Okay, so I think we're at a special time now where at least in some directions AI has become superhuman at least on certain tasks. And that's what led to these recent papers that resolve a problem that was puzzling physicists experts in the field for over a year. And they were unable to resolve it and AI was able to do it very quickly. So I think that's a certain milestone that we've passed. I think maybe for the average person on the street who doesn't care about theoretical physics, this is not very noticeable, but I think it's a very profound change and we've really passed some kind of a threshold. Welcome to the AI for Science podcast, part of Lean Space Network. I'm Brandon... joined by my co-host RJ Honicky... it's a pleasure to introduce Alex Lubyansky, Professor at Vanderbilt University and fellow at OpenAI. He has quite a storied background... winner of the 2024 New Horizons Breakthrough Prize. It's the Oscars for science. Right now he's having fun at OpenAI doing some really cool research of pushing the foundation of theoretical physics using GPT models.

The one message I wanted to convey is that I think we're on this trajectory which I personally find very surprising and yeah, kind of surreal but also amazing. Where I would say a little over a year ago AI was very useful for email but not the kind of work that I do that I consider important theoretical physics calculations. I thought, "Oh, that's special. It's much harder than email and AI is not going to be able to do that." And then there were a series of developments that came in rapid succession that completely changed my mind. Specifically in particular ChatGPT o3 was the first really strong reasoning model that could do actual math that was useful for my research and could save me a lot of time. That's when I started to really pay attention and use it a lot more. Then when GPT-5 came out it was able to reproduce one of my best papers that took me a very long time to come up with in like 30 minutes. And that's when I really became AI pilled. I thought, "Oh my god, this changes everything. It's the most important discovery in my lifetime. It's going to affect everything about how we do research." To understand that this is happening and not be a part of it is a huge mistake so I have to go to OpenAI.

In physics there are two basic principles of nature that we think every law should respect... relativity... you cannot transmit information faster than the speed of light. But then there's another principle which is the uncertainty principle... everything is a little fuzzy. There's a tension between these two principles. The great achievement of 20th century physics is the elaboration of this framework called quantum field theory. What you're trying to compute are the probabilities for certain events to occur. Probability distributions are obtained by squaring certain complex quantities... which we call quantum amplitudes. These quantum amplitudes... called scattering amplitudes... describe the following scenario. Suppose you have a bunch of particles that you throw at one another... smash them together. Stuff happens. Scattering amplitude is the object that describes the probability for a particular type of attraction. It turns out in quantum field theory that if you have a particular force and you're able to compute the n-point amplitudes... then you know everything about the theory.

The title says single minus gluon tree amplitudes are non-zero. Gluons are the particles that carry the strong force. Single minus... single minus amplitudes. It's been known for a long time that actually in that case, the amplitude is just zero. Which means the interaction is forbidden and cannot happen. Andy, David and Alfredo understood a year ago that this statement is not exactly correct because the usual argument in the lecture notes and textbooks has a loophole... in a certain regime where the particles are exactly aligned, we say they're collinear. Alfredo did a lot of really hard work to compute these things by hand. By the time you get to six terms, it explodes in your face. 32 terms. But ChatGPT... it says, "Hop. Yep, simplifies to this." And we thought, "Whoa, okay. That is really nice." Instead of 32 terms, it reduces to just four terms. It's not a sum... it's a product. The amazing thing is that the formula that it proposed, instead of having this factorial growth which is super exponential... here it's actually linear. It's the nicest possible behavior you could imagine. This is the equivalent of the Parke-Taylor formula.

We wrote this paper which is called single minus graviton tree amplitudes are non-zero. It came out three weeks after the first one, which is really fast. I think this is a great example of AI accelerating science. Most of the time was spent verifying the answer, not writing, which is insane. We gave it the gluon paper as a seed. We said, "Read and understand the paper... generalizations to the gravity case." GPT Pro was able to do the graviton calculation, which is really different mathematically. There's a crucial application of something called the directed matrix tree theorem. We're like, "Whoa, that's really cool. We hadn't thought of that." From section three onwards, it's pretty much very close to what the AI wrote. All the math was derived by ChatGPT Pro.

How do we train the next generation? Because the way we were trained is by going through these difficult rites of passage where you have to do these really arduous calculations. These models can probably crush these problems. AI can totally help you with that. It's the best teacher and knows everything. There's two key ways in which my research is completely changed. One is that I spend much less time being confused. With GPT, the amount of time you spend confused just dramatically shrinks. The other is that you can launch 10 instances of chat and have each one try a different route and send it as a scout that moves very fast into the unknown. AI right now is a very good physicist, but one of the things that it doesn't quite have yet is knowing what is the right question to ask. The difference between a good physicist and a great physicist is knowing what is the right question to ask.

I spend so much of my time writing papers... it just feels like not the right way somehow to store and communicate knowledge. The huge bottleneck is writing it up. If you ask me would I be confident that in 20 years we'll have these sort of like static documents in which we publish our results as papers, I would think not. Maybe some kind of interactive paper which lives in some LLM. We haven't seen an AI yet solve a question that stumps an entire community of physicists for decades. That hasn't happened yet. But I think given the trajectory that we're on, at some point hopefully not too far in the future we should see that. I think we should just raise the bar for what it means to write a good paper. Like we should aim higher basically.