No Priors: Artificial Intelligence | Technology | Startups
00:47:25 · Alex Rives, Sarah Guo
[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. [00:47:39] S3: Sorry. Very basic question. Yes. Virtual cell model. Like, what is the input and output I should expect? [00:47:44] S2: Yeah. I mean, I think there's different views on that. But I think kind of what you ultimately want is a system that can really model each of the levels of complexity. So, you know, the the proteomic layer, the genetic layer, the transcriptomic layer, and connect that to the phenotype. And you need enough generality so that you can ask the model questions about a new intervention in a context that it hasn't been trained on and and kind of get an answer from it. And, you know, the gap that we we need to close as a field is being able to really make those predictions that can generalize. So that's gonna require an enormous effort to generate data.
00:48:27 · Mark Zuckerberg, Elad Gil
[00:48:27] S0: Yeah. And then, I mean, in terms of what you decide to do next, I think this is like you know, a pretty normal process of constraint management. Right? I mean, it's like like, I think every lab in every field across the world probably feels compute constrained. I think that that's probably true here too. Right? It's like so, I mean, I know, like, you know, there's always questions. It's like, okay. Should we double down more on advancing the protein piece? Should we do more of the cellular stuff? I think those are kind of ongoing debates in terms of how you sequence that. And then, yeah, within that, there's kind of being at the Pareto frontier about how much you wanna train the different models in order to like, and and the size of the models is also dependent on the scale of the data that you have because you have yeah. For for obvious reasons. So, yeah, I mean, I think it's there's some of that as just where you wanna be on the curves and then normal constraints, but I think that this is, like, probably the same process that, like, any research organization goes through of, like, you wanna go in all these different directions, and you're just trying to constraint optimize and make enough progress to do world class work at one thing at a time while planting some seeds that can blossom over the next couple years as well. Yeah. This has been the most dynamic [00:49:37] S4: period of technology, at least, I've seen my career. Mean it's so exciting in terms of everything that's happening with AI. Every week there's something new that's changed.
00:49:37 · Elad Gil, Priscilla Chan, Mark Zuckerberg, Sarah Guo
[00:49:37] S4: period of technology, at least, I've seen my career. Mean it's so exciting in terms of everything that's happening with AI. Every week there's something new that's changed. [00:49:44] S1: Are you tired or invigorated? [00:49:46] S4: I'm both. I everybody's feel in manic phase. [00:49:52] S0: Yes. It's a combination of invigorated and exhausting. [00:49:55] S4: Yeah. It's wonderful. And so I guess, know, things are very unpredictable right now. It's really hard to know what's coming. We have this almost like early signs of exponentation on the model side with agentic flows that we're starting to see in really interesting ways. Models starting to help more and more with models, but that's still very very early days for that. If you're thinking back five years from now and you were to define what success was relative to your efforts, and I know things are very dynamic, things changed a lot. But you have this common thread of tooling for the Biohub. You have a common thread of empowering scientists at scale. You're looking back five years from now, is there a specific thing that you really want to make sure that you've accomplished or achieved or a primary goal? [00:50:37] S3: Well, I mean, I think we
00:50:37 · Sarah Guo, Mark Zuckerberg
[00:50:37] S3: Well, I mean, I think we [00:50:37] S0: have a pretty clear view of this hierarchical set of world models that we wanna build around biology. And the other part of that is that we wanna do the highest quality work in the world. Right? I mean, I and I think we're basically set up to do that between having a world class AI research team and this collection of of Biohubster world class life sciences research organizations. I think that that's, like, fundamentally a setup that no other organization in the world has. But, you know, you can have a lot of great ingredients, and that doesn't guarantee that you succeed. And so, I mean, to me, five years from now looking back, I think, you know, I'm sure other labs or efforts will try to produce things that approximate what we're trying to do. And I just think that we should be able to do something that is meaningfully better and a unique intellectual contribution to the world. I think that that's kind of what you, whenever you do any kind of research, that's what you're trying to do. Right? So, yeah, so if we do that, think we'll all feel very good. I would also expect that at some point we'll just start seeing a lot more idea generation from the people using the models. But I have enough faith that that part will materialize that for me, it's more just about, like, making sure that we do world class work. And I think if we do, like, the rest almost will take care of itself.
00:50:37 · Mark Zuckerberg, Sarah Guo
[00:50:37] S0: have a pretty clear view of this hierarchical set of world models that we wanna build around biology. And the other part of that is that we wanna do the highest quality work in the world. Right? I mean, I and I think we're basically set up to do that between having a world class AI research team and this collection of of Biohubster world class life sciences research organizations. I think that that's, like, fundamentally a setup that no other organization in the world has. But, you know, you can have a lot of great ingredients, and that doesn't guarantee that you succeed. And so, I mean, to me, five years from now looking back, I think, you know, I'm sure other labs or efforts will try to produce things that approximate what we're trying to do. And I just think that we should be able to do something that is meaningfully better and a unique intellectual contribution to the world. I think that that's kind of what you, whenever you do any kind of research, that's what you're trying to do. Right? So, yeah, so if we do that, think we'll all feel very good. I would also expect that at some point we'll just start seeing a lot more idea generation from the people using the models. But I have enough faith that that part will materialize that for me, it's more just about, like, making sure that we do world class work. And I think if we do, like, the rest almost will take care of itself. [00:51:54] S3: Very last question for you. Snapshot of it's mid twenty twenty six. What's the biggest update in your own thinking about Biohub or the domain from the last year?
00:52:05 · Mark Zuckerberg
[00:52:05] S0: Well, from the last year. I mean, you joined in the last year. I mean, I think the the biggest thing that that we basically rotated and and I think in the last year, we basically kinda formalized that Biohub is the main focus of our philanthropy. So I think this has been a very big shift. But Alex and the team coming in, I think, been interesting, not only because it's a world class group. Right? I mean, you guys have worked together for a while. I think also, I mean, you talked about how stuff is changing so much in the field. I think one thing that's underrated is this is an extremely talented group of people who also know each other and work well together and are stable and good. And I think that that also is underestimated in terms of the compounding benefit of people being able to work well in a stable environment over time. So I think that that's a really important piece. But part of what we wanted to do was, prior to Alex leading the effort, the previous leaders of the Biohub were basically primarily biologists who were interested in technology. Now I think this is the point where we really flip that, where, I mean, obviously you have a background in biology as well, but you are primarily an AI researcher who has a background in biology. I think that that's a deep reflection on the way that we expect that this is going to drive more value in the future. So those are probably the biggest updates in the last year in terms of the work that we're doing. I mean, it's a new leader, not just the leader, but a team that I think has been is like really good.