Abstract
Large language models provide a pre-trained foundation for training many interesting AI systems. However, they have many shortcomings: They are expensive to train and to update, their non-linguistic knowledge is poor, they make false and self-contradictory statements, and these statements can be socially and ethically inappropriate. This talk will review these shortcomings and current efforts to address them within the existing LLM framework. It will then argue for a different, more modular architecture that decomposes the functions of existing LLMs and adds several additional components. We believe this alternative can address many of the shortcomings of LLMs.
Speaker Biography
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Distinguished Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University. Dietterich is one of the pioneers of the field of machine learning and has authored more than 200 refereed publications and two books. He is a fellow of the ACM, the American Association for the Advancement of Science, and the Association for the Advancement of Artificial Intelligence. His current research topics include robust artificial intelligence, robust human-AI systems, and applications in sustainability.
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