OpenAI's Dan Roberts: Why AI Can Now Make Discoveries
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What recent AI breakthroughs suggest AI is becoming a scientist?
One of the things that ChatGPT was able to do was assume it was false. When you go against the green and do something contrarian like that, you really have to have strong conviction in what you're doing in order to persevere down a really long calculation path. I feel really excited that we will get to really answer a lot of fundamental questions in the fields of science that that we care about, with the aid or the models being the driving force. And so that's just really thrilling.
Hi, I'm Matt Turk. Welcome to the Matt Podcast. It's been yet another extraordinary last few days in AI, with OpenAI, DeepMind, and Anthropic cracking some of the most famous long unsolved questions in mathematics, known as the AirDosh problems. A moment many view as a stunning breakthrough and yet another signal that AI is moving from doing the work we ask of it to autonomously making deep science discoveries. To unpack the moment, And the fundamental advances in model reasoning that make it possible, I'm excited to welcome Dan Roberts, a top AI researcher at OpenAI, who comes from a deep background in theoretical physics and has a particular interest in the intersection of science and AI. In this conversation, we go deep on what reinforcement learning actually is, why it's the most important paradigm in AI right now, and what's ahead for AI and science.
Please enjoy my video. Conversation with Dan Roberts. Hey Dan, excited to do this. Thanks for taking the time.
Of course. Very happy to be here.
You are the lead of the foundations of reinforcement learning team at OpenAI. So what what does that mean? What does the name mean?
The larger team that that we're on is called Foundations, and we think about reinforcement learning. So very boring foundations of reinforcement learning. But the team co comes from a mandate of of thinking about the science of reinforcement learning. And a long time ago, which in AI speak is like six months ago, maybe a year. I guess now two years. So before we released O one. And thinking reasoning models, we were studying this internally and and one of the advantages to being first, or at least to being forced and and spending a lot of resources on scaling things up is that you can empower a group of people to not just work on making the thing work, but work on understanding how it works. And then beyond that, how do we scale how should we think about scaling reinforcement learning versus scaling pre-training?
So what what are scaling laws look like? But then going beyond that, what what sort of things does this kind of training teach us, what doesn't it teach us? We're very interested in at the frontier for exploratory scenarios, how do we either improve or understand better what reinforcement learning is doing? We have all this compute that Famously we are in the process of of of of acquiring and we would like to turn that compute into intelligence. And to do that, we need to make thinking models and some somewhere along the way we interact with that process, usually at the earlier stage uh for models, you know, not the next model, but things that are like the next model or the next, next model.
Great. And uh uh quickly what was your path to open AI? So how did you go from studying physics to being where you are today?
I did a PhD in in theoretical physics uh from from MIT, thinking about the intersection of quantum gravity and quantum information. Thought a lot about black holes and quantum chaos, kind of thing of what if you throw something into a black hole? What happens to the information? Does it does it come out? How if we think about black holes as computers, how fast are they? I I was very interested in this fundamental question. question in theoretical physics, which is how do you find a quantum theory of gravity? I also got very interested in this interplay between computation and the laws of physics. You know, any computer exists in the universe in in you know behaves according to physical law. So the sort of computations you can do are bounded by the laws of physics and there's some sort of interesting relationship there.
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Chapters
5 chapters
1
What recent AI breakthroughs suggest AI is becoming a scientist?
0:00–6:53
2
How does OpenAI's Foundations of Reinforcement Learning team work?
6:53–17:42
3
What is Dan Roberts' journey from theoretical physics to AI?
17:42–30:37
4
Are AI systems now useful for real scientific discovery?
30:37–43:00
5
How do reinforcement learning and test‑time compute enable models to think?
43:00–49:05
Speakers
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