Periodic Labs: A chatbot walks into a laboratory

Dogus Cubuk (0:00)

LLMs, if you talk to them, they're incredibly intelligent. Their performance is insane. But then you haven't seen that kind of impact on the physical world. Why is that?

Liam Fedus (0:07)

Our goal is to build a system that can intentionally synthesize matter. This isn't going to emerge just from reading textbooks. It's really necessary to use simulations, model the physical world, but then actually carry out the experiment.

Dogus Cubuk (0:19)

If we are successful with periodic, it will actually increase the number of scientists in the world.

Mario Gabriele (0:23)

Maybe let's get really granular to understand how this all comes together.

Liam Fedus (0:27)

Anyone could go into a lab and mix a bunch of powders and say they ran a lot of experiment. However, if you're not seeing what you're doing, there's no error correction loop, there's no intentionality to it. And so those are some of the first AI systems we have to build. What we're targeting now are areas where the frontier is just not sufficient, or we can do it vastly cheaper. A system that can literally engineer matter is going to be of key importance to everybody.

Mario Gabriele (0:51)

If you had unlimited resources and no operational constraints, what is an experiment you'd really like to run?

Liam Fedus (0:56)

I would love to see...

Mario Gabriele (1:04)

AI has gotten very good at writing software. That's no accident. After all, code has an answer key a computer can check in a fraction of a second. The program either runs or it doesn't. Physics has an answer key too. The physical world. But consulting it means running actual experiments, something an AI model can't do on its own.

Liam Fedus and Dogus Cubuk are building a company to change that. They're the founders of Periodic Labs, a startup building infrastructure to create what they call synthetic superintelligence.

Liam previously led post-training at OpenAI and was one of the creators of ChatGPT, while Dogus was a research scientist at Google DeepMind.

They've brought their experience together to build what might be described as an AI scientist, capable of conducting thousands of experiments a day, and learning from each one of them.

In this episode, we discuss the limits of LLMs, why science is so hard, and what Liam learned from launching ChatGPT. I'm Mario, and this is The Generalist. I'm really excited to have you both here. I've been really looking forward to this conversation. I think you're building such a fascinating company. So maybe we just start there. Tell me a little bit about Periodic. What are you trying to do?

Liam Fedus (2:17)

Our goal is to build a system that can intentionally synthesize matter. So we're calling us a synthesis superintelligence. Our core belief is in order to do this, you need to make connection with the real world. So a system like this isn't going to emerge just from reading textbooks, reading papers. It's really necessary to use simulations, model the physical world, but then actually carry out the experiments.

We kind of talk about like thinkism is not enough. It's really necessary to get the new information from the world to conjecture how it might be, get the experiments back, and do this end-to-end loop. By doing this end-to-end training against the physical world, new types of intelligence and AI systems will emerge. So rather than a system that has just been trained on the body of final scientific literature, it's been trained on the scientific process itself. So rather than knowing the state of science, it knows how to do science.

Mario Gabriele (3:12)

I've heard one way that you've maybe described the mission in the past is almost to build an AI scientist, right? Decomposing the different pieces of that puzzle through that sort of lens. How do you think about, fundamentally, what does a scientist do and what do you need to replicate?

Dogus Cubuk (3:29)

The traditional way to look at science is to say, you come up with an hypothesis.

And then based on the hypothesis, you do some experiments. And the experiments either validate that hypothesis or invalidate it. And then you update your hypothesis. And I think what's really critical here for us is this observation that science always tries to change the boundary of our understanding, which by definition means it cannot just rely on what we already know because science is trying to figure out what we don't yet know. And this is almost as with the traditional description of machine learning, where machine learning trains on a training set and does well on a test set that has the same distribution as a training set. So there's like a clear tension between what we call machine learning and what we call science. And that's where we're basically trying to make progress. And how have humans gotten around this problem, right? Because at the end of the day, humans are also machine learning algorithms. And they often have figured out things that they didn't know before. And they had to iterate. Like they had to run experiments, or they had to write down a theory, get yelled at by reviewers, and then update it.