No Priors: Artificial Intelligence | Technology | Startups
00:05:50 · Mark Zuckerberg
And I think it's just a very exciting thing that, going back to what you're saying, if the scientific field, it primarily needs kind of tool development that now is going to empower scientists across the field to be able to do their work faster, that's what we think we can provide through this kind of long term focus on tool development.
00:06:10 · Priscilla Chan
[00:06:10] S1: But I think there's a fun through line on where we started and, you know, bringing us to our work that Alex is driving now, is that our very first request for application, RFA here, was around single cell sequencing. And we wanted to look at sort of like the RNA that is transcribed in individual cells. And that was possible, but it was still pretty early on in understanding how different cells were expressing their DNA to the point where at the beginning we were just funding methods, like getting people to describe how to do it so that others could share that methodology. And then that became us funding the Human Cell Atlas, which is now one of the largest databases of single cell transcriptomes, it was getting hard for scientists to annotate the data. So we built CellByGene, which was like a very simple annotation tool that scientists could use to make use of that data. Then a community came around Cell by Gene, built around Cell by Gene, and started contributing more and more data that we had nothing to do with sort of creating or funding or making happen in the world. And now Cell by Gene is a corpus of knowledge that a lot of the transcriptomic based models are based off of and is used regularly by the scientific community. But still there are always critiques. Like, this is just stamp collecting. Like, you're just gathering bits of knowledge sorry, bits of data, and we're not going to be able to pull scientific knowledge and wisdom and insights out of. And we're like, well, we didn't have an answer for a while. And then imagine our delight when large language models became a huge topic of conversation that could make sense of large amounts of data. And I just for me, it was like, what if we could actually understand how biology worked? Move it from a discovery based science to an engineering based science where we could systematically understand how living beings, living cells worked and be able to understand why things go wrong. And so when we saw that moment, we're like, this is it. Something really big could happen here.
00:08:27 · Sarah Guo, Alex Rives
[00:08:27] S3: Alex, you started at MetaFair, but you were on the path to, you know, you'd assemble the team at evolutionary scale and you'd raise venture and you were making progress in your models. What was the pitch from Mark and Priscilla where you said like, that's actually the right way to go after the mission? [00:08:43] S2: Well, I think for me it was really kind of the moment when I understood that, you know, they they really saw this as as an integration of frontier AI and frontier biology. And I think I had developed conviction that, you know, this is really a new era of science that's just beginning, kind of what's gonna be possible with artificial intelligence. And, you know, we're in the age of information theory at scale, and we have these systems that can basically kind of predict the next token, and they can, learn world models from that. They can learn biology from the data. And so, you know, I think that it was really clear that, you know, to build kind of that next kind of institution for the next era, you would really need to have frontier artificial intelligence. You would have to have frontier biology. You would need to start to put those things in feedback and really have models that are learning from the biology. And I think, you know, and you need the right scale on the right people. And so this just really felt, I think, like the way to do that.
00:09:45 · Elad Gil
[00:09:45] S4: There's a variety of different models that you all have been working on. And I think it's kind of interesting because some of the earliest breakthroughs in biology were things like AlphaFold, where there was a Google model that showed that you could do protein folding at scale in a really interesting way that people didn't realize was very tractable. And this was pre sort of the really big transformer waves that came later. And then you're working on a variety of different things at different scale, right? You're doing incremental molecular modeling and protein folding. You're doing cell based stuff. You're thinking about interrogating larger scale systems in biology. How well do you think that extends from sort of the micro to the macro? You mentioned almost starting with building blocks and building up, but modeling cellular behavior is very different from modeling protein folding. The data is very different, the modeling is different. I'm just curious, do you think it's all similar in terms of it's just data and you train stuff? Or do you think there's some differences in terms of how you actually have to deal with these systems?
00:10:35 · Mark Zuckerberg
[00:10:35] S0: I mean, are probably some differences. I mean, you can probably talk more to the specifics around this. But I mean, I think each layer is gonna end up being somewhat qualitatively different. Right? But you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to cells in a way without understanding the protein modeling. And then if you're trying to understand something like the, you know, the way the immune system works or a bunch of cells interact together, then, you know, it's tough to do that without first understanding cells. I mean, you might be able to, at, like, a very high level of abstraction, simulate a system. But if you really wanna, like, understand how it's gonna work, you kind of wanna build the simulations at each level hierarchically. So that's basically the approach that we're going through starting with the building blocks and the and the protein. But yeah. I mean, I think that there's gonna be different types of data that you wanna collect for each. The modeling techniques, think we'll see. I mean, that'll all keep on advancing across the board. But I do think that, like, a big part of the strategy is this view that you need to build it up hierarchically.
00:11:35 · Priscilla Chan, Elad Gil, Mark Zuckerberg
[00:11:35] S1: And, you know, one of the things that's unique about us in this space is we are very intentional that the AI efforts and the wet lab efforts were a single effort. And we've done a lot of work to bring them together. And the really neat thing that we can do is really try to pull and gather data that helps us connect across sort of the hierarchy. You know, you can look at transcriptomics with space within a cell and look at where it's localizing. We can look at translucent zebrafish and look at the development across different cells and when the brain develops. We have sensors that allow us to look at cell cell communication in different molecules. And so we can be strategic about the types of experiments and data we want to collect that helps us bridge across these, that makes it so that there's some connective tissue that helps drive the modeling that you know, the modeling magic that happens. [00:12:35] S4: Yeah. The reason I asked the question, by the way, is I used to be a biologist. I have a PhD in biology, and I worked in wet labs for almost a decade and everything else. [00:12:40] S1: Are looking for a job. [00:12:46] S4: We can talk about that later. [00:12:48] S0: It's not a no. At [00:12:49] S4: this point [00:12:49] S0: in my career, know? [00:12:51] S1: Hearing, I [00:12:52] S0: love my aggressive reaction.