No Priors: Artificial Intelligence | Technology | Startups
00:16:37 · Sarah Guo, Alex Rives, Mark Zuckerberg
[00:16:37] S3: And at the extreme, it could be, you know, we're going to understand systems in the body that we didn't before or the mechanism of action for a new treatment because we can ask the model, right, interrogate that representation. [00:16:47] S2: That's right. The hope is that you kind of really learn the underlying basis for how it's making the predictions. And so you open up the black box and you can actually understand kind of the biology that the model is representing. [00:16:58] S3: So asking for a friend, you know, you guys all believe in venture backed companies as a way to have impact on the world. Was it like collecting data on zebrafish or the span of the data, or the wet lab work, or just the scale? Like what makes this a better fit for this big nonprofit, you know, ecosystem effort versus a venture backed company? [00:17:24] S0: Well, I think we just want to give tools to the whole scientific community. [00:17:27] S3: And I mean, like, so I I think
00:17:29 · Mark Zuckerberg
[00:17:29] S0: in order to have the biggest impact, I mean, part of it is just we're I mean, it's not actually clear that we couldn't run it as a business if we wanted to. I just think that we'll have a bigger impact by getting this in more scientists' hands quicker by doing it as open source projects instead. So, yeah, I mean, I think that that's kind of the approach. But I don't know. It's an interesting question. I'm not sure that I mean, obviously, you were doing it as a for profit company, a bunch of the modeling before, then you run into certain issues. You have to raise a large amount of money in order to build the compute clusters. I think in a lot of ways, the data is actually even more of a constraint. Because if you look at the scale of these models compared to language models, they're smaller, but they're smaller because the amount of data is less. In order to get the data, it's not just like there's some factory somewhere that you can pay to produce the data. You actually need to invent new, novel, scientific approaches to be able to do the, know, for example, the type of cellular engineering we're doing in New York or the types of devices in Chicago, which is why, you know, when we're talking about this concept of frontier biology and frontier AI, the frontier biology is you need to do real science to advance different biological methods in order to be able to observe the things that create the data that go into the model. Mhmm. So it's not just like an off the shelf thing that you can create. Now that's a pretty big effort. I don't know that there are, like, that many things like that that are done as as biotechs. I think it's just the scale of the ambition of what we're doing, the time horizon over which we're committed to doing it. I think part of the theory is, like, if you're building tools that are this complicated, you kinda wanna have a ten to fifteen year time horizon on on building out these efforts. And then the scale of capital required I mean, I guess there's no rule that said that you couldn't do it as an incredibly well funded startup, but I think that this just made more sense. And then it also is is simplifying strategically to not have to think about you're gonna make money with the different things. I mean, we just we wanna get the models in people's hands. We release them as open source.
00:19:38 · Mark Zuckerberg
I think that that's like a very valuable thing to do. And again, I mean, the the theory isn't that we're gonna cure the diseases. We're not. It's that we wanna help accelerate the pace of progress for the whole scientific field.
00:19:50 · Priscilla Chan
[00:19:50] S1: As the person least experienced with making money here, I would say that there the sort of neutral nonprofit nature of our work actually helps harness more people to enter this effort. And to actually achieve the mission of like understanding the totality of human biology and to cure, prevent, manage all disease, you actually do need the entire academic biotech industry to come together and to work on this in a sort of unified way, in part because there's a lot of talent out there. And it's not helpful to leave any talent, exclude any talent from the effort. And there's a super long tail of diseases. There are the common ones. And even the common ones, I think if you unbundle heart disease, cancer, neurodegenerative diseases, even if you unbundle like dementia or depression, there are many, many, many subcategories that become more and more niche. And that's not even looking at the long, long tail of rare diseases. Those often get orphaned and don't get brought along when we're sort of looking at what the most efficient way to impact the lives of many. But if you sort of decentralize the effort and put the tools in many people's hands, you start getting people who are like, you know what? I am super interested in spinal muscular atrophy, and that's something I care deeply about. And if you put the tools in that person's hands, they're gonna be able to make progress. In a way, if you had to focus your efforts and make big bets, you probably wouldn't because it's just a niche individual small group disease that actually will in turn, if we can understand that disease process, helps us unlock knowledge about a lot more about how the human body works.
00:21:41 · Alex Rives, Elad Gil
[00:21:41] S2: Mhmm. Do you [00:21:42] S4: have any thoughts or predictions in terms of what disease areas this work will impact first? I know it's very hard to be predictive about these things. But just given the nature of the work and the nature of the models, are there areas you're most optimistic about in the short to medium term?
00:21:55 · Priscilla Chan
[00:21:55] S1: That's actually not how I think about it at least. The way I think about it is like we want to understand how biology works. The ideal world is you would say, I understand the genetics of this person. So I want to think about people at the individual level. I want to understand the genetics of this person. I want to understand the risks they have to different illnesses. I want to understand the mechanistic connection between, say, gene variant, a protein, and a disease process. Because if you understand that through chain, then you can design a protein, design a drug, bespoke to them, and actually make an intervention. And right now, I'm sure we've all had experiences being sick. And if you have something that's even remotely nonstandard, you go into PubMed, you look up a paper, you look up the supplement, and then you start going through the methods. And you're like, am I represented in this paper? And we're just making guesses. We really have no mechanistic understanding. We're saying like, okay, you're kind of like these people that we studied, and this drug kind of impacts the pathway that we think is implicated. Let's try and see if anything happens. And time passes. And sometimes it works and sometimes it doesn't. So my goal is to be able to treat the individual as an individual, understand the mechanisms, and be able to intervene. And there are different diseases that are at different stages of filling out that whole through line. And so for some diseases, you just want to understand which gene variants actually cause disease and which don't. And that in itself can be super empowering to patients. And if beyond that, there are some diseases where we understand the chain. We just can't intervene and change a specific protein function. That's super exciting too. Like, if we could design a protein to actually change the physiology, then we can actually cure someone. But to me, like, that is just as exciting as understanding contributing to our understanding of like how someone gets sick in