Integrating AI Agents into Human Lives via a Simulation Approach (Talk)
As the rapid growth of AI techniques, we might witness the emergence of AI agents entering our lives, reminiscent of new species. Ensuring these AI agents can well integrate into human life would be a profounding challenge. We urge these agents to be highly performant, safe, and well-aligned with human values. However, directly training and testing AI agents in real-world environments to guarantee their performance and safety is costly and can disrupt everyday life. Thus, we are exploring a simulation-based approach to incubate these AI agents. In this talk, we will highlight the role of simulation in two key scenarios: large language models (LLMs) and autonomous driving. Through these two studies, I will demonstrate how simulation can effectively facilitate the development of LLM agents and driving agents, ensuring they are both powerful and safe for human use.
Biography: Siheng Chen is a tenure-track associate professor of Shanghai Jiao Tong University. Before joining Shanghai Jiao Tong University, he was a research scientist at Mitsubishi Electric Research Laboratories (MERL), and an autonomy engineer at Uber Advanced Technologies Group (ATG), working on the perception and prediction systems of self-driving cars. Dr. Chen received his doctorate from Carnegie Mellon University. Dr. Chen received the 2018 IEEE Signal Processing Society Young Author Best Paper Award, ASME SHM/NDE 2020 Best Journal Paper Runner-Up Award, the Best Student Paper Award at 2018 IEEE Global Conference on Signal and Information Processing, and MERL President's Award. His research interests include collective intelligence and autonomous driving.
