Events Generalization in People and Machines
본문
Title Generalization in People and Machines
Content How does the human ability to generalize work? This talk will examine two sources of this capability. The first are qualitative representations, which provide abstract causal and spatial models that are easier to learn than detailed quantitative models. This will be illustrated with the use of QR to model phenomena where quantitative models do not yet exist. The second is analogy, where the process of analogical matching provides a means of constructing generalizations by identifying what is common across a set of examples. How our analogy stack consisting of cognitive models of matching, retrieval, and generalization will be discussed, along with how they have been used in cognitive simulations and performance-oriented AI systems. The Continuum of Knowledge Hypothesis will be discussed, which proposes that knowledge starts out concrete and is incrementally and partially abstracted in stages. Finally, a set of open questions will be discussed.
Date July 31st, 2024(Wed) 16:00
Place Makerspace, 2nd floor of Building 942, and online (Live streaming via SNU AIIS YouTube channel)
Attendees AIIS faculty, students, and pre-registered external attendees
Place Makerspace, 2nd floor of Building 942, and online (Live streaming via SNU AIIS YouTube channel)
Attendees AIIS faculty, students, and pre-registered external attendees
Speaker Kenneth D. Forbus, Professor of Computer Science and Education at Northwestern University
Background Kenneth D. Forbus received his bachelor's and master's degrees in Computer Science, and a PhD in Artificial Intelligence, from MIT. He then served as an assistant professor in the Department of Computer Science at the University of Illinois before currently holding positions as a professor in the Department of Computer Science and the Department of Education and Social Policy at Northwestern University. He serves as a technical advisor for the OECD/NASEM Study on AI and the Future of Work and is on the advisory board for the AI graduate program at Dongguk University. His primary research interests include qualitative reasoning, analogical reasoning and learning, spatial reasoning, sketch understanding, natural language understanding, cognitive architecture, reasoning system design, intelligent educational software, and AI in interactive entertainment. He is a Fellow of the American Association for the Advancement of Science and other societies. He has also served as President of the Cognitive Science Society and received the Humboldt Research Award.
The lecture will be given in English.
댓글목록
등록된 댓글이 없습니다.
