No Priors: Artificial Intelligence | Technology | Startups
00:36:20 · Mark Zuckerberg
[00:36:20] S0: Yeah. The recruitment could also change. We have this program, Rare as one, and the basic idea is that a lot of people focus on the most common diseases, but there's this long tail. And the economics don't quite work out for companies to focus on those diseases, but if you can make it so that the groups of patients can kind of come together and organize and say, hey, we would take an experimental drug on this, then it actually, because of the cost that you're talking about and how that's a huge amount of the overall cost, if you can flip that, then it actually makes it so the economics make a lot more sense to then if you can generate something more easily and you can pair it with a group of people. I think one of the interesting things from science and engineering is that often can hit your head against the wall on the common problems, and in this case diseases. But a lot of times you learn a lot more about a system from finding some kind of rare or weird side thing that's happening in each case. I don't know. I think that that's always been kind of an interesting part of this that actually connects pretty well to this because now you're gonna be able to enable a long tail of new kind of ideas to get tried and enable them to potentially get tested more easily.
00:37:34 · Priscilla Chan, Mark Zuckerberg, Elad Gil
[00:37:34] S1: Yeah. That's a really good point on rare. In our rare disease cohorts, first of all, they're incredibly inspiring and powerful. But patient groups are self organizing patient registries, natural history registries, biobanks. They're organizing their own clinical trials. There's gene therapy that one disease group has moved forward over the course of like, I want to say like three to five years rather than decades. And the speed is so fast because the patients themselves have organized the resources that a scientist or a clinician might need. And it's incredible. [00:38:14] S0: But I think to some degree you're going to need something like this because are gonna be many more new things that can get created. But that doesn't mean that for, like, the general population that you're not gonna want the same level of vetting that we've had historically. But making it so that people who wanna be on more of the frontier have the ability to do that is I think also going be pretty helpful. [00:38:33] S4: Yeah, letting people opt in to be part of trials I think is one of the big shifts that is starting to happen but could really help accelerate biology in general.
00:38:33 · Elad Gil, Sarah Guo
[00:38:33] S4: Yeah, letting people opt in to be part of trials I think is one of the big shifts that is starting to happen but could really help accelerate biology in general. [00:38:41] S3: All three of you have mentioned at different points like the power of open ecosystems in such a large space. Like I think some of that logic around open source and the breadth or diversity of data collection that you guys were describing, it should also apply in the like language model world and the multimodal AI world. Do you think that's right? Does any of the work you're doing here change how you think about AI and meta? [00:39:07] S4: I mean, I think
00:39:08 · Mark Zuckerberg
[00:39:08] S0: it's sort of a similar philosophy overall. And, you know, Priscilla was talking about this, that a lot of our focus is building tools that empower individuals to do things. And that's sort of a common theme across a lot of the things that that I work on is just kind of putting the technology in individuals' hands. We don't believe in this, like, very centralized future where there should be a small number of institutions that that basically are are advancing all of this stuff. Our vision is not that there's gonna be, like, some central superintelligence that solves all of science. I think, people are really important, and I think will be more important in the future. And giving people more tools to be more productive is gonna be a critical part of any kind of positive future that both and that's how progress has always been made historically. Right? It's not through centralization. It's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought they were good ideas that already had been done. So I think that that's very central to the whole ethos of I mean, to some degree, it's like why you create something like social media, right, to give people a voice. It's, you know, I think a lot of the the stuff that we that I care about in terms of empowering people with individual AI. Open source is one instantiation of it. It's not the only way to do it. It certainly is one way that you basically are saying we're going to take this technology and put it in everyone's hands. In terms of science, I think it really makes sense, and we're deeply committed to open source. There are obviously interesting considerations on this that are important too because there's a lot of considerations around biosafety and things like that that we're going to need to balance and think through how to handle. But I think overall, this is very deep in the ethos of the work that we're doing both at Biohub and probably a theme for a lot of the stuff that I do is just we believe that a positive future is one where you build a technology as a tool, you put it in individuals' hands, and that's kind of how society makes progress.
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.