Event Details

In this talk, I pose a major challenge for AI researchers: to develop
systems that learn in a human-like manner. I illustrate this idea with
two domains — mathematics and driving — where people are effective
learners. I review briefly the history of machine learning, noting that
early work made close contact with results from cognitive psychology
but that this is no longer the case. After this, I identify characteristics
of human behavior that can serve as a ‘computational gauntlet’ and
that, if reproduced, will offer better ways to acquire expertise than
statistical induction over massive training sets. In addition, I review
five AI systems — some older and others more recent — that pass
most of the gauntlet’s obstacles and thus can serve as role models
for future work. In closing, I suggest some ways to encourage more
research on the important problem of human-like learning.

Langley, P. (2022). The computational gauntlet of human-like learning.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial
Intelligence ((pp. 12268-12273). Vancouver, BC: AAAI Press.

file:///Users/d22admin/Downloads/21489-Article%20Text-25502-1-2-20220628.pdf

Speaker Bio

Dr. Pat Langley serves as Director of the Institute for the Study of
Learning and Expertise and as a Research Scientist at Stanford
University's Center for Design Research. He has contributed to AI
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, learning procedures
from written instructions, and induction of dynamic causal models
from time series and background knowledge.

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Host: Mike Pazzani and Mohammad Rostami, POC: Pete Zamar