No Priors: Artificial Intelligence | Technology | Startups
00:00:00 · Mark Zuckerberg, Priscilla Chan, Alex Rives, Sarah Guo, Elad Gil
[00:00:00] S0: We just wanna give tools to the whole scientific community. [00:00:02] S1: We wanna understand how biology works. I wanna understand the genetics of this person. I wanna understand the risks they have to different illnesses. My goal is to be able to treat the individual as an individual, understand the mechanisms, and be able to intervene. [00:00:17] S0: We'll have a bigger impact by getting us in more scientists hands quicker by doing it as open source projects instead. 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. 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 [00:00:36] S2: whole scientific field. We folded over 1,100,000,000 proteins and predicted their structures, [00:00:42] S3: and we didn't design a model for antibodies. We didn't design a model to be able to bind one particular target. We just designed a model that could understand proteins. [00:00:50] S1: If we could design a protein to actually change the physiology, then we can actually cure someone. [00:01:05] S4: Today on No Priors, we're joined by Mark Zuckerberg, Priscilla Chan, and Alex Reeves. We'll be talking about Biohub and all their various efforts to now start applying AI at scale to do world models of cells and different levels of interactions across biology. [00:01:19] S3: Mark, Priscilla, thank you for doing this. [00:01:22] S0: Thanks for having [00:01:22] S3: It us [00:01:23] S2: was fun.
00:01:23 · Alex Rives, Sarah Guo, Priscilla Chan, Mark Zuckerberg
[00:01:23] S2: was fun. [00:01:24] S3: Congratulations on new missions. [00:01:26] S2: Thank you. [00:01:26] S3: You guys made Biohub your primary philanthropic effort and then committed $500,000,000 to this virtual biology initiative. Can you tell us a little bit about, you know, why do that and how did you go from we should fund this to this is like [00:01:41] S1: who we are? So Biohub in its current form, we're super excited about. We feel like it's a really good fit for who we are and what we bring to the table and what we can achieve together. But this work started ten years ago when we were thinking about how can we give back? And wanted to build an organization that could cure, prevent, and manage all disease by the end of the century. And we had a series of hilarious meetings with scientists that, like, famous Nobel Prize winning scientists were just laughing at us. [00:02:15] S3: Was that your starting line? We're just gonna cure all disease? [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.
00:02:17 · 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.