講座主題👩👩👧👧：Towards Trustworthy AI Generalist
主 講 人👨👩👧：趙鼎🤸🏻，卡內基梅隆大學 副教授
邀 請 人🎉：張頌安 助理教授
時 間：2024.05.20 15:30 - 16:30
地 點👨🏽🦳：意昂2体育平台包玉剛圖書館東翼樓200報告廳
講座摘要：
As AI becomes more integrated into physical autonomy, it presents a dual spectrum of opportunities and risks. In this talk, I will introduce our efforts in deploying trustworthy intelligent autonomy at a large scale for vital civil usage such as self-driving cars and assistant robots. During the deployment and transition, training data often exhibit significant imbalance, multi-modal complexity, and nonstationarity. I will initiate the discussion by analyzing 'long-tailed' problems with rare events and their connection to safety evaluation and safe reinforcement learning. I will then discuss how modeling multi-modal uncertainties as ‘tasks’ may enhance generalizability by learning across domains. In cases involving unknown-unknown tasks with severely limited data, we explore the potential of leveraging external knowledge from legislative sources, causal reasoning, and large language models. Lastly, I will discuss the potential social benefits/concerns regarding deploying intelligent autonomy at a large scale.
主講人簡介🎥：
Ding Zhao is the Dean's Early Career Fellow Professor and Associate Professor of Mechanical Engineering at Carnegie Mellon University. He directs the CMU Safe AI Lab, where his research focuses on trustworthy AI, covering algorithmic, physical, and societal aspects. His work encompasses self-driving technology, autonomous medical robots, and privacy and cybersecurity of autonomy. He has been actively working with world-renowned industrial partners including Google, Amazon, Microsoft, Ford, Uber, IBM, Adobe, Bosch, Toyota, and Rolls-Royce. He also collaborates with leading healthcare providers including Mayo Clinic and Cleveland Clinic. He worked as a visiting scholar with the robotic team at Google DeepMind. Ding Zhao has received numerous awards, including the George N. Saridis Best Transactions Paper Award from IEEE, the National Science Foundation CAREER Award, George Tallman Ladd Research Award, MIT Technology Review 35 under 35 Award in China, Ford University Collaboration Award, Qualcomm Innovation Award, Carnegie-Bosch Research Award, Struminger Teaching Award, and various industrial fellowship awards from Google DeepMind, Adobe, Toyota, and Bosch. His work has garnered attention from media outlets like the New York Times, TIME, Telegraph, and Wired.
