🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences

**Brandon** (0:00)
But not just TechPio, what do you do in terms of science?

**Andy Beam** (0:03)
We are all in on the bitter lesson and scale. We think that methods that scale and that are general, beat those that are not. You know, as Elia said at NeurIPS last year, we have but one Internet. It's the fossil fuel. We fracked. We got every ounce of data that we could out of the Internet, but it's gone. And so the question AI is like, where is the next Internet scale data set coming from?

**Brandon** (0:24)
You know, people normally talk about different scaling axes. You have compute, you have data. And for science, data is not necessarily an infinite resource. And your point is that we now want to add a new scaling axis for data.

**Andy Beam** (0:35)
We think that the lab of the future should feel like a data center. Rows of server racks, as densely packed as possible, and also as energy efficient as possible and things like that.

**Brandon** (0:46)
Welcome to Latent Space Science. I'm Brandon. I'm here with my co-host, RJ.
Today we have Rafa Gómez-Bombarelli and Andy Beam from Lila Science. We'll just start off and let you introduce yourself.

**Andy Beam** (0:58)
Yeah. Thanks for having us on the podcast. A long time listener, first time caller. Excited to be here. I'm Andy. I'm the Chief Technology Officer at Lila. I've been an AI researcher now for something like 20 years, going back to the pre-deep learning days, SVMs, random forest, things like that. I did a neural net PhD around 2010 to 2014, as deep learning was taking off. It's clear neural nets were the thing to back, but autograd libraries really hadn't been developed yet, so I did the backprop by hand back in my day, walking uphill both ways kind of thing.
Got very interested in AI for health care and life sciences. My wife's a physician, so I watched her struggle through different things and thought that AI was obviously a natural solution for a lot of those problems. Did a postdoc at Harvard in the medical school, doing early work on medical AI, and I'm in it for the AI. I was really interested in what problems could AI solve. But I've also always been like startup curious. So I took a break from academia for a year and helped start a company called Generate Biomedicines, which was an early generative biology company. I was the founding head of machine learning there and got to do the fun kind of hybrid professor startup founder thing for the next five or six years. I had a lab at Harvard, again, sort of between the School of Public Health and the medical school, doing methods research, but also a lot of applied work.
That's fun. Those are a great set of jobs. But I got a sense that the AI moment was changing in a very significant way, and I wanted to be a part of it. I started to think about where could I work at the frontier of AI on really, really exciting problems.
Academia has a lot going for it.
Access to scaled compute is not one of the things that it has going for it, or scaled resources. I'd been an early advisor for Lila and got very excited once the thesis crystallized. But basically, science is as an infinite token generator to train models at scale. Why would I want to work on anything other than creating a new frontier model that can solve scientific problems? So I kind of joked that I hung up the tweed jacket two years ago, left my position in academia and joined Lila full time as the inaugural CTO.

**Rafa Gómez-Bombarelli** (3:07)
Yeah, I go by Rafa.
I'm the Chief Scientific Officer for Physical Sciences at Lila and a co-founder. I was a computational chemist back in the day. We used a commodity resource that is compute. So it was clear that we could scale up the compute to do molecular simulations. And that's sort of something that produce enough data that in the early 20-teens, we realized we had a data problem. And sort of things switched gear for me right around then. I worked with David Duvernot and Ryan Adams in sort of blending what I think first like they felt like the first instances of deep learning for science.
I was one of the first people to do generative AI for chemistry and with auto-encoder on tokenized molecules. And so I'm deeply in love with Latent Space. We actually have a very similar to your guys' logo, but for molecules. And that has taken its own life, that figure.

**Andy Beam** (4:06)