No Priors: Artificial Intelligence | Technology | Startups
00:33:07 · Priscilla Chan
[00:33:07] S1: actually with biological models. Because right now some of the off target effects are we just didn't know, you know, your kidney cell also expressed this receptor. And then when we test it in human, like we see it happening and we see renal toxicity. And so being in If you have a single cell atlas that looks at all the different cell types, some of which actually were not predicted before we modeled them, you can start looking at which cells actually do have receptors for the target you thought you were exclusively targeting and be able to predict some of these downstream effects before we get into the human trials. And I think that that's actually one of the more exciting applications of like a transcriptomic model to understand actually how the different cells will react when you intervene and do something. But I think when you think about delivery mechanisms and patient care, that's where you start having to be creative about when you ask like, what disease do you want to care first? There are certain diseases that will be easier to like deliver a therapeutic to, or the risk reward makes more sense. And, you know, I think we were all inspired by baby KJ, I think last year now, when the team at CHOP was able to deliver a CRISPR therapeutic to edit a mutation that he had that would have inevitably led him to significant neurotoxicity and altered his life. But we were able to, that disease was very carefully chosen because we needed to target his liver cells and if we could easily deliver a product that would work in his liver. And I think that's when the creativity, the wherewithal to choose the right applications can help us unlock the first applications.
00:35:08 · Alex Rives, Elad Gil, Sarah Guo
[00:35:08] S2: Maybe something just to add to that also, you know, because, I mean, kind of you described the conventional, you know, drug development process. Right? And I think, you know, these kind of tools have the potential to have a lot of impact on that process. But, you know, what's interesting is to really start to think about kind of the new paradigms that can open up. And, you know, what does it mean if if you can you know, the barrier to develop a drug, to design a molecule, you know, to kind of get through all of those stages is so much lower. And so you have programmable biology, and you can, really start to create a medicine for every individual patient. I think that has enormous implications for how we do drug development and what the future of medicine looks like. [00:35:52] S4: Mhmm. It'll be an exciting day when the FDA accepts like a [00:35:54] S3: virtual clinical trial for the phase one or something, or, know, it's based on some person's view of that person. Yeah. Or even short of that, like thinking about the specific like mechanisms where you see this acceleration, like I imagine if people feel like they can predict impact in kidney cells or have a stronger perspective on tox because they have this broader understanding, they'll be willing to try many more programs.
00:36:20 · Mark Zuckerberg
[00:36:20] S0: Yeah. The recruitment could also change. We have this program, Rare as one, and the basic idea is that a lot of people focus on the most common diseases, but there's this long tail. And the economics don't quite work out for companies to focus on those diseases, but if you can make it so that the groups of patients can kind of come together and organize and say, hey, we would take an experimental drug on this, then it actually, because of the cost that you're talking about and how that's a huge amount of the overall cost, if you can flip that, then it actually makes it so the economics make a lot more sense to then if you can generate something more easily and you can pair it with a group of people. I think one of the interesting things from science and engineering is that often can hit your head against the wall on the common problems, and in this case diseases. But a lot of times you learn a lot more about a system from finding some kind of rare or weird side thing that's happening in each case. I don't know. I think that that's always been kind of an interesting part of this that actually connects pretty well to this because now you're gonna be able to enable a long tail of new kind of ideas to get tried and enable them to potentially get tested more easily.
00:37:34 · Priscilla Chan, Mark Zuckerberg, Elad Gil
[00:37:34] S1: Yeah. That's a really good point on rare. In our rare disease cohorts, first of all, they're incredibly inspiring and powerful. But patient groups are self organizing patient registries, natural history registries, biobanks. They're organizing their own clinical trials. There's gene therapy that one disease group has moved forward over the course of like, I want to say like three to five years rather than decades. And the speed is so fast because the patients themselves have organized the resources that a scientist or a clinician might need. And it's incredible. [00:38:14] S0: But I think to some degree you're going to need something like this because are gonna be many more new things that can get created. But that doesn't mean that for, like, the general population that you're not gonna want the same level of vetting that we've had historically. But making it so that people who wanna be on more of the frontier have the ability to do that is I think also going be pretty helpful. [00:38:33] S4: Yeah, letting people opt in to be part of trials I think is one of the big shifts that is starting to happen but could really help accelerate biology in general.
00:38:33 · Elad Gil, Sarah Guo
[00:38:33] S4: Yeah, letting people opt in to be part of trials I think is one of the big shifts that is starting to happen but could really help accelerate biology in general. [00:38:41] S3: All three of you have mentioned at different points like the power of open ecosystems in such a large space. Like I think some of that logic around open source and the breadth or diversity of data collection that you guys were describing, it should also apply in the like language model world and the multimodal AI world. Do you think that's right? Does any of the work you're doing here change how you think about AI and meta? [00:39:07] S4: I mean, I think
00:39:08 · Mark Zuckerberg
[00:39:08] S0: it's sort of a similar philosophy overall. And, you know, Priscilla was talking about this, that a lot of our focus is building tools that empower individuals to do things. And that's sort of a common theme across a lot of the things that that I work on is just kind of putting the technology in individuals' hands. We don't believe in this, like, very centralized future where there should be a small number of institutions that that basically are are advancing all of this stuff. Our vision is not that there's gonna be, like, some central superintelligence that solves all of science. I think, people are really important, and I think will be more important in the future. And giving people more tools to be more productive is gonna be a critical part of any kind of positive future that both and that's how progress has always been made historically. Right? It's not through centralization. It's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought they were good ideas that already had been done. So I think that that's very central to the whole ethos of I mean, to some degree, it's like why you create something like social media, right, to give people a voice. It's, you know, I think a lot of the the stuff that we that I care about in terms of empowering people with individual AI. Open source is one instantiation of it. It's not the only way to do it. It certainly is one way that you basically are saying we're going to take this technology and put it in everyone's hands. In terms of science, I think it really makes sense, and we're deeply committed to open source. There are obviously interesting considerations on this that are important too because there's a lot of considerations around biosafety and things like that that we're going to need to balance and think through how to handle. But I think overall, this is very deep in the ethos of the work that we're doing both at Biohub and probably a theme for a lot of the stuff that I do is just we believe that a positive future is one where you build a technology as a tool, you put it in individuals' hands, and that's kind of how society makes progress.