Breaking Boundaries: AI CoScientist to Accelerate Science Research with Gabe Gomes, Professor at CMU

**Erik Torenberg** (0:00)
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**Gabe Gomes** (0:46)
At 6 AM on the Sunday after that janky prototype, I woke up with this idea. Okay, there is a way for us to go from natural language all the way to the codes that allows for running these experiments. The new sense screenshots and messages was like saying, AGI is here.
I'm like, you cannot joke about this. I came to the office right away to see what he was talking about. He had put together something that showed us there is lightning in a bottle here.

**Nathan Labenz** (1:16)
I probably weighed out 4.5 milligrams of palladium acetate like a thousand times over the course of a year.
And I just thought to myself, man, this should be automated. You know, like I am not learning that much. I would just dream of the ability to have a machine do that.

**Gabe Gomes** (1:37)
The idea of cloud labs is that you have not so different from cloud compute, where you have, let's say, a warehouse, where instead of just running CPU clocks or GPU clocks, now you have hundreds of different types of instruments and hundreds of copies of those instruments as well as technicians and robots that can perform the operations. I am so excited about what we're going to be able to do because we are not encumbered by, you know, having to worry so much about these things that took a lot of time, but you're not advanced in technology.
It's going to be awesome.

**Nathan Labenz** (2:10)
Hello, and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Erik Torenberg. Hello, and welcome back to The Cognitive Revolution. My guest today is Gabe Gomes, Professor of Chemistry and Chemical Engineering at Carnegie Mellon University, and author of the recent Nature paper, Autonomous Chemical Research with Large Language Models, which describes the pioneering and highly influential coscientist system that he and his graduate students built immediately upon the release of GPT-4.
This episode was super fun for me for a couple of reasons. On a personal level, I studied chemistry in college, and as an undergraduate research assistant, my job was to optimize a palladium-catalyzed organic chemistry reaction for yield. In other words, I explored the space of possible configurations for the reaction to see which would produce the desired product at the highest rate. It was, to be honest, mostly brute force grunt work, and over the course of that year, I spent most of my time weighing out small amounts of fine powders. I used to joke that I felt more like a low-level drug dealer than a scientist, and only once over the course of a year did I observe an unexpected result that led to meaningful new knowledge. Often, as I worked, I would daydream about a future in which all of that could be automated. To hear how Gabe and his team have used GPT-4 in combination with a remote-controlled life sciences lab called Emerald Cloud Lab, to automate a significant part of this work, and to see how they applied it specifically to the optimization of a palladium-catalyzed organic reaction was, for me, uncanny. If this technology had existed back then, I might have followed through on my plan to become a chemist.
Far more importantly than my story, though, is how this paper, along with just a handful of others released over the course of 2023, represent the beginning of the AI-powered automation of science. While large language model breakthrough insights are still exceedingly rare, with appropriate prompting, scaffolding, and affordances, AI systems can now generate new knowledge. This is already working at a level that can create leverage for graduate students, and it sets the stage, in my view, for the next generation of models to do much more still.