AI in the physical world: from observation to discovery
Session Abstract
In 2026, AI is moving beyond digital tasks into the physical world. It increasingly interacts with instruments, experiments, and real-world data. Physicists stand at this frontier, using deep learning, LLMs, and agents to analyze nature itself. What have we learned about AI when it meets reality?
Session Description
Artificial intelligence is moving beyond text generation and digital optimization into domains where uncertainty, scale, and scientific rigor dominate. Modern physics provides a uniquely demanding testbed for this shift: deep learning is used to reconstruct complex events, identify rare phenomena, and search for anomalies in high-dimensional datasets characterized by sparse signals and strict statistical constraints. At the same time, AI is taking on more structured roles in scientific workflows — from code generation and literature synthesis to emerging agent-based approaches — raising fundamental questions about how far AI can support scientific reasoning in practice.
A central challenge is operational integration. AI methods are increasingly explored for decision support in complex research facilities: tuning accelerator parameters, assisting telescope operations, and adapting to evolving environmental and hardware conditions. Yet claims of autonomous discovery or fully AI-driven infrastructure have often proven difficult to reproduce outside controlled settings. A balanced, engineering-focused assessment of both successes and limitations is therefore essential.
In this talk, I will survey real-world applications of AI across modern physics, from collider experiments to large-scale astronomy systems. I will highlight measurable gains alongside negative results, sources of bias, and stability issues that matter for production environments. The presentation concludes with a concrete case study from gamma-ray astrophysics, illustrating both the opportunities and the practical limits of integrating AI into data analysis pipelines and next-generation observatory infrastructure.
