🔬 Automating Science: World Models, Scientific Taste, Agent Loops — Andrew White

**Andrew White** (0:00)
MD was supposed to be the protein-folding solution. There is a great counter-example. The counterfactual is basically a group called Desres, DE Shaw Research. They had 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 silicon 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 he was a pretty famous guy, kind of rich. And he gave an amazing presentation about these special computers and special room and outside of Times Square and what they can do with it. It was beautiful, amazing. And I always thought that protein folding would be solved by them, but it would require a special machine. Maybe the government would buy five of these things and we could fold 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 Gpt or desktop, it was so mind-blowing. I forget that protein folding was solved. I always thought that was inevitable. But the fact that it was solved and on your desktop, you can do it, was just completely floored, changed everything.

**Brandon Anderson** (1:12)
This is the first episode of the new AI for Science podcast on the Latent Space Network. I'm Brandon. I work on RNA therapeutics using machine learning at Atomic AI.

**RJ Honicky** (1:23)
My name is RJ Honicky. I'm the co-founder of Miraomics, where we build spatial transcript Atomic AI models.

**Brandon Anderson** (1:30)
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 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 one-size-fits-all solution. You need domain expertise. You need 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.

**RJ Honicky** (2:17)
So without further ado, let's get started on the first podcast. We're really happy to have in the studio today, Andrew White, co-founder of Future House and newly formed startup, Edison Scientific. Rather than introduce him, I'll let him introduce himself.

**Andrew White** (2:35)
Hi, I'm Andrew from San Francisco, former professor, now running two startups. One that's a non-profit research lab and one that's a for-profit venture-backed company, and we're trying to automate science.

**RJ Honicky** (2:47)
We're going to get into all those points.

**Andrew White** (2:50)
I'm really happy to be here. Thanks for having me on.

**RJ Honicky** (2:52)
I want to know personally about jump from academia to industry and quasi-industry. So I would love to hear that story.

**Andrew White** (3:02)
Yes, I guess that's the whole story, right? So I did my PhD at University of Washington, and I worked in a group with, I think, 19 people doing experiments and like two people doing simulations.
And I was working on a topic called Molecular Dynamics, 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 basically everything that's molecules moving around in dynamic systems, like biology, things like that. Of course, the complement in material sciences, things like density functional theory, where you can model chemical reactions in these like solid systems. So I was working on that. We were working on biomaterials. And so the goal of my PhD was trying to find what are called non-fouling materials. So in biological systems, whenever you put like a foreign object into the body, it will trigger a response. And that response called the foreign body response, basically encapsulates it in like this layer of collagen. This actually is exploited for some implants. Like if you get a heart, sorry, pacemaker installed, like it coats it with this collagen so that if you go to change the battery, you can almost change the battery out like without even bleeding because like the body has like completely encased. And this is great for pacemakers, but for like a glucose sensor or like a brain cognitive interface, BCI is what they call it now. Yeah. There, it's not so great. And so that's why some of those things have like a limited lifetime because eventually your body treats it as like a wound and heals.