🔬 Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White
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What is Andrew White’s background and why did he leave academia for AI‑driven science?
MD was supposed to be the protein folding solution. There is a great counterexample. The counterfactual is basically a group called Desres, Deeshaw Research. They had, you know, similar funding to DeepMind, probably more actually. They tested the hypothesis to death that MD could fold proteins. They built their own silicon, they built their own clusters, they had them taped out all themselves. They burned into the silicone. The algorithms to run MD, they ran MD at huge speeds, huge scales. I remember David Shaw came to a conference once on MD and he flew in by helicopter and like to this pretty pretty famous guy, kind of rich. And um he he gave uh an amazing presentation about the special computers and special room and out outside of Times Square and like what they can do with it.
It was beautiful, amazing. And I always thought that protein filming would be solved by them. But it would require a special machine. Maybe the government would buy like five of these things and we could fold, you know, maybe one protein a day or two proteins a day. And when AlphaFold came out and it's like you can do it in Google Colab, you know, or on a GP or desktop, it was so mind blowing. I forget like that protein folding was solved. I always thought that was inevitable. But the fact that it was solved and on like your desktop you can do it was just completely floored, changed everything.
This is the first episode of the new AI for science podcast on the Lecent Space Network. I'm Brandon. I work on RNA therapeutics using machine learning at Atomic AI. My name
is R.J. Haneke. I'm the co-founder of Mira Omics, where we build spatial transcriptomics.
The point of this podcast is to bring together AI engineers and scientists or bring together the two communities. These are two communities which have been developed independently for quite some time, but there's been some attempt to combine them. And only now, after you know many years, are we starting to see some of the big developments start to play out in the real world and start to solve key scientific problems? There's no like One size fits all solution. You need domain expertise. You need ki people on both sides of the aisle who can really talk to each other and really work together and understand both the modeling and all of the real subtleties of the system you're actually trying to work on.
We hope that we can connect these communities and that we can provide a starting point for this new era of AI and science to move forward. So without further ado, let's get started.
Yes. We're really happy to have in the studio today Andrew White, co founder of Future House and newly formed startup Edison Scientific. Um, rather than introduce him, I'll let him introduce himself.
Uh hi, I'm Andrew from San Francisco, former professor, now running two startups. Uh one that's a nonprofit research lab and one that's a for-profit venture-backed company. And we're trying to automate science. We're gonna get into all those points. Yeah, really happy to be here. Thanks for having me on.
I wanna know personally about jump from academia to industry and or quasi industry. So like I would love to hear that story.
Yes. Um I guess like that's the whole story, right? Uh so I did my PhD at University of Washington and uh I worked in a group with um I think nineteen people doing experiments and like two people doing simulations. And I was working on a topic uh called molecular dynamics, um, which I think is actually suddenly becoming interesting again as everyone's looking for ways to generate data from first principle simulation. And molecular dynamics, you know, covers uh basically everything that's molecules moving around and dynamic systems so like biology, things like that. Of course the compliment in material sciences. Things like density functional theory where you can model chemical reactions in these like solid systems.
So it's working on that and we work on biomaterials. And so the goal of my my PhD was trying to find what are called non-fouling materials. So in biological systems, whenever you put like a um foreign object into the body, it will trigger a response.
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Chapters
8 chapters
1
What is Andrew White’s background and why did he leave academia for AI‑driven science?
0:00–9:33
2
How did the ChemCrow project spark a White House briefing and raise dual‑use concerns?
9:33–18:18
3
Why does Andrew consider “scientific taste” the next frontier for AI agents?
18:18–27:52
4
What is a world model in the context of scientific agents and how is it similar to a Git repository?
27:52–36:28
5
Why does Andrew argue that molecular dynamics and DFT are overrated for real‑world chemistry?
36:28–44:36
6
How did AlphaFold’s breakthrough change the perception of first‑principles protein folding?
44:36–54:10
7
What lessons were learned from the E3‑Zero reward‑hacking saga and the “Ether Zero” project?
54:10–1:04:12
8
How does the Cosmos system integrate literature search, data analysis, and experiments to automate the scientific method?
1:04:12–1:13:48
Speakers
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