Masters of Scale
00:06:05 · Demis Hassabis
[00:06:05] S2: Yes, sure. Well, biology is the most complex systems in the universe. That's why it's so hard because of the emergent behaviour, the interactions, and proteins are the building blocks of life. That's how I've always seen them. The protein folding problem, which is what we tried to solve with AlphaFold, was considered to be like a fifty year grand challenge in biology. It's sometimes described to me as like Fermat's Last Theorem, but for biology. It's critical to understand the structure of a protein from its genetic sequence, its amino acid sequence, to understand its function. And that's really important to understand basic biological mechanisms, but also to understand what goes wrong in disease and what things to target. You know, if you imagine you're designing a drug, then you need to know what the three d surface of the protein looks like so you know which part of that protein to bind to. And so it was always a critical, but not obviously only still a small part of the entire drug discovery process. But it's an absolutely critical part of that. And we were able to, you know, with AlphaFold, accelerate it by orders of magnitude by building AlphaFold. And interesting is that at the time... And if you think about it, we finished AlphaFold two back in 2020, which is, six plus years ago now, and it's still probably the best example of... Concrete example of AI applied to a really hard scientific problem that was thought to be sort of uncrackable. That just shows you how far ahead of its time it was, And now, you know, we're really proud of of the impact it's made. Like, 4,000,000 researchers around the world use it. Pretty much every biology and medical researcher in the world, almost every drug in future will have made use of AlphaFold, all the structures. But it's not enough. We want to expand it to biochemistry and also predicting things like toxicity or acne properties, all of these other things that you need to know about before you can have a drug that's safe to use and that's efficacious you know, efficient in whatever it is you're trying to tackle. Yep.
00:08:02 · Reid Hoffman
[00:08:02] S3: So speaking of accelerations, let's go to one of the hot topics of the day, recursive self improvement. Mhmm. What is your kind of theory of the game? And I say this because you, you know, are a master games person. So I use that as kind of deliberate. And if you don't know what I'm referring to, either look at the documentary or look it up on Wikipedia. So like RSI, opportunities, concerns? Yeah.
00:08:28 · Demis Hassabis
[00:08:28] S2: Well, look, RSI, it's obviously an extremely useful technique to make progress. It mostly works best in what I call verifiable domains, what's called verifiable domains. So math is one, which is why we're seeing so much advance there, coding's another, but originally it was games. So actually, one of the first sort of famous recursive self improving systems was one we invented quite a lot of years ago now called AlphaZero, built off the back of AlphaGo, which is the kind of, you know, famous program that beat the world champion of Go back in '20... You know, ten years ago now, 2016, and kind of was the beginning, I think, of the modern AI era. And then we built a second version that was in some ways more consequential, AlphaZero, that didn't need to use any human data anymore, and, it just basically played against itself. Started at random, and then after, you know, around a dozen to two dozen different epochs, you know, it played like a million games against itself. It would then train a new version of itself that was slightly better than the old version, and then that would play a million games against itself and so on. And for example... And then it could play any game, not just Go. It could play any two player per information game. And for me, it was especially visceral as a chess player because I remember watching it play chess. And within, you know, twelve hours, basically, or less than twelve hours, it would start from random play in the morning to, you know, you'd have your coffee break at eleven a. M, and I could still just about beat it. Then by lunchtime, it was Grand Master level, and then by tea time, it was better than any chess playing entity.
00:10:06 · Reid Hoffman
[00:10:06] S3: Tea time is dinner time for The
00:10:07 · Demis Hassabis
[00:10:07] S2: US folks. Yes, computer time. Or computer, know, kind of handcrafted chess computer is better than all of them. So we saw the power of that recursive self improvement and obviously the thing that all these domains have in common is you can verify the answer. Is it a correct answer? Is it an optimal answer? Is the equation correct? Does the code run? Did you win the game? So those domains are... And and and the important thing about a verifiable domain is you can verify the answer, so you know when you've you've you've you've the right point. But, also, that means you can create almost infinite synthetic data because you can just use the current best versions you have to set new problems or make new moves and then just sort of verify whether the synthetic data was correct. The problem with synthetic data a lot of the time is you can't verify that it's right from the right distribution, but in those cases you can. So we've seen the enormous progress with maths and with coding, and coding's obviously become enormously valuable, and everyone is having to do that now. But I do worry about it because in the limit... So there's different forms of RSI. Right now, we're in the early days of RSI, I would say, where what's happening is, and you're probably all experiencing this as well with your teams, is, you know, it's making a human engineer maybe three, four, five times more productive. Okay? So... But the but the human is very much still in the loop and reviewing code and architecting things and and writing code still, but it's all just enhanced. The next phase, which I think carries the most risk, is closed RSI, closed loop, where you basically manage to somehow figure out how to do it without the human reviewer process in the whole procedure. That will obviously be faster, but I think is also a very dangerous way to get to full AGI because we're likely for two reasons. It sometimes used to be referred to twenty years ago as like an intelligence explosion because you don't quite know how do you control, keep control of such a system, such a process. And then the end result will likely be quite alien. It's gonna be like an alien intelligence. There's no reason you... To think it would be sort of in the similar kind of mind space that humans are in. So that will make it very hard for it to understand... To...
00:12:23 · Demis Hassabis, Reid Hoffman
For us to understand it and maybe for it to understand us. So I think it's... You know, we should be very cautious as we get closer towards that closed loop form of RSI. And I think all the AI researchers sort of know that, but there is a sort of race to the there's this sort of intense, obviously, competitive race to improve your coding systems. [00:12:43] S3: So presume that the incentives to be in a race continue, and you and I, some people already know this, have tried to figure out if there's various ways you can get global collaboration to have less. You've dedicated a huge amount of energy to that, so thank you. It's a hard game Mhmm. As as you know. But presume that the competitive pressure for continuing to do RSI and coding continues. In that environment, what are the best ways that we can potentially navigate that? Because we have to see kind of where our SI is going, what it's pointed at. What are the kinds of things that the, you know, the leading labs such as your own Yeah. And the others, what should they be doing Yeah. To try to navigate this