A shared data-and-model loop is driving Biohub's reorganization

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 28:06

   So you guys are announcing some big news this week. Do you want to give us a sneak preview? Well, the big news is thinking about how we are going to be coming together as one team. And, you know, in the past, we have done, we've run biohubs and we've done built software. We've done some AI research. But all of it has been really thinking about – has been a little bit decentralized. But now, under Alex's leadership, we are going to come together as the biohub, an operating philanthropy where we are doing the science in service of a singular goal together.

2. 28:45

   And how do we actually advance the state of biology and research at the intersection of AI and biology? Amazing. Alex is amazing. Yeah, no, he's great. And then the other thing is that the piece that I mentioned earlier, which is just, yeah, I mean, CCI has focused on a number of different things. We've really just found over time that we feel like we've been able to make the biggest difference in science.

3. 29:06

   So we've just kept on doubling down on it. And we're going to continue doing work in education. We're going to continue supporting local communities and those different pieces. But going forward, the biohub is really going to be the main thrust of our philanthropy. And we're very excited about that because I think that this is, there has been, you know, when we started, the mission to see if we could help the scientific community cure and prevent diseases by the end of the century.

4. 29:30

   I do think with the advances in AI, that should be possible to do significantly sooner. And that is a very worthy and important and very exciting goal that we think we kind of have a unique place in the ecosystem that we can help empower others to make fast progress on that. So there's obviously plenty of advantages to decentralization from a management communication overhead and so forth. And so, like, what are you trying to add by adding this kind of new layer slash unification on top?

5. 30:02

   Like, what are the outputs? And then I guess what are the complexities to that? Because that's, I'm sorry to ask a CEO question. No, no. I mean, I'm like, ask us for a friend. We think about this. You want to go for it? And then I can jump in. Yeah, so there are obviously amazing groups doing Frontier AI and a lot of groups doing great Frontier biology. And where we think we can do uniquely is actually tie these two together.

6. 30:28

   And we are, we've funded data sets, we've built data sets. We're like building the instrumentation now to be able to look at the cell, whether it's, you know, at the tissue cell-cell communication, our cryo-EM, where we can look at the cell at nearly atomic level. So we have the ability to not only build the data sets, but actually shape and form them the way we want based on what we see as necessary to complement the existing body of knowledge.

7. 30:59

   And so we have amazing teams doing that work and we're building these AI models. And so the reason to do it together is then we can actually complete the flywheel. Like, you know, the model is looking like it has some gaps and blind spots in this area. Okay, who do we talk to? How do we build the next data set? And, you know, we're seeing this in the lab. Like, the metadata is going to be so rich that we can feed back into the way that we do this modeling.

8. 31:28

   And so if we can close that loop, which is our goal in bringing everyone together, I think it's going to be incredibly powerful. And it's more than just, like, you know, writing down a spec and saying, like, please deliver this. Like, these people need to be sort of working shoulder to shoulder and shaping each other's work for this to actually be the more and more accurate model of how the human cell works.