No Priors: Artificial Intelligence | Technology | Startups
00:02:17 · Mark Zuckerberg, Priscilla Chan
[00:02:17] S0: No. No. And to be clear, we don't think that we're gonna be the ones curing the diseases. Our goal is always to build tools that could accelerate the whole scientific field, so that way the scientific field collectively could cure all the diseases. But still people thought that by the end of the century it was a stretch. Now I think it's like too conservative. [00:02:36] S1: And so we kept being like, okay, well, we had these series of funny, awkward educational conversations where we were like, okay, but like, why? Like, why do you think it's impossible? And like, you know, just being the person in the room is just like, oh, I don't know why. You tell me. Finally, we got people to like, they're like, fine. If you really must know. And we're like, you know, we do. It seems important. You know, they were like, well, we work in silos. And when you publish, information doesn't get shared. It gets locked up for long periods of time. And we don't have tooling. You know, they gave the example of like, we build a great tool by one postdoc in a lab and it lives on their computer. And when they graduate, the tool is gone. And we heard was very hard to build shared tools to move science faster, build a shared knowledge base to quickly move science faster. And that's sort of where we began in thinking about, okay, like if those are the problems, like what can we contribute?
00:03:40 · Mark Zuckerberg
[00:03:40] S0: Yeah. I mean, so the original Biohub model was basically focus on long term tool development by bringing together engineers and scientists across multiple universities to focus on long term tool development. And basically, it like worked. And, you know, we started off with with CZI doing a number of different things. And I think over time, we just felt like, okay. The science piece is really working, and we just kept on investing more and more and more in it until now it is basically the primary and main thing that we're doing. And we've expanded the original San Francisco Biohub to a handful now at this point. There's New York. There's Chicago. The real focus and the unifying theme at this point the virtual biology initiative around taking the unique data sets that are able to be generated in order to model effectively starting with the smallest pieces of proteins, but then eventually cells and whole biological systems. But that's kind of how we've evolved, this idea that we talk about around that some of this is an AI problem, and you want to build a frontier AI lab, but you need to couple that with a frontier biology effort that can do the work of basically being able to understand and get the data that you need to actually be able to build these models. Because unlike language models, there's just like a lot of data out there on the Internet. That's not really the case with biology. I mean, there are obviously a bunch of different data sets that exist that academia and scientists have generated over the the decades, but a lot of the stuff that I think we wanna put into this, it doesn't exist. Right? It's like you wanna be able to visualize things that people haven't been able to see before, which is why we were doing the the imaging work. You wanna be able to record things that are going on inside the body, which is why we're doing the kind of cellular engineering work. Or you want to be able to measure things like inflammation in ways that haven't been possible, which is why the Chicago Biohub is focused on building those kind of devices and being able to do that. And that will fundamentally create new types of datasets that will allow new types of models.
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?