Masters of Scale
00:00:00 · Jeff Berman
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00:01:41 · S1, Demis Hassabis, Jeff Berman
Ad[00:01:41] S1: AI is full of possibilities. Enterprises run on realities. That's why Pega combines the world's most powerful AI with the governance, transparency, and control enterprises demand so you can move faster, adapt faster, and scale AI with confidence. All the power of AI, none of the uncertainty. Learn more at pega.com/ai. [00:02:11] S2: A lot of the cyber issues that we've seen in the last six months or so could have been addressed with better engineering, more robust engineering. The human's very much still in the loop. And the next phase, which I think carries the most risk, is closed RSI, closed loop, without the human reviewer process. And that will obviously be faster, but I think is also a very dangerous way to get to full AGI. I do think there are some things that generally all the leading labs do agree on, like the idea of third party auditors or evaluators coming in. Obviously, there's a question mark on which third parties, and I think we need a healthy kind of ecosystem of that. You could have good start ups and companies doing that. There's enough revenue there for them given the amount of investment going into AI as a whole. [00:02:58] S0: This is Masters of Scale. I'm Jeff Berman, your host. In today's episode, our very own Reid Hoffman sits down with the incredible Demis Hassabis. Demis is the cofounder and chair of Google DeepMind and chief scientist of Alphabet. Reid and Demis discuss the race to AGI, the future of AI fueled scientific discovery, the need for more cooperation on safety efforts, and much, much more. I hope you enjoy it.
00:02:58 · Jeff Berman, Demis Hassabis, Reid Hoffman
[00:02:58] S0: This is Masters of Scale. I'm Jeff Berman, your host. In today's episode, our very own Reid Hoffman sits down with the incredible Demis Hassabis. Demis is the cofounder and chair of Google DeepMind and chief scientist of Alphabet. Reid and Demis discuss the race to AGI, the future of AI fueled scientific discovery, the need for more cooperation on safety efforts, and much, much more. I hope you enjoy it. [00:03:37] S2: Demis, thank you for joining us. Great to be here. Thanks for having me. [00:03:40] S3: We've known each other for, I think, well over ten years now. Maybe approaching twelve or something like that. It was an honor when you asked me to be on the AI ethics board for Google for for our various funny meetings. Yes. Yeah. Yeah. So let's start. Because one of the things we've been trying to do at the summit is to be envisioning the world as we'd wanna make it. The theme has been agency. And, you know, as you have been a leader in and knew well before most people were thinking about AI, that one of the key things for thinking about AI for human benefit is medicine and medical. And so say a little bit about how you envision this, how this helps. You know, people say, oh, if I could be getting that, I understand why you AI guys are actually trying to build something that creates a longer human experience, a more quality of experience, you call it anti x risk.
00:04:38 · Demis Hassabis, Reid Hoffman
[00:04:38] S2: Yeah. For sure. I mean, we take a step back for a moment, it's like big picture, and you know this knowing us from quite early in our DeepMind journey, that the whole point of me working on AI for my whole career was to use it as a kind of like the ultimate tool to advance science and medicine and our understanding of the world around us, but also improving human health. I've always said that the number one thing we should use these amazing tools that we've now built, and we were, you know, dreaming about twenty years ago, is to improve the human condition and improve human health. And I've tried to do that certainly with my own more personal projects, obviously things like AlphaFold, but also the spin out drug discovery company, Isomorphic. I've tried to push it really hard in that direction, as I know you have too more recently. And I think it's exactly the sorts of things we need to be showing the world that AI can do. I think what better thing than to use AI to help us cure terrible diseases. [00:05:34] S3: So some of the protein folding was this kind of intersection of the idea of, like, we have really good data. We could use scale learning because the whole learning... The cognitive revolution from learning starting with even Pong, but the... You know, and self play and all [00:05:50] S2: the rest
00:05:50 · Demis Hassabis, Reid Hoffman
[00:05:50] S2: the rest [00:05:50] S3: is like, okay, that's... This would be a really good way to do it. How does AlphaFold lead to medicines? Because one of the things that is a question I get frequently is like, yeah, you're talking about that, but is it true? Is it going to happen anytime soon? So I'm passing along the question I get to you.
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.