Speaker
Description
Generative AI is increasingly integrated into nuclear and hadron physics research, offering powerful tools for data analysis and interpretation. However, it also introduces underappreciated risks, including confidently incorrect outputs, misinterpretation of complex datasets, and the generation of physically inconsistent or “hallucinated” results. In high-precision environments, uncritical reliance on such systems can lead to flawed conclusions or unsafe assumptions.
This poster highlights these failure modes and emphasizes the need for rigorous validation, cross-checks, and sustained human oversight. Notably, the work itself carries a slightly humorous, meta dimension: both the poster and this analysis were produced using generative AI, illustrating firsthand the dual role of these systems as both useful tools and potential sources of error.