Latent Space: The AI Engineer Podcast
00:48:37 · Fabian Theis
In this case, a good friend and colleague from University of Cambridge who was doing hematoquiesis in addition, had a sort of a rapid cycle system where we actually set up three, four lab in the loops where we iteratively improved on a model. And this is, I think, a really hard thing. And this sort of goes into your second question, Brandon, right? Like how do you bring up a culture where once your computational person or model or whatever predicts something that you actually get to convince experimental people to put their lifetime in, because these experiments often take some time, right, to actually buy into and actually measure some of these follow ups. And sometimes, you know, this works a bit better if it's sort of molecules, because then, you know, so the stakes are not as high, but, you know, if you, like, say, you know, you need to do this new cell and you need add these very strange things, do you really want to do this? Like, shouldn't we maybe not take a bit of our gut feeling, and then you know, this sort of these intermediate solutions come. So I think really setting this up is far from trivial. That needs culture, and it might be the same person doing it. A lot of people then start throwing around robotics and so on as well, but I I think this is a tad too early because the the cycle times in terms of our essays are still a bit on the long side. But I do think this will come. And, you know, obviously the the ultimate vision being that we want to RL everything, we want to do RL also in in in in this loop. It's a bit out there, but, you know, I think we're we're slowly getting that. The moment is more maybe active.
00:49:50 · RJ Hanaki, Fabian Theis
[00:49:50] S1: I think that very much like we talked to a Lila there, that is their their kind of their core thesis is is is that basically the lab is part of the RL environment kind of sort of thing. Yeah. No, that's great. Fabian, it is late at night for you now. I really appreciate you taking the time out of your evening to to talk to us. Look forward to to watching your research. And when I say you, I mean your lab and colleagues and you as well. I'm a really look forward to watching this progress and keeping careful track of the things that we talked about today. [00:50:23] S0: Yeah. Thanks so much, RJ, Brandon, for having me. I I I think these links are important. This sort of look in beyond what maybe you do, like in your own sort of smaller disciplines, and these discussions helped me a lot. I really enjoyed the discussion. Had a lot of fun. Thanks. [00:50:37] S1: Great. Thanks. Take care. Thank you.