Towards Safe, Calibrated and Accessible Artificial General Intelligence

Abstract:

Recent advances in AI have led to the emergence of generalist agents with astounding capabilities (e.g., language, image and video generation, autonomous driving, complex reasoning, advanced decision making, etc.).  I will present recent developments in inverse constraint reinforcement learning to learn safety constraints from human demonstrations.  I will also discuss uncertainty quantification techniques to help agents know what they don't know.  Finally, I will also discuss recent advances in reward guided text generation that avoid prohibitively expensive fine tuning of large language models (LLMs) to modularize LLM alignment and improve their accessibility.

Bio:

Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He serves on the advisory board of the NSF AI Institute for Advances in Optimization (2022-present) at Georgia Tech, UC Berkeley and University of Southern California. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab at the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing and Material Discovery. He is most well-known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team is currently working on include democratizing large language models, inverse constraint learning, mean field RL, RL foundation models, Bayesian federated learning, uncertainty quantification, probabilistic deep learning, conversational agents, transcription error correction, sport analytics, adaptive satisfiability and material discovery for CO2 recycling.