ECE SEMINAR Speaker: Yuandong Tian, Meta AI Title: Towards Efficient, Effective and Explainable AI-guided Decision Making Date: Tuesday, March 26, 2024 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Jason Lee Abstract: Decision-making is ubiquitous in our everyday life and plays an essential role throughout the entire human history. With the emergence of strong or even superhuman Artificial Intelligence (AI), AI-guided decision making becomes a viable choice in situations that require fast yet precise decisions with overwhelming, fast-changing or intricate input, and limited prior experience and resources. Such a high bar demands our AI-guided decision making to be more effective, efficient and explainable. In this talk, I will cover our work ranging from superhuman Go bot to strategy finding in various industrial-level applications such as Antenna Design and Network Planning, from standard RL and optimization formulation to effective representations beyond pre-defined state/action spaces, and from blackbox to explainable improvement for LLMs training and inference. Bio: Yuandong Tian is a Research Scientist and Senior Manager in Meta AI Research (FAIR), working on more efficient training and inference of Large Language Models (LLMs), understanding of LLM, optimization and reinforcement learning. He has been the main mentor of recent works StreamingLLM and GaLore that improves the training and inference of LLM, and the project lead for OpenGo that beats professional players with a single GPU during inference. He is the first-author recipient of 2021 ICML Outstanding Paper Honorable Mentions and 2013 ICCV Marr Prize Honorable Mentions, and also received the 2022 CGO Distinguished Paper Award. Prior to that, he worked in Google Self-driving Car team in 2013-2014 and received a Ph.D in Robotics Institute, Carnegie Mellon University in 2013. He has been appointed as area chairs for NeurIPS, ICML, AAAI, CVPR and AIStats.