Towards Building the Trust of Complex AI Systems in the LLM Era

In recent years, deep learning-enabled systems have made remarkable progress, powering a surge in advanced intelligent applications. This growth and its real-world impact have been further amplified by the advent of large foundation models (e.g., LLM, Stable Diffusion). Yet, the rapid evolution of these AI systems often proceeds without comprehensive quality assurance and engineering support. This gap is evident in the integration of standards for quality, reliability, and safety assurance, as well as the need for mature toolchain support that provides systematic and explainable feedback of the development lifecycle. In this talk, I will present a high-level overview of our team's ongoing initiatives to lay the groundwork for Trustworthy Assurance of AI Systems and its industrial applications, e.g., including (1) AI software testing and analysis, (2) our latest trustworthiness assurance efforts for AI-driven Cyber-physical systems with an emphasis on sim2real transition. (3) risk and safety assessment for large foundational models, including those akin to large language models, and vision transformers.