Within the general excitement about artificial intelligence, there has been special interest in the technology’s application to discovery of scientific knowledge. Like AI itself, this subfield has a long history and many successes, but also outstanding challenges. In this talk, I focus on two problems that have received considerable attention: discovery of numeric equations and construction of qualitative process models. In each case, I define the computational task, review basic approaches, and report successes that led to scientific insights. After this, I turn to a third paradigm – inductive process modeling – that moves beyond earlier efforts to generate quantitative explanations of scientific data in terms of domain knowledge. I present multiple approaches to this problem, present encouraging results, and discuss open issues that merit further attention from the AI community.
26/06/2024 at 13.00
Artificial Intelligence and Scientific Discovery: Paradigms, Progress, and Potential
Pat Langely
26/06/2024 at 13.00
Artificial Intelligence and Scientific Discovery: Paradigms, Progress, and Potential
Institute for the Study of Learning and Expertise, Palo Alto, California
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 systems for 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 and the discovery of explanatory process models in science.
Pat Langely
Institute for the Study of Learning and Expertise, Palo Alto, California
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 systems for 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 and the discovery of explanatory process models in science.