Uploaded January 2024 | Updated September 2026, 1 day ago
Abstract: In this talk, I challenge the AI research community to develop and evaluate integrated discovery systems. There has been a steady stream of AI work on scientific discovery since the 1970s, much of it leading to published results in fields like astronomy, biology, chemistry, and physics. However, most efforts have focused on isolated tasks rather than addressing how they interact. I start by noting distinguishing features of scientific discovery and examine eight of its component abilities, in each case specifying the problem and reviewing progress in the area. After this, I note some previous successes at partial integration and consider the remaining hurdles that the AI community must leap to transform the vision for integrated discovery into reality. In closing, I discuss promising scientific domains, both natural and synthetic, in which to test such computational artifacts.
Bio: Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise. He has contributed to AI and cognitive science for more than 40 years, publishing over 300 papers and five books on these topics. Dr. Langley developed some of the first computational approaches to scientific knowledge discovery, and he was an early champion of experimental studies of machine learning and its application to real-world problems. He is the founding editor of two journals, Machine Learning in 1986 and Advances in Cognitive Systems in 2012, and he is a Fellow of both AAAI and the Cognitive Science Society. Dr. Langley's current research focuses on architectures for embodied agents, learning procedures from written instructions, and induction of dynamic causal models from time series and background knowledge.
Abstract: In this talk, I challenge the AI research community to develop and evaluate integrated discovery systems. There has been a steady stream of AI work on scientific discovery since the 1970s, much of it leading to published results in fields like astronomy, biology, chemistry, and physics. However, most efforts have focused on isolated tasks rather than addressing how they interact. I start by noting distinguishing features of scientific discovery and examine eight of its component abilities, in each case specifying the problem and reviewing progress in the area. After this, I note some previous successes at partial integration and consider the remaining hurdles that the AI community must leap to transform the vision for integrated discovery into reality. In closing, I discuss promising scientific domains, both natural and synthetic, in which to test such computational artifacts.
Bio: Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise. He has contributed to AI and cognitive science for more than 40 years, publishing over 300 papers and five books on these topics. Dr. Langley developed some of the first computational approaches to scientific knowledge discovery, and he was an early champion of experimental studies of machine learning and its application to real-world problems. He is the founding editor of two journals, Machine Learning in 1986 and Advances in Cognitive Systems in 2012, and he is a Fellow of both AAAI and the Cognitive Science Society. Dr. Langley's current research focuses on architectures for embodied agents, learning procedures from written instructions, and induction of dynamic causal models from time series and background knowledge.
