Thomas Dietterich, distinguished professor emeritus at Oregon State University, discusses large language models (LLMs) and their limitations, including lack of modularity, hallucinations, and struggles with novel situations. The conversation also explores uncertainty quantification in machine learning models, the concept of hallucination in LLMs, emergent properties in machine learning, promising opportunities in AI research, and challenges in mixing instructions and parameters in language models.

Thomas Dietterich, pioneer in machine learning, discusses the early days of AI, philosophy of science in AI, levels of understanding and sentience, error correcting output codes, hierarchical reinforcement learning, computational sustainability, flaws of language models, and more.