AI Making Theoretical Physics Breakthroughs

A.I. is now directly advancing science. "SuperChat", a powerful internal OpenAI model, recently helped crack a particle physics problem that had stumped researchers for over a year. Here's what happened:

THE PROBLEM

• Four theoretical physicists (from Harvard, the Institute for Advanced Study, Cambridge and Vanderbilt) had been studying interactions involving gluons — the particles that "glue" quarks together inside protons and neutrons, essentially holding all matter together.

• For decades, textbooks said a specific type of gluon interaction (called "single-minus" configurations) had a "scattering amplitude" of zero (i.e., these interactions simply could not occur).

• The team suspected otherwise, and proved it for small numbers of gluons... but as they tried to generalize the formula, the expressions became dozens of terms long and unworkable. After about a year of grinding away by hand, they were stuck.

THE BREAKTHROUGH

• They fed their complicated formulae into GPT-5.2 Pro. The model simplified an expression with 32 variables down to a compact product fitting on a single line.

• Asked to generalize for any number of gluons, the model replied within minutes with what it called (I love this!) the "obvious" generalization.

• A more powerful internal OpenAI model (which the researchers called "SuperChat") then produced a formal proof after about 12 hours of autonomous reasoning. The physicists checked step by step and confirmed it was correct.

• The team then extended the approach to gravitons (hypothetical particles thought to carry the gravitational force), releasing the results in their second arXiv preprint a few weeks later.

CAVEATS

• These are preprints, not yet peer-reviewed papers.

• The results apply to a very specific mathematical regime at the simplest level of calculation ("tree level").

• Human physicists were essential for defining the problem, providing the initial data and verifying the output.

WHY IT MATTERS

• As one researcher put it: The hard part is no longer the physics itself; the hard part is now verifying the results and writing them up. AI compressed months of work into weeks.

• This may be a template for AI-assisted research more broadly: AI generates conjectures from patterns in the data, human experts verify those conjectures through rigorous math and physical consistency checks.

• It's not autonomous AI science; it's augmented human science. And that model could scale across disciplines, from pure math to drug discovery to materials science

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How A.I. is Transforming Science

A.I. is not just a tool, but a driving force in reshaping the landscape of science. In today's episode, I dive into the profound implications A.I. holds for scientific discovery, citing applications across nuclear fusion, medicine, self-driving labs and more.

Here are some of the ways A.I. is transforming science that are covered in today's episode:

• Antibiotics: MIT researchers uncovered two new antibiotics in a single year (antibiotic discovery is very rare so this is crazy!) by using an ML model trained on the efficacy of known antibiotics to sift through millions of potential antibiotic compounds.

• Batteries: Similar sifting was carried out by A.I. at the University of Liverpool to narrow down the search for battery materials from 200,000 candidates to just five highly promising ones.

• Weather: Huawei's Pangu-Weather and NVIDIA's FourCastNet use ML to offer faster and more accurate forecasts than traditional super-compute-intensive weather simulations — crucial for predicting and managing natural disasters.

• Nuclear Fusion: AI is simplifying the once-daunting task of controlling plasma in tokamak reactors, thereby contributing to advancements in clean energy production.

• Self-Driving Labs: Automate research by planning, executing, and analyzing experiments autonomously, thereby speeding up scientific experimentation and unveiling new possibilities for discovery.

• Generative A.I.: Large Language Models (LLMs) tools are pioneering new frontiers in scientific research. From improving image resolution to designing novel molecules, these tools are yielding tangible results, with several A.I.-designed drugs currently in clinical trials. Tools like Elicit are streamlining the process of scientific literature review over vast corpora, allowing connections within or between fields to be uncovered automatically and suggesting new research directions.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Machine Learning at NVIDIA

This week's guest is absolute rockstar Dr. Anima Anandkumar, who's both professor at prestigious Caltech and director of ML research at NVIDIA. The episode is exceptionally content-rich but also lots of fun; Anima was a joy to film with.

In the episode, Anima fills us in on:

• The cutting-edge interdisciplinary research she carries out (applying highly optimized mathematical operations to allow state-of-the-art ML models to be executed on NVIDIA's state-of-the-art GPUs)

• How this blending of leading software and leading hardware enables world-changing innovations across disparate fields, from healthcare to robotics

• What it's like in the workweek of a researcher bridging the academic and industrial realms

• The open-source data science tools and techniques that she most highly recommends

Listen or watch here.