University of Birmingham > Talks@bham > IRLab Seminars: Robotics, Computer Vision & AI > Forty Years of Machine Learning: Myths, Metaphors, Paradigms, and Challenges

Forty Years of Machine Learning: Myths, Metaphors, Paradigms, and Challenges

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The machine learning community declared itself as a new branch of artificial intelligence at its first workshop in 1980. In this talk, I review the field’s forty-year history, covering developments that led to its intellectual advances and its application successes. I also examine some myths that have emerged along the way, metaphors that have guided research, and paradigms that have drawn attention in different periods. In closing, I describe some open challenges that, despite recent impressive successes, suggest the field remains far from reproducing the breadth and depth of human learning.

References Langley, P. (2011). The changing science of machine learning. Machine Learning, 82, 275-279.

Langley, P. (2016). The central role of cognition in learning. Advances in Cognitive Systems, 4, 3-12.

Langley, P. (2021). The computational gauntlet of human-like learning. Submitted to the Thirty-Sixth AAAI Conference on Artificial Intelligence.

Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as Research Scientist at Stanford University’s Center for Design Research. He has contributed to artificial intelligence and cognitive science for more than 40 years, having published 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, abductive methods for plan understanding, and learning procedures from written instructions.

This talk is part of the IRLab Seminars: Robotics, Computer Vision & AI series.

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