Applications at RHIC and beyond

Rapid advances in artificial intelligence (AI) and machine learning are beginning to reshape how we design, operate, and analyze experiments at the Relativistic Heavy Ion Collider (RHIC). In this talk I will present recent developments and applications of AI/ML at RHIC, spanning data taking, reconstruction, and physics analysis in experiments such as sPHENIX and STAR, as well as applications to accelerator controls. I will then outline future opportunities at the Electron Ion Collider and other facilities, where AI based approaches to detector optimization, streaming readout, and end to end analysis workflows can increase data quality and efficiency, expand the physics reach for precision measurements, and deepen our understanding of quantum chromodynamics.