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).

Next Event

Thursday, November 13, 2025

3 am EST (Eastern Standard Time, US)

8 am UTC (Universal Coordinated Time)

9 am CET (Central European Time)

5 pm JST (Japan Standard Time)

Tadahiro Taniguchi

Kyoto University, Japan

"Developing Collective Minds: Symbol Emergence and Co-creative Learning via Collective Predictive Coding"

Abstract

This talk introduces the Collective Predictive Coding (CPC) hypothesis, a computational theory for how collective minds develop. CPC extends the Free Energy Principle (FEP) to the societal level, positing that interacting agents (both human and artificial) collaboratively minimize their collective prediction error through interaction.

We argue that this process, functioning as a form of decentralized Bayesian inference embodied in interaction games, is the engine for symbol emergence. It provides a mechanism for agents to integrate their partial and heterogeneous perceptual information, leading to a shared, co-created understanding that exceeds the capabilities of any single agent.

The CPC framework provides a scientific foundation for "co-creative learning," a new paradigm for human-AI interaction where systems learn

with humans, rather than merely from them. This contrasts with traditional AI alignment, which often assumes a unilateral transfer of knowledge. CPC enables a bilateral, organic alignment that emerges from continuous, mutual interaction.

This bottom-up, co-creative process offers a compelling alternative to top-down alignment methods, paving the way for a future of human-AI symbiosis. It allows humans and AI to co-evolve, generate new knowledge together, and realize the development of true collective minds.

Short Bio

Tadahiro Taniguchi is a Professor at the Graduate School of Informatics, Kyoto University. He received his M.E. in 2003 and Ph.D. in Engineering in 2006, both from Kyoto University.

He began his academic career at Ritsumeikan University, serving as an Assistant Professor, Associate Professor, and Professor in the College

of Information Science and Engineering before assuming his current position at Kyoto University in 2024. During his tenure at Ritsumeikan, he was also a Visiting Associate Professor at Imperial College London from September 2015 to September 2016.

Concurrently, he serves as a Senior Technical Advisor at Panasonic Holdings Corporation, an Affiliate Professor at Ritsumeikan University, a Director of the Tomorrow Never Knows association, Representative Director of the Bibliobattle Association, Director of the AI Robot Association (AIRoA), a Technical Advisor for ABEJA, Inc., and the Chair of the IEEE Cognitive and Developmental Systems Technical Committee.

His research interests include artificial intelligence, emergent systems, and cognitive and developmental robotics. He is a pioneer in the field of "Symbol Emergence in Robotics," a constructive approach to understanding the mechanisms of language acquisition and semantic understanding from the perspective of the symbol grounding problem. Dr. Taniguchi has received numerous awards, including the Academic

Encouragement Award from the Society of Instrument and Control Engineers (SICE), the Paper Award from the Institute of Systems,

Control and Information Engineers (ISCIE), and the Advanced Robotics Best Survey Paper Award.

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Upcoming Events

• 2025-11-13: Tadahiro Taniguchi, Kyoto University, Japan

• 2025-12-04: Jenny Saffran, University of Wisconsin-Madison, USA

Previous Events

• 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