Physicists at the University of California, Irvine, have developed an artificial intelligence system called the Autonomous Model Builder, or AMBer, that can autonomously design theoretical particle physics models to help explain the non-zero mass and behavior of neutrinos. The system was developed by UC Irvine doctoral candidates Victoria Knapp-Pérez and Jake Rudolph in the Department of Physics and Astronomy, and the work is described in a study published in Nature Communications Physics. Unlike traditional machine learning that identifies patterns in pre-existing data, AMBer utilizes reinforcement learning to learn through trial and error, constructing models by selecting mathematical symmetry groups, assigning particle behaviors, and evaluating each model's alignment with experimental data while actively minimizing the number of adjustable parameters. The Standard Model of particle physics serves as the baseline framework that AMBer seeks to expand upon by addressing its inability to account for neutrino mass. Beyond neutrinos, this computational approach can be expanded to efficiently navigate other vast, unexplored theoretical model-building problems across physics.