No Priors: Artificial Intelligence | Technology | Startups
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
00:24:12 · Alex Rives, Elad Gil
[00:24:12] S2: the first time. [00:24:13] S4: And that's a very exciting vision because you're basically saying you can bring generalizable tools to provide very personalized things for each individual person. Yes. And that's the power of the approach, is you have these big models that you build that can then apply anywhere. I know that you mentioned earlier that you were gonna try and cure prevent all diseases within one hundred years. And you mentioned, hey, could actually be sooner now given all the advances in AI. Do you have some thought of when we think we'll be closer to that goal or something?
00:24:37 · Mark Zuckerberg
[00:24:37] S0: Mean, I'm optimistic it'll be sooner. I mean, I think the the thing that's complicated is that it's a dynamic system. Right? So if you fix something, there will obviously be future things that you need to work on. So I don't think that the current set of things that we're aware of are gonna be the only things that need to get worked out. But I don't know. I think that the progress with AI is really is is obviously very exciting on this. The other thing that I'd say, just adding to what you were saying a second ago, is we really look at more systems than than specific diseases. So for example, one area that seems really important to understand is inflammation. We talked about this a bunch. This is a big focus of the Chicago Biohub. There's a lot of data on that. It seems quite clear that it's connected to a bunch of different diseases, but rather than studying the specific diseases, we think that by trying to understand inflammation more broadly, that will make it so that other companies that can then use these tools can work on specific therapies. Another example is I think that the immune system, I think, is a very good case to study for some of the work that we're doing in cellular engineering and when we kind of ladder up from proteins to cells to, like, whole dynamic systems within the body, I think that that one makes sense. I mean, it's sort of privileged. It can and the cells can travel around through the body, all that. You know? So, obviously, that has a big part in addressing different diseases. How do you make the immune system function better? But exactly how do you connect that last mile I think is gonna be more something that biotech or other academics individually studying things will be better suited to do. So this is kind of how we think about building out the toolset that just helps accelerate all these other folks.