No Priors: Artificial Intelligence | Technology | Startups
00:41:08 · Sarah Guo, Mark Zuckerberg, Priscilla Chan, Alex Rives
[00:41:08] S3: You have this, I think, incredibly ambitious mission at Biohub. And yet, you know, the AI scientists that work here could also go work in commercial enterprises. How do you think about the talent and, like, how to bring people to Biohub? [00:41:25] S0: I mean, where do you to start? I think, you know, yeah, I mean, it's a very hot market for AI researchers. But I think that part of what that means is that there's a lot of demand, and they're very in demand and can work on the things that they wanna work on. And I think this gets back to this point again about frontier AI and frontier biology. So yeah, I mean, think the AI researchers who work here could go work on language models or things at any of the main labs, But those labs don't have the frontier biology part attached to it. So I think that there's also a just very large mission component of this, which is there's an ability to do this unique work here that you just can't really do at the other places. So if that's what your focus is, then this then I don't actually think that there's any other organization in the world that's doing both the frontier biology and the frontier AI. [00:42:27] S1: Yeah. Why are you here, Alex? [00:42:28] S2: I mean, I think it's it's really simple. Yeah. Our our mission is to take care of prevent disease. And and I think, you know, there's it's it's just such a [00:42:37] S3: powerful with a straight face in a less than hundred year timeline. [00:42:40] S1: Serious now. There's no more. [00:42:42] S0: That's yeah.
00:42:42 · Mark Zuckerberg, Alex Rives, Sarah Guo
[00:42:42] S0: That's yeah. [00:42:44] S2: Yeah. It's it's a really powerful mission. And I I think, you know, you yeah. I mean, it's it's just, you know, scientists, I think, are [00:42:53] S3: very motivated by science. Yes. [00:42:54] S2: Yeah. It's it's something people are deeply motivated by. And I think, you know, we're at this moment in time where that actually seems like something that can be achieved. And I think, you know, we're building a really unique place where where we're tackling that problem. And, you know, we have the resources, I think, kind of the right things to actually really go after that and do that. [00:43:20] S3: Yeah. I mean, that resonates with me as somebody who, you know, talks to and hires a lot of research scientists. They want to know if you have the data, if you have the tools, if you have the compute, if you have the talent, and then what the mission is. And so I actually think that's super competitive.
00:43:20 · Sarah Guo, Mark Zuckerberg
[00:43:20] S3: Yeah. I mean, that resonates with me as somebody who, you know, talks to and hires a lot of research scientists. They want to know if you have the data, if you have the tools, if you have the compute, if you have the talent, and then what the mission is. And so I actually think that's super competitive. [00:43:34] S0: The other thing is that you don't need a very large team. Right? So I think it's like an interesting thing about the world is that people care about different missions, and that's good. I think that's part of the whole I mean, part of why building these tools and giving people the ability to explore what they care about, whether it's across science or just across everything, is such a powerful way to make progress in society is that people care about different things. And in order to make progress in AI, you don't need many, many hundreds of AI researchers or thousands or anything like that. I think you can really make progress with a very strong group of a dozen or a couple dozen people. And, yeah, mean, finding people who care about this mission is not a particularly hard thing. I mean, this is like a super important thing in the world. So I think that that's yeah, it's just kind of a cool thing about the world is that people obviously are drawn to different missions.
00:43:34 · Mark Zuckerberg, Sarah Guo
[00:43:34] S0: The other thing is that you don't need a very large team. Right? So I think it's like an interesting thing about the world is that people care about different missions, and that's good. I think that's part of the whole I mean, part of why building these tools and giving people the ability to explore what they care about, whether it's across science or just across everything, is such a powerful way to make progress in society is that people care about different things. And in order to make progress in AI, you don't need many, many hundreds of AI researchers or thousands or anything like that. I think you can really make progress with a very strong group of a dozen or a couple dozen people. And, yeah, mean, finding people who care about this mission is not a particularly hard thing. I mean, this is like a super important thing in the world. So I think that that's yeah, it's just kind of a cool thing about the world is that people obviously are drawn to different missions. [00:44:28] S3: So I think the simplest mental models that folks have, even if they're paying attention to the space, are essentially like, okay, you know, structured prediction models for proteins and protein protein interaction models. And then, so there's this one piece, which is fundamental understanding. And then there's this like theory of someday we're just going to be able to like zero shot things into either the clinic or the clinic with much, much better hit rate. What needs to happen for us to go from ESM Fold two to this other piece? Is that feasible?
00:45:01 · Alex Rives, Sarah Guo
[00:45:01] S2: I think that's a great question. I mean, I would say that I'm really optimistic on that. So I think, you know, on the one hand, you know, these are problems that historically, you know, people could spend kind of an entire career working on. Like, how do you how do you figure out how to effectively optimize a drug? How do you get it, you know, get it through preclinical? How do you do the early safety? I think that, you know, when you have a new scientific paradigm, kind of, you know, questions that were once hard kind of become simplified through the new paradigm. And so I'm very optimistic that kind of many of these core problems will be solved kind of in an emergent way through these models. Mhmm. And I think one great example of that is is toxicity. Whereas if if you can kind of really digitally digitally kind of simulate everything and be able to predict, you know, where a drug is going to distribute and bind across the human body. You know, like you kind of have the beginning of a solution to that kind of problem. So I think that once you have these kind of accurate representations at the molecular level, we're going to start to see really rapid progress on a lot of these core problems. [00:46:13] S3: What is the most exciting use or experimentation with the models you've seen in the last week since release?
00:46:13 · Sarah Guo, Alex Rives
[00:46:13] S3: What is the most exciting use or experimentation with the models you've seen in the last week since release? [00:46:19] S2: Yeah, I mean, it's just been great to kind of see it get integrated in all kinds of things. I think one of the really interesting things that we've been seeing is people kind of connecting it with AgenTex systems to just kind of do automated design and kind of just automate that whole process. So it's really, I think, another example of how you can kind of see bringing together AgenTeq and Frontier AI with, you know, the ability to have a world model for biology and actually reason about biology and, you know, really kind of start to automate the entire design process. [00:46:53] S3: How do you decide what the next step in the research agenda is? It's like world model for biology, and then I could I'm just gonna be very coarse here. Like I could scale it up, I could add more data, I could add, like adding data is a nontrivial thing in terms of new methods and domains. Like what is, do you take input from the larger ecosystem about, you know, how people are using it and what would make it more useful? Or is it really like we we understand, like, the next step of structures or coverage that we're looking for? [00:47:25] S2: I mean, I think there's two things. So, like, we have a view on kind of the next big challenge, which I think is, you know, the the virtual cell. Mhmm. And, you know, really being able to kind of ladder up the hierarchy of biological complexity to the cell.