Séminaire ayant lieu dans le cadre du Doctorat en informatique cognitive, en collaboration avec le centre de recherche CRIA          

TITRE : Is it really easier to build a child AI than an adult AI?

Emmanuel DUPOUX

Jeudi le 4 décembre 2025 à 10h30

Local PK-5115 (Il est possible d'y assister en virtuel en vous inscrivant ici)            

RÉSUMÉ

This talk reexamines Turing’s proposal to achieve machine intelligence by building an artificial child. With language acquisition as a testbed, I examine whether recent advances in self-supervised learning and large language models applied to child-centered audio or audio/video data take into account early phonetic and lexical developmental landmarks in real children. Focussing on the issues of robustness and data efficiency in child language learning, I will recast the long-standing controversy between statistical learning, social approaches and nativist hypotheses as an investigation of inductive biases in AI models in the light of ecologically realistic data. 

BIOGRAPHIE

Emmanuel DUPOUX is Professor at the École des Hautes Études en Sciences Sociales (EHESS) and directs the Cognitive Machine Learning team at the Laboratoire de Sciences Cognitives et Psycholinguistique (LSCP, ENS/CNRS/EHESS). He is also a part-time scientist at Meta AI Research. His research focuses on the mechanisms underlying cognitive and linguistic development in infants, combining experimental psychology, brain imaging, and machine learning. He holds a PhD in Cognitive Science (EHESS), an MA in Computer Science, and a BA in Applied Mathematics (ENS). He is recipient of an ERC Advanced Grant and organizer of the Zero Resource Speech Challenge, developing computational approaches to understanding how children learn language from their environment.

RÉFÉRENCES

Lavechin, M., de Seyssel, M., Métais, M., Metze, F., Mohamed, A., Bredin, H., Dupoux, E., & Cristia, A. (2024). Modeling early phonetic acquisition from child-centered audio data. Cognition, 245, 105734.

Rita, M., Strub, F., Chaabouni, R., Michel, P., Dupoux, E., & Pietquin, O. (2024). Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning. ACL Findings 2024. 

Rita, M, Michel, P., Chaabouni, R., Pietquin, O., Dupoux, E., Strub, F. (2025). Language Evolution with Deep Learning. Chapter to appear in the Oxford Handbook of Approaches to Language Evolution

Benchekroun, Y., Dervishi, M., Ibrahim, M., Gaya, J.-B., Martinet, X., Mialon, G., Scialom, T., Dupoux, E., Hupkes, D., & Vincent, P. (2023). WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models. arXiv:2311.15930.

Poli, M., Schatz, T., Dupoux, E., & Lavechin, M. (2025). Modeling the initial state of early phonetic learning in infants. Language Development Research, 5(1).