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, December 4, 2025

8:00 am CT (Central Time, USA)

 2:00 pm UTC (Universal Coordinated Time)

 15:00 CET (Central European Time)

 23:00 JST (Japan Standard Time)

Jenny Saffran

University of Wisconsin-Madison, USA

"Learning to understand: Statistical learning and language development"

Abstract

Infants rapidly develop from being naïve listeners, who experience language as a sea of sounds, to understanding their native language(s). How does this remarkable learning process unfold? One potentially useful source of information lies in the statistical patterns that characterize natural languages, which signal structures ranging from phonemes to words to grammatical structures. Beyond merely tracking these patterns, how might infants use statistical regularities to support language development? In my presentation, I will explore the hypothesis that infants exploit statistical regularities in the service of efficiently processing information in their linguistic environments. 

Short Bio

Dr. Jenny Saffran has been on the faculty of the University of Wisconsin-Madison since she received her Ph.D. in 1997. Her research program focuses on how infants learn, particularly in the domain of language. She is a highly engaged teacher and mentor to students at all levels. Dr. Saffran is the recipient of numerous awards for her scholarship and teaching, including as the inaugural recipient of the Jeffrey Elman Prize for Scientific Achievement and Community Building from the Cognitive Science Society. She was elected to the American Academy of Arts and Sciences in 2015. 

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Upcoming Events

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

Previous Events

• 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