First Steps Toward Automated AI Research

Talk transcript

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   [outro music] All right. Hello, everyone. Really excited to be here.

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   It's a big room. Very, uh, very cool conference so far. Uh, I wanna talk to you today about something that's been on my mind for many, many years. This is actually the first time I, I talk about it, sort of my version of going to Mars, um, and that is the Eureka machine, a machine that will eventually

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   invent pretty much all future inventions for humanity. Uh, and the way we're gonna get there is, uh, by taking a step back and thinking about what else has given us a lot of really incredible inventions, uh, namely evolution, and how that leads us to automating research a-and pushing the scientific frontier forward.

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   And this is, uh, joint work with a lot of, uh, amazing folks, uh, at Recursive, You.com, uh, and even some, uh, folks at AIX Ventures. And some of these slides are, uh, actually inspired by, uh, and taken, uh, partially from one of my co-founders at Recursive, Tim Rocktäschel.

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   So, uh, why do I talk about evolution, and why is it so important? Uh, I think basically evolution is this, like, open-ended process that has gotten us to a lot of different things that we really like.

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   Uh, it started in biology. It's moving to science, technology, and eventually AI, and I think it can inspire us in a lot of different ways to build better AI systems as well.

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   In fact, uh, whenever we take out-- And there's this famous saying, "Whenever I fire a linguist, my accuracy goes up." Uh, I think that's true for machine translation back in the day, and it may be true that we should fire all the AI engineers, uh, and-- that, that are here, uh, and have them mostly manage an actual

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   AI engineer that is AI and works on AI. Uh, and so that may be, uh, one of the conclusions of this talk. Uh, and I think most of us are gonna be excited about it 'cause it means that we'll all become managers of such an AI rather than having to do the nitty-gritty ourselves.

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   All right. So let's start with evolution, right? The really, really big picture, three and a half billion years or so. Uh, this is kind of the incredible process, uh, that has led from, you know, simple bacteria and plants a-and fish and amphibians and so on to, after many billions of years, us.

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    So that's, that's a good starting point. That gives us some indication that evolutionary processes can do pretty amazing things, right? But now let's zoom in and, uh, go maybe down to a few million years.

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    There we can also see how, in very first primitive ways, technological evolution, uh, has basically increased the world's, uh, sort of product, uh, in terms of monetary value. It's a little bit harder to estimate in the beginning, but we can see these sort of sequences of exponentials, and most exponentials eventually become S curves.

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    They flatten out. But humanity has done pretty well by basically developing, uh, many of these very basic technologies, hunting, farming, but then also thinking about science, the scientific method, um, in the early days of the Enlightenment and, of course, the Industrial Revolution.

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    So now we can zoom even further. Uh, and no worries, we're eventually gonna get to nanochat and actual autoresearch and, and what we're doing. Uh, it's a very, very quick zoom.

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    Um, and now we can zoom down to the last few thousands of years. And what we're seeing there is that with more technology, we were able to sustain more people, right?

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    So when we're working on pushing that frontier forward, uh, we're very certain that that will lead to more human flourishing, right? And especially in the last few, uh, hundred years, we're seeing this incredible explosion in the population of people because of technology and the evolution, uh, that it brings.

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    And in many cases, that evolutionary process is run by us, so it's sort of conscious, uh, but there are sort of interesting, uh, inspirations that we can take from that as we're thinking about the evolution of AI in the next cycles.

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    Uh, in fact, and I might not agree with everything with Marc Andreessen, but, uh, he is very smart, and we agree on a lot of things. Uh, and so I think he wrote this really great, uh, techno-optimist manifesto in which he, I think, correctly points out that the only perpetual source of growth for the entire economy-- A

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    lot of people worry about AI taking jobs and things like that, but the truth is it will very, very likely increase, uh, the economy massively, and that will benefi-benefit a lot of us.

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    And so the perpetual source of growth is technology. Uh, in fact, we can go even further and say that there's no material problem, and again, it's not sort of psychological problems and things like that, but no material problems, uh, that cannot be solved with even more technology, right?

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    For the problem of starvation, we invented the Green Revolution. Darkness, light. Uh, cold, indoor heating. Heat, air conditioning. And the list goes on. So I think we can kind of realize that this evolutionary process has been going on for a very long time and continues to make a huge amount of progress.

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    In fact, the progress is so fast that there can, within one lifetime, be a major, major shift, right? If you were born in [REDACTED:dob], uh, then [REDACTED:age] [REDACTED:age]-- when you're [REDACTED:age], the first human ever was able to, thanks to the Wright brothers, kind of have sustained motored flight.

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    And then about sixty-ish years later, in 1969, humans flew all the way to the Moon, right? So that within one lifetime, humanity went from, like, no one can fly for a very long time, other than sort of gliding down a hill or something.

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    No one can really fly to we all fly to the Moon.

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    Right? And so for us, I think what that means is we're probably, and I sometimes say this, we're, like, too late to explore Earth, we're too early to explore the stars, but we're right on time to build an AI that could actually do what flying did for some in one lifetime due to intelligence.

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    We can build and move from AI being worse at everything that we do to possibly being better at any specific task that we do. Right? And that, that will probably be our, our sixty-year timeframe, and because everything moves faster, it might only be thirty years or so.

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    So then, uh, there's an interesting connection between technology and science and theory, right? Like, sometimes the application comes first, and then we develop the theory later and then improve, uh, the technology.

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    Sometimes the theory comes first, and from that we can build new kinds of technologies. And so it's very helpful to think a little bit about the philosophy of science, and no better, uh, to be inspired there than Karl Popper, who wrote that, just like in other types of evolution, when we choose a theory, we also choose one

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    that is best, uh, in competition with other theories. Of course, you need-- if you wanted LLMs to do that, they need to find them. You need web search, for instance.

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    Um, but, uh, in the theory that best holds its own, uh, it's one that, just like evolution, has a certain natural selection process, right? It proves itself, uh, and there is also a sort of survival of the fittest, uh, going on in scientific theories.

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    And, uh, in fact, uh, a lot of science, according to Popper, is basically us proposing a new theory, hypothesis or explanation or description, and then subjecting it to rigorous empirical testing.

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    That is the, uh, essentially evolution-- e-evolutionary pressure of scientific theories.

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    And basically, that was a very short, uh, run-through, uh, sort of the history of, of open-ended evolution, uh, which hopefully makes us all realize that more science will lead to more technology, which will lead to more growth, which will lead to more human flourishing.

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    And so that then begs the question, does it make sense for us, uh, to try to just scale up and spend a lot of our resources as humanity to scale up scientific discovery in order to lead, uh, to this flourishing?

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    Uh, when, when you double-click into that, you kind of realize, um, which Stanisław Lem, uh, already realized a long time ago, uh, that the exponential growth of science will actually be at some point halted by the lack of people working on it, right?

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    There are so many niche subfields now in all the different areas of science that it's very hard to get a million people to work on that particular thing. Uh, and so as a result of this incredible widening of the scope, uh, he says, uh, "The number of people focusing on any single section of it has decreased."

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    And that then leads us to really thinking about how could we automate this and automate scientific discovery, and that then leads us to what I call the Eureka machine.

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    This is basically, uh, our attempt at trying to

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    build a machine that automates the process of scientific discoveries. And, uh, in fact, I-- Like, in a couple of months, I'll have a book coming out on, on this, uh, exact idea.

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    Uh, and so I'll just give you a super high-level highlight of how such a Eureka machine could be built for basically everything from physics, chemistry, biology, neuroscience, medicine, uh, economics, astrophysics, and so on.

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    And there are essentially four pillars that are all extremely important to this machine. One is, of course, you have to understand what knowledge is already out there, uh, what, uh, things humanity has already invented.

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    Uh, you have to get all the scientific measurement, uh, data into as the second pillar. This machine, uh, then for things that you cannot yet measure, we don't yet know, you should try to then f- build simulations.

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    Anything you can simulate, you can verify, and, uh, you can then solve with AI. Uh, and if all else fails, or at the very end of these processes, you still need to have some kind of, uh, physical, industrial, like, lab, uh, that actually can run real experiments in the real world.

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    And on top of all of this, uh, you'll have, uh, basically, uh, an agent swarm that will deal with all of these different sources of knowledge and data and experimentations and, and rewards.

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    Uh, and in terms of, you know, the foundational model of knowledge, of course, we also, you know, it basically is, is a good example of how every single technology we've built so far, especially in AI, but also before that, the internet, browsers, GPUs, and so on, we can rethink, and there are a lot of startups possible in

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    rethinking every single one of the layers of technology as infrastructure for superintelligence. All right. At You.com, for instance, we work on web search for LLMs, right, and agents and so on.

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    Uh, and that actually is quite different, right? Uh, agents can read thousands of very long snippets, um, rather than just ten blue links with, like, a very short snippet.

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    And so you can rethink each of these different, uh, layers of technology that we've built for people, uh, and, uh, rebuild them for AI in order to use them as tools to then build, uh, superintelligence.

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    Now, that is essentially, uh, the sort of why. Like, like, we wanna build superintelligence in order to automate science. Uh, and to me, that will be the next big step function change, uh, in, in humanity, uh, and technology as we know it.

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    Now, how do we actually build it? Uh, I think the best way to build it is to have it build itself, right? We've moved as a field in, especially natural language processing, for instance, which I've worked on for many years.

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    We've moved from not having linguists. This feels like ancient, you know, BC, uh, history. Uh, but before ChatGPT, um, we, we moved from having linguists tell us a bunch of things about language and then Training statistical models on top of that.

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    And when we allowed neural networks to actually automate learning those features with word vectors and, uh, other neural network architectures and back-to-back, uh, end-to-end learning and back propagation, we basically, uh, were able to get much bigger improvements.

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    Uh, then we did a bunch of architecture engineering. Now a bunch of people at least are working on a unified architecture. Uh, but even that unified architecture has a lot of manual processes.

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    And so it's clear over and over again in AI that when we take out a manual process and we replace it with a learned system, improvements will follow. Uh, and so that's why I think, uh, we should try to build this Eureka machine by having, uh, an RSI that builds itself.

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    And the beauty is that only now, um, AI can actually do this because AI is code and AI can code now. This, this ability to really code in longer and longer time horizons has really only happened in the last, like, six to eight months, and that now enables such an RSI to work on itself, to develop almost

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    a certain sense of self-awareness of its own shortcomings and then fix those shortcomings. Uh, and then once we have that machine that has gotten really, really good at doing research in AI itself, we can then use it to do AI research for a lot of other things, uh, in, in other scientific fields.

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    And so at a high level, it's quite easy, right? We have three steps: ideation, implementation, and validation of ideas. That's true for basically almost every scientific field.

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    And so, uh, to end maybe on some very specific examples, uh, we have built this first kind of version of such a Eureka machine, uh, and we wanted to just show that it works on some small, uh, samples that a lot of people know a-and are aware of.

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    And so we basically started, uh, with three things that show you and give you a very first glimpse of and sort of simple proof points, uh, of what such a machinery can do, and that was basically better training, faster training, and, and better kernels, uh, for Nvidia GPUs.

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    Um, the first one, nanochat, um, I'm sure many of you have heard of it. A lot of people think that's already recursive self-improvement, and it is kind of a weak form in the sense that usually when you do auto-research, it's, it's not recursive self-improvement, right?

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    True recursive self-improvement is when you have an AI that has a sense of self-awareness of its own shortcomings, full access over everything, uh, in its arsenal from pre-training to RL training and harnesses and everything, and then actually updates that entire system in the next version of itself.

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    Now, you can also take such a system and just ask it to improve some other process, some other AI, like a small nanochat run where you can train something in five minutes, and that is really exciting and it's an important milestone, but it's not actual RSI.

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    So here we basically showed three examples of such an autoresearch, um, uh, system and what it can do. And, uh, after a very, very short time, it essentially was able to outperform many, uh, different teams and teams that also use, uh, other AI research.

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    So let's double-click into some of these. Nanochat, uh, is a really exciting example. Uh, basically you train a very small, uh, chat model, uh, in less than, uh, five minutes, and you basically want to have it get to the best possible bits per byte, uh, number.

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    And so the whole community had worked on this, uh, for, uh, quite some time and got to, uh, zero point nine three, and after training this for a little more than a day or two, uh, we basically got it down to zero point nine one, um, which is pretty exciting.

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    Now, it wouldn't be that exciting if all it did was just find a couple of hyperparameters, um, and tune them carefully. But it actually did find truly interesting novel ideas like hashed bigrams and trigram embeddings and tables for those, um, and mixing that i-into, uh, various, uh, value paths of, uh, the intention through a variety of learned

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    gates. So it actually started to doing more and more interesting things rather than just kind of tuning hyperparameters.

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    Um, another one, a nanoGPT speedrun. Uh, obviously speed's very important. Uh, and so here we're able to work on this again, apply the system, and after a very short amount of time, it got better than, uh, people working often together with the AI for over a year, uh, on, on this very-- on this benchmark and made the

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    whole thing another two seconds, over two seconds faster, um, at seventy seconds. And again, discovering, uh, very interesting ideas in the process.

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    And then the third one is CUDA kernels. Of course, we all care about not burning through our GPU budgets too quickly, um, uh, and trying to be very efficient.

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    I think in general it's actually kind of shocking how inefficient a lot of mixture of expert, uh, models still are run in very large clusters that cost billions of dollars and, and only have, like, thirty percent or so utilization.

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    There's a lot of work, uh, that's ongoing in the world, uh, to improve that and different fields, uh, or different groups of people are various different, um, yeah, stages of that.

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    Uh, but long story short, um, lots of different CUDA kernels are used during training and testing. And here, um, we basically again took that system and after, uh, a couple of days, it discovered better kernels, uh, than the leaderboard's best, uh, on the NVIDIA, uh, benchmark website, um, by again quite, quite a sizable margin across all the

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    different, uh, categories o-of those kernels. And while we are pretty good at AI and, like, we actually in the team didn't have any particular CUDA kernel experts who just spent their entire careers writing good kernels.

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    Uh, but still, you know, we do just enough to make sure and worked together with NVIDIA to make sure that there are no reward hacks here and, and other issues, but actually- Found, uh, that eventually these all checked out and were indeed, uh, pretty much all the different kernels, uh, found the best solutions there.

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    And so with that, I hope I could convince you, uh, that indeed RSI could be that next big, uh, S-curve, um, and exponential that's gets layered, uh, on top of previous exponentials.

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    And, uh, that should help us, uh, with not just AI, but eventually science and then all of technology, and then, uh, allowing many more people, uh, to flourish on our planet.

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    Uh, and so maybe I'll end on this note here, which is, uh, a lot of people wonder how much longer AI can go, right? Every exponential eventually flattens out.

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    And, um, it's actually quite hard to know, like when we even talk about exponential growth in AI, what does that even mean? There are many different, I call them spaces of intelligence, and we won't have time to go into all of, all of these.

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    But as soon as we actually try to define multiple different dimensions of each of these 10 spaces, uh, that make up this complex sort of, uh, volumetric, uh, thing that is intelligence, you'll realize that there's still so much more to go.

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    Like on the upper bounds of intelligence, we're still astronomically far away from reaching those, uh, in across pretty much every single one of, uh, these dimensions and the spaces, uh, that they make up.

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    Uh, so if any of that is interesting and you wanna help us build that, um, we'd love to hear from you. Thank you. [outro music]