The rise of large AI models has amplified concerns about safety, reliability, and accountability in critical domains. Traditional programming languages research has long confronted similar challenges: how to specify intended behavior, prevent errors, and enforce guarantees through design. Yet modern AI systems often appear unprogrammable—opaque, stochastic, and lacking explicit semantics. In this talk, I will ask whether programming can still serve as a foundation for AI safety. I will discuss how concepts such as specification, semantics, and verification might be reimagined for AI-based systems, and share insights from our recent research that explores programming-inspired approaches to making AI systems more predictable and trustworthy.

SUN, Jun is a professor at Singapore Management University (SMU). He received Bachelor and PhD degrees in computing science from National University of Singapore (NUS) in 2002 and 2006. In 2007, he received the prestigious LEE KUAN YEW postdoctoral fellowship in School of Computing of NUS. From 2010 to 2019, he was an Assistant/Associate Professor at Singapore University of Technology and Design (SUTD). He was a visiting scholar at MIT from 2011-2012. Since 2019, he joined SMU as an associate professor. Jun’s research interests include software engineering, formal methods, program analysis and cyber-security. He is the co-founder of the PAT model checker.