Particle Physics meets Artificial Intelligence: How can machines see meaningful patterns that equations miss?

Artificial Intelligence has been established as a powerful tool for recognizing complex patterns and generating synthetic data. This talk explores the parallels between generative AI and more traditional approaches, in which experimental data is tested against simulations grounded in theory. The case in question is high-energy particle physics, where collision experiments generate vast datasets to answer key questions about the fundamental constituents of matter. However, the lessons learned may have a much broader range of applications.