In 1950, Alan Turing asked "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's?" Today, 75 years later, constructing a computer program that can learn like a child and that develops a human-like general intelligence and consciousness is still considered a grand, if not the ultimate, challenge for artificial intelligence (AI). An interdisciplinary community of scientists from AI, Cognitive Science, Psychology, Engineering, and Neuroscience are tackling this grand challenge. In the Developing Minds global lecture series we showcase the progress being made. It is organized by the Developmental AI Task Force of the IEEE Technical Committee on Cognitive and Developmental Systems of the IEEE Computational Intelligence Society. See also: IEEE Int. Conference on Development and Learning (ICDL), IEEE Transactions on Cognitive and Developmental Systems (TCDS). If you enjoy this lecture series, you may also enjoy the keynote lectures and all oral presentations from the last ICDL conference.

Next Event

Thursday, June 25, 2026

10:00 am EDT (Eastern Daylight Time, USA)

 14:00 UTC (Universal Coordinated Time)

 16:00 CEST (Central European Summer Time)

 23:00 JST  (Japan Standard Time)

Andrew Barto

Prof. Emeritus of Univ. of Massachusetts Amherst, USA

"Rediscovering Reinforcement Learning"

Abstract

The idea of reinforcement learning (RL) as a key principle of animal learning has been around at least since Edward Thorndike proposed the “Law of Effect” in 1898. Machine implementation of this principle began with electro-mechanical machines in the 1930s, and the earliest idea for computer implementation was probably Turing’s 1948 proposal of a computer implementation of a “pleasure-pain system”.  In this talk I review what has followed Turing’s unimplemented proposal, starting with the first computer experiments in 1954, up to what we now know as modern computational RL.

Short Bio

Andrew Barto is Professor Emeritus of Computer Science, University of Massachusetts Amherst, having retired in 2012. He served as Chair of the UMass Department of Computer Science from 2007 to 2011. He received a B.S. with distinction in mathematics from the University of Michigan in 1970, and a Ph.D. in Computer Science in 1975, also from the University of Michigan. He joined the Computer Science Department of the University of Massachusetts Amherst in 1977. Before retiring he co-directed the Autonomous Learning Laboratory at UMass Amherst, which produced many notable machine learning researchers. Professor Barto is a Fellow of the American Association for the Advancement of Science (AAAS) and a Fellow and Life Member of the IEEE. He received the 2004 IEEE Neural Network Society Pioneer Award for contributions to the field of reinforcement learning, the IJCAI-17 Award for Research Excellence for groundbreaking and impactful research in both the theory and application of reinforcement learning, and a University of Massachusetts Neurosciences Lifetime Achievement Award in 2019. Most recently, he and his former doctoral student, Richard Sutton, were co-recipients of the 2024 Association for Computing Machinery’s A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning, and he and Sutton received the 2026 IEEE Frank Rosenblatt Award for contributions to reinforcement learning and artificial intelligence.  He has published over one hundred papers or chapters in journals, books, and conference and workshop proceedings. He is co-author with Sutton of "Reinforcement Learning: An Introduction," MIT Press, 1998. A much expanded second edition was published in 2018.

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Upcoming Events

• 2026-06-25: Andrew Barto, University of Massachusetts Amherst, USA (10:00 am Boston time)

Previous Events

• 2026-04-02: Lisa Oakes, "Learning to look and looking to learn: Developmental cascades in infant attention". Video

• 2025-12-04: Jenny Saffran, "Learning to understand: Statistical learning and language development". Video

• 2025-11-13: Tadahiro Taniguchi, "Developing Collective Minds: Symbol Emergence and Co-creative Learning via Collective Predictive Coding". Video

• 2025-10-16: Denis Mareschal, "The challenges and rewards of pursuing real-world Developmental Science". Video

• 2025-09-25: Uri Hasson,  "Developing cognitively feasible learning agents that can acquire language like children through real-life experiences". Video not yet available.

• 2024-12-12: Daniel Messinger, "Does Interaction Drive Development? Lessons from infant emotion, autism, and preschool language". Video

• 2024-11-12: Justin N. Wood, "Radical empiricism: The origins of knowledge as a mini-evolution". Video

• 2024-04-18: Sabine Hunnius, "Early cognitive development: Five lessons from infant learning". Video

• 2024-01-24: Caroline Rowland, "What predicts how quickly children learn language?" Video

• 2023-11-30: Brenden Lake, "Addressing two classic debates in cognitive science with deep learning". Video

• 2023-06-28: Angelo Cangelosi, "Developmental Robotics for Language Learning, Trust and Theory of Mind". Video

• 2023-04-27: Karl Friston, "Active Inference and Artificial Curiosity". Video

• 2023-03-02: Masashi Sugiyama, "Theory and Algorithm towards Reliable Machine Learning". Video

• 2022-12-08: Karen E. Adolph, "Development of intelligent behavior: Lessons from Infants". Video

• 2022-11-17: Gary Marcus, "Towards a Proper Foundation for Robust Artificial Intelligence". Video

• 2022-07-28: Sergey Levine, UC Berkeley, "From Reinforcement Learning to Embodied Learning". Video

• 2022-06-01: Susan Goldin-Meadow, U. of Chicago, "The Mind Hidden in Our Hands". Video

• 2022-03-31 : Atsushi Iriki, Riken, "Self-in-the-world map evolved in the primate brain as a basis of civilized Homo sapiens". Video

• 2022-01-27: Josh Tenenbaum, MIT, "Reverse Engineering Human Cognitive Development: What do we start with, and how do we learn the rest?". Video

• 2021-11-11: Linda B. Smith, Indiana University, "Babies, bodies, brains and machines". Video  

• 2021-09-30: Pierre-Yves Oudeyer, INRIA, "Developmental Artificial Intelligence: machines that learn like children and help children learn better".  Video

Initiators