#306 – Oriol Vinyals: Deep Learning and Artificial General Intelligence

Oriol Vinyals is the Research Director and Deep Learning Lead at DeepMind. Please support this podcast by checking out our sponsors: – Shopify: https://shopify.com/lex to get 14-day free trial – Weights & Biases: https://lexfridman.com/wnb – Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off – Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium EPISODE LINKS: Oriol’s Twitter: https://twitter.com/oriolvinyalsml Oriol’s publications: https://scholar.google.com/citations?user=NkzyCvUAAAAJ DeepMind’s Twitter: https://twitter.com/DeepMind DeepMind’s Instagram: https://instagram.com/deepmind DeepMind’s Website: https://deepmind.com Papers: 1. Gato: https://deepmind.com/publications/a-generalist-agent 2. Flamingo: https://deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model 3. Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.14165 4. Emergent Abilities of Large Language Models: https://arxiv.org/abs/2206.07682 5. Attention Is All You Need: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (05:18) – AI (20:14) – Weights (26:33) – Gato (1:01:22) – Meta learning (1:15:21) – Neural networks (1:37:46) – Emergence (1:44:30) – AI sentience (2:08:27) – AGI

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Oriol Vinyalis, his second time in the podcast. Oriol 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. And now, a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We've got Shopify for e-commerce, Weights and Biases for machine learning, Magic Spoon for breakfast, and Blinkist for nonfiction. Choose wisely, my friends. And now, onto the full ad reads. No ads in the middle. I try to make this interesting, but if you skip them, please still check out our sponsors. I enjoy their stuff. Maybe you will too.

• 0:44Lex Fridman?

  This show is brought to you by Shopify, a platform designed for anyone to sell anywhere. The great-looking online store that brings your ideas to life. And, of course, it has tools to manage day-to-day operation. A bunch of folks asked me to have some kind of merch. As a fan of a bunch of podcasts, I find merch to be pretty cool. It's a cool way to celebrate people you connect with because of a podcast, or a show, or any kind of thing. It's just fun. No matter what your goal is, Shopify is a great platform to make it happen. It makes the whole thing easy from beginning to end, including daily management.

• 1:27Lex Fridman?

  Anyway, Shopify powers over 1.7 million entrepreneurs. You can get a free trial and full access to Shopify's entire suite of features when you sign up at Shopify.com/lex. That's all lowercase, Shopify.com/lex. This show is also brought to you by Weights and Biases, the company helping machine learning teams build better models faster. You can debug, compare,

• 1:57Lex Fridman?

  We produce models, architectures, hyperparameters, Git commits, model weights, GPU usage, and even data sets and predictions. And you can do all of this while collaborating with teammates. It's like Google Docs type of collaboration, but for neural network hyperparameters. It's an amazing visualization, collaboration ecosystem for machine learning researchers. Their tools are free for personal use, and their Teams feature is available for free to academic researchers. Weights and biases gives you a better way to stay organized and be more productive. Join over 200,000 machine learning engineers and data scientists when you sign up

• 2:42Lex Fridman?

  at lexfridman.com/wnb. This episode is also brought to you by an old friend, Magic Spoon. Low carb, keto-friendly cereal. It has zero grams of sugar, 13 to 14 grams of protein, and only 4 net grams of carbs, and also 140 calories in each serving. They have been a good companion throughout this journey on this little podcast, and I've loved them throughout, both having them as a sponsor and to eat them. My favorite flavor of theirs is the cocoa. It reminds me of childhood, of happiness.

• 3:26Lex Fridman?

  It's delicious, like a cereal that makes you happy in childhood. And when I say childhood, by the way, I mean high school. Cereal was one of the few ways I would indulge. Magic Spoon has a 100% happiness guarantee, so if you don't like it, they will refund it. You can get a discount on your order if you go to magic spoon.com/lex and use code Lex.

• 3:50Lex Fridman?

  This show is brought to you by Blinkist, my favorite app for learning new things. Blinkist takes the key ideas from thousands of nonfiction books and condenses them down into just 15 minutes that you can read or listen to. I can recommend Sapiens: Meditations by Marcus Aurelius, Beginning of an Infinity by David Deutsch, The Snowden Book, all of it. There's the best nonfiction books ever written, or many of the best nonfiction books ever written are on there. I use it to both review the books I've already read, choose the books I'm going to read, or summarize books that I just don't have the time to read, but they're still a big part

• 4:33Lex Fridman?

  or an important part of public discourse. I have never found a place that's able to summarize the key insights in nonfiction books as well as Blinkist. I mean, it's an art form. You can claim a special offer for savings at blinkist.com/lex. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now to your friends, here's Ariel Venialas.

• 5:18Lex Fridman?

  You are one of the most brilliant researchers in the history of AI, working across all kinds of modalities. Probably the one common theme is it's always sequences of data. So we're talking about languages, images, even biology, and games, as we talked about last time. So you're a good person to ask this. In your lifetime, will we be able to build an AI system that's able to replace me as the interviewer in this conversation?

• 5:46Lex Fridman?

  In terms of ability to ask questions that are compelling to somebody listening. 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? How far away are we, do you think?

• 6:03Speaker 02

  It's a good question. I think partly I would say, do we want that? I really like when we start now with very powerful models, interacting with them and thinking of them more closer to us. The question is, if you remove the human side of the conversation, is that an interesting? You know, is that an interesting artifact? And I would say probably not. I've seen, for instance, last time we spoke, like was, we were talking about Starcraft. And creating, you know, agents that play games involves self-play. But ultimately, what people care about was, how does this agent behave when the opposite side is a human?

• 6:46Speaker 02

  So without a doubt, we will probably be more empowered by AI. Maybe you can source some questions from an AI system. 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. We call this cherry picking sometimes in the field of language. And likewise, if I had now the tools on my side, I could say, look, you're asking this interesting question. From this answer, I like the words chosen by this particular system that created a few words. Completely replacing it feels not exactly exciting to me. Although in my lifetime, I think way, I mean, given the trajectory, I think it's possible

• 7:30Speaker 02

  that perhaps 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.

• 7:46Lex Fridman?

  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? So there's probably a huge amount of data 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. So you can probably optimize the question that's most created an engaging conversation in the past. So actually, if you strictly use the word exciting. There is probably a way to create a optimally exciting conversations that are involved AI systems, at least one side is AI.

• 8:28Speaker 02

  Yeah, that makes sense. I think maybe looping back a bit to to games and the game industry. When you design algorithms, you're thinking about winning as the objective, right? Or the reward function. But in fact, when we discussed this with Blizzard, the creators of StarCraft in this case, I think what's exciting, fun, if you could measure that and optimize for that, that's probably why we play video games, or why we interact, or listen, or look at cat videos, or whatever on the internet. So it's true that modeling reward beyond the obvious reward functions we've used to in reinforcement learning is definitely very exciting. And again, there is some progress actually into a particular

• 9:13Speaker 02

  aspect of AI, which is quite critical, which is, for instance, is a conversation that or is the information truthful, right? So you could start trying to evaluate these from accepts from the internet, right, that has lots of information. And then if you can learn a function automated ideally, so you can also optimize it more easily, then you could actually have conversations that optimize for non-obvious things such as excitement. So yeah, that's quite possible. And then I would say, in that case, it would definitely be fun, a fun exercise and quite unique to have at least one side that is fully driven

• 9:52Speaker 02

  by an excitement reward function. But obviously, there would be still quite a lot of humanity in the system, both from who are who is building the system, of course, and also ultimately, if 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. As far as I understand, there's no such thing.

• 10:19Lex Fridman?

  Well, you mentioned truth also. I would actually venture to say that excitement is easier to label than truth, or is perhaps has lower consequences of failure. But there is perhaps The humanness that you mentioned, that's perhaps part of a thing that could be labeled. And that could mean an AI system that's doing dialogue, that's doing conversations, should be flawed, for example. Like, that's the thing you optimize for, which is have inherent contradictions by design, have flaws by design. Maybe it also needs to have a strong sense of identity.

• 11:02Lex Fridman?

  So it has a backstory it told itself that it sticks to. It has memories, not in terms of the how the system is designed, but it's able to tell stories about its past. It's able to have mortality and fear of mortality in the following way, that it has an identity. And like, if it says something stupid and gets canceled on Twitter, that's the end of that system. So it's not like you get to rebrand yourself. That system is, that's it. So maybe that, the high stakes nature of it, because like, you can't say anything stupid now, or, because you'd be canceled on Twitter. And that, there's, there's stakes to that. And then I think part of the reason that makes it interesting.

• 11:47Lex Fridman?

  And then you have a perspective, like you've built up over time, that you stick with. And then people can disagree with you. So holding that perspective strongly, holding sort of a, maybe a controversial, at least a, a strong opinion. 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. And then combine that with a metric of excitement, you can start to create something that, as opposed to trying to optimize for, sort of grammatical clarity and truthfulness, the, the factual consistency over many sentences, you optimize for the humanness. And there's obviously data for humanness on the internet.

• 12:32Lex Fridman?

  So I wonder, I wonder if there's a future where that's part, well, I mean, I, I sometimes wondered that about myself. I'm a huge fan of podcasts, and I listen to podcasts, some podcasts, and I think like, what is interesting about this? What is compelling?

• 12:49Lex Fridman?

  The same way you watch other games, like you said, watch play Starcraft or have Magnus Carlsen play chess. So I'm not a chess player, but it's still interesting to me. What is that? That's the stakes of it, maybe the end of a domination of a series of wins. 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.

• 13:27Speaker 02

  Yes, I actually recall, I was just listening to our first podcast where we discussed Turing test. So I would say from a neural network, AI builder perspective, there's, usually you try to map many of these interesting topics you discuss to benchmarks and then also to actual architectures on the how these systems are currently built, how they learn, what data they learn from, what are they learning, right? We're talking about weights of a mathematical function. And then looking at the current state of the game, maybe what do we need leaps forward

• 14:09Speaker 02

  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, it's a large vast of variety of human interactions online, and then you're distilling these sequences, right? Going back to my passion, like sequences of words, letters, images, sound, there's more modalities here to be at play. And then you're trying to

• 14:46Speaker 02

  Just learn a function that will be happy, that maximizes the likelihood of seeing all this through a neural network. Now, I think there's a few places where the way currently we train these models would clearly lack to be able to develop the kinds of capabilities you say. I'll tell you maybe a couple. One is the lifetime of an agent or a model. So you learn from this data offline, right? So you're just passively observing and maximizing this. You know, it's almost like a mountains, like a landscape of mountains. 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.

• 15:29Speaker 02

  And then these models generally don't then experience themselves this. They just are observers, right? They're passive observers of the data. And then we're putting them to then generate data when we interact with them. But that's very limiting. The experience they actually experience when they could maybe be optimizing or further optimizing the weights, we're not even doing that. So to be clear, and again, mapping to AlphaGo, AlphaStar, we trained the model. 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? They're not learning in the sense of the weights that you've learned from the data, they don't keep changing.

• 16:13Speaker 02

  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 weights changing, maybe that's mapping to how neurons interconnect and how we learn over our lifetime. But it's true that the context of the conversation that they, that takes, takes place with when you talk to these systems, it's held in their working memory, right? 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 the, these agents, as we call them, or start calling them, build upon.

• 16:57Speaker 02

  Now, this memory is very limited. I mean, right now, we're talking, to be concrete, about 2,000 words that we hold. And then beyond that, we start forgetting what we've seen. So you can see that there's some short-term coherence already, right? 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 you say, oh, what's your name? It could remember that, but then it might forget beyond 2,000 words, which is not that long of context. If we think even of these podcast books are much longer. So technically speaking, there's a limitation there.

• 17:38Speaker 02

  Super exciting from people that work on deep learning to be working on. But I would say we lack maybe benchmarks and the technology to have this lifetime-like experience of memory that keeps building up. However, the way it learns offline is clearly very powerful, right? So I, you know, you asked me three years ago, I would say, oh, we're very far. I think we've seen the power of this imitation again on the internet scale that has enabled this to feel like at least the knowledge, the basic knowledge about the world now is incorporated into the weights. But then this experience is lacking.

• 18:20Speaker 02

  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. So that's the dynamic part, but they don't learn in the same way that you and I have learned, right? When, from basically when we were born and probably before. So lots of fascinating, interesting questions you asked there. 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. And then the second maybe issue that I see is All these models, we train them from scratch.

• 19:04Speaker 02

  That's something I would have complained three years ago, or six years ago, or 10 years ago. 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 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. And that, from a just purely neural network perspective, even though we would like to make it work,

• 19:46Speaker 02

  it's proven very hard to not throw away the previous weights, right? This landscape we learn from the data and refresh it with a brand new set of weights, given maybe a recent snapshot of this dataset we train on, etc., or even a new game we're learning. So that feels like something is missing fundamentally. We might find it, but it's not very clear how it will look like. There's many ideas and it's super exciting as well.

• 20:14Lex Fridman?

  Yes, just for people who don't know, when you're approaching 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. So that's what you mean by starting from scratch, and it seems like it's a waste every time you solve 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 has solved some of the toughest problems in the world. And so some of that is...

• 20:58Lex Fridman?

  What is that? Methods, how to reuse weights? How to learn, extract was generalizable, or at least has a chance to be, and throw away the other stuff. And maybe the neural network itself should be able to tell you that. Like, what? Yeah, how do you? What ideas do you have for better initialization of weights?

• 21:21Speaker 02

  Maybe stepping back. If we look at the field of machine learning, but especially deep learning, right? At the core of deep learning, there's this beautiful idea that is a single algorithm can solve any task, right? 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 that is you take a neural network of uninitialized weights, so like a blank 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. I mean, image classification is very clear example.

• 22:06Speaker 02

  Images to maybe one of a thousand categories. That's what ImageNet is like. But many, many, if not all, problems can be mapped this way. And then there's a generic recipe, right? That you can use. 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. We have not found this recipe, but I think the field is 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?

• 22:51Speaker 02

  So at an algorithmic level, I would say we have something general already, which is this formula of training a very powerful model, a neural network, on a lot of data. And in many cases, You need some specificity to the actual problem you're solving. Protein folding being such an important problem has some basic recipe that is learned from before, right? Like transformer models, graph neural networks, ideas coming from NLP like, you know, something called BERT that is a kind of loss that you can in place to help the model. Knowledge distillation is another technique, right? So this is the formula. We still had to find some particular things that

• 23:34Speaker 02

  were specific to AlphaFold, right? 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. And it's possible that some of these learnings will apply then to the next iteration of this recipe that deep learners are about. But it is true that so far, the recipe is what's common, but the weights you generally throw away, which feels very sad. 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. That idea and some progress has been had,

• 24:16Speaker 02

  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. These, these actually, you could teach it by a prompting, it's called. 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. But now you're doing it through language, which is very natural way for us to learn from one another. I tell you, hey, you should do this new task. I'll tell you a bit more. Maybe you ask me some questions and now you know the task, right?

• 25:01Speaker 02

  You didn't need to retrain it from scratch. And we've seen these magical moments almost in this way to do few-shot prompting through language on language-only domain. And then in the last Two years, we've seen this expanded to beyond language, adding vision, adding actions and games. Lots of progress to be had, 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. The problem of this model is it's hard to grow in weights or capacity, but the model is certainly so powerful that you can teach it some tasks, right?

• 25:42Speaker 02

  In this way that I teach you, I could teach you a new task now if we were all at a text, a text-based task or a classification, a vision style task. But it still feels like more breakthroughs should be had, but it's a great beginning, right? We have a good baseline. We have an idea that this maybe is the way we want to benchmark progress towards AGI. And I think in my view, that's critical to always have a way to benchmark. The community is sort of converging to this overall, which is good to see. And then this is actually what excites me in terms of also next steps for deep learning is how to make these models more powerful? How do you train them? How to grow them if they must grow?

• 26:26Speaker 02

  Should they change their weights as you teach it task or not? There are some interesting questions, many to be answered.

• 26:32Lex Fridman?

  Yeah, you've opened a door to a bunch of questions I want to ask, but let's first return to the, to your tweet and read it like it's Shakespeare. You wrote, Gato is not the end, it's the beginning. And then you wrote meow and then an emoji of a cat. So first, two questions. First, can you explain the meow and the cat emoji? And second, can you explain what Gato is and how it works?

• 26:57Speaker 02

  Right, indeed. I mean, thanks, thanks for reminding me that we're all exposed on Twitter and.

• 27:03Lex Fridman?

  Permanently there.

• 27:04Speaker 02

  Yes, permanently there.

• 27:05Lex Fridman?

  One of the greatest AI researchers of all time, meow and cat emoji.

• 27:10Speaker 02

  Yes.

• 27:11Lex Fridman?

  There you go.

• 27:11Speaker 02

  Right, so.

• 27:12Lex Fridman?

  Can you imagine like touring, tweeting meow and cat? Probably he would, probably would.

• 27:17Speaker 02

  Probably. So yeah, the tweet is important, actually. You know, I put thought on the tweets. I hope people.

• 27:24Lex Fridman?

  Which part do you think? Okay, so there's three sentences. Gato is not the end. Gato is the beginning. Meow cat emoji, which is the important part.

• 27:35Speaker 02

  The meow, no, no, definitely that it is the beginning. I mean, I probably was just explaining a bit where the field is going. But let me tell you about Gato. So first, the name Gato comes from maybe a sequence of releases that DeepMind had that named, like, used animal names to name some of their models that are based on this idea of large sequence models. Initially, they're only language, but we're expanding to other modalities. So we had, you know, we had Gopher, Chinchilla. These were language only. And then more recently, we released Flamingo, which adds vision to the equation.

• 28:19Speaker 02

  And then Gato, which adds vision and then also actions in the mix, right? As we discussed, actually, actions, especially discrete actions like up, down, left, right. I just told you the actions, but they're words. So you can kind of see how actions naturally map to sequence modeling of words, which these models are very powerful. So Gato was named after, I believe, I can only from memory, right? These, you know, these things always happen with an amazing team of researchers behind. So before the release,

• 28:54Lex Fridman?

  Yeah.

• 28:54Speaker 02

  we had a discussion about which animal would we pick, right? And I think because of the word general agent, right? And this is a property quite unique to Gato. We kind of were playing with the GA words and then, you know, Gato.

• 29:09Lex Fridman?

  Rhymes with cat.

• 29:10Speaker 02

  Yes. And Gato is obviously a Spanish version of cat. I had nothing to do with it, although I'm from Spain.

• 29:15Lex Fridman?

  Oh, how do you, wait, sorry. How do you say cat in Spanish?

• 29:18Speaker 02

  Gato.

• 29:18Lex Fridman?

  Oh, Gato, okay.

• 29:19Speaker 02

  Yeah. I see, I see, I see.

• 29:21Lex Fridman?

  Now it all makes sense.

• 29:22Speaker 02

  Okay.

• 29:23Lex Fridman?

  How do you say miau in Spanish? No, that's probably the same.

• 29:25Speaker 02

  I think you say it the same way, but you write it as M I A U.

• 29:31Lex Fridman?

  Okay, it's universal.

• 29:32Speaker 02

  Yeah.

• 29:33Lex Fridman?

  All right, so then how does the thing work? So you said general is, so you said language, vision.

• 29:41Speaker 02

  And action.

• 29:41Lex Fridman?

  Action. How does this? Can you explain what kind of neural networks are involved? What does the training look like? Maybe.

• 29:52Lex Fridman?

  What you are some beautiful ideas within the system?

• 29:55Speaker 02

  Yeah, so maybe the basics of GATO are not that dissimilar from many, many work that comes. So here is where the recipe hasn't changed too much. There is a transformer model, that's the kind of recurrent neural network that essentially takes a sequence of modalities, observations that could be words, could be vision, or could be actions. And then its own objective that you train it to do when you train it is to predict what the next anything is. And anything means what's the next action? 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?

• 30:40Speaker 02

  So you think of these really as a sequence of bytes, right? So take any sequence of words, a sequence of interlaced words and images, a sequence of maybe observations that are images and moves in Atari, up, down, left, right. And these, you just think of them as bytes, and you're modeling what's the next byte gonna be like. And you might interpret that as an action and then 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. So GATO basically can be thought as inputs, images, text, video, actions.

• 31:25Speaker 02

  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. And then at the output, similarly, it outputs words, actions. It does not output images. That's just by design, we decided not to go that way for now. That's also in part why it's the beginning, because there's more to do, clearly. But that's kind of what the GATO is, is this brain that essentially you give it any sequence of these observations and modalities, and it outputs the next step. And then off you go, you feed the next step into and predict the next one and so on. Now, It is more than a language model because even though you can chat with Gato,

• 32:10Speaker 02

  like you can chat with Chinchilla or Flamingo, it also is an agent, right? So that's why we call it A of Gato, like the word, the letter A. And also it's general. It's not an agent that's been trained to be good at only StarCraft or only Atari or only Go. It's been trained on a vast variety of data sets. So.

• 32:35Lex Fridman?

  What makes it an agent? If I may interrupt, the fact that it can generate actions?

• 32:39Speaker 02

  Yes, so when we call it, I mean, it's a, it's a good question, right? What, why, 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.

• 33:00Lex Fridman?

  This, this actually, this reminds me of the question from the side of biology. What is life? Which is actually a very difficult question as well. What is living? What is living? When you think about life here on this planet Earth? And a question interesting to me about aliens. What is life when we visit another planet? Would we be able to recognize it? And this feels like it's, it sounds perhaps silly, but I don't think it is. At which point is the neural network a being versus a tool? And it feels like action, ability to modify its environment is that fundamental leap.

• 33:38Speaker 02

  Yeah, I, I think it's, it certainly feels like action is a necessary condition to, to be more alive, but probably not sufficient either. So sadly.

• 33:48Lex Fridman?

  This whole consciousness thing, whatever.

• 33:50Speaker 02

  Yeah, yeah, we can get back to that later. But anyways, going back to the meow and the Gato, right? So 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? So I told you Gato is this transformer neural network, models, actions, sequences of actions, words, et cetera. And then the way we train it is by essentially pulling data sets of.

• 34:21Speaker 02

  Observations, right? 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 used before, right? So these are these web-scale style data sets of people writing, you know, on webs or chatting or whatnot, right? So that's an obvious source that we use on all language work. But then we also took a lot of agents that we have at DeepMind. I mean, as you know, DeepMind, we're quite, you know, we're quite interested in learning reinforcement learning and learning agents that play in different environments. So we kind of created a data set of these trajectories, as we call them, or agent experiences.

• 35:06Speaker 02

  So in a way, there are other agents we train for a single mind purpose to, let's say, you know, control a 3D game environment and navigate a maze. So we had all the experience that was created through the one agent interacting with that environment. And we added this to the data set, right? And as I said, we just see all the data, all these sequences of words or sequences of this agent interacting with that environment or, you know, agents playing Atari and so on. We see this as the same kind of data. And so we mix these data sets together and we train GATO. That's the G part, right? It's general because it really has mixed. It doesn't have different brains for each modality

• 35:51Speaker 02

  or each narrow task. It has a single brain. It's not that big of a brain compared to most of the neural networks we see these days. It has 1 billion parameters. 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. So the actual agent is relatively small, but it's been trained on a very challenging diverse data set, not only containing all of internet, but containing all this agent experience playing very different distinct environments. So these Brings us to the part of the tweet of this is not the end is the beginning.

• 36:32Speaker 02

  It feels very cool to see Gato in principle is able to control any sort of environments that 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.

• 36:54Lex Fridman?

  Not obvious. It's not obvious that it wouldn't be more proficient. 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.

• 37:07Speaker 02

  Right, so it's not as good, although I would argue size matters here. So the fact that.

• 37:12Lex Fridman?

  I would argue always size always matters. That's a different conversation.

• 37:16Speaker 02

  Yes, but for neural networks, certainly size does matter. So it's the beginning because it's relatively small. So obviously scaling this idea up might make the connections that exist between, you know, text on the internet and playing Atari and so on more synergistic with one another. And you might gain and that moment we didn't quite see, but obviously that's why it's the beginning.

• 37:42Lex Fridman?

  That synergy might emerge with scale.

• 37:44Speaker 02

  Right, might emerge with scale. 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. 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. 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. So text might actually be a good driver to enhance the data, right?

• 38:28Speaker 02

  So then these connections might be made more easily, right? That's an idea that we start seeing in language, but you know, obviously beyond this is going to be effective, right? It's not like I don't show you a screen and you from scratch, you're supposed to learn a game. There is a lot of context we might set. So there might be some work needed as well to set that context. But. Anyways, there's a lot of work. Yeah.

• 38:54Lex Fridman?

  So that context puts all the different modalities on the same level ground.

• 38:58Speaker 02

  Exactly.

• 38:58Lex Fridman?

  If you provide the context best. 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 that then could cross modalities somehow. So what's tokenization? How do you tokenize text? How do you tokenize images? How do you tokenize games and actions and robotics tasks?

• 39:30Speaker 02

  Yeah, that's a great question. So 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. We break down anything into these puzzle pieces and then we just model, what's this puzzle look like, right? When you make it, you know, lay down in a line, so to speak, in a sequence. So in GATO, the text, there's a lot of work. You tokenize text usually by looking at commonly used substrings, right? So there's, you know, ing in English is a very common substring. So that becomes a token. There's quite well-studied problem on tokenizing text.

• 40:12Speaker 02

  And GATO just use the standard techniques that have been developed from many years, even starting from N-gram models in the 1950s and so on.

• 40:21Lex Fridman?

  Just for context, how many tokens? Like, what order, magnitude, number of tokens is required for a word?

• 40:28Speaker 02

  Yeah.

• 40:28Lex Fridman?

  Well, what are we talking about?

• 40:29Speaker 02

  Yeah, for a word in English, right? I mean, every language is very different. The current level or granularity of tokenization generally means it's maybe two to five. 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.

• 40:55Lex Fridman?

  It's bigger than letters, smaller than words.

• 40:56Speaker 02

  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.

• 41:06Lex Fridman?

  Have you ever tried to tokenize emojis?

• 41:08Speaker 02

  Emojis are actually just. Um, sequences of letters, so.

• 41:13Lex Fridman?

  Maybe to you, but to me, they mean so much more.

• 41:16Speaker 02

  Yeah, you can render the emoji, but you you might, if you actually just.

• 41:20Lex Fridman?

  Yeah, this is a philosophical question. Is emojis an image or a text?

• 41:26Speaker 02

  The way we do these things, these things is they're actually mapped to small sequences of characters.

• 41:32Lex Fridman?

  Yeah.

• 41:33Speaker 02

  So you can actually play with these models and input emojis, it will output emojis back, which is actually quite a fun exercise. You probably can find other tweets about this out there. But yeah, so anyways, text, there's like, it's very clear how this is done. 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,

• 42:02Speaker 02

  less like every pixel with every intensity that would mean we have a very long sequence, right? Like if we we're talking about hundred by hundred pixel images, that would make the sequences far too long. So what was done there is you just use a technique that essentially compresses an image into maybe sixteen by sixteen patches of pixels and then that is map. Again, tokenize, you just essentially quantize this space into a special word that actually maps to this little sequence of pixels. And then you put the pixels together in some raster order and then that's how you get out or in or in the image that you're you're processing.

• 42:44Lex Fridman?

  But there's no semantic aspect to that, so you're doing some kind of, you don't need to understand anything about the image in order to tokenize it currently.

• 42:53Speaker 02

  No, you're only using this notion of compression. So you're trying to find common, it's like JPG or all these algorithms. It's actually very similar at the tokenization level. 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.

• 43:15Lex Fridman?

  Although you could probably argue that JPG does have some understanding of images, like a.

• 43:22Lex Fridman?

  Because visual information, maybe color, compressing based crudely based on color does capture some, something important about an image that's about its meaning, not just about some statistics.

• 43:37Speaker 02

  Yeah, I mean JP, as I said, these very, the algorithms look actually very similar to, they use this, the cosine transform in JPG. The, 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. So rather than have some sort of Fourier basis for how, you know, frequencies appear in natural, in the natural world, we actually just use the, the statistics of the images and then quantify them based on the statistics, much like you do in words, right? So common substrates, substrings are allocated a token and images is, is very similar.

• 44:18Speaker 02

  But there's no connection, the token space, if you think of, oh, like the tokens are an integer and in the end of the day. 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? Certainly more than characters because we have groups of characters and so on. So from one to 10,000, those are representing all the language and the words we'll see. And then images occupy the next set of integers. So they're completely independent, right? So from 10,001 to 20,000, those are the tokens that represent these other modality images. And that is an interesting aspect that makes it orthogonal.

• 45:02Speaker 02

  So what connects these concepts is the data, right? Once you have a data set, for instance, that captions images that tells you, oh, this is someone playing a frisbee on, on a, on a green field. Now, 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. So these connections happen as the algorithm learns. And then the last, if we think of these integers, the first few are words, the next few are images. In GAT, we also allocated the highest order of integers to actions, right? Which we discretize and actions are very diverse, right? In Atari, there's, I don't know if 17 discrete actions.

• 45:47Speaker 02

  In robotics, actions might be torques and forces that we apply. So we just use kind of similar ideas to compress these actions into tokens. And then we just, that's how we map now all the space to this sequence of integers. But they occupy different space and what connects them is then the learning algorithm. That's where the magic happens.

• 46:09Lex Fridman?

  So the modalities are orthogonal to each other in token space.

• 46:13Speaker 02

  Right.

• 46:14Lex Fridman?

  So in the input, everything you add, you add extra tokens.

• 46:18Speaker 02

  Right.

• 46:19Lex Fridman?

  And then you're shoving all of that into one place.

• 46:24Speaker 02

  Yes, the transformer.

• 46:25Lex Fridman?

  And that transformer, that transformer tries, 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. How's that possible? If you were to sort of like put your psychoanalysis hat on and try to psychoanalyze this neural network, is it schizophrenic? Does it try to, given this very few weights, represent multiple disjoint things and somehow have them not interfere with each other? Or is this somehow building on the?

• 47:09Lex Fridman?

  And the joint strength, and whatever is common to all the different modalities. Like what, if you were to ask it questions, is it schizophrenic? Or is it, is it of one mind?

• 47:22Speaker 02

  I mean, it is, it is one mind. And it's actually the very, the simplest algorithm, which that's kind of in a way how it feels like the field hasn't changed since backpropagation and gradient descent was purpose for learning neural networks. So there is obviously details on the architecture. This has evolved. The current iteration is still the transformer, which is a powerful sequence modeling architecture. But then the goal of this, you know, 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? We, we take, let's say, an Atari game.

• 48:08Speaker 02

  We map it to a string of numbers that will all be probably image space and action space interlaced. And all we're gonna do is say, okay, given the numbers, you know, 10,001, 10,004, 10,005, the next number that comes is 20,006, which is in the action space. And you're just optimizing these weights via very simple gradients, like, you know, mathematical is almost the most boring algorithm you could imagine. We settle 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.

• 48:52Speaker 02

  And then the algorithm does this for many, many, many iterations, looking at different modalities, different games, right? That's the mixture of the data set we discussed. So in a way, it's a very simple algorithm. And the weights, right, they're all shared, right? So in terms of, is it focusing on one modality or not? The intermediate weights that are converting from this input of integers To the target integer you're predicting next. Those weights certainly are common. 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.

• 49:35Speaker 02

  Like real numbers, we can optimize them with gradient descent, right? The functions we learn are actually surprisingly differentiable. That's why we compute gradients. So this step is the only one that this orthogonality you mentioned applies. 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. If you look at the field back many years ago, people were talking about word vectors or word embeddings. These are the same. We have word vectors or embeddings. We have image vector or embeddings and action vector of embeddings. And the beauty here is that as you train this model, if you visualize these little vectors,

• 50:20Speaker 02

  it might be that they start aligning even though they're independent parameters. There could be anything. But then it might be that you take the word gato or cat, which maybe is common enough that 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? So by learning from this vast amount of data, the model is realizing the potential connections between these modalities. Now, I will say there would be another way, at least in part, to not have these different vectors for each different modality. For instance, when I tell you about actions in certain space,

• 51:03Speaker 02

  I'm defining actions by words, right? So you could imagine a world in which I'm not learning that the action app in Atari is its own number. The action "up" in Atari maybe is literally the word or the sentence "up" in Atari. Right? And that would mean we now leverage much more from the language. 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? So all this to say that GATTO is indeed the beginning, that it is a radical idea to do this way, but there's probably a lot more to be done.

• 51:46Speaker 02

  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.

• 51:54Lex Fridman?

  So just to elaborate quickly, you mean one possible next step or one of the paths that you might take next is

• 52:06Lex Fridman?

  doing the tokenization fundamentally as a kind of linguistic communication. So like you convert even images into language. So doing something like a crude semantic segmentation, trying to just assign a bunch of words to an image that like have almost like a dumb entity explaining as much as it can about the image. And so you convert that into words and then you convert games into words and then you provide the context in words and all of it.

• 52:37Lex Fridman?

  And eventually getting to a point where everybody agrees with Noam Chomsky that language is actually at the core of everything. That's it's the base layer of intelligence and consciousness and all that kind of stuff. Okay.

• 52:51Lex Fridman?

  You mentioned early on like it's hard to grow. What did you mean by that? Because we're talking about scale might change. 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 emerging. So certain like performance we can see only with scale. And there's some kind of threshold of scale. So it, why is it hard to grow something like this Meow network?

• 53:20Speaker 02

  So the Meow network is not, it's not hard to grow if you retrain it.

• 53:25Lex Fridman?

  Yeah.

• 53:26Speaker 02

  What's hard is, well, we have now one billion parameters. We trained them for a while. We, we spent some amount of work towards building these, these weights that are an amazing initial brain for doing this kind of task we care about. Could we reuse the weights and expand to a larger brain? And that is extraordinarily hard, but also exciting from a research perspective and a practical perspective point of view, right? So there is this notion of modularity in software engineering. And we're starting to see some examples and work that leverages modularity.

• 54:07Speaker 02

  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. Flamingo did not deal with actions, but it definitely dealt with images in, in a, in an interesting way, kind of akin to what a GATO did, but slightly different technique for tokenizing. But we don't need to go into that detail. But what Flamingo also did, which GATO didn't do, and that just happens because these projects, you know, they're, they're, they're different, you know, it's, it's a bit of like the exploratory nature of research, which is great.

• 54:40Lex Fridman?

  The research behind these projects is also modular.

• 54:44Speaker 02

  Yes, exactly. And it has to be, right? We need, we need to have creativity and sometimes you need to protect pockets of, you know, people, researchers and so on.

• 54:54Lex Fridman?

  By we, you mean humans?

• 54:55Speaker 02

  Yes.

• 54:56Lex Fridman?

  Okay.

• 54:56Speaker 02

  And also in particular researchers and maybe even further, you know, DeepMind or, or other such labs.

• 55:02Lex Fridman?

  And then the, the neural networks themselves. So it's modularity all the way down.

• 55:07Speaker 02

  All the way down. 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. So we took Chinchilla, we took the weights of Chinchilla. And then we froze them. We said, these don't change. We trained them to be very good at predicting the next word. It's a very good language model, state of the art at the time you release it, et cetera, et cetera. We're gonna add a capability to see, right? We are gonna add the ability to see to this language model. So we're gonna attach small pieces of neural networks at the right places in the model.

• 55:47Speaker 02

  It's almost like injecting the network with some weights and some substructures in the ways, in a good way, right? So you need the research to say what is effective? How do you add this capability without destroying others, et cetera? So we created a small sub-network initialized not from random, but actually from self-supervised learning, that, you know, a model that understands vision in general. And then we took data sets that connect the two modalities, vision and language. And then we froze the main part, the largest portion of the network, which was Chinchilla, that is 70 billion parameters.

• 56:29Speaker 02

  And then we added a few more parameters on top, trained from scratch, and then some others that were pre-trained from like, with the capacity to see. Like it was, it was not tokenization in the way I described for Gato, but it's a similar idea. And then we trained the whole system. Parts of it were frozen, parts of it were new. And all of a sudden, we developed Flamingo, which is an amazing model that is essentially, I mean, describing it is a chatbot where you can also upload images and start conversing about images. But it's also kind of a dialogue style chatbot.

• 57:07Lex Fridman?

  So the input is images and text and the output is text?

• 57:10Speaker 02

  Exactly. And

• 57:12Lex Fridman?

  How many parameters? You said 70 billion for Chinchilla?

• 57:15Speaker 02

  Yeah, Chinchilla is 70 billion. And then the ones we add on top, which kind of almost, 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. That adds an extra 10 billion parameters, right? So it's total eighty billion, the largest one we released. And then you train it on a few data sets that contain vision and language. 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.

• 57:59Speaker 02

  You can teach it a new vision task, right? It does things beyond the capabilities that in theory the data sets 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, but once developed, then as you play with the interface, you can start seeing, wow, okay, yeah, it's cool. 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 waiting themselves and it's kind of a joke style image. And it's notable because I think Andre Karpathy a few years ago said, no computer vision

• 58:44Speaker 02

  system can understand the subtlety of this joke in this image, all the things that go on. And so what we tried to do, and it's very anecdotal, 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 is a joke because the person waiting themselves doesn't see that someone behind is making the weight higher and so on and so forth. So it's a fascinating capability. And it comes from this key idea of modularity, where we took a frozen brain and we just added a new capability. So the question is, should we?

• 59:24Speaker 02

  So in a way, you can see even from DeepMind, we have Flamingo that this moderate approach and thus could leverage the scale a bit more reasonably because we didn't need to retrain a system from scratch. And on the other hand, we had GAT, which used the same data sets, but then it trained it from scratch, right? And so I guess, big question for the community is, Should we train from scratch or should we embrace modularity? And this lies like this goes back to modularity as a way to grow. But reuse seems like natural and it was very effective, certainly.

• 59:58Lex Fridman?

  The next question is if you go the way of modularity, is there a systematic way of freezing weights and joining different modalities across, 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 like Starcraft and play all the other video games and they, you can keep adding them in without significant effort, 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.

• 1:00:41Speaker 02

  Yeah, so that that vision is beautiful. I think there's still the question about within single modalities, like Chinchilla was reused, but now if we train an X iteration of language models, are we going to use Chinchilla or not?

• 1:00:55Lex Fridman?

  Yeah, how do you swap out Chinchilla?

• 1:00:56Speaker 02

  Right, so there's, there's still big questions, but that idea is actually really akin to software engineering, which we're not re-implementing, you know, libraries from scratch. We're reusing and then building ever more amazing things, including neural networks with software that we're reusing. So I think this idea of modularity, I like it. I think it's here to stay. And that's also why I mentioned it's just the beginning, not the end.

• 1:01:22Lex Fridman?

  You've mentioned meta learning. So given this promise of GAT, 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. But what do you think meta learning? Is and looks like now in the five years, 10 years, will it look like the system like Gato, but scaled? What's your sense of what is, what does meta learning look like? Do you think with all the wisdom we've learned so far?

• 1:01:54Speaker 02

  Yeah, great, great question. Maybe it's good to give another data point looking backwards rather than forward. So when, when we talk in 2019, meta learning meant something that has changed mostly through the revolution of GPT three and beyond. So what meta learning meant at the time was driven by what benchmarks people care about in meta learning. And the benchmarks were about a capability to learn about object identities. So it was very much overfitted to vision and object classification. And the part that was meta about that was that, oh, we're not just learning a thousand

• 1:02:38Speaker 02

  categories that ImageNet tells us to learn. We're going to learn object categories that can be defined when we interact with the model. So it's interesting to see the evolution, right? The way, 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. I'll give you one image and the name, which was an integer at the time of the image and a different image and so on. So you have a small prompt in the form of a data set, a machine learning data set. And then you got then a system that could then predict or classify these objects that

• 1:03:20Speaker 02

  you just defined kind of on the fly. So fast forward, it was revealed that language models are future learners. That's the title of the paper. So very good title. Sometimes titles are really good. So this one is really, really good because that's, that's the point of GPT three that showed that, look, Sure, we can focus on object classification and what meta-learning means within the space of learning object categories. This goes beyond, or before rather, to also OmniGlaive before ImageNet and so on. So there's a few benchmarks. To now, all of a sudden, we're a bit unlocked from benchmarks. And through language, we can define tasks, right? So we're literally telling the model some logical task

• 1:04:05Speaker 02

  or little thing that we wanted to do. We prompted much like we did before, but now we prompted through natural language. 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? So they meta-learn this new capability. Now, that's where we are now. Flamingo expanded this to visual and language, but it basically has the same abilities. 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, oh, that's, I mean, when I show you 3 plus 6, you know, I want you to output 9. And you show it a few examples and now it does that.

• 1:04:50Speaker 02

  So it went way beyond the, oh, this ImageNet sort of categorization of images that we were a bit stuck maybe before this revelation moment that happened in 2000. I believe it was 19, but it was after we chat.

• 1:05:05Lex Fridman?

  In that way, it has solved meta-learning as was previously defined.

• 1:05:09Speaker 02

  Yes, it expanded what it meant. So that's what you say, what does it mean? So it's an evolving term. 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. And I would certainly hope to see the following. And this is a pretty drastic hope, but in five years, maybe we chat again. And We have a system, right, a set of weights that we can teach it to play StarCraft. Maybe not at the level of AlphaStar, but play StarCraft, a complex game, we teach it through interactions, through prompting. You can certainly prompt a system, that's what Gato shows,

• 1:05:53Speaker 02

  to play some simple Atari games. So imagine if you start talking to a system, teaching it a new game, showing it examples of, you know, in this, in this particular game, this user did something good. Maybe the system can even play and ask you questions, say, "Hey, I played this game. I just played this game. Did I do well? Can you teach me more?" So five, maybe to 10 years, these capabilities, or what meta-learning means, will be much more interactive, much more rich, and through, through domains that we were specializing, right? So you see the difference, right? We built AlphaStar specialized to play StarCraft. The algorithms were general, but the weights were specialized. And what, what we're hoping

• 1:06:35Speaker 02

  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, of StarCraft. Like, this is in the horizon, and obviously there are details need to be, to be filled and research need to be done. But that's how I see meta-learning above, which is going to be beyond prompting, it's going to be a bit more interactive, it's gonna, you know, the system might tell us to give it feedback after it maybe makes mistakes or it loses a game. But it's nonetheless very exciting, because if you think about this this way, the benchmarks are already there, we just repurpose the benchmarks, right?

• 1:07:16Speaker 02

  So in a way, I like to map the space of what maybe AGI means to say, "Okay, like, we went 100, 1% performance in Go, in Chess, in StarCraft."

• 1:07:31Speaker 02

  The next iteration might be 20% performance across quote unquote all tasks. Right? And even if it's not as good, it's fine. We actually, we have ways to also measure progress because we have those special agents, specialized agents and so on. So this is to me very exciting. And these next iteration models are definitely hinting at that direction of progress, which hopefully we can have. 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.

• 1:08:12Speaker 02

  But that's, if you ask me five to 10 years, you might see these models that start to look more like weights that are already trained and then it's more about teaching or make, they're meta learn what you're trying, you're trying to induce in terms of tasks and so on. Well beyond the simple now task, we're starting to see emerge like, you know, small arithmetic tasks and so on.

• 1:08:37Lex Fridman?

  So a few questions around that. This is fascinating. So that kind of teaching, interactive, not so it's beyond prompting, so it's interacting with a neural network. That's different than the training process. So it's different than the optimization over differentiable functions. This is already trained and now you're teaching. I mean, it's almost like akin to the brain, then the neurons are already set with their connections. On top of that, you know, using that infrastructure to build up further knowledge.

• 1:09:16Lex Fridman?

  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. Cause you always think for a neural network to learn, it has to be retrained, trained and retrained. But maybe, and prompting is a way of teaching.

• 1:09:37Lex Fridman?

  And you'll now work a little bit of context about whatever the heck you're trying it to do. So you can maybe expand this prompting capability by making it interact. That's really, really.

• 1:09:47Speaker 02

  Yeah, by the way, this is not, if you look at way back, at different ways to tackle even classification tasks. So this, this is, this comes from, from like longstanding literature in machine learning. What I'm suggesting could sound to some like a bit like nearest neighbor. So nearest neighbor is almost the simplest algorithm that you can, that, that does not require learning. So it has this interesting, like you don't need to compute gradients. And what nearest neighbor does is you quote unquote, have a data set or upload a data set. And then all you need to do is a way to measure distance between points. And then to classify a new point, you're just simply computing what's the closest

• 1:10:32Speaker 02

  point in this massive amount of data. And that's my answer. So you can think of prompting in a way as you're uploading not, not just simple points and, and you know, the, 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? You, you simply are uploading some knowledge to this pre-trained system in nearest neighbor. Maybe the metric is learned or not, but you don't need to further train it. And then now you immediately get a classifier out, out of this, right? Now it's just an evolution of that concept, very classical concept in machine learning, which is, um, yeah, just learning

• 1:11:14Speaker 02

  through what's the closest point, closest by some distance, and that's it. It, it's an evolution of that. And I will say, how, how I saw meta learning when we worked on a few ideas in, in 2016 was precisely through the lens of.

• 1:11:30Speaker 02

  Nearest neighbor, which is very common in computer vision community, right? 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? All I'm saying is now these distances and the points are not just images. They're like words or sequences of words and images and actions that teach you something new. But it might be that technique wise, those come back. And I will say that it's not necessarily true that you might not ever train the weights a bit further. Some aspect of meta learning, some techniques in meta learning do actually do a bit of fine tuning, as it's called, right?

• 1:12:12Speaker 02

  They train the weights a little bit when they get a new task. So as I call the how or how we're going to achieve this. As a deep learner, I'm very skeptic. 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. We'll see, right? 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. I think this is basically what's exciting about machine learning period to me.

• 1:12:55Lex Fridman?

  Well, the interactive aspect of that is also very interesting. The interactive version of nearest neighbor. To help you pull out the classifier from this giant thing. Okay, is this the way we can go in five, 10 plus years from any task? Sorry, from many tasks to any task? So. And what does that mean? Like, what does it need to be actually trained on? Which point is the network had enough? So what?

• 1:13:31Lex Fridman?

  What does a network need to learn about this world in order to be able to perform any task? Is it just as simple as language, image and action? Or do you need some set of representative images? Like, if you only see land images, will you know anything about underwater? Is that some fundamentally different? I don't know.

• 1:13:52Speaker 02

  Those, I mean, those are upper questions, I would say. I mean, the way you put, let me maybe further your example, right? If, if all you see is land images, but you're reading all about land and water worlds, but in books, right? The magic, like, would that be enough? I mean, good question. We don't know, but I guess maybe you can, you can, you can join us if you want in our quest to find this. That's, that's precisely.

• 1:14:17Lex Fridman?

  Water world, yeah.

• 1:14:18Speaker 02

  Yes, that's precisely, I mean, the beauty of research and, and that's the, the, the, the, the research business we're in, I guess, is to figure this out and ask the right questions. And then iterate with, with the whole community, publishing like findings and so on. But yeah, these are, this is a question. It's not the only question, but it's certainly, as you ask, is, is on my mind constantly, right? And so we'll, we'll need to wait for maybe the, let's say, five years. Let's hope it's, it's not 10 to, to see what, what are the answers?

• 1:14:53Speaker 02

  Some people will largely believe in unsupervised or self-supervised learning of single modalities and then crossing them. Some people might think end-to-end learning is the answer. Modularity is maybe the answer. So we don't know, but we're just definitely excited to find out.

• 1:15:11Lex Fridman?

  But it feels like this is the right time and we're at the beginning of this.

• 1:15:14Speaker 02

  Yes.

• 1:15:15Lex Fridman?

  This journey, we're finally ready to do these kind of general, big models and agents. What do you sort of specific technical thing about Gato, Flamingo?

• 1:15:28Lex Fridman?

  Chinchilla, gopher, any of these that is especially beautiful, that was surprising. Maybe, is there something that just jumps out at you? 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. Or maybe the, how small of a network, relatively speaking, Gato is, all that kind of stuff. But is there some weird little things that were surprising?

• 1:15:58Speaker 02

  Look, I, I'll give you an answer that's very important because maybe people don't quite realize this, but the teams behind these efforts, the actual humans. That's maybe the surprising, in a obviously positive way. So anytime you see these, these breakthroughs, I mean, it's, it's easy to map it to a few people. There's people that are great at explaining things and so on. That, that's very nice, but maybe the, the, 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, how important are all the pieces of, of these projects? How do they come together?

• 1:16:43Speaker 02

  So I'll, I'll give you maybe some of the ingredients of success that are common across these, but not the obvious ones on machine learning. I, I can always, always also give you those. But basically, there is, engineering is, is critical. So, so very good engineering, because ultimately we're, we're collecting data sets, right? So the, 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. And it's hard to believe that details matter so much.

• 1:17:25Speaker 02

  We, we would like to believe that it's true that there is more and more of a standard formula, as I was saying, like, right, this recipe that works for everything. But then, when you zoom in into this, each of these projects, then you realize the, the devil is indeed in the details. And then the teams. Have to work kind of together towards these goals. So engineering of data and obviously clusters and large scale is very important. And then one that is often not, maybe nowadays it is more more clear, is benchmark progress. Right. So we're talking here about multiple months of, you know, tens of researchers

• 1:18:06Speaker 02

  and and and people that are trying to organize the research and so on, working together. And you don't know that you can get there. I mean, it is, this is, this is the beauty. 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. So the benchmarks that you build are critical. I've seen this beautifully pay out in many projects. I mean, maybe the one I've seen it more consistently, which means we we establish the metric, actually, the community did, and then we leverage that massively is AlphaFold. This is a project where the data, the metrics were all there.

• 1:18:49Speaker 02

  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 which is one way to do research that is valid, but aim very high and work literally for years to iterate over that process and working for years with the team. I mean, it is, it is tricky. That also happened, happened to happen partly during a pandemic and so on. So I think my meta learning from all this is the teams are critical to the success. And then if now going to the machine learning, the part that's surprising is. So we like architectures like neural networks.

• 1:19:32Speaker 02

  And I would say this was a very rapidly evolving field until the transformer came. So attention might indeed be all you need, which is the title. Also, good title, although in hindsight is good. I don't think at the time I thought this is a great title for a paper. But that that architecture is proving that the dream of modeling sequences of any bytes.

• 1:19:57Speaker 02

  There is something there that will stick. And I think this advance in architectures, in how neural networks are architected to do what they do. 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. So that is a surprising, is a surprise that keeps recurring into other projects.

• 1:20:21Lex Fridman?

  Can you try to, on a philosophical or technical level, introspect what is the magic of attention? What is attention? That's attention in people that study cognition, so human attention. I think there's giant wars over what attention means, how it works in the human mind. So what, there's very simple looks at what attention is in neural network. From the days of attention is all you need, but broad, do you think there's a general principle that's really powerful here?

• 1:20:52Speaker 02

  Yeah, so a distinction between transformers and LSTMs, which were what came before. And, you know, there was a transitional period where you could, you could use both. In fact, when we talked about AlphaStar, we used transformers and LSTMs. So it was still the beginning of transformers. They were very powerful, but LSTMs were still very, also very powerful sequence models. So 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? Like we discussed this before, right? This is a sequence of words.

• 1:21:34Speaker 02

  When you, 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. 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...

• 1:22:02Speaker 02

  You're looking for something, right? So you're sort of, when you're, you, you, you just read a piece of text. Now you're thinking what comes next. You might wanna relook at the text or look it from scratch. I mean, the, literally is, is because there's no recurrence. You're just thinking what comes next. And it's almost hypothesis driven, right? So if, if I'm thinking the next word that I'll write is cat or dog. Okay. The way the transformer works almost philosophically is it has these two hypotheses. Is it, is it gonna be cat or is it gonna be dog? And then it says, okay, if it's cat, I'm gonna look for certain words, not necessarily cat, although cat is an obvious word you would look in the past to, to see whether it makes

• 1:22:47Speaker 02

  more sense to output cat or dog. And then it does some very deep computation over the words and beyond, right? So it combines the words and, but, but it has the query, as we call it, that is cat. And then similarly for dog, right? And so it's, it's very, it's a very computational way to think about, look, if I'm, if I'm thinking deeply about text, I need to go back to, to look at all of the text, attend over it. But it's not just attention, like what, what is guiding the attention? And that was the key insight from an earlier paper is not how far away is it? I mean, how far away is it is important. What, what, what did I just write about? That's critical. But what you wrote about 10 pages ago might also be critical.

• 1:23:32Speaker 02

  So you're looking not positionally, but content wise, right? And you, transformers have this beautiful way to query for certain content and pull it out in a compressed way. So then you can make a more informed decision. I mean, that's one way to explain transformers. But I think it's, it's very, it's a very powerful inductive bias. 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. Transformer itself has flaws.

• 1:24:12Speaker 02

  And I think the main one, the main challenge is these prompts that we, we just were talking about. They can be a thousand words long, but if I'm teaching you StarCraft, I mean, I'll have to show you videos, I'll have to, I'll have to point you to whole Wikipedia articles about the game. We'll have to interact probably as you play, you'll ask me questions. 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. What, what I think goes well beyond the current capabilities. So the question is, how do we benchmark this? And then how do we change the structure of the architectures? I think there's ideas on both sides, but we'll have to see empirically, right? Obviously what ends up working in the.

• 1:24:57Lex Fridman?

  And as, 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 to where you can start being much clever in how you use those thousand tokens.

• 1:25:12Speaker 02

  Indeed.

• 1:25:14Lex Fridman?

  Yeah, that's really interesting, but it also is possible that this attention mechanism where you basically, you don't have a recency bias, but you, you, you look more generally. You, you make it learnable, the mechanism in which way you look back into the past. You make that learnable. It's also possible where at the very beginning of that. Because that you might become smarter and smarter in the way you query the past.

• 1:25:42Lex Fridman?

  So recent past and distant past and maybe very, very distant past. So almost like the attention mechanism will have to improve and evolve as good as the, the tokenization mechanism where so you can represent long-term memory somehow.

• 1:25:58Speaker 02

  Yes. And I mean, hierarchies are very, I mean, it's a very nice board that sounds appealing. There's lots of work adding hierarchy to the memories. In practice, it does seem like we keep coming back to the main formula or main architecture.

• 1:26:17Speaker 02

  That sometimes tells us something. There is such a sentence that a friend of mine told me, like, whether it wants to work or not. So Transformer was clearly an idea that wanted to work. And then I think there's some principles we believe will be needed, but finding the exact details, details matter so much, right? That's gonna be tricky.

• 1:26:37Lex Fridman?

  I love the idea that there's, 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 which more likely the model will win in the end. Because it's the one, you don't have to do any work. The model's the one that has to do the work, so you should listen to the model. And I really love this idea that you talked about the humans in this picture. If I could just briefly ask, one is you're saying the benchmarks about the modular humans working on this, the benchmarks providing a sturdy ground of a wish to do these things that seem impossible. They give you, in the darkest of times, give

• 1:27:21Lex Fridman?

  you hope, because little signs of improvement, you could, you could, like, you're not, you're, somehow you're not lost if you have metrics to measure your improvement. And then there's other aspect, you said elsewhere and here today, like, titles matter. I wonder how much humans matter in the evolution of all of this, meaning individual humans. You know, something about their interactions, something about their ideas, how much they change the direction of all of this. Like, if you change the humans in this picture, like, is it that the model is sitting there and it wants you, it wants some idea to work, or is it the

• 1:28:06Lex Fridman?

  humans, or maybe the model is providing you 20 ideas that could work, and depending on the humans you pick, they're going to be able to hear some of those ideas? Like, in all the, 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. How much variability is created by the humans in all of this?

• 1:28:27Speaker 02

  Yeah, I mean, I do believe humans matter a lot. At the very least, at the

• 1:28:34Speaker 02

  You know, time scale of years on when things are happening and what's the sequencing of it, right? 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. And then some other people might be more practical, like, I don't care what idea works. I care about, you know, cracking protein folding. Yes. And these, at least these two kind of seem opposite sides. We need both. And we've clearly had both historically, and that made certain things happen earlier or later.

• 1:29:12Speaker 02

  So definitely, humans involved in all of this endeavor have had, I would say, years of change or of ordering, how things have happened, which breakthroughs came before, which other breakthroughs, and so on. So certainly, that does happen. And so, one other, maybe one other axis of distinction is what I called, and this is most commonly used in reinforcement learning, is the exploration-exploitation trade-off as well. It's not exactly what I meant, although quite related. So when you start trying to help others, right, like you're, you know, you become a

• 1:29:53Speaker 02

  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, you're identifying quickly, right, some things that are explorative or exploitative. And it's tempting to try to guide people, obviously. I mean, that's what makes like our experience, we bring it and we try to shape things, sometimes wrongly, and there's many times that I've been wrong in the past, that's great. But It would be wrong to dismiss any sort of of the research styles that I'm observing. And I often get asked, well, you're in industry, right?

• 1:30:36Speaker 02

  So we do have access to large compute scale and so on. So there's certain kinds of research. I almost feel like we need to do responsibly and so on. But it is kind of, we have the particle accelerator here, so to speak, in physics. So we need to use it. We need to answer the questions that we should be answering right now for the scientific progress. But then, at the same time, I look at many advancements, including attention, which was discovered in Montreal initially because of lack of compute, right? 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. And then I think Montreal was a bit more limited in the scale.

• 1:31:19Speaker 02

  But then they discovered this content-based attention concept that then has obviously triggered things like transformer. Not everything obviously starts transformer. There's always a history that is important to recognize because then you can make sure that then those whom I 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. Perhaps it's not as short term as some of these advancements, or perhaps it's a different time scale. But the people and the diversity of the field is quite critical that we maintain it.

• 1:31:59Speaker 02

  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. But the humans definitely are critical. And I can think of, yeah, quite a few personal examples where also someone told me something that had a huge, you know, huge effect on to some idea. And then that's why I'm saying at least in terms of years, probably some things do happen.

• 1:32:28Lex Fridman?

  Yeah, it's fascinating. Yeah. And it's also fascinating how constraints somehow are essential for innovation. And the other thing you mentioned about engineering, I have a sneaking suspicion, maybe I over, you know, my love is with engineering. So I have a sneaking suspicion that all the genius, a large percentage of the genius is in the tiny details of engineering. So like, I think. We like to think our genius, the genius is in the big ideas. There's, I have a sneaking suspicion that like, because I've seen the genius of details, of engineering details make, uh, like make the night and day difference.

• 1:33:12Lex Fridman?

  And I wonder if those kind of have a ripple effect over time. So that, that too, so that's, 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. 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.

• 1:33:40Speaker 02

  Yes.

• 1:33:40Lex Fridman?

  Which is interesting to think about.

• 1:33:42Speaker 02

  Yeah, I mean, engineering, there's also kind of a historical, it might be a bit random. Because if you think of the history of how, especially deep learning and neural networks took off, feels like a bit random, because GPUs happened to be there at the right time for a different purpose, which was to play video games. So even the engineering that goes into the hardware, and it might have a time, like the timeframe might be very different. I mean, these, the GPUs were evolved throughout many years where we didn't even, were looking at that, right? So even at that level, right, that revolution, so to speak, the ripples are like, like we'll see when they stop, right?

• 1:34:25Speaker 02

  But in terms of thinking of why is this happening, right? There's, there's, 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. We are surfing thanks to that. Data centers as well, I mean, data centers are where, like, I mean, 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. Software is an important one. 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.

• 1:35:06Speaker 02

  Yeah, clearly the times have changed and thankfully we are in a much better software position as well. And then. I mean, obviously, there's research that happens at scale and more people enter the field. That's great to see, but it's almost enabled by these other things. And last but not least is also data, right? Curating data sets, labeling data sets, these benchmarks we think about. Maybe we'll want to have all the benchmarks in one system, but it's still very valuable that someone put the thought and the time and the vision to build certain benchmarks. We've seen progress, thanks to, but we're going to repurpose the benchmarks. That's the beauty of Atari is like, we solved it in a way, but we use it in Gato.

• 1:35:49Speaker 02

  It was critical and I'm sure it's, 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, you know, iteration of architectures? That's what maybe the field recognizes, but we need to, that's another thing. We need to balance in terms of humans behind. We need to recognize all these aspects because they're all critical. And we tend to, yeah, we tend to think of the genius, the scientist and so on, but I'm glad you're, I know you have a strong engineering background, so.

• 1:36:21Lex Fridman?

  But also I'm a lover of data and it gives a pushback on the engineering comment. Ultimately, it could be the creatives of benchmarks who have the most impact. Andre Capati, 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. And you're saying that that's actually, that benchmark is holding back the field. Because, I mean, especially in his context on Tesla Autopilot, that's looking at real world behavior of a system. It's, you, you, there's something fundamentally missing about ImageNet that doesn't capture

• 1:37:01Lex Fridman?

  the real worldness of things, that we need to have data sets, benchmarks that have the unpredictability, the edge cases, the whatever the heck it is that makes the real world so difficult to operate in. We need to have benchmarks of that, so. 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. 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 benchmark for the community to mark and push progress?

• 1:37:46Lex Fridman?

  You have this amazing paper you co-authored, a survey paper called Emergent Abilities of Large Language Models. It has, again, the philosophy here that I'd love to ask you about. What's the intuition about the phenomena of emergence in neural networks? Transformers language models. Is there a magic threshold beyond which we start to see certain performance? And is that different from task to task? Is that us humans just being poetic and romantic? Or is there literally some level at which we start to see breakthrough performance?

• 1:38:21Speaker 02

  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 curves. When you analyze how much the data set size affects the performance, or how the model size affect the performance, or how long you train the system for affects the performance, right?

• 1:39:03Speaker 02

  So, you know, if we think of ImageNet, like the training curves look fairly smooth and predictable in a way. And I would say that's probably because of the, it's kind of a one, a one-hop reasoning task, right? 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. So.

• 1:39:33Speaker 02

  In language, we are seeing benchmarks that require more pondering and more thought in a way, right? This is just kind of you, you, you need to look for some subtleties that it involves inputs that you, you might think of, or if, even if the input is a sentence describing a mathematical problem, there is, there is a bit more processing required as a human and more introspection. So I think the, how these benchmarks work means that there is actually a threshold, just going back to how transformers work in this way of querying for the right questions to

• 1:40:13Speaker 02

  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. And then, only, only then, you might start seeing performance going from random to non-random. And this is more empirical, there's, there's no formalism or, or theory behind this yet, although it might be quite important, but we're seeing these phase transitions of random performance until some, let's say, scale of a model, and then it goes beyond that.

• 1:40:50Speaker 02

  And it might be that you need to fit a few low-order bits of thought before you can make progress on the whole task. And if you could measure actually those breakdown of the task, maybe you would see more smooth, oh, like, yeah, this, you know, once, once you get this and this and this and this and this, then you start making progress in the task. 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. And one thing that conversely, I've seen great progress on in the last couple of years

• 1:41:32Speaker 02

  is this notion of science of deep learning and science of scale in particular, right? So on the negative is that there's some benchmarks for which progress might need to be measured at, at minimum and at certain scale until you see then what details of the model matter to make that performance better, right? So that's a bit of a con, but... What we've also seen is that you can empirically analyze behavior of models at scales that are smaller, right? So let's say, to put an example, we had this Chinchilla paper that revised the so-called scaling laws of models.

• 1:42:15Speaker 02

  And that whole study is done at a reasonably small scale, right? That may be hundreds of millions up to 1 billion parameters. And then the cool thing is that you create some laws, right? Some laws that, some trends, right? 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. And these laws, 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, extrapolating from a small scale. But then this emergence is sadly that not everything can be extrapolated from scale depending on the benchmark.

• 1:43:00Speaker 02

  And maybe the harder benchmarks are not so good for extracting these laws. But we have a variety of benchmarks at least.

• 1:43:07Lex Fridman?

  So I wonder to which degree the threshold, the phase shift scale is a function of the benchmark. Some of the science of scale might be engineering benchmarks where that threshold is low. Sort of taking a main benchmark and reducing it somehow, where the essential difficulty is left, but the emergent, the scale at which the emergence happens is lower. Just for the science aspect of it versus the actual real world aspect.

• 1:43:40Speaker 02

  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.

• 1:43:50Speaker 02

  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. But that is not to say that this extrapolation will hit its limits. And when it does, then how much we scale or how we scale will sadly be a bit suboptimal until we find better loss, right? And these laws, again, are very empirical laws. They're not like physical laws of models, although I wish there would be better theory about these things as well. But so far, I would say empirical theory, as I call it, is way ahead than actual theory of machine learning.

• 1:44:31Lex Fridman?

  Let me ask you almost for fun. So this is not Oriol as a DeepMind person or anything to do with DeepMind 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 making an official report and the claim that he believes there's evidence that this system has achieved sentience. And I think this is a really interesting case on a human level, on a psychological

• 1:45:14Lex Fridman?

  level, on a 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. So what did you... What do you find interesting? What's your take on all of this as a machine learning engineer and a researcher and also as a human being?

• 1:45:37Speaker 02

  Yeah, I mean, a few reactions. Quite a few, actually.

• 1:45:42Lex Fridman?

  Have you ever...

• 1:45:44Speaker 02

  Briefly thought, is this thing sentient? Right, so never, absolutely never. Like even with like AlphaStar? Wait a minute, what? Sadly, though, I think, yeah, sadly, I have not. Yeah, I think, I think the current, any of the current models, although very useful and very good. Yeah, I think we're quite far from that.

• 1:46:06Speaker 02

  And there's kind of a converse side story. So one of, one of the, my passions is about science in general. And I think, I feel I'm a bit of like a failed scientist. That's why I came to machine learning, because you always feel, and you start seeing this, that machine learning is maybe the science that can help other sciences, as we've seen, right? Like you, you know, it's such a powerful tool. 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. But having, especially with, when I was a bit more involved in AlphaFold,

• 1:46:49Speaker 02

  learning a bit about proteins and about biology and about life.

• 1:46:55Speaker 02

  The complexity, it feels like, it really is like, I mean, if you start looking at the things that are going on at, you know, at the atomic level. And also, I mean, there's, there's obviously that

• 1:47:10Speaker 02

  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 just makes me like, wow, there's such level of complexity difference still, right? 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.

• 1:47:49Speaker 02

  It just feels like it's just not possible to achieve the same level of complexity behavior. And but 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 fetched and far in the future to be calling or to be thinking, well, this this mathematical function that is differentiable is is in fact sentient and so on.

• 1:48:22Lex Fridman?

  There's something on that point. It's very interesting. So you know enough about machines and enough about biology to know that there's many orders of magnitude of difference in complexity. But you know how machine learning works. So the interesting question for human beings that are interacting with a system that don't know about the underlying complexity. And I've seen people, probably including myself, that have fallen in love with things that are quite simple.

• 1:48:51Speaker 02

  Yeah, so.

• 1:48:52Lex Fridman?

  And so maybe the complexity is one part of the picture, but maybe that's not a necessary.

• 1:48:59Lex Fridman?

  That's not a necessary condition for sentience, for perception

• 1:49:06Lex Fridman?

  or emulation of sentience.

• 1:49:08Speaker 02

  Right. So I mean, I guess the other side of this is that's how I feel personally. I mean, you asked me about the person, right?

• 1:49:16Speaker 02

  Now it's very interesting to see how other humans feel about things, right? 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, yeah, how I got to learn about it and how I see the curve a bit more smooth. Because I, you know, like, just seen the progress of language models since Shannon in the 50s. And actually, looking at that time scale, we're not that fast progress, right? I mean, what what we were thinking at the time, like, almost 100 years ago. Is not that dissimilar to what we're doing now. But at the same time, yeah, obviously others, my experience, right? That the personal experience, I think no one should, you know,

• 1:50:01Speaker 02

  I think no one should, should, should, should tell others how they should feel. I mean, the feelings are very personal, right? 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. And then when I maybe disagree or I don't understand or see that, yeah, maybe this, this is not something I think right now is reasonable, knowing all that I know. 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, let's make, 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.

• 1:50:46Speaker 02

  And then just a dump of the internet. So versus like then the complexity of like the creation of humans from, from their inception, right? And also the complexity of evolution of the whole universe to where we are. That is feels orders of magnitude more complex and fascinating to me. So I think, yeah, maybe part of the only thing I'm, I'm thinking about trying to tell you is, yeah, I, I think explaining a bit of the magic, there is a bit of magic. It's good to be in love, obviously, with what you do at work. And I'm, I'm certainly fascinated and surprised quite, quite often as well. But I think, hopefully, as experts in biology, hopefully will tell me this is not as magic.

• 1:51:30Speaker 02

  And I'm happy to learn that through, through interactions with the larger community, we can 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're starting to interact with this in products and so on. 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? Otherwise, the technology will not be used properly for good, which is obviously the goal of all of us, I hope.

• 1:52:04Lex Fridman?

  So let me then ask the next question. Do you think in order to solve intelligence or to do to replace the leg spot that does interviews? As we started this conversation with, do you think the system needs to be sentient? Do you think it needs to achieve something like consciousness? And do you think about what consciousness is in the human mind? That could be instructive for creating AI systems?

• 1:52:30Speaker 02

  Yeah, honestly, I think probably not to to the degree of intelligence that there's this brain that can learn, can be extremely useful, can challenge you, can teach you. Conversely, you can teach it to do things. I'm not sure it's necessary personally speaking. But if consciousness or any other biological or evolutionary lesson can be repurposed to then influence our next set of algorithms. That is a great, that is a great way to actually make progress, right?

• 1:53:09Speaker 02

  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? So there's a distinction between

• 1:53:23Speaker 02

  details of how the brain might be doing computation. 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 to be able to replicate it. But then more if you 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. I think these clearly can inform algorithmic level. Research 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?

• 1:54:05Speaker 02

  So I think biology and what we know about ourselves can help a whole lot to build essentially like what we call AGI, this general, the real ghetto, right? The last step of the chain, hopefully. But but consciousness in particular, I don't, I don't myself at this thing too hard about how to add that to the system. But maybe, maybe my understanding is also very personal about what it means, right? I think this even, even that in itself is a long debate that I know people, people have often, and maybe I should learn more about this.

• 1:54:40Lex Fridman?

  Yeah, and I personally, I notice the magic often on a personal level, especially with physical systems like robots. 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 has to do more with effective communication and less with any of these kind of dramatic things. It, it seems like a useful part of communication. Having the perception of consciousness seems like useful for us humans. We treat each other more seriously.

• 1:55:24Lex Fridman?

  We are able to do a nearest neighbor shoving of that entity into your memory correctly, all that kind of stuff seems useful, at least to fake it, even if you never make it.

• 1:55:35Speaker 02

  So maybe like, yeah, mirroring the question, and since you talk 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 world?

• 1:55:50Lex Fridman?

  Yeah, I personally believe yes, but I don't even think it'll be like a separate island we'll have to travel to. I think it'll emerge quite naturally.

• 1:55:59Speaker 02

  Okay, that's easier than for us, then, thank you.

• 1:56:03Lex Fridman?

  But the reason I think it's important to think about is you will start, I believe, like with this Google engineer, you'll 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. And we have to prepare for that. Because there'll be, I do believe there'll be a civil rights movement for robots, as silly as it is to say. 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 want to lose them. They've come to be a part of my life and they mean a lot. They have a name, they have a story, they have a memory. And we start to ask questions about ourselves.

• 1:56:45Lex Fridman?

  Well, what, this thing sure seems like it's capable of suffering because it tells all these stories of suffering. It doesn't want to die and all those kinds of things. And we have to start to ask ourselves questions. What is the difference between a human being and this thing? And so when you engineer, I believe from an engineering perspective, from like a DeepMind or anybody that builds systems, 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. So if you have a system that's just doing customer support, you're legally not allowed to display sentience.

• 1:57:27Lex Fridman?

  We'll start to like ask ourselves that question and then so that that's going to be part of the software engineering process. Which features do we have? And one of them is communications of sentience. But it's important to start thinking about that stuff, especially how much it captivates public attention.

• 1:57:44Speaker 02

  Yeah, absolutely. It's definitely a topic that is important we think about. 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, 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 like 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?

• 1:58:28Speaker 02

  So I think we're never going to be fully ready unless we talk about this and we start also, as I said, just kind of expanding the...

• 1:58:42Speaker 02

  The people we talk to, to not include only our, our own researchers and so on, and in fact, places like DeepMind but elsewhere, there's more interdisciplinary groups forming up to start asking and really working with us on these questions. Because obviously, this is not initially what your passion is when you do your PhD, but certainly, it is coming, right? So it's, it's fascinating, kind of. It's, it's the, the thing that brings me to one of my passions that is learning. So the, in this sense, this is kind of a new area that, as a learning system myself, I want to keep exploring. And I think it's, it's great that, um, to see, you know, parts of the debate and, and

• 1:59:25Speaker 02

  even I seen a level of maturity in the conferences that deal with AI. If you look five years ago, um, to now, just the amount of workshops and so on has changed so much. It's, it's impressive to see how much topics of, um, you know, safety ethics and so on, come to, to the surface, which is great. And if we were too early, clearly, it's fine. I mean, it's a big field and there's lots of people with lots of, um, interest that will do progress or make progress. Um, and obviously, I don't believe we're too late. So that in that, in that sense, like, I think it's great that we're doing this already.

• 2:00:01Lex Fridman?

  It's better be too early than too late when it comes to super intelligent AI systems. Let me ask, speaking of sentient AIs, you gave props to your friend Ilya Sutskever for being elected, uh, the Fellow of the Royal Society. So just as a shout out to a fellow researcher and a friend, what's the secret to the genius of Ilya Sutskever? And also, do you believe that his tweets of, as you have hypothesized and Andre Karpati did as well, are generated by a language model?

• 2:00:31Speaker 02

  Yeah. So. I strongly believe I, Ilya is gonna be visiting in a few weeks, actually. So I'll, I'll ask him in person, um, but.

• 2:00:41Lex Fridman?

  Will he tell you the truth?

• 2:00:42Speaker 02

  Yes, of course, hopefully. I mean, we're, you know, ultimately, we, we all have shared paths and, and there's friendships that go beyond, obviously, institutional, institutions and so on. So I hope he tells me the truth.

• 2:00:55Lex Fridman?

  Well, maybe the AI system is holding him hostage somehow. Maybe he has some videos about, he doesn't want to release. So maybe it has taken control over him, so he can.

• 2:01:03Speaker 02

  Well, I, if I see him in person, then.

• 2:01:05Lex Fridman?

  He will, he will know.

• 2:01:06Speaker 02

  Yeah, I'll, I'll, but, but I think the, um, I think it's a good, I think Ilya's personality, just knowing him for a while.

• 2:01:16Speaker 02

  Yeah, he's, he's, everyone in Twitter, I guess, gets a different persona. And, and I, and I think Ilya's one, um, does not surprise me, right? So I think knowing Ilya from before social media and before AI was so prevalent, I recognize a lot of his character. So that's something for me that I feel good about, a friend that hasn't changed or like is still true to himself, right? Obviously, there is, 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 advancements. So I think that the tricky bit here is how to balance your true self with the responsibility that your worth carry.

• 2:01:57Speaker 02

  So in this sense, I think, yeah, like I, I, I appreciate the style and I understand it, but, um, it created debates on like some, some of his tweets, right? That maybe it's good, we have them early anyways, right? But, um, but yeah, it's, it's, then the reactions are usually polarizing. I think we're just seeing kind of the reality of social media a bit there as well, reflected on, on that, on that particular topic or set of topics he is tweeting about.

• 2:02:23Lex Fridman?

  Yeah, I mean, it, it's funny that you speak to this tension. He was one of the early. Seminal figures in the field of deep learning. And so there's a responsibility with that. But he's also, from having interacted with him quite a bit, he's just a brilliant thinker about ideas. And which, as are you. And that there's a tension between becoming the manager versus like the actual thinking through very novel ideas. The, yeah, the scientist versus the manager. And he's, he's one of the great scientists of our time. This was quite interesting. And also, people tell me quite silly, which I haven't quite detected yet.

• 2:03:06Lex Fridman?

  But in private, we'll have to see about that.

• 2:03:09Speaker 02

  Yeah. Yeah, I mean, just, just on the point of, I mean, Ilya has been an inspiration. I mean, quite a few colleagues I can think shaped, you know, the person you are, like Ilya certainly gets probably the top spot, if not close to the top. And if we go back to the question about people in the field, like how the role would have changed the field or not. I think Ilya's case is interesting because he really has a deep belief in the scaling up of neural networks. There was a talk that, that, that is still famous to this day, from the sequence to sequence paper, where, where he was just claiming, just give me supervised data

• 2:03:54Speaker 02

  and a large neural network and then, you know, you'll solve basically all the problems, right? That, that, that vision, right, was already there many years ago. So it's, it's good to see like 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. The funny bit about that talk is that we rehearsed the talk in a hotel room before and The original version of that talk would have been even more controversial. So maybe I'm the only person that has seen the unfiltered version of the talk. And you know, maybe when the time comes, maybe we should revisit some of the skip slides

• 2:04:38Speaker 02

  from the talk from Ilya. But I really think the deep belief into some certain style of research pays out, right? It's good to be practical sometimes. And I actually think Ilya and myself are like practical, but it's also good. There's some sort of long term belief and trajectory. Obviously, there's a bit of lack involved, but it might be that that's the right path. Then you clearly are ahead and hugely influential to the field, as he has been.

• 2:05:07Lex Fridman?

  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. Do you think that intuition is ultimately correct? General methods, leverage 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.

• 2:05:50Speaker 02

  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. We don't want to pre-process data sets. We want to see the bytes, right? Like the true data as it is, and then learn everything on top. So very much agree with that. And I think scaling up feels, at the very least, again, necessary for building incredible complex systems. It's possibly not sufficient, barring that we need a couple of breakthroughs. I think Rich Sutton mentioned search being part of the equation of scale and search.

• 2:06:35Speaker 02

  I think search, I've seen it.

• 2:06:39Speaker 02

  That's been more mixed in my experience. 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. But then in some other tasks, it's not very clear how you would do that. Although recently, one of our recent works, which actually was mostly mimicking or a continuation, and even the team and the people involved were pretty much very like 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 kind of very interesting

• 2:07:19Speaker 02

  result of being able to have human-level code competition. So I've seen examples of it being literally mapped to search and scale. I'm not so convinced about the search bit, but certainly I'm convinced scale will be needed. So we need general methods. 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. And that's 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, we don't have

• 2:08:03Speaker 02

  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.

• 2:08:27Lex Fridman?

  Do you think in your lifetime, we will build an AGI system that would undeniably be a thing that achieves human-level intelligence and goes far beyond?

• 2:08:42Speaker 02

  I definitely think it's possible that it will go far beyond, but I'm definitely convinced that it will be human-level intelligence. And I'm hypothesizing about the beyond because 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? So we can certainly imitate humans at language and beyond. So getting at human level through imitation feels very possible. Going beyond will require reinforcement learning and other things. And I think in some areas, that certainly already has paid out.

• 2:09:27Speaker 02

  I mean, Go being an example, that's my favorite so far in terms of going beyond human capabilities. But in general, I'm not sure we can define reward functions that from a seat of imitating human-level intelligence that is general and then going beyond. That bit is not so clear in my lifetime. But certainly, human level, yes. And I mean, that in itself is already quite powerful, I think. So going beyond, I think is 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.

• 2:10:11Lex Fridman?

  Well, especially if human level or slightly beyond is integrated deeply with human society and there's billions of agents like that. Do you think there's a singularity moment beyond which our world will be just very deeply transformed by these kinds of systems? Because now you're talking about intelligence systems that are... Just, I mean, this is no longer just a going from horse and buggy to the car. It feels like a very different kind of shift. It wouldn't means to be a living entity on earth. Are you afraid or are you excited of this world?

• 2:10:49Speaker 02

  I'm afraid if there's a lot more. 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. As we know, there's quite a few, you know, issues with having too many people coexisting in a resource limited way. So for digital entities is an interesting question. I think such a limit maybe should exist, but maybe it's going to be imposed by energy availability, because this also consumes energy.

• 2:11:33Speaker 02

  In fact, most systems are more inefficient than we are in terms of energy required.

• 2:11:39Lex Fridman?

  Currently, yeah.

• 2:11:40Speaker 02

  But definitely, I think as a society, we'll need to just work together to find what would be reasonable in terms of growth or how we coexist if that is to happen. 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. I think those are the applications in a way that I'm most exciting to see and to personally work towards.

• 2:12:14Lex Fridman?

  Yeah, there's going to be significant improvements in productivity and the quality of life across the whole population, which is very interesting. But I'm looking even far beyond us becoming a multiplanetary species.

• 2:12:26Lex Fridman?

  And just as a quick bet, last question, do you think as humans become multiplanetary species, go outside our solar system, all that kind of stuff, do you think there will be more humans or more robots in that future world? So will humans be 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?

• 2:12:57Speaker 02

  I think we're all excited to build AGI to empower or make us more powerful as human species. Not to say there might be some hybridization. I mean, this is obviously speculation, but there are companies also trying to, the same way medicine is making us better. Maybe there are other things that are yet to happen on that. But if the ratio is not at most one to one, I would not be happy. So I would hope that we are part of the equation. But maybe there's maybe a one to one ratio feels like possible,

• 2:13:38Speaker 02

  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.

• 2:13:50Lex Fridman?

  Well, this is how I know you're human, and this is how you've passed the Turing test. And you are one of the special humans, Oriol. 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. Thank you again for talking today. Thank you for the amazing work you do. You're a shining example of a researcher and a human being in this community.

• 2:14:16Speaker 02

  Thanks a lot. Yeah, looking forward to before the singularity, certainly.

• 2:14:20Lex Fridman?

  And maybe after. Thanks for listening to this conversation with Oriol Venialas. To support this podcast, please check out our sponsors in the description. And now, let me leave you with some words from Alan Turing.

• 2:14:34Lex Fridman?

  Those who can imagine anything can create the impossible. Thank you for listening and hope to see you next time.