The Information's TITV
00:27:13 · Stephanie Palazzolo, Lori Goler
[00:27:13] S0: And you talked a bit about the obstacles of just getting data for a field like biology, where obviously you can't just scrape the entire internet the way that, you know, different labs have done to train large language models. What is going to go into this process of finding or developing new data sets? And Just tell me a bit about how you guys plan to [00:27:35] S6: do that. Sure, sure. The history really of data in biology is that it's happened mostly in proprietary silos, because the incentives are such that research institutes have their own data, pharmas have their data, all the players have their own data. It's meant to be kept proprietary in the current incentive structure and the way that it works. The idea is really to bring together the research community with people who are interested in creating the datasets to match potential vendors, whether they're commercial vendors or philanthropic vendors, or to align with the government in terms of the investments they're making to be sure that we're all coordinated on a path to a common scientific agenda. We're really working with all the different players to understand what data they're looking for, what data they have, what new data should be created, novel data sets that need to be created through high throughput means to bring it to everyone. We're looking for all the commonalities across all of that so that we can come up with a core set of priorities, really, that we can help to move forward. It's really a role that we're trying to play.
00:28:39 · Stephanie Palazzolo, Lori Goler
[00:28:39] S0: And for those companies and different groups that are involved, obviously by working together, I'm assuming that they get access to data sets that maybe some of the other organizations had, but were maybe keeping to themselves. But I guess what else is in it for them? You know, do they, I know that maybe you want to at some point open source the datasets, but I believe there's something where does these companies get access for some period of time, right? [00:29:03] S6: Yes. It'll work a little bit differently depending on the funding structure for each of the areas, in the commercial lane of funding. One of the incentives for the companies to participate is that they're going to spend $100,000,000 but they're going to get access to several $100,000,000 worth of data. It really creates some leverage for them in terms of what their return is on the dataset. Basically the business model in that area is that they get a twelve month embargo on the data before it becomes open. But the idea is that all the data becomes open, it becomes available to the public and to the research community and to other commercial and philanthropic players as well over time.
00:29:03 · Lori Goler, Stephanie Palazzolo
[00:29:03] S6: Yes. It'll work a little bit differently depending on the funding structure for each of the areas, in the commercial lane of funding. One of the incentives for the companies to participate is that they're going to spend $100,000,000 but they're going to get access to several $100,000,000 worth of data. It really creates some leverage for them in terms of what their return is on the dataset. Basically the business model in that area is that they get a twelve month embargo on the data before it becomes open. But the idea is that all the data becomes open, it becomes available to the public and to the research community and to other commercial and philanthropic players as well over time. [00:29:40] S0: I think something interesting is, you mentioned earlier, this goal of wanting to, you know, prevent and or cure all diseases. That's obviously super ambitious. And I think it's a sentiment that's been echoed by a lot of leaders in technology and AI, not just Mark and Priscilla, but also folks like Dario from Anthropic, Sam from OpenAI. I guess I'm curious, after a certain point, do you kind of feel like these words have begun to not really resonate as much with people because I think a lot of folks I talk to, at least they tell me that, you know, whenever I hear Sam Altman talking about curing all diseases, that sounds great. But where's the evidence and how do I see the progress that's being made towards that? It feels like the super far off thing that, you know, it just feels like there's no way to get there right now.
00:30:26 · Lori Goler
[00:30:26] S6: Yeah. You know, it's funny because when Mark and Priscilla first started this about ten years ago, when they said those words and they said by the end of the century. At that time, people laughed at them because they were like, by the end of the century, there's no way that's going to happen. But now with the new tools and technology that we have, it really brings in the frontier on what we think can get done. Is it a few years? Is it five years? Is it ten years? Not sure, but it's definitely compressing the timeline. Think everyone's excited about it because you can see the vision of what it's going to be, but we're really focused on the substance of bringing it to life and making it happen, which is why we're looking at the building blocks like data. It's like, Okay, why isn't it here yet? Why can't any of us do this yet? What is the next bottleneck for us to attack? Because we're a philanthropy without commercial interest in it, we're really well positioned. It's what we call a Biohub sized problem. We're really well positioned to be able to bring the industry together to kind of address the bottlenecks that there are in the industry and kind of move forward from, you know, more hype to more substance to really helping people. You know, all of us have people in our lives who need these cures and preventions, and so we're really working with great urgency to kind of bring it all to life.
00:31:36 · Stephanie Palazzolo, Lori Goler
[00:31:36] S0: And when it comes to building these datasets, are there milestones that you're looking forward to, to kind of show the progress you're making? Can you walk me through some of those milestones? [00:31:46] S6: Sure. Yeah. I mean, the first thing will be just that we're creating novel datasets. So, you know, we'll know when we have those. I And hope we'll have those in the next twelve months. I hope they'll start becoming more there an example [00:31:56] S0: of one type of data set? [00:31:58] S6: Yeah, areas that us and other people are very interested in are areas like spatial biology and perturbation data, because a cell exists not by itself at a moment in time, but in context. In context means both over time, but also in context with other cells in your body, and things that are happening, and changes. To address something that's happening in your body, need some intervention. You want to have enough different types of data that the AI can really come up with new insights or observations or discoveries, not just tell us what we already know. The idea is to create this almost an oracle that's a generalizable reasoning model for the cell and for biology. How will we know when we get there? There are a lot of players now who are working on models. They'll be using this data. You know, we'll take a step back together and think about, like, okay. Well, this model did this. This one did this. Like, what is missing? What data is missing? And then together, we can go and create the datasets.
00:31:58 · Lori Goler, Stephanie Palazzolo
[00:31:58] S6: Yeah, areas that us and other people are very interested in are areas like spatial biology and perturbation data, because a cell exists not by itself at a moment in time, but in context. In context means both over time, but also in context with other cells in your body, and things that are happening, and changes. To address something that's happening in your body, need some intervention. You want to have enough different types of data that the AI can really come up with new insights or observations or discoveries, not just tell us what we already know. The idea is to create this almost an oracle that's a generalizable reasoning model for the cell and for biology. How will we know when we get there? There are a lot of players now who are working on models. They'll be using this data. You know, we'll take a step back together and think about, like, okay. Well, this model did this. This one did this. Like, what is missing? What data is missing? And then together, we can go and create the datasets. [00:32:58] S0: So it sounds like some milestones are, you know, getting the datasets in the first place and then this reasoning model you're talking about that will be some sort of model of the way cells Yes. Yeah. [00:33:07] S6: And you know, the model that we'll build will be a little bit different from the model that other folks will build. And so together, we'll be able to see how well it's working. And eventually, we're actually going have to validate it in the, you know, in the lab. So that's another step and another milestone that will happen down the road.