Andreas Stuhlmüller: disagreement

Transcript context

…Yeah, okay, cool. I'm sorry I missed that in my prep, but I'll again point my at the documentation. How about, so I'm a little bit of an AI for science mini arc right now. I'd be interested in your take on other big picture approaches to AI for science. You guys are obviously coming at it with a very systematic reasoning angle. There is the sort of close the loop angle where we'll empower these models to actually run experiments through a cloud lab or whatever, and then they'll be getting feedback from reality. That seems like it could go somewhere quite interesting. Then there's of course training models on other modalities of data. We've of course seen how proteins fold and There's what's the, what if I do this perturbation to a cell, like what's its next state going to be? Or you can go on and on in that domain. Interesting takes that you think might be non-consensus that you'd like to share. I think all of this stuff is like super exciting. I think sometimes people like come to us and are like, well, who's going to win in AI for science? And I think that's just an absurd thing to say because science is such a big space. And as you just said, there's like many layers of abstraction from, I don't know, understanding single cell dynamics through automated experiments, protein models to like making, like when we talk to the pharma companies, they're like, we are trying to make like a multi-year plan that accounts for the changing technological environment, but also accounts for the fact that clinical trials have certain intrinsic time scales. And there's just like such a different reasoning problem from modeling the single cell dynamics. I don't know, it's a big space, it's a big pie. I think I'm excited that people are excited about it. Yeah. Do you have any controversial takes here? I think how does it all come together is maybe a really interesting, like open question. When people think about what is the automated company of the future? Does it look like you take the existing, like top 20 pharma companies and they over time will morph into like a different functional form? Or is it going to be the case that a small biotech comes along and they're just like much more end-to-end integrated? And in 10 years, the top 20 companies will all be replaced by companies that we don't even know the names of today. I actually don't know how it's going to shake out. I think, yeah, I really think it could go either way, depending on how quickly people at the existing companies wake up and understand how much everything is going to transform. So yeah, maybe that's not a very interesting controversial take, given that I don't have a take between those two futures. Do you think that there's One big question I think about a lot is how integrated will the models themselves be? Obviously, we have the tool calling paradigm coming along very nicely. And this could be extended to tool call to run an experiment in a cloud lab and get a result. And that result could come back as a data printout of the same sort that a human would read. And then there's this other paradigm of integration that we see, I think a leading indicator of with image and now also video with Google's latest Omni model. There's this sort of deep integration of language and pixel space where in the early JetGPT image generation experience, you would talk to the model or even if you gave it a photo, it would try to caption that photo, describe it, and then use language to go over to the separate cool call image generation model and ask for something. And of course, the people never quite looked like the ones that you put in, right? Because you just can't describe a face in language with that level of fidelity. But now you have this deeper weights level integration where I can give you an image and say, make this a line drawing or whatever, and It has both, right? It understands conceptually what I want, but it also sees, it sees in some sense the structure of the face and can preserve that through the transformation. So I really wonder if that's coming to all the modalities of science as well. And if it's a good idea, I guess is maybe. I think it probably is coming, but I wonder if you think it's a good idea, because it certainly would in some ways make or at least it naively seems to me like it would make process supervision more difficult if I can trace like, okay, you called the protein folding model. This is what you got back. Okay, there's where you went wrong, right? I can see us digging in and interrogating those traces a lot better versus it's just, I asked you for this, you like spit out a new protein sequence because in your weights, you were, oh, I intuitively know what sequence will do that function that you just asked for. But obviously that could be really powerful in image, it's like a major unlock and I don't see any reason it wouldn't be a major unlock in designing new proteins or what have you as well. So do you think that's coming and do you think it would be a good or bad idea if it does in fact?…

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