER_01
So you said general is, so you said language,
1
SPEAKER_01
vision, and action.
1
SPEAKER_01
How does this, can you explain what kind of neural networks are involved?
1
SPEAKER_01
What does the training look like?
1
SPEAKER_01
And maybe...
1
SPEAKER_01
what to you are some beautiful ideas within the system.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
So maybe the basics of GATO are not that dissimilar from many, many work that comes.
1
SPEAKER_00
So here is where the sort of the recipe, I mean, hasn t changed too much.
1
SPEAKER_00
There is a transformer model that s the kind of recurrent neural network that essentially takes a sequence of
1
SPEAKER_00
modalities, observations that could be words, could be vision, or could be actions.
1
SPEAKER_00
And then its own objective that you train it to do when you train it is to predict what the next anything is.
1
SPEAKER_00
And anything means what s the next action.
1
SPEAKER_00
If this sequence that I m showing you to train is a sequence of actions and observations, then you re predicting what s the next action and the next observation, right?
1
SPEAKER_00
So you think of
1
SPEAKER_00
of these really as a sequence of bytes, right?
1
SPEAKER_00
So take any sequence of words, a sequence of interleaved words and images, a sequence of maybe observations that are images and moves in Atari up, down, left, right.
1
SPEAKER_00
And these, you just think of them as bytes and you re modeling what s the next byte going to be like.
1
SPEAKER_00
And you might interpret that as an action and then
1
SPEAKER_00
play it in a game or you could interpret it as a word and then write it down if you re chatting with the system and so on.
1
SPEAKER_00
So Gato basically can be thought of as inputs, images, text, video,
1
SPEAKER_00
actions.
1
SPEAKER_00
It also actually inputs some sort of proprioception sensors from robotics, because robotics is one of the tasks that it s been trained to do.
1
SPEAKER_00
And then at the output, similarly, it outputs words, actions.
1
SPEAKER_00
It does not output images.
1
SPEAKER_00
That s just by design.
1
SPEAKER_00
We decided not to go that way for now.
1
SPEAKER_00
That s also in part why it s the beginning, because there s more to do clearly.
1
SPEAKER_00
But that s kind of what the gaito is, is this brain that essentially you give it any sequence of these observations and modalities and it outputs the next step.
1
SPEAKER_00
And then off you go, you feed the next step into and predict the next one and so on.
1
SPEAKER_00
Now,
1
SPEAKER_00
It is more than a language model because even though you can chat with Gato, like you can chat with Chinchilla or Flamingo, it also is an agent, right?
1
SPEAKER_00
So that s why we call it A of Gato, the letter A, and also it s general.
1
SPEAKER_00
It s not an agent that s been trained to be good at only StarCraft or only Atari or only Go.
1
SPEAKER_00
It s been trained on a vast variety of datasets.
1
SPEAKER_01
What makes it an agent, if I may interrupt, the fact that it can generate actions?
1
SPEAKER_00
Yes, so when we call it, I mean, it s a good question, right?
1
SPEAKER_00
When do we call a model, I mean, everything is a model, but what is an agent in my view is indeed the capacity to take actions in an environment that you then send to it, and then the environment might return with a new observation, and then you generate the next action
1
SPEAKER_01
This actually, this reminds me of the question from the side of biology, what is life?
1
SPEAKER_01
Which is actually a very difficult question as well.
1
SPEAKER_01
What is living?
1
SPEAKER_01
What is living when you think about life here on this planet Earth?
1
SPEAKER_01
And a question interesting to me about aliens, what is life when we visit another planet?
1
SPEAKER_01
Would we be able to recognize it?
1
SPEAKER_01
And this feels like, it sounds perhaps silly, but I don t think it is.
1
SPEAKER_01
At which point is the neural network a being versus a tool?
1
SPEAKER_01
And it feels like action, ability to modify its environment, is that fundamental leap.
1
SPEAKER_00
Yeah, I think it certainly feels like action is a necessary condition to be more alive, but probably not sufficient either.
1
SPEAKER_00
So sadly, I- It s a full consciousness thing, whatever.
1
SPEAKER_00
Yeah, yeah, we can get back to that later.
1
SPEAKER_00
But anyways, going back to the meow and the gato, right?
1
SPEAKER_00
one of the leaps forward and what took the team a lot of effort and time was, as you were asking, how has Gato been trained?
1
SPEAKER_00
So I told you Gato is this transformer neural network, models actions, like sequences of actions, words, et cetera.
1
SPEAKER_00
And then the way we train it is by essentially pulling data sets of
1
SPEAKER_00
observations, right?
1
SPEAKER_00
So it s a massive imitation learning algorithm that it imitates obviously to what is the next word that comes next from the usual data sets we use before, right?
1
SPEAKER_00
So these are these web scale style data sets of people writing on webs or chatting or whatnot, right?
1
SPEAKER_00
So that s an obvious source that we use
2
SPEAKER_01
on all language work.
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SPEAKER_01
But then we also took a lot of agents that we have at DeepMind.
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SPEAKER_01
I mean, as you know, DeepMind, we re quite interested in learning reinforcement learning and learning agents that play in different environments.
2
SPEAKER_01
So we kind of created a data set of these trajectories, as we call them, or agent experiences.
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SPEAKER_01
So in a way,
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SPEAKER_01
There are other agents we trained for a single mind purpose to, let s say, control a 3D game environment and navigate a maze.
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SPEAKER_01
So we had all the experience that was created through one agent interacting with that environment.
2
SPEAKER_01
And we added this to the dataset, right?
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SPEAKER_01
And as I said, we just see all the data, all these sequences of words or sequences of this agent interacting with that environment.
2
SPEAKER_01
or agents playing Atari and so on.
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SPEAKER_01
We see this as the same kind of data.
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SPEAKER_01
And so we mix these data sets together and we train Gato.
2
SPEAKER_01
That s the G part, right?
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SPEAKER_01
It s general because it really has mixed, it doesn t have different brains for each modality or each narrow task.
2
SPEAKER_01
It has a single brain.
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SPEAKER_01
It s not that big of a brain compared to most of the neural networks we see these days.
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SPEAKER_01
It has 1 billion parameters.
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SPEAKER_01
some models we re seeing getting the trillions these days, and certainly 100 billion feels like a size that is very common from when you train these jobs.
2
SPEAKER_01
So the actual agent is relatively small, but it s been trained on a very challenging, diverse data set, not only containing all of the internet, but containing all this agent experience, playing very different, distinct environments.
2
SPEAKER_01
So these
2
SPEAKER_01
brings us to the part of the tweet of, this is not the end, it s the beginning.
2
SPEAKER_01
It feels very cool to see Gato, in principle, is able to control any sort of environments, especially the ones that it s been trained to do, these 3D games, Atari games, all sorts of robotics tasks and so on, but obviously it s not as proficient as the teachers it learned from on these environments.
2
SPEAKER_00
Is that why it was not obvious?
2
SPEAKER_00
it s not obvious that it wouldn t be more proficient.
2
SPEAKER_00
It s just the current beginning part is that the performance is such that it s not as good as if it s specialized to that task.
2
SPEAKER_01
Right.
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SPEAKER_01
So it s not as good, although I would argue size matters here.
2
SPEAKER_01
So the fact that
2
SPEAKER_00
I would argue size always matters.
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SPEAKER_01
That s a different conversation.
2
SPEAKER_01
But for neural networks, certainly size does matter.
2
SPEAKER_01
So it s the beginning because it s relatively small.
2
SPEAKER_01
So obviously scaling this idea up might make
2
SPEAKER_01
the connections that exist between text on the internet and playing Atari and so on, more synergistic with one another, and you might gain.
2
SPEAKER_01
And that moment we didn t quite see, but obviously that s why it s the beginning.
2
SPEAKER_00
That synergy might emerge with scale.
2
SPEAKER_01
Right, might emerge with scale.
2
SPEAKER_01
And also, I believe there s some new research or ways in which you prepare the data that you might need to sort of make it more clear to the model that you re not only playing Atari and it s just you start from a screen and here is up and a screen and down.
2
SPEAKER_01
Maybe you can think of playing Atari as there s some sort of context that is needed for the agent before it starts seeing, oh, this is an Atari screen, I m going to start playing.
2
SPEAKER_01
You might require, for instance, to be told in words, hey, in this sequence that I m showing, you re going to be playing an Atari game.
2
SPEAKER_01
So text might actually be a good driver to enhance the data, right?
2
SPEAKER_01
So then these connections might be made more easily, right?
2
SPEAKER_01
That s an idea that we start seeing
2
SPEAKER_01
in language, but obviously beyond this is going to be effective, right?
2
SPEAKER_01
It s not like I don t show you a screen and you from scratch, you re supposed to learn a game.
2
SPEAKER_01
There is a lot of context we might set.
2
SPEAKER_01
So there might be some work needed as well to set that context.
2
SPEAKER_01
But
2
SPEAKER_01
Anyways, there s a lot of work, yeah.
2
SPEAKER_00
So that context puts all the different modalities on the same level ground, if you provide the context best.
2
SPEAKER_00
So maybe on that point, so there s this task, which may not seem trivial, of tokenizing the data, of converting the data into pieces, into basic atomic elements.
2
SPEAKER_00
that then could cross modality somehow.
2
SPEAKER_00
So what s tokenization?
2
SPEAKER_00
How do you tokenize text?
2
SPEAKER_00
How do you tokenize images?
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SPEAKER_00
How do you tokenize games and actions and robotics tasks?
2
SPEAKER_01
Yeah, that s a great question.
2
SPEAKER_01
Tokenization is the entry point to actually make all the data look like a sequence, because tokens then are just kind of these little puzzle pieces.
2
SPEAKER_01
We break down anything into these puzzle pieces, and then we just model what s this puzzle look like, right?
2
SPEAKER_01
When you make it, you know, lay down in a line, so to speak, in a sequence.
2
SPEAKER_01
So in Gato,
2
SPEAKER_01
The text, there s a lot of work.
2
SPEAKER_01
You tokenize text usually by looking at commonly used substrings, right?
2
SPEAKER_01
So there s, you know, ing in English is a very common substring, so that becomes a token.
2
SPEAKER_01
There s quite well-studied problem on tokenizing text, and Gato just used the standard techniques that have been developed from many years, even starting from N-gram models in the 1950s and so on.
2
SPEAKER_00
Just for context, how many tokens, like what order, magnitude, number of tokens is required for a word?
2
SPEAKER_01
Yeah.
2
SPEAKER_00
Well, what are we talking about?
2
SPEAKER_01
Yeah, for a word in English, right, I mean, every language is very different.
2
SPEAKER_01
The current level or granularity of tokenization generally means it s maybe two to five.
2
SPEAKER_01
I mean, I don t know the statistics exactly, but to give you an idea, we don t tokenize at the level of letters, then it would probably be like, I don t know what the average length of a word is in English, but that would be, you know, the minimum set of tokens you could use.
2
SPEAKER_00
So it s bigger than letters, smaller than words.
2
SPEAKER_01
Yes, yes, and you could think of very, very common words like the, I mean, that would be a single token, but very quickly you re talking two, three, four, four tokens or so.
2
SPEAKER_00
Have you ever tried to tokenize emojis?
2
SPEAKER_01
Emojis are actually just sequences of letters, so.
2
SPEAKER_00
Maybe to you, but to me, they mean so much more.
2
SPEAKER_01
Yeah, you can render the emoji, but you might, if you actually just.
2
SPEAKER_00
Yeah, this is a philosophical question.
2
SPEAKER_00
Is emojis an image or a text?
2
SPEAKER_01
The way we do these things is they re actually mapped to small sequences of characters.
2
SPEAKER_01
So you can actually play with these models and input emojis.
2
SPEAKER_01
It will output emojis back, which is actually quite a fun exercise.
2
SPEAKER_01
You probably can find other tweets about this out there.
2
SPEAKER_01
But yeah, so anyway, text, there s like, it s very clear how this is done.
2
SPEAKER_01
And then in Gato, what we did for images is we map images to essentially, we compressed images, so to speak, into something that looks more like...
2
SPEAKER_01
less like every pixel with every intensity, that would mean we have a very long sequence, right?
2
SPEAKER_01
Like if we were talking about 100 by 100 pixel images, that would make the sequences far too long.
2
SPEAKER_01
So what was done there is you just use a technique that essentially compresses an image into maybe 16 by 16 patches of pixels, and then that is mapped.
2
SPEAKER_01
Again, tokenized, you just essentially quantize this space into
2
SPEAKER_01
a special word that actually maps to these little sequence of pixels.
2
SPEAKER_01
And then you put the pixels together in some raster order, and then that s how you get out or in the image that you re processing.
2
SPEAKER_00
But there s no semantic aspect to that.
2
SPEAKER_00
So you re doing some kind of, you don t need to understand anything about the image in order to tokenize it currently.
2
SPEAKER_01
No, you re only using this notion of compression.
2
SPEAKER_01
So you re trying to find common... It s like JPG or all these algorithms.
2
SPEAKER_01
It s actually very similar at the tokenization level.
2
SPEAKER_01
All we re doing is finding common patterns and then making sure, in a lossy way, we compress these images, given the statistics of the images that are contained in all the data we deal with.
2
SPEAKER_00
Although you could probably argue that JPEG does have some understanding of images.
2
SPEAKER_00
Because visual information, maybe color, compressing crudely based on color does capture something important about an image that s about its meaning, not just about some statistics.
2
SPEAKER_01
Yeah, I mean, as I said, the algorithms look actually very similar.
2
SPEAKER_01
They use the cosine transform in JPG.
2
SPEAKER_01
The approach we usually do in machine learning when we deal with images and we do this quantization step is a bit more data-driven.
2
SPEAKER_01
So rather than have some sort of Fourier basis for how frequencies appear in the natural world, we actually just use
2
SPEAKER_01
the statistics of the images and then quantize them based on the statistics, much like you do in words, right?
2
SPEAKER_01
So common substrings are allocated a token, and images is very similar.
2
SPEAKER_01
But there s no connection.
2
SPEAKER_01
The token space, if you think of, oh, like the tokens are an integer at the end of the day.
2
SPEAKER_01
So now, like we work on, maybe we have about, let s say, I don t know the exact numbers, but let s say 10,000 tokens for text, right?
2
SPEAKER_01
Certainly more than characters, because we have groups of characters and so on.
2
SPEAKER_01
So from one to 10,000, those are representing all the language and the words we ll see.
2
SPEAKER_01
And then images occupy the next set of integers.
2
SPEAKER_01
So they re completely independent, right?
2
SPEAKER_01
So from 10,001 to 20,000, those are the tokens that represent these other modality images.
2
SPEAKER_01
And that is an interesting
2
SPEAKER_01
aspect that makes it orthogonal.
2
SPEAKER_01
So what connects these concepts is the data, right?
2
SPEAKER_01
Once you have a data set, for instance, that captions images, that tells you, oh, this is someone playing frisbee on a green field.
2
SPEAKER_01
Now,
2
SPEAKER_01
the model will need to predict the tokens from the text green field to then the pixels, and that will start making the connections between the tokens.
2
SPEAKER_01
So these connections happen as the algorithm learns.
2
SPEAKER_01
And then the last, if we think of these integers, the first few are words, the next few are images.
2
SPEAKER_01
In Gato, we also allocated
2
SPEAKER_01
the highest order of integers to actions, right, which we discretize.
2
SPEAKER_01
And actions are very diverse, right?
2
SPEAKER_01
In Atari, there s, I don t know, 17 discrete actions.
2
SPEAKER_01
In robotics, actions might be torques and forces that we apply.
2
SPEAKER_01
So we just use kind of similar ideas to compress these actions into tokens and then
2
SPEAKER_01
We just, that s how we map now all the space to these sequence of integers.
2
SPEAKER_01
But they occupy different space and what connects them is then the learning algorithm.
2
SPEAKER_01
That s where the magic happens.
2
SPEAKER_00
So the modalities are orthogonal to each other in token space.
2
SPEAKER_00
Right.
2
SPEAKER_00
So in the input, everything you add, you add extra tokens.
2
SPEAKER_01
Right.
2
SPEAKER_00
And then you re shoving all of that into one place.
2
SPEAKER_01
Yes, the transformer.
2
SPEAKER_00
And that transformer, that transformer
2
SPEAKER_00
tries to look at this gigantic token space and tries to form some kind of representation, some kind of unique wisdom about all of these different modalities.
2
SPEAKER_00
How s that possible?
2
SPEAKER_00
If you were to sort of put your psychoanalysis hat on and try to psychoanalyze this neural network, is it schizophrenic?
2
SPEAKER_00
does it try to, given this very few weights, represent multiple disjoint things and somehow have them not interfere with each other?
2
SPEAKER_00
Or is it somehow building on the joint strength, on whatever is common to all the different modalities?
2
SPEAKER_00
If you were to ask a question, is it schizophrenic or is it of one mind?
2
SPEAKER_01
I mean, it is one mind and it s actually the simplest algorithm, which that s kind of in a way how it feels like the field hasn t changed since back propagation and gradient descent was purpose for learning neural networks.
2
SPEAKER_01
So there is obviously details on the architecture.
2
SPEAKER_01
This has evolved.
2
SPEAKER_01
The current iteration is still the transformer, which is a powerful sequence modeling architecture, but then
2
SPEAKER_01
The goal of setting these weights to predict the data is essentially the same as basically I could describe, I mean, we described a few years ago, AlphaStar, language modeling, and so on, right?
2
SPEAKER_01
We take, let s say, an Atari game.
2
SPEAKER_01
We map it to a string of numbers that will all be probably image space and action space interleaved.
2
SPEAKER_01
And all we re gonna do is say, okay,
2
SPEAKER_01
given the numbers 10,001, 10,004, 10,005, the next number that comes is 20,006, which is in the action space.
2
SPEAKER_01
And you re just optimizing these weights via very simple
2
SPEAKER_01
gradients, like, you know, mathematical is almost the most boring algorithm you could imagine.
2
SPEAKER_01
We set all the weights so that given this particular instance, these weights are set to maximize the probability of having seen this particular sequence of integers for this particular game.
2
SPEAKER_01
And then the algorithm does this for many, many, many iterations, looking at different modalities, different games, right?
2
SPEAKER_01
That s the mixture of the data set we discussed.
2
SPEAKER_01
So in a way, it s a very simple algorithm.
2
SPEAKER_01
And the weights, right, they re all shared, right?
2
SPEAKER_01
So in terms of, is it focusing on one modality or not?
2
SPEAKER_01
The intermediate weights that are converting from these input of integers to the target integer you re predicting next, those weights,
2
SPEAKER_01
certainly are common.
2
SPEAKER_01
And then the way the tokenization happens, there is a special place in the neural network, which is we map this integer, like number 10,001, to a vector of real numbers.
2
SPEAKER_01
Like real numbers, we can optimize them with gradient descent, right?
2
SPEAKER_01
The functions we learn are actually surprisingly differentiable.
2
SPEAKER_01
That s why we compute gradients.
2
SPEAKER_01
So this step is the only one that this orthogonality dimension applies.
2
SPEAKER_01
So mapping a certain token for text or image or actions, each of these tokens gets its own little vector of real numbers that represents this.
2
SPEAKER_01
If you look at the field back many years ago, people were talking about word vectors or word embeddings.
2
SPEAKER_01
These are the same.
2
SPEAKER_01
We have word vectors or embeddings.
2
SPEAKER_01
We have image vector or embeddings and action vector of embeddings.
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SPEAKER_01
And the beauty here is that as you train this model, if you visualize these little vectors, it might be that they start aligning even though
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they re independent parameters.
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SPEAKER_01
There could be anything, but then it might be that you take the word gato or cat, which maybe is common enough that it actually has its own token, and then you take pixels that have a cat, and you might start seeing that these vectors look like they align, right?
2
SPEAKER_01
So by learning from this vast amount of data, the model is realizing the potential connections between these modalities.
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SPEAKER_01
Now, I will say there will be another way, at least in part, to not have these different vectors for each different modality.
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SPEAKER_01
For instance, when I tell you about actions in certain space, I m defining actions by words, right?
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SPEAKER_01
So you could imagine a world in which I m not learning that the action app in Atari is its own number.
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SPEAKER_01
the action app in Atari maybe is literally the word or the sentence app in Atari, right?
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SPEAKER_01
And that would mean we now leverage much more from the language.
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SPEAKER_01
This is not what we did here, but certainly it might make these connections much easier to learn and also to teach the model to correct its own actions and so on, right?
2
SPEAKER_01
So all this to say that Gato is indeed the beginning,
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SPEAKER_01
that it is a radical idea to do this this way, but there s probably a lot more to be done and the results to be more impressive, not only through scale, but also through some new research that will come hopefully in the years to come.
2
SPEAKER_00
So just to elaborate quickly, you mean one possible next step or one of the paths that you might take next is
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doing the tokenization fundamentally as a kind of linguistic communication.
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SPEAKER_00
So you convert even images into language.
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SPEAKER_00
So doing something like a crude semantic segmentation, trying to just assign a bunch of words to an image that have almost like a dumb entity explaining as much as it can about the image.
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SPEAKER_00
And so you convert that into words, and then you convert games into words,
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SPEAKER_00
and then you provide the context and words and all of it, eventually getting to a point where everybody agrees with Noam Chomsky that language is actually at the core of everything, that it s the base layer of intelligence and consciousness and all that kind of stuff, okay.
3
SPEAKER_00
You mentioned early on, like, it s hard to grow.
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SPEAKER_00
What did you mean by that?
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SPEAKER_00
Because we re talking about scale might change.
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SPEAKER_00
There might be, and we ll talk about this too, like, there s a emergent, there s certain things about these neural networks that are emergent.
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SPEAKER_00
So certain, like, performance we can see only with scale, and there s some kind of threshold of scale.
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SPEAKER_00
So why is it hard to grow something like this meow network?
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SPEAKER_01
So the Meow Network, it s not hard to grow if you retrain it.
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SPEAKER_01
What s hard is, well, we have now one billion parameters.
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SPEAKER_01
We train them for a while.
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SPEAKER_01
We spend some amount of work towards building these weights that are an amazing initial brain for doing this kind of task we care about.
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SPEAKER_01
could we reuse the weights and expand to a larger brain?
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SPEAKER_01
And that is extraordinarily hard, but also exciting from a research perspective and a practical perspective point of view, right?
3
SPEAKER_01
So there s this notion of
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SPEAKER_01
modularity in software engineering.
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SPEAKER_01
And we re starting to see some examples and work that leverages modularity.
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SPEAKER_01
In fact, if we go back one step from Gato to a work that I would say trained much larger, much more capable network called Flamingo.
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SPEAKER_01
Flamingo did not deal with actions, but it definitely dealt with images in an interesting way, kind of akin to what Gato did, but slightly different technique for tokenizing.
3
SPEAKER_01
But we don t need to go into that detail.
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SPEAKER_01
But what Flamingo also did, which Gato didn t do, and that just happens because these projects, you know, they re different.
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SPEAKER_01
You know, it s a bit of like the exploratory nature of research, which is great.
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SPEAKER_00
The research behind these projects is also modular.
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SPEAKER_01
Yes, exactly.
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SPEAKER_01
And it has to be, right?
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SPEAKER_01
We need to have creativity, and sometimes you need to protect pockets of people, researchers, and so on.
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SPEAKER_00
By we, you mean humans?
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SPEAKER_00
Yes.
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SPEAKER_01
And also, in particular, researchers, and maybe even further, DeepMind or other such labs.
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SPEAKER_00
And then the neural networks themselves.
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SPEAKER_00
So it s modularity all the way down.
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SPEAKER_01
Okay.
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SPEAKER_01
All the way down.
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SPEAKER_01
So the way that we did modularity very beautifully in Flamingo is we took Chinchilla, which is a language-only model, not an agent if we think of actions being necessary for agency.
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SPEAKER_01
So we took Chinchilla, we took the weights of Chinchilla,
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And then we froze them.
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SPEAKER_01
We said, these don t change.
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SPEAKER_01
We trained them to be very good at predicting the next word.
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SPEAKER_01
It s a very good language model, state-of-the-art at the time you release it, et cetera, et cetera.
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SPEAKER_01
We re gonna add a capability to C, right?
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SPEAKER_01
We are gonna add the ability to C to this language model.
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SPEAKER_01
So we re gonna attach small pieces of neural networks at the right places in the model.
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SPEAKER_01
It s almost like injecting the network with some weights and some substructures
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SPEAKER_01
in a good way, right?
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SPEAKER_01
So you need the research to say what is effective, how do you add this capability without destroying others, etc.
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SPEAKER_01
So we created a small subnetwork
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SPEAKER_01
initialized, not from random, but actually from self-supervised learning, a model that understands vision in general.
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SPEAKER_01
And then we took datasets that connect the two modalities, vision and language.
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SPEAKER_01
And then we froze the main part, the largest portion of the network, which was Chinchilla, that is 70 billion parameters.
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SPEAKER_01
And then we added a few more parameters on top.
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SPEAKER_01
trained from scratch, and then some others that were pre-trained with the capacity to see.
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SPEAKER_01
It was not tokenization in the way I described for Gato, but it s a similar idea.
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SPEAKER_01
And then we trained the whole system.
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SPEAKER_01
Parts of it were frozen, parts of it were new.
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SPEAKER_01
And all of a sudden, we developed Flamingo, which is an amazing model that is essentially, I mean, describing it is,
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SPEAKER_01
a chatbot where you can also upload images and start conversing about images.
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SPEAKER_01
But it s also kind of a dialogue style chatbot.
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SPEAKER_00
So the input is images and text and the output is text.
3
SPEAKER_01
Exactly.
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SPEAKER_00
And how many parameters?
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SPEAKER_00
You said 70 billion for Chinchilla.
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SPEAKER_01
Yeah, Chinchilla is 70 billion.
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SPEAKER_01
And then the ones we add on top, which is almost like a way to overwrite its little activations so that when it sees vision, it does kind of a correct computation of what it s seeing, mapping it back to words, so to speak.
3
SPEAKER_01
That adds an extra 10 billion parameters, right?
3
SPEAKER_01
So it s total 80 billion, the largest one we released.
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SPEAKER_01
And then you train it on...
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SPEAKER_01
a few data sets that contain vision and language.
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SPEAKER_01
And once you interact with the model, you start seeing that you can upload an image and start sort of having a dialogue about the image, which is actually not something, it s very similar and akin to what we saw in language only, these prompting abilities that it has.
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SPEAKER_01
You can teach it a new vision task, right?
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SPEAKER_01
It does things beyond the capabilities
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SPEAKER_01
that, in theory, the datasets provided in themselves, but because it leverages a lot of the language knowledge acquired from Chinchilla, it actually has this few-shot learning ability and these emerging abilities that we didn t even measure once we were developing the model.
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SPEAKER_01
But once developed, then
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SPEAKER_01
as you play with the interface, you can start seeing, wow, OK, yeah, it s cool.
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SPEAKER_01
We can upload, I think, one of the tweets talking about Twitter was this image from Obama that is placing a weight and someone is kind of weighting themselves.
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SPEAKER_01
And it s kind of a joke style image.
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SPEAKER_01
And it s notable because I think Andrej Karpathy a few years ago said,
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SPEAKER_01
no computer vision system can understand the subtlety of this joke in this image, all the things that go on.
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SPEAKER_01
And so what we try to do, and it s very anecdotally, I mean, this is not a proof that we solved this issue, but it just shows that you can upload now this image and start conversing with the model, trying to make out if it gets that there s a joke,
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SPEAKER_01
because the person weighting themselves doesn t see that someone behind is making the weight higher and so on and so forth.
3
SPEAKER_01
So it s a fascinating capability.
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SPEAKER_01
And it comes from this key idea of modularity, where we took a frozen brain and we just added a new capability.
3
SPEAKER_01
So the question is,
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SPEAKER_01
So in a way, you can see, even from DeepMind, we have Flamingo, this moderate approach, and thus could leverage the scale a bit more reasonably, because we didn t need to retrain a system from scratch.
3
SPEAKER_01
And on the other hand, we had Gato, which used the same datasets, but then it trained it from scratch, right?
3
SPEAKER_01
And so I guess, big question for the community is,
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SPEAKER_01
should we train from scratch or should we embrace modularity?
3
SPEAKER_01
And this goes back to modularity as a way to grow, but reuse seems natural and it was very effective, certainly.
3
SPEAKER_00
The next question is, if you go the way of modularity, is there a systematic way of freezing weights and joining different modalities across
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SPEAKER_00
you know, not just two or three or four networks, but hundreds of networks from all different kinds of places, maybe open source network that looks at weather patterns, and you shove that in somehow, and then you have networks that, I don t know, do all kinds of, play StarCraft and play all the other video games, and they, you can keep adding them in without significant effort,
3
SPEAKER_00
like that maybe the effort scales linearly or something like that, as opposed to like, the more network you add, the more you have to worry about the instabilities created.
3
SPEAKER_01
Yeah, so that that vision is beautiful.
3
SPEAKER_01
I think
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SPEAKER_01
there s still the question about within single modalities, like Chinchilla was reused, but now if we train a next iteration of language models, are we gonna use Chinchilla or not?
3
SPEAKER_00
Yeah, how do you swap out Chinchilla?
3
SPEAKER_01
Right, so there s still big questions, but that idea is actually really akin to software engineering, which we re not re-implementing libraries from scratch.
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SPEAKER_01
We re reusing and then building ever more amazing things, including neural networks.
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SPEAKER_01
with software that we re reusing.
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SPEAKER_01
So I think this idea of modularity, I like it.
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SPEAKER_01
I think it s here to stay.
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SPEAKER_01
And that s also why I mentioned it s just the beginning, not the end.
3
SPEAKER_00
You ve mentioned meta-learning.
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SPEAKER_00
So given this promise of Gatto, can we try to redefine this term that s almost akin to consciousness because it means different things to different people throughout the history of artificial intelligence.
3
SPEAKER_00
But what do you think meta-learning
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SPEAKER_00
is and looks like now in the five years, 10 years, will it look like the system I got, but scaled?
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SPEAKER_00
What s your sense of, what does meta-learning look like, do you think, with all the wisdom we ve learned so far?
3
SPEAKER_01
Yeah, great question.
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SPEAKER_01
Maybe it s good to give another data point looking backwards rather than forward.
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SPEAKER_01
So when we talked in 2019, meta-learning meant something that has changed mostly through the revolution of GPT-3 and beyond.
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SPEAKER_01
So what meta-learning meant at the time was driven by what benchmarks people care about in meta-learning.
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SPEAKER_01
And the benchmarks were about a capability to learn about object identities.
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SPEAKER_01
So it was very much overfitted to vision and object classification.
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SPEAKER_01
And the part that was meta about that was that, oh, we re not just learning a thousand categories that ImageNet tells us to learn.
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SPEAKER_01
We re going to learn object categories that can be defined
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SPEAKER_01
when we interact with the model.
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SPEAKER_01
So it s interesting to see the evolution, right?
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SPEAKER_01
The way this started was we have a special language that was a data set, a small data set that we prompted the model with saying, hey, here is a new classification task.
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SPEAKER_01
I ll give you one image and the name.
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SPEAKER_01
which was an integer at the time of the image and a different image and so on.
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SPEAKER_01
So you have a small prompt in the form of a data set, a machine learning data set.
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SPEAKER_01
And then you got then a system that could then predict or classify these objects that you just defined kind of on the fly.
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SPEAKER_01
So
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SPEAKER_01
Fast forward, it was revealed that language models are future learners.
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SPEAKER_01
That s the title of the paper.
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SPEAKER_01
So very good title.
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SPEAKER_01
Sometimes titles are really good.
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SPEAKER_01
So this one is really, really good because that s the point of GPT-3 that showed that, look,
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SPEAKER_01
Sure, we can focus on object classification and what meta-learning means within the space of learning object categories.
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SPEAKER_01
This goes beyond, or before, rather, to also Omniglot, before ImageNet, and so on.
3
SPEAKER_01
So there s a few benchmarks.
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SPEAKER_01
To now, all of a sudden, we re a bit unlocked from benchmarks.
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SPEAKER_01
And through language, we can define tasks, right?
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SPEAKER_01
So we re literally telling the model some logical task or little thing that we wanted to do.
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SPEAKER_01
We prompt it much like we did before, but now we prompt it through natural language.
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SPEAKER_01
And then, not perfectly, I mean, these models have failure modes and that s fine, but these models then are now doing a new task, right?
3
SPEAKER_01
So they meta-learn this new capability.
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SPEAKER_01
Now, that s where we are now.
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SPEAKER_01
expanded this to visual and language, but it basically has the same abilities.
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SPEAKER_01
You can teach it, for instance, an emergent property was that you can take pictures of numbers and then do arithmetic with the numbers just by teaching it.
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SPEAKER_01
When I show you three plus six, I want you to output nine and you show it a few examples and now it does that.
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SPEAKER_01
So it went way beyond the
3
SPEAKER_01
or this ImageNet sort of categorization of images that we were a bit stuck maybe before this revelation moment that happened in 2000.
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SPEAKER_01
I believe it was 19, but it was after we chatted.
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SPEAKER_00
And that way it has solved meta-learning as was previously defined.
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SPEAKER_01
Yes, it expanded what it meant.
3
SPEAKER_01
So that s what you say.
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SPEAKER_01
What does it mean?
3
SPEAKER_01
So it s an evolving term.
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SPEAKER_01
But here is maybe now looking forward, looking at what s happening, you know, obviously in the community with more modalities, what we can expect.
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SPEAKER_01
And I would certainly hope to see the following.
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SPEAKER_01
And this is a pretty drastic hope.
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SPEAKER_01
But in five years, maybe we chat again.
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SPEAKER_01
And
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SPEAKER_01
we have a system, right, a set of weights that we can teach it to play StarCraft.
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SPEAKER_01
Maybe not at the level of AlphaStar, but play StarCraft, a complex game.
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SPEAKER_01
We teach it through interactions to prompting.
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SPEAKER_01
You can certainly prompt a system, that s what Gato shows, to play some simple Atari games.
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SPEAKER_01
So imagine if you start talking to a system, teaching it a new game, showing it examples of, you know, in this particular game, this user did something good.
3
SPEAKER_01
Maybe the system can even play and ask you questions, say, hey, I played this game.
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SPEAKER_01
I just played this game.
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SPEAKER_01
Did I do well?
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SPEAKER_01
Can you teach me more?
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SPEAKER_01
So five, maybe to 10 years, these capabilities, or what meta-learning means, will be much more interactive, much more rich, and through domains that we were specializing, right?
3
SPEAKER_01
So you see the difference, right?
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SPEAKER_01
We built AlphaStar specialized
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SPEAKER_01
to play StarCraft.
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SPEAKER_01
The algorithms were general, but the weights were specialized.
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SPEAKER_01
And what we re hoping is that we can teach a network to play games, to play any game, just using games as an example, through interacting with it, teaching it, uploading the Wikipedia page of StarCraft.
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SPEAKER_01
This is
3
SPEAKER_01
in the horizon.
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SPEAKER_01
And obviously, there are details that need to be filled and research needs to be done.
3
SPEAKER_01
But that s how I see meta-learning above, which is going to be beyond prompting.
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SPEAKER_01
It s going to be a bit more interactive.
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SPEAKER_01
It s going to, you know, the system might tell us to give it feedback after it maybe makes mistakes or it loses a game.
3
SPEAKER_01
But it s nonetheless very exciting, because if you think about this this way,
3
SPEAKER_01
The benchmarks are already there.
3
SPEAKER_01
We just repurpose the benchmarks, right?
3
SPEAKER_01
So in a way, I like to map the space of what maybe AGI means to say, OK, like we went 101% performance in Go, in Chess, in StarCraft.
3
SPEAKER_01
the next iteration might be 20% performance across quote unquote all tasks, right?
3
SPEAKER_01
And even if it s not as good, it s fine.
3
SPEAKER_01
We actually, we have ways to also measure progress because we have those special agents, specialized agents, and so on.
3
SPEAKER_01
So this is, to me, very exciting.
3
SPEAKER_01
And
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SPEAKER_01
these next iteration models are definitely hinting at that direction of progress, which hopefully we can have.
3
SPEAKER_01
There are obviously some things that could go wrong in terms of we might not have the tools, maybe transformers are not enough, then we must... there s some breakthroughs to come, which makes the field more exciting to people like me as well, of course.
3
SPEAKER_01
But that s
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SPEAKER_01
If you ask me, five to 10 years, you might see these models that start to look more like weights that are already trained.
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SPEAKER_01
And then it s more about teaching or make their meta learn what you re trying to induce in terms of tasks and so on.
3
SPEAKER_01
Well beyond the simple now tasks we re starting to see emerge, like, you know, small arithmetic tasks and so on.
3
SPEAKER_00
So a few questions around that, this is fascinating.
3
SPEAKER_00
So that kind of teaching, interactive, so it s beyond prompting, so it s interacting with the neural network, that s different than the training process.
3
SPEAKER_00
So it s different than the optimization over differentiable functions.
3
SPEAKER_00
This is already trained and now you re teaching, I mean,
3
SPEAKER_00
It s almost like akin to the brain, the neurons are already set with their connections.
3
SPEAKER_00
On top of that, you re now using that infrastructure to build up further knowledge.
3
SPEAKER_00
Okay, so that s a really interesting distinction that s actually not obvious from a software engineering perspective, that there s a line to be drawn.
3
SPEAKER_00
Because you always think for neural networks
4
SPEAKER_01
to learn, it has to be retrained, trained and retrained.
4
SPEAKER_01
But maybe, and prompting is a way of teaching.
4
SPEAKER_01
and you ll now work a little bit of context about whatever the heck you re trying to do.
4
SPEAKER_01
So you can maybe expand this prompting capability by making it interact.
4
SPEAKER_01
That s really, really interesting.
4
SPEAKER_00
Yeah, by the way, this is not, if you look at way back at different ways to tackle even classification tasks.
4
SPEAKER_00
So this comes from like long standing literature in machine learning.
4
SPEAKER_00
What I m suggesting could sound to some like a bit like nearest neighbor.
4
SPEAKER_00
So nearest neighbor is almost the simplest algorithm that does not require learning.
4
SPEAKER_00
So it has this interesting, like you don t need to compute gradients.
4
SPEAKER_00
And what nearest neighbor does is you quote unquote, have a data set or upload a data set.
4
SPEAKER_00
And then all you need to do is a way to measure distance between points.
4
SPEAKER_00
And then to classify a new point, you re just simply computing what s the closest point in this massive amount of data, and that s my answer.
4
SPEAKER_00
So you can think of prompting in a way as you re uploading, not just simple points and the metric is not the distance between the images or something simple, it s something that you compute that s much more advanced, but in a way, it s very similar, right?
4
SPEAKER_00
You simply...
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SPEAKER_00
are uploading some knowledge to this pre-trained system in nearest neighbor.
4
SPEAKER_00
Maybe the metric is learned or not, but you don t need to further train it.
4
SPEAKER_00
And then now you immediately get a classifier out of this, right?
4
SPEAKER_00
Now it s just an evolution of that concept, very classical concept in machine learning, which is
4
SPEAKER_00
Yeah, just learning through what s the closest point, closest by some distance, and that s it.
4
SPEAKER_00
It s an evolution of that.
4
SPEAKER_00
And I will say how I saw meta learning when we worked on a few ideas in 2016 was precisely through the lens of
4
SPEAKER_00
nearest neighbor, which is very common in computer vision community, right?
4
SPEAKER_00
There s a very active area of research about how do you compute the distance between two images, but if you have a good distance metric, you also have a good classifier, right?
4
SPEAKER_00
All I m saying is now these distances and the points are not just images, they re like words or sequences of
4
SPEAKER_00
words and images and actions that teach you something new, but it might be that technique-wise, those come back.
4
SPEAKER_00
And I will say that it s not necessarily true that you might not ever train the weights a bit further.
4
SPEAKER_00
Some aspects of meta-learning, some techniques in meta-learning, do actually do a bit of fine-tuning, as it s called.
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SPEAKER_00
They train the weights a little bit when they get a new task.
4
SPEAKER_00
So
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SPEAKER_00
as I call the how, or how we re going to achieve this.
4
SPEAKER_00
As a deep learner, I m very skeptic.
4
SPEAKER_00
We re going to try a few things, whether it s a bit of training, adding a few parameters, thinking of this as nearest neighbor, or just simply thinking of there s a sequence of words, it s a prefix, and that s the new classifier.
4
SPEAKER_00
We ll see, right?
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SPEAKER_00
There s the beauty of research, but what s important is that is a good goal in itself that I see as very worthwhile pursuing for the next stages of not only meta-learning.
4
SPEAKER_00
I think this is basically what s exciting about machine learning, period, to me.
4
SPEAKER_01
Well, the interactive aspect of that is also very interesting.
4
SPEAKER_01
The interactive version of nearest neighbor.
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SPEAKER_01
to help you pull out the classifier from this giant thing.
4
SPEAKER_01
Okay, is this the way we can go in five, 10 plus years from any task, sorry, from many tasks to any task?
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SPEAKER_01
And what does that mean?
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SPEAKER_01
What does it need to be actually trained on?
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SPEAKER_01
At which point is the network had enough?
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SPEAKER_01
What does a network need to learn about this world in order to be able to perform any task?
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SPEAKER_01
Is it just as simple as language, image, and action?
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SPEAKER_01
Or do you need some set of representative images
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SPEAKER_01
Like, if you only see land images, will you know anything about underwater?
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SPEAKER_01
Is that somehow fundamentally different?
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SPEAKER_01
I don t know.
4
SPEAKER_00
Those, I mean, those are open questions, I would say.
4
SPEAKER_00
I mean, the way you put, let me maybe further your example, right?
4
SPEAKER_00
If all you see is land images, but you re reading all about land and water worlds, but in books, right?
4
SPEAKER_00
Yes.
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SPEAKER_00
Would that be enough?
4
SPEAKER_00
Good question.
4
SPEAKER_00
We don t know, but I guess maybe you can join us if you want in our quest to find this.
4
SPEAKER_01
That s precisely... Waterworld, yeah.
4
SPEAKER_00
Yes, that s precisely, I mean, the beauty of research and that s...
4
SPEAKER_00
the research business we re in, I guess, is to figure this out and ask the right questions, and then iterate with the whole community, publishing findings and so on.
4
SPEAKER_00
But yeah, this is a question.
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SPEAKER_00
It s not the only question, but it s certainly, as you ask, it s on my mind constantly, right?
4
SPEAKER_00
And so we ll need to wait for
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SPEAKER_00
maybe let s say five years, let s hope it s not 10, to see what are the answers.
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Some people will largely believe in unsupervised or self-supervised learning of single modalities and then crossing them.
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Some people might think end-to-end learning is the answer.
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Modularity is maybe the answer.
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So we don t know, but
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We re just definitely excited to find out.
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But it feels like this is the right time and we re at the beginning of this journey.
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We re finally ready to do these kind of general big models and agents.
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What do you sort of specific technical thing about Gato, Flamingo, Chinchilla, Gopher, any of these that is especially beautiful, that was surprising maybe?
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Is there something that just jumps out at you?
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Of course, there s the general thing of like, you didn t think it was possible and then you realize it s possible in terms of the generalizability across modalities and all that kind of stuff.
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Or maybe how small of a network, relatively speaking, Gatoway is, all that kind of stuff.
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But is there some weird little things that were surprising?
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Look, I ll give you an answer that s very important because maybe people don t quite realize this, but
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the teams behind these efforts, the actual humans, that s maybe the surprising, in an obviously positive way.
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So anytime you see these breakthroughs, I mean, it s easy to map it to a few people.
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There s people that are great at explaining things and so on.
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That s very nice.
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But maybe the learnings or the meta-learnings that I get as a human about this is, sure, we can move forward, but the surprising bit is how
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How important are all the pieces of these projects?
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How do they come together?
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So I ll give you maybe some of the ingredients of success that are common across these, but not the obvious ones in machine learning.
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I can always also give you those.
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But basically there is...
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engineering is critical.
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So very good engineering, because ultimately, we re collecting data sets, right?
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So the engineering of data, and then of deploying the models at scale into some compute cluster that cannot go understated, that is a huge factor of success.
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And it s hard to believe that
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details matter so much.
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We would like to believe that it s true that there is more and more of a standard formula, as I was saying, like this recipe that works for everything.
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But then when you zoom into this age of these projects, then you realize the devil is indeed in the details.
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And then the teams
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have to work kind of together towards these goals.
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So engineering of data and obviously clusters and large scale is very important.
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And then one that is often not, maybe nowadays it is more clear, is benchmark progress, right?
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So we re talking here about multiple months of, you know, tens of researchers and people that are trying to organize the research and so on, working together.
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And
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you don t know that you can get there.
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I mean, this is the beauty.
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Like if you re not risking to trying to do something that feels impossible, you re not gonna get there, but you need a way to measure progress.
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So the benchmarks that you build are critical.
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I ve seen this beautifully play out in many projects.
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I mean, maybe the one I ve seen it more consistently, which means we establish the metric
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actually the community did, and then we leveraged that massively is AlphaFold.
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This is a project where the data, the metrics were all there, and all it took was, and it s easier said than done, an amazing team working not to try to find some incremental improvement and publish, which is one way to do research that is valid, but aim very high and work literally for years.
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to iterate over that process.
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And working for years with a team, I mean, it is tricky.
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That also happened to happen partly during a pandemic and so on.
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So I think my meta learning from all this is the teams are critical to the success.
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And then if now going to the machine learning, the part that s surprising is, so we like architectures like neural networks and
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I would say this was a very rapidly evolving field until the transformer came.
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So Attention might indeed be all you need, which is the title, also a good title, although in hindsight is good.
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I don t think at the time I thought this is a great title for a paper, but that architecture is proving that the dream of modeling sequences of any bytes
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there is something there that will stick.
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And I think these advance in architectures, in kind of how neural networks are architectured to do what they do.
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It s been hard to find one that has been so stable and relatively has changed very little since it was invented five or so years ago.
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So that is a surprising, is a surprise that keeps recurring into other projects.
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try to, on a philosophical or technical level, introspect what is the magic of attention?
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What is attention?
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That s attention in people that study cognition, so human attention.
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I think there s giant wars over what attention means, how it works in the human mind.
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So what, there s very simple looks at what attention is in neural network.
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from the days of attention is all you need, but do you think there s a general principle that s really powerful here?
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SPEAKER_00
Yeah, so a distinction between transformers and LSTMs, which were what came before, and there was a transitional period where you could use both.
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In fact, when we talked about AlphaStar, we used transformers and LSTMs, so it was still the beginning of transformers.
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They were very powerful, but LSTMs were still also very powerful sequence models.
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The power of the transformer is that it has built in what we call an inductive bias of attention that makes the model, when you think of a sequence of integers, right?
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SPEAKER_00
Like we discussed this before, right?
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This is a sequence of words.
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When you have to do very hard tasks over these words, this could be, we re gonna translate a whole paragraph, or we re gonna predict the next paragraph given 10 paragraphs before.
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There s some loose intuition from how we do it as a human that is very nicely mimicked and replicated, structurally speaking, in the Transformer, which is this idea of
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You re looking for something, right?
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So you re sort of, when you re, you just read a piece of text, now you re thinking what comes next.
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You might wanna re-look at the text or look it from scratch.
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I mean, readily is because there s no recurrence.
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You re just thinking what comes next and
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It s almost hypothesis driven, right?
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So if I m thinking the next word that I ll write is cat or dog, OK, the way the transformer works almost philosophically is it has these two hypotheses.
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Is it going to be cat or is it going to be dog?
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And then it says, OK, if it s cat, I m going to look for certain words, not necessarily cat, although cat is an obvious word you would look in the past to see whether it makes more sense to output cat or dog.
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And then it does some very
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deep computation over the words and beyond, right?
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So it combines the words, but it has the query, as we call it, that is cat.
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And then similarly for dog, right?
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And so it s a very computational way to think about
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Look, if I m thinking deeply about text, I need to go back to look at all of the text, attend over it.
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But it s not just attention.
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What is guiding the attention, and that was the key insight from an earlier paper, is not how far away is it.
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I mean, how far away is it is important.
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SPEAKER_00
What did I just write about?
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That s critical.
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But what you wrote about 10 pages ago might also be critical.
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So you re looking not positionally, but content wise, right?
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And transformers have this beautiful way to query for certain content and pull it out in a compressed way.
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So then you can make a more informed decision.
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SPEAKER_00
I mean, that s one way to explain transformers.
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But I think it s a very powerful inductive bias.
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SPEAKER_00
There might be some details that might change over time, but I think that is what makes Transformers so much more powerful than the recurrent networks that were more recency bias-based, which obviously works in some tasks, but it has major flaws.
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SPEAKER_00
Transformer itself has flaws, and I think the main one, the main challenge is these prompts that we just were talking about.
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SPEAKER_00
They can be a thousand words long, but if I m teaching you StarCraft, I mean, I ll have to show you videos.
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I ll have to point you to whole Wikipedia articles about the game.
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We ll have to interact probably as you play, you ll ask me questions.
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The context required for us to achieve me being a good teacher to you on the game, as you would want to do it with a model.
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what I think goes well beyond the current capabilities.
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SPEAKER_00
So the question is, how do we benchmark these?
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SPEAKER_00
And then how do we change the structure of the architectures?
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SPEAKER_00
I think there s ideas on both sides, but we ll have to see empirically, right?
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SPEAKER_01
Obviously what ends up working in the- And as you talked about, some of the ideas could be, you know, keeping the constraint of that length in place, but then forming like hierarchical representations.
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SPEAKER_01
to where you can start being much cleverer in how you use those thousand tokens.
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SPEAKER_01
Yeah, that s really interesting, but it also is possible that this attentional mechanism where you basically, you don t have a recency bias, but you look more generally, you make it learnable.
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SPEAKER_01
The mechanism in which way you look back into the past, you make that learnable.
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SPEAKER_01
It s also possible where at the very beginning of that,
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SPEAKER_01
because that, you might become smarter and smarter in the way you query the past.
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SPEAKER_01
So recent past and distant past, and maybe very, very distant past.
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SPEAKER_01
So almost like the attention mechanism will have to improve and evolve as good as the tokenization mechanism, so you can represent long-term memory somehow.
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SPEAKER_00
And I mean, hierarchies are very, I mean, it s a very nice word that sounds appealing.
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There s lots of work adding hierarchy to the memories.
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SPEAKER_00
In practice, it does seem like we keep coming back to the main formula or main architecture.
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That sometimes tells us something.
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There s such a sentence that a friend of mine told me, like, whether it wants to work or not.
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So Transformer was clearly an idea that wanted to work.
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SPEAKER_00
And then I think there s some principles we believe will be needed, but finding the exact details, details matter so much, right?
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SPEAKER_00
That s going to be tricky.
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SPEAKER_01
I love the idea that there s...
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Like you as a human being, you want some ideas to work, and then there s the model that wants some ideas to work, and you get to have a conversation to see
5
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which more likely the model will win in the end.
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Because it s the one, you don t have to do any work.
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The model s the one that has to do the work, so you should listen to the model.
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SPEAKER_01
And I really love this idea that you talked about the humans in this picture, if I could just briefly ask.
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One is you re saying the benchmarks about the modular humans working on this.
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The benchmarks providing a sturdy ground of a wish to do these things that seem impossible.
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They give you, in the darkest of times, give you hope, because little signs of improvement.
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You could, like you re not, somehow you re not lost if you have metrics to measure your improvement.
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SPEAKER_01
And then there s other aspect, you said elsewhere, and here today, like titles matter.
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I wonder,
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how much humans matter in the evolution of all of this, meaning individual humans.
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You know, something about their interaction, something about their ideas, how much they change the direction of all of this.
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SPEAKER_01
Like if you change the humans in this picture, like is it that the model is sitting there and it wants some idea to work, or is it the humans, or maybe the model is providing you 20 ideas that could work, and depending on the humans you pick,
5
SPEAKER_01
they re going to be able to hear some of those ideas.
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Because you re now directing all of deep learning at DeepMind, you get to interact with a lot of projects, a lot of brilliant researchers.
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SPEAKER_01
How much variability is created by the humans in all of this?
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SPEAKER_00
Yeah, I mean, I do believe humans matter a lot, at the very least,
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SPEAKER_00
you know, timescale of years on when things are happening and what s the sequencing of it, right?
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SPEAKER_00
So you get to interact with people that, I mean, you mentioned this, some people really want some idea to work and they ll persist.
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And then some other people might be more practical, like, I don t care.
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what idea works?
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I care about, you know, cracking protein folding.
5
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And these, at least these two kind of seem opposite sides.
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We need both.
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And we ve clearly had both historically and that made certain things happen earlier or later.
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So definitely humans involved in all of this endeavor have had
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I would say years of change or of ordering, how things have happened, which breakthroughs came before which other breakthroughs and so on.
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SPEAKER_00
So certainly that does happen.
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SPEAKER_00
And so one other, maybe one other axis of distinction is what I called, and it is most commonly used in reinforcement learning is the exploration exploitation trade off as well.
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SPEAKER_00
It s not exactly what I meant, although quite related.
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So when you start
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trying to help others, right?
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Like you become a bit more of a mentor to a large group of people, be it a project or the deep learning team or something, or even in the community when you interact with people in conferences and so on.
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You re identifying quickly some things that are explorative or exploitative.
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And it s tempting to try to guide people, obviously.
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I mean, that s what makes our experience.
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We bring it and we try to shape things, sometimes wrongly.
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And there s many times that I ve been wrong in the past.
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That s great.
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it would be wrong to dismiss any sort of the research styles that I m observing.
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And I often get asked, well, you re in industry, right?
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So we do have access to large compute scale and so on.
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So there s certain kinds of research, I almost feel like
5
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we need to do responsibly and so on.
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But it is, Carlos, we have the particle accelerator here, so to speak, in physics.
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So we need to use it.
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We need to answer the questions that we should be answering right now for the scientific progress.
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But then at the same time, I look at many advances, including attention, which was discovered
5
SPEAKER_00
in Montreal, initially, because of lack of compute, right?
5
SPEAKER_00
So we were working on sequence-to-sequence with my friends over at Google Brain at the time, and we were using, I think, eight GPUs, which was somehow a lot at the time.
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And then I think Montreal was a bit more limited in the scale, but then they discovered this content-based attention concept that then has obviously triggered things like Transformer.
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Not everything obviously starts
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transformer, there s always a history that is important to recognize, because then you can make sure that then those who might feel now, well, we don t have so much compute, you need to then help them optimize that kind of research that might actually produce amazing change.
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SPEAKER_00
Perhaps it s not
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as short term as some of these advancements, or perhaps it s a different timescale.
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SPEAKER_00
But the people and the diversity of the field is quite critical that we maintain it.
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SPEAKER_00
And at times, especially mixed a bit with hype or other things, it s a bit tricky to be observing maybe too much of the same thinking across the board.
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SPEAKER_00
But the humans definitely are critical.
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SPEAKER_00
And I can think of quite a few personal examples where also
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SPEAKER_00
someone told me something that had a huge, you know, huge effect on to some idea.
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SPEAKER_00
And then that s why I m saying at least in terms of years, probably some things do happen.
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SPEAKER_00
Yeah, it s fascinating.
5
SPEAKER_01
It s also fascinating how constraints somehow are essential for innovation.
5
SPEAKER_01
And the other thing you mentioned about engineering, I have a sneaking suspicion, maybe I over, you know, my love is with engineering.
5
SPEAKER_01
So,
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I have a sneaking suspicion that a large percentage of the genius is in the tiny details of engineering.
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SPEAKER_01
So I think we like to think the genius is in the big ideas.
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SPEAKER_01
I have a sneaking suspicion that like, because I ve seen the genius of details, of engineering details, make the night and day difference.
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SPEAKER_01
And I wonder if those kind of have a ripple effect over time.
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SPEAKER_01
So that too, so that s sort of taking the engineering perspective, that sometimes that quiet innovation at the level of an individual engineer, or maybe at the small scale of a few engineers, can make all the difference.
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SPEAKER_01
That scales, because we re doing, we re working on computers that are scaled across large groups, that one engineering decision can lead to ripple effects.
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SPEAKER_01
Which is interesting to think about.
5
SPEAKER_00
Yeah, I mean, engineering, there s also kind of a historical, it might be a bit random.
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SPEAKER_00
Because if you think of the history of how, especially deep learning and neural networks took off, feels like
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a bit random because GPUs happened to be there at the right time for a different purpose, which was to play video games.
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SPEAKER_00
So even the engineering that goes into the hardware and it might have a time, like the timeframe might be very different.
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SPEAKER_00
I mean, the GPUs were evolved throughout many years where we didn t even, we re looking at that, right?
5
SPEAKER_00
So even at that level, right, that revolution, so to speak,
5
SPEAKER_00
The ripples are like, we ll see when they stop, right?
5
SPEAKER_00
But in terms of thinking of why is this happening, right?
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SPEAKER_00
I think that when I try to categorize it in sort of things that might not be so obvious, I mean, clearly there s a hardware revolution.
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SPEAKER_00
We are surfing thanks to that.
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Data centers as well.
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SPEAKER_00
I mean, data centers.
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SPEAKER_00
At Google, for instance, obviously they re serving Google, but there s also now, thanks to that and to have built such amazing data centers, we can train these models.
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SPEAKER_00
Software is an important one.
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SPEAKER_00
I think if I look at the state of how I had to implement things to implement my ideas, how I discarded ideas because they were too hard to implement.
5
SPEAKER_00
Yeah, clearly the times have changed and thankfully we are in a much better software position as well.
5
SPEAKER_00
And then, I mean, obviously there s research that happens at scale and more people enter the field.
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SPEAKER_00
That s great to see, but it s almost enabled by these other things.
5
SPEAKER_00
And last but not least is also data, right?
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SPEAKER_00
curating data sets, labeling data sets, these benchmarks we think about.
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SPEAKER_00
Maybe we ll want to have all the benchmarks in one system, but it s still very valuable that someone put the thought and time and the vision to build certain benchmarks.
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SPEAKER_00
We ve seen progress thanks to that.
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SPEAKER_00
We re going to repurpose the benchmarks.
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SPEAKER_00
That s the beauty of Atari.
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SPEAKER_00
It s like
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we solved it in a way, but we use it in Gato.
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It was critical.
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SPEAKER_00
And I m sure there s still a lot more to do thanks to that amazing benchmark that someone took the time to put, even though at the time maybe, oh, you have to think what s the next iteration of architectures.
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SPEAKER_00
That s what maybe the field recognizes, but we need to, that s another thing we need to balance in terms of a human s behind.
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SPEAKER_00
We need to recognize all these aspects because they re all critical and we tend to,
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SPEAKER_00
Yeah, we tend to think of the genius, the scientist and so on.
5
SPEAKER_00
But I m glad you re I know you have a strong engineering background.
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SPEAKER_01
But also I m a lover of data and it is a pushback on the engineering comment.
5
SPEAKER_01
ultimately could be the creators of benchmarks who have the most impact.
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SPEAKER_01
Andrej Karpathy, who you mentioned, has recently been talking a lot of trash about ImageNet, which he has the right to do because of how critical he is about, how essential he is to the development and the success of deep learning around ImageNet.
5
SPEAKER_01
And you re saying that that s actually, that benchmark is holding back the field.
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SPEAKER_01
because, I mean, especially in his context on Tesla autopilot, that s looking at real world behavior of a system.
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SPEAKER_01
There s something fundamentally missing about ImageNet that doesn t capture the real worldness of things, that we need to have data sets, benchmarks that have the unpredictability, the edge cases, whatever the heck it is that makes the real world so difficult to operate in, we need to have benchmarks of that.
5
SPEAKER_01
But just to think about the impact of ImageNet as a benchmark, and that really puts a lot of emphasis on the importance of a benchmark, both sort of internally at DeepMind and as a community.
5
SPEAKER_01
So one is coming in from within, like how do I create a benchmark for me to mark and make progress, and how do I make a benchmark for the community to mark,
5
SPEAKER_01
and push progress.
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SPEAKER_01
You have this amazing paper you co-authored, a survey paper called Emergent Abilities of Large Language Models.
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SPEAKER_01
It has, again, the philosophy here that I d love to ask you about.
5
SPEAKER_01
what s the intuition about the phenomena of emergence in neural networks, transformers, language models?
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SPEAKER_01
Is there a magic threshold beyond which we start to see certain performance?
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SPEAKER_01
And is that different from task to task?
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SPEAKER_01
Is that us humans just being poetic and romantic?
5
SPEAKER_01
Or is there literally some level at which we start to see breakthrough performance?
5
SPEAKER_00
Yeah, I mean, this is a property that we start seeing in systems that actually tend to be, so in machine learning, traditionally, again, going to benchmarks, I mean, if you have some input-output, right, like that is just a single input and a single output, you generally, when you train these systems, you see reasonably smooth,
5
SPEAKER_00
curves when you analyze how much the data set size affects the performance, or how the model size affects the performance, or how long you train the system for affects the performance, right?
5
SPEAKER_00
So, if we think of ImageNet, the training curves look fairly smooth and predictable in a way.
5
SPEAKER_00
And I would say that s probably because it s kind of a one hop reasoning task, right?
5
SPEAKER_00
It s like, here is an input and you think for a few milliseconds or 100 milliseconds, 300 as a human, and then you tell me, yeah, there s an alpaca in this image.
5
SPEAKER_00
So in language, we are seeing benchmarks that require
5
SPEAKER_00
more pondering and more thought in a way, right?
5
SPEAKER_00
This is just kind of you need to look for some subtleties that it involves inputs that you might think of, even if the input is a sentence describing a mathematical problem, there is a bit more processing required as a human and more introspection.
5
SPEAKER_00
So I think the
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SPEAKER_00
how these benchmarks work means that there is actually a threshold.
5
SPEAKER_00
Just going back to how transformers work in this way of querying for the right questions to get the right answers, that might mean that performance becomes random until the right question is asked by the querying system of a transformer or of a language model like a transformer.
5
SPEAKER_00
And then
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SPEAKER_00
Only then you might start seeing performance going from random to non-random.
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SPEAKER_00
And this is more empirical.
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SPEAKER_00
There s no formalism or theory behind this yet, although it might be quite important.
5
SPEAKER_00
But we re seeing these phase transitions of random performance and until some, let s say, scale of a model.
5
SPEAKER_00
And then it goes beyond that.
5
SPEAKER_00
And it might be that you need to fit a few low order bits of thought
5
SPEAKER_00
before you can make progress on the whole task.
5
SPEAKER_00
And if you could measure, actually, those breakdown of the task, maybe you would see more smooth, oh, like, yeah, this, you know, once you get this and this and this and this and this, then you start making progress in the task.
5
SPEAKER_00
but it s somehow a bit annoying because then it means that certain questions we might ask about architectures possibly can only be done at certain scale.
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SPEAKER_00
And one thing that conversely I ve seen great progress on in the last couple of years is this notion of science of deep learning and science of scale in particular, right?
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SPEAKER_00
On the negative is that there s some benchmarks for which progress might need to be measured at minimum and at certain scale until you see then what details of the model matter to make that performance better, right?
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SPEAKER_00
So that s a bit of a con.
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But what we ve also seen is that you can sort of empirically analyze behavior of models at scales that are
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smaller, right?
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So let s say, to put an example, we had this chinchilla paper that revised the so-called scaling laws of models.
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And that whole study is done at a reasonably small scale, right?
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That may be hundreds of millions up to one billion parameters.
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And then the cool thing is that you create some loss, right?
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Some loss that some trends, right?
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SPEAKER_00
You extract trends from data that you see, okay, like it looks like the amount of data required to train now a 10X larger model would be this.
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SPEAKER_00
And these loss so far, these extrapolations have helped us safe compute and just get to a better place in terms of the science of how should we run these models at scale, how much data, how much depth and all sorts of questions we start asking
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extrapolating from small scale.
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SPEAKER_00
But then this emergence is, sadly, that not everything can be extrapolated from scale, depending on the benchmark.
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SPEAKER_00
And maybe the harder benchmarks are not so good for extracting these laws.
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SPEAKER_00
But we have a variety of benchmarks at least.
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SPEAKER_01
So I wonder to which degree the threshold, the phase shift scale is a function of the benchmark.
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SPEAKER_01
Some of the science of scale might be engineering benchmarks where that threshold is low.
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Sort of taking a main benchmark and reducing it somehow, where the essential difficulty is left but the emergent
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SPEAKER_01
the scale at which the emergence happens is lower.
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SPEAKER_01
Just for the science aspect of it versus the actual real world aspect.
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SPEAKER_00
Yeah, so luckily we have quite a few benchmarks, some of which are simpler or maybe they re more like, I think people might call this systems one versus systems two style.
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SPEAKER_00
So I think what we re not seeing luckily is that extrapolations from maybe slightly more smooth or simpler benchmarks are translating to the harder ones.
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But that is not to say that this extrapolation will hit its limits.
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And when it does,
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then how much we scale or how we scale will sadly
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be a bit suboptimal until we find better laws, right?
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SPEAKER_00
And these laws, again, are very empirical laws.
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SPEAKER_00
They re not like physical laws of models, although I wish there would be better theory about these things as well.
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SPEAKER_00
But so far, I would say empirical theory, as I call it, is way ahead than actual theory of machine learning.
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SPEAKER_01
Let me ask you, almost for fun, so this is not, Oriel, as a deep mind person or anything to do with deep mind or Google, just as a human being, and looking at these news of a Google engineer who claimed that, I guess, the Lambda language model was sentient, or had the, and you still need to look into the details of this, but sort of,
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making an official report and a claim that he believes there s evidence that this system has achieved sentience.
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And I think this is a really interesting case on a human level, on a psychological level, on a
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technical machine learning level of how language models transform our world and also just philosophical level of the role of AI systems in a human world.
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So what did you, what do you find interesting?
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SPEAKER_01
What s your take on all of this as a machine learning engineer and a researcher and also as a human being?
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SPEAKER_00
Yeah, I mean, a few reactions, quite a few actually.
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SPEAKER_01
Have you ever,
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briefly thought, is this thing sentient?
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SPEAKER_00
Right.
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SPEAKER_00
So never, absolutely never.
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SPEAKER_00
Even with like AlphaStar?
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SPEAKER_00
Wait a minute.
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SPEAKER_00
Sadly, no, I think, yeah, sadly I have not.
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SPEAKER_00
Yeah, I think the current, any of the current models, although very useful and very good, yeah, I think we re quite far from that.
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SPEAKER_00
And there s kind of a converse
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side story.
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So one of my passions is about science in general.
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And I think I feel I m a bit of like a failed scientist.
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That s why I came to machine learning, because you always feel, and you start seeing this, that machine learning is maybe
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the science that can help other sciences, as we ve seen, right?
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SPEAKER_00
Like you, you know, it s such a powerful tool.
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SPEAKER_00
So thanks to that angle, right, that, okay, I love science, I love, I mean, I love astronomy, I love biology, but I m not an expert and I decided, well, the thing I can do better at is computers.
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SPEAKER_00
But having, especially with, when I was a bit more involved in AlphaFold, learning a bit about proteins and about biology and about life,
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SPEAKER_00
The complexity, it feels like, it really is like, I mean, if you start looking at the things that are going on at the atomic level, and also, I mean, there s obviously the
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SPEAKER_00
we are maybe inclined to try to think of neural networks as like the brain, but the complexities and the amount of magic that it feels when, I mean, I don t, I m not an expert, so it naturally feels more magic, but looking at biological systems, as opposed to these computer computational brains,
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SPEAKER_00
just makes me like, wow, there s such level of complexity difference still, right?
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SPEAKER_00
Like orders of magnitude complexity that, sure, these weights, I mean, we train them and they do nice things, but they re not at the level of biological entities, brains, cells.
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SPEAKER_00
It just feels like it s just not possible to achieve the same level of complexity behavior.
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SPEAKER_00
And my belief, when I talk to other beings, is certainly shaped by this amazement of biology that maybe because I know too much, I don t have about machine learning, but I certainly feel it s very far
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SPEAKER_00
fetched and far in the future to be calling or to be thinking, well, this mathematical function that is differentiable is in fact sentient and so on.
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SPEAKER_01
There s something on that point.
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SPEAKER_01
It s very interesting.
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So you know enough about machines and enough about biology to know that there s many orders of magnitude of difference and complexity.
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but you know how machine learning works.
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So the interesting question for human beings that are interacting with a system that don t know about the underlying complexity.
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And I ve seen people, probably including myself, that have fallen in love with things that are quite simple.
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SPEAKER_01
And so maybe the complexity is one part of the picture, but maybe that s not a necessary condition for sentience, for perception
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SPEAKER_01
or emulation of sentience.
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SPEAKER_00
Right.
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SPEAKER_00
So, I mean, I guess the other side of this is, that s how I feel personally.
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SPEAKER_00
I mean, you asked me about the person, right?
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SPEAKER_00
Now it s very interesting to see how other humans feel about things, right?
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SPEAKER_00
This is, this, we are like, again, like I m not as amazed about things that I feel are, this is not as magical as this other thing because of maybe how I got to learn about it and how,
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SPEAKER_00
I see the curve a bit more smooth because I, you know, like just seeing the progress of language models since Shannon in the 50s and actually looking at that timescale, we re not that fast progress, right?
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SPEAKER_00
I mean, what we were thinking at the time, like almost a hundred years ago,
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is not that dissimilar to what we re doing now.
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SPEAKER_00
But at the same time, yeah, obviously others, my experience, right, the personal experience, I think no one should, you know, I think no one should tell others how they should feel.
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SPEAKER_00
I mean, the feelings are very personal, right?
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SPEAKER_00
So how others might feel about the models and so on, that s one part of the story that is important to understand for me personally as a researcher.
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SPEAKER_00
And then
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SPEAKER_00
when I maybe disagree or I don t understand or see that, yeah, maybe this is not something I think right now is reasonable, knowing all that I know.
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SPEAKER_00
One of the other things, and perhaps partly why it s great to be talking to you and reaching out to the world about machine learning is, hey,
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SPEAKER_00
let s demystify a bit the magic and try to see a bit more of the math and the fact that literally to create these models, if we had the right software, it would be 10 lines of code and then just a dump of the internet.
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SPEAKER_00
So versus like then the complexity of like the creation of humans from their inception, right?
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SPEAKER_00
And also the complexity of evolution of the whole universe to where we are.
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that feels orders of magnitude more complex and fascinating to me.
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SPEAKER_00
So I think, yeah, maybe part of the only thing I m thinking about
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SPEAKER_00
trying to tell you is, yeah, I think explaining a bit of the magic, there is a bit of magic.
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SPEAKER_00
It s good to be in love, obviously, with what you do at work.
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SPEAKER_00
And I m certainly fascinated and surprised quite often as well.
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SPEAKER_00
But I think, hopefully, as experts in biology, hopefully you will tell me this is not as magic.
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And I m happy to learn that through interactions with the larger community, we can
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also have a certain level of education that in practice also will matter because, I mean, one question is how you feel about this, but then the other very important is you starting to interact with this in products and so on.
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SPEAKER_00
It s good to understand a bit what s going on, what s not going on, what s safe, what s not safe and so on, right?
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SPEAKER_00
Otherwise, the technology will not be used properly for good, which is obviously the goal of all of us, I hope.
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SPEAKER_01
So let me then ask the next question.
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SPEAKER_01
Do you think in order to solve intelligence, or to replace the legs bot that does interviews, as we started this conversation with, do you think the system needs to be sentient?
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SPEAKER_01
Do you think it needs to achieve something like consciousness?
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SPEAKER_01
And do you think about what consciousness is in the human mind that could be instructive for creating AI systems?
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SPEAKER_00
Yeah, honestly, I think probably not to the degree of intelligence that there s this brain that can learn, can be extremely useful, can challenge you, can teach you.
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SPEAKER_00
Conversely, you can teach it to do things.
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SPEAKER_00
I m not sure it s necessary, personally speaking, but
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SPEAKER_00
if consciousness or any other biological or evolutionary lesson can be repurposed to then influence our next set of algorithms, that is a great way to actually make progress, right?
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SPEAKER_00
And the same way I tried to explain Transformers a bit how it feels we operate when we look at text specifically, these insights are very important, right?
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SPEAKER_00
So there s a distinction between
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details of how the brain might be doing computation.
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I think my understanding is, sure, there s neurons and there s some resemblance to neural networks, but we don t quite understand enough of the brain in detail, right, to be able to replicate it.
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SPEAKER_00
But then more, if you zoom out a bit, how we then, our thought process, how memory works, maybe even how evolution got us here, what s exploration, exploitation, like all the, how these things happen.
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I think these clearly can inform algorithmic level.
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research.
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And I ve seen some examples of this being quite useful to then guide the research, even it might be for the wrong reasons, right?
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SPEAKER_00
So I think biology and what we know about ourselves can help at
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SPEAKER_00
whole lot to build essentially what we call AGI, this general, the real gato, right?
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SPEAKER_00
The last step of the chain, hopefully.
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SPEAKER_00
But consciousness in particular, I don t myself at least think too hard about how to add that to the system.
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SPEAKER_00
But maybe my understanding is also very personal about what it means, right?
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SPEAKER_00
I think this, even that in itself is a long debate that I know people have often, and maybe I should learn more about this.
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SPEAKER_01
Yeah, and I personally, I notice the magic often on a personal level, especially with physical systems like robots.
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SPEAKER_01
I have a lot of legged robots now in Austin that I play with, and even when you program them, when they do things you didn t expect, there s an immediate anthropomorphization, and you notice the magic, and you start to think about things like sentience, that,
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SPEAKER_01
has to do more with effective communication and less with any of these kind of dramatic things.
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SPEAKER_01
It seems like a useful part of communication.
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SPEAKER_01
Having the perception of consciousness seems like useful for us humans.
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SPEAKER_01
We treat each other more seriously.
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SPEAKER_01
We are able to do a nearest neighbor shoving of that entity into your memory correctly, all that kind of stuff.
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SPEAKER_01
It seems useful, at least to fake it, even if you never make it.
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SPEAKER_00
So maybe like, yeah, mirroring the question, and since you talked to a few people, then you do think that we ll need to figure something out in order to achieve intelligence in a grander sense of the word?
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SPEAKER_01
Yeah, I personally believe yes, but I don t even think it ll be like a separate island we ll have to travel to.
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SPEAKER_01
I think it ll emerge quite naturally.
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SPEAKER_00
Okay, that s easier for us then, thank you.
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SPEAKER_01
But the reason I think it s important to think about is you will start, I believe, like with this Google engineer, you will start seeing this a lot more, especially when you have AI systems that are actually interacting with human beings that don t have an engineering background.
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SPEAKER_01
And we have to prepare for that.
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because there ll be, I do believe there ll be a civil rights movement for robots, as silly as it is to say.
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SPEAKER_01
There s going to be a large number of people that realize there s these intelligent entities with whom I have a deep relationship and I don t wanna lose them.
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SPEAKER_01
They ve come to be a part of my life and they mean a lot.
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SPEAKER_01
They have a name, they have a story, they have a memory, and we start to ask questions about ourselves, well,
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SPEAKER_01
what this thing sure seems like it s capable of suffering, because it tells all these stories of suffering.
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SPEAKER_01
It doesn t wanna die and all those kinds of things, and we have to start to ask ourselves questions.
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SPEAKER_01
What is the difference between a human being and this thing?
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SPEAKER_01
So when you engineer, I believe, from an engineering perspective, from like a deep mind, or anybody that builds systems,
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SPEAKER_01
There might be laws in the future where you re not allowed to engineer systems with displays of sentience, unless they re explicitly designed to be that, unless it s a pet.
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SPEAKER_01
So if you have a system that s just doing customer support, you re legally not allowed to display sentience.
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SPEAKER_01
We ll start to ask ourselves that question, and then so that s going to be part of the software engineering process.
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SPEAKER_01
Which features do we have, and one of them is,
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SPEAKER_01
communications of the sentience.
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SPEAKER_01
But it s important to start thinking about that stuff, especially how much it captivates public attention.
6
SPEAKER_00
Yeah, absolutely.
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SPEAKER_00
It s definitely a topic that is important we think about.
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SPEAKER_00
And I think in a way, I always see, not every movie is equally on point with certain things, but certainly science fiction in this sense,
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SPEAKER_00
at least has prepared society to start thinking about certain topics that, even if it s too early to talk about, as long as we are reasonable, it s certainly going to prepare us for both the research to come and how to... I mean, there s many important challenges and topics that come with building an intelligent system, many of which you just mentioned, right?
6
SPEAKER_00
So I think
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SPEAKER_00
being, we re never going to be fully ready unless we talk about this.
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SPEAKER_00
And we start also, as I said, just kind of expanding the people we talk to, to not include only our own researchers and so on.
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SPEAKER_00
And in fact, places like DeepMind, but elsewhere, there s more interdisciplinary
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SPEAKER_00
groups forming up to start asking and really working with us on these questions.
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SPEAKER_00
Because obviously, this is not initially what your passion is when you do your PhD, but certainly it is coming, right?
6
SPEAKER_00
So it s fascinating, kind of.
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SPEAKER_00
It s the thing that brings me to one of my passions that is learning.
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SPEAKER_00
So in this sense, this is kind of a new area that
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SPEAKER_00
As a learning system myself, I want to keep exploring, and I think it s great to see parts of the debate, and even I ve seen a level of maturity in the conferences that deal with AI.
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SPEAKER_00
If you look five years ago,
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SPEAKER_00
to now, just the amount of workshops and so on has changed so much.
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SPEAKER_00
It s impressive to see how much topics of safety ethics and so on come to the surface, which is great.
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SPEAKER_00
And if we were too early, clearly it s fine.
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SPEAKER_00
I mean, it s a big field and there s lots of people with lots of interests that will do progress or make progress.
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SPEAKER_00
And obviously, I don t believe we re too late.
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SPEAKER_00
So in that sense, I think it s great that we re doing this already.
6
SPEAKER_01
Better be too early than too late when it comes to super-intelligent AI systems.
6
SPEAKER_01
Let me ask, speaking of sentient AIs, you gave props to your friend Ilyas Itzkever for being elected the Fellow of the World Society.
6
SPEAKER_01
So just as a shout-out to a fellow researcher and a friend, what s the secret to the genius of Ilyas Itzkever?
6
SPEAKER_01
And also, do you believe that his tweets, as you ve hypothesized and Andrei Karpathy did as well, are generated by a language model?
6
SPEAKER_00
Yeah, so I strongly believe Ilya is gonna visit in a few weeks, actually, so I ll ask him in person.
6
SPEAKER_01
But- Will he tell you the truth?
6
SPEAKER_00
Yes, of course, hopefully.
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SPEAKER_00
I mean, we re, you know, ultimately, we all have shared paths and there s friendships that go beyond, obviously, institutions and so on.
6
SPEAKER_00
So I hope he tells me the truth.
6
SPEAKER_01
Well, maybe the AI system is holding him hostage somehow.
6
SPEAKER_01
Maybe he has some videos that he doesn t want to release.
6
SPEAKER_01
So maybe,
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SPEAKER_01
It has taken control over him, so he can t tell the truth.
6
SPEAKER_00
Well, if I see him in person, then he will know.
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SPEAKER_00
But I think the, I think it s a good, I think Ilya s personality, just knowing him for a while.
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SPEAKER_00
Yeah, he s, everyone in Twitter, I guess, gets a different persona.
6
SPEAKER_00
And I think Ilya s one.
6
SPEAKER_00
does not surprise me, right?
6
SPEAKER_00
So I think knowing Ilya from before social media and before AI was so prevalent, I recognized a lot of his character.
6
SPEAKER_00
So that s something for me that I feel good about.
7
SPEAKER_01
a friend that hasn t changed or is still true to himself, right?
7
SPEAKER_01
Obviously, there is though a fact that your field becomes more popular and he is obviously one of the main figures in the field having done a lot of advancement.
7
SPEAKER_01
So I think that the tricky bit here is how to balance your true self with the responsibility that your words carry.
7
SPEAKER_01
So
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SPEAKER_01
In this sense, I appreciate the style and I understand it, but it created debates on some of his tweets.
7
SPEAKER_01
Maybe it s good we have them early anyways, but then the reactions are usually polarizing.
7
SPEAKER_01
I think we re just seeing the reality of social media a bit there as well, reflected on that particular topic or set of topics he s tweeting about.
7
SPEAKER_00
Yeah, I mean, it s funny that you speak to this tension.
7
SPEAKER_00
He was one of the early seminal figures in the field of deep learning, and so there s a responsibility with that.
7
SPEAKER_00
But he s also, from having interacted with him quite a bit, he s just a brilliant thinker about ideas.
7
SPEAKER_00
Which, as are you, and there s a tension between becoming the manager versus the actual thinking through very novel ideas.
7
SPEAKER_00
Duh.
7
SPEAKER_00
Yeah, the scientist versus the manager.
7
SPEAKER_00
And he s one of the great scientists of our time.
7
SPEAKER_00
This was quite interesting.
7
SPEAKER_00
And also people tell me quite silly, which I haven t quite detected yet, but in private, we ll have to see about that.
7
SPEAKER_01
Yeah, I mean, just on the point of, I mean, Ilya has been an inspiration.
7
SPEAKER_01
I mean, quite a few colleagues I can think shaped, you know, the person you are, like Ilya certainly,
7
SPEAKER_01
gets probably the top spot, if not close to the top.
7
SPEAKER_01
And if we go back to the question about people in the field, like how their role would have changed the field or not, I think Ilya s case is interesting because
7
SPEAKER_01
He really has a deep belief in the scaling up of neural networks.
7
SPEAKER_01
There was a talk that is still famous to this day from the Sequence to Sequence paper, where he was just claiming, just give me supervised data and a large neural network, and then you ll solve basically all the problems, right?
7
SPEAKER_01
That vision was already there many years ago.
7
SPEAKER_01
So it s good to see someone who is, in this case, very deeply into this style of research and clearly has had a tremendous track record of successes and so on.
7
SPEAKER_01
The funny bit about that talk is that we rehearsed the talk in a hotel room before and
7
SPEAKER_01
the original version of that talk would have been even more controversial.
7
SPEAKER_01
So maybe I m the only person that has seen the unfiltered version of the talk.
7
SPEAKER_01
And maybe when the time comes, maybe we should revisit some of the skipped slides from the talk from Ilya.
7
SPEAKER_01
But I really think
7
SPEAKER_01
The deep belief into some certain style of research pays out, right?
7
SPEAKER_01
It s good to be practical sometimes, and I actually think Ilya and myself are practical, but it s also good there s some sort of long-term belief and trajectory.
7
SPEAKER_01
Obviously, there s a bit of luck involved, but it might be that that s the right path.
7
SPEAKER_01
Then you clearly are ahead and hugely influential to the field, as he has been.
7
SPEAKER_00
Do you agree with that intuition that maybe was written about by Rich Sutton in The Bitter Lesson, that the biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective?
7
SPEAKER_00
Do you think that intuition is ultimately correct?
7
SPEAKER_00
General methods, leveraged computation, allowing the scaling of computation to do a lot of the work, and so the basic task of us humans is to design methods that are more and more general versus more and more specific to the tasks at hand.
7
SPEAKER_01
I certainly think this essentially mimics a bit of the deep learning research, almost like philosophy, that on the one hand, we want to be data agnostic.
7
SPEAKER_01
We don t want to pre-process data sets.
7
SPEAKER_01
We want to see the bytes, right, like the true data as it is, and then learn everything on top.
7
SPEAKER_01
So very much agree with that.
7
SPEAKER_01
And I think scaling up feels at the very least, again, necessary for building incredible complex systems.
7
SPEAKER_01
It s possibly not sufficient, barring that we need a couple of breakthroughs.
7
SPEAKER_01
I think Rich Sutton mentioned search being part of the equation of scale and search.
7
SPEAKER_01
I think search, I ve seen it
7
SPEAKER_01
That s been more mixed in my experience.
7
SPEAKER_01
So from that lesson in particular, search is a bit more tricky because it is very appealing to search in domains like Go, where you have a clear reward function that you can then discard some search traces.
7
SPEAKER_01
But then in some other tasks, it s not very clear how you would do that.
7
SPEAKER_01
Although recently, one of our recent works, which actually was mostly mimicking
7
SPEAKER_01
or a continuation, and even the team and the people involved were pretty much intersecting with AlphaStar, was AlphaCode, in which we actually saw the bitter lesson how scale of the models and then a massive amount of search yielded this very interesting result of being able to
7
SPEAKER_01
have human level code competition.
7
SPEAKER_01
So I ve seen examples of it being literally mapped to search and scale.
7
SPEAKER_01
I m not so convinced about the search bit, but certainly I m convinced scale will be needed.
7
SPEAKER_01
So we need general methods.
7
SPEAKER_01
We need to test them and maybe we need to make sure that we can scale them given the hardware that we have in practice, but then maybe we should also shape how the hardware looks like based on which methods might be needed to scale.
7
SPEAKER_01
And that s an interesting
7
SPEAKER_01
an interesting contrast of this GPU comment that is, we got it for free almost because games were using this, but maybe now if sparsity is required,
7
SPEAKER_01
We don t have the hardware, although in theory, I mean, many people are building different kinds of hardware these days, but there s a bit of this notion of hardware lottery for scale that might actually have an impact at least on the year, again, scale of years on how fast we ll make progress to maybe a version of neural nets or whatever comes next that might enable truly intelligent agents.
7
SPEAKER_00
Do you think in your lifetime we will build an AGI system?
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that would undeniably be a thing that achieves human-level intelligence and goes far beyond?
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I definitely think it s possible that it will go far beyond, but I m definitely convinced that it will be human-level intelligence.
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And I m hypothesizing about the beyond because
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The beyond bit is a bit tricky to define, especially when we look at the current formula of starting from this imitation learning standpoint, right?
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So we can certainly imitate humans.
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at language and beyond.
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So getting at human level through imitation feels very possible.
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Going beyond will require reinforcement learning and other things.
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And I think in some areas that certainly already has paid out.
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I mean, Go being an example that s my favorite so far in terms of going beyond human capabilities.
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But in general,
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I m not sure we can define reward functions that from a seed of imitating human level intelligence that is general and then going beyond.
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That bit is not so clear in my lifetime, but certainly human level, yes.
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And I mean, that in itself is already quite powerful, I think.
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So going beyond
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I think it s obviously not, we re not gonna not try that if then we get to superhuman scientists and discovery and advancing the world, but at least human level is also, in general, is also very, very powerful.
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Well, especially if human level or slightly beyond is integrated deeply with human society and there s billions of agents like that.
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Do you think there s a singularity moment beyond which our world would be just very deeply transformed by these kinds of systems?
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Because now you re talking about intelligent systems that are just, I mean, this is no longer just going from horse and buggy to the car.
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It feels like a very different kind of shift in what it means to be a living entity on Earth.
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Are you afraid or are you excited of this world?
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I m afraid if there s a lot more.
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So I think maybe we ll need to think about if we truly get there, just thinking of limited resources, like, you know, humanity clearly hits some limits, and then there s some balance, hopefully, that biologically, the planet is imposing, and we should actually try to get better at this.
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As we know, there s quite a few issues with having too many
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people coexisting in a resource-limited way.
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So for digital entities, it s an interesting question.
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I think such a limit maybe should exist, but maybe it s going to be imposed by energy availability because this also consumes energy.
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In fact, most systems are more inefficient than we are in terms of energy required.
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But definitely, I think as a society, we ll need to
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just work together to find what would be reasonable in terms of growth or how we coexist if that is to happen.
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I am very excited about, obviously, the aspects of automation that make people that obviously don t have access to certain resources or knowledge, for them to have that access.
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I think those are the applications in a way that I m most excited to see and to personally work towards.
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Yeah, there s going to be significant improvements in productivity and the quality of life across the whole population, which is very interesting.
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But I m looking even far beyond us becoming a multi-planetary species.
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And just as a quick bet, last question, do you think as humans become multi-planetary species, go outside our solar system, all that kind of stuff, do you think there ll be more humans or more robots in that future world?
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So will humans be,
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the quirky, intelligent being of the past, or is there something deeply fundamental to human intelligence that s truly special, where we will be part of those other planets, not just AI systems?
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I think we re all excited to build AGI to
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empower or make us more powerful as human species.
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Not to say there might be some hybridization.
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I mean, this is obviously speculation, but there are companies also trying to, the same way medicine is making us better.
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Maybe there are other things that are yet to happen on that.
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But
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if the ratio is not at most one-to-one, I would not be happy.
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So I would hope that we are part of the equation, but maybe there s maybe a one-to-one ratio feels like possible, constructive and so on, but it would not be good to have a misbalance, at least from my core beliefs and the why I m doing what I m doing when I go to work and I research what I research.
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Well, this is how I know you re human, and this is how you ve passed the Turing test.
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And you are one of the special humans, Oriel.
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It s a huge honor that you would talk with me, and I hope we get the chance to speak again, maybe once before the singularity, once after, and see how our view of the world changes.
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Thank you again for talking today.
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Thank you for the amazing work you do.
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You re a shining example of a researcher and a human being in this community.
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Thank you.
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Thanks a lot, Lex.
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Yeah, looking forward to before the singularity, certainly.
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And maybe after.
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Thanks for listening to this conversation with Ariel Vinales.
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To support this podcast, please check out our sponsors in the description.
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And now let me leave you with some words from Alan Turing.
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Those who can imagine anything can create the impossible.
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Thank you for listening and hope to see you next time.