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Developmental Machine Learning: From Human Learning to Machines and Back (Dagstuhl Seminar 22422)

Authors James M. Rehg, Pierre-Yves Oudeyer, Linda B. Smith, Sho Tsuji, Stefan Stojanov, Ngoc Anh Thai and all authors of the abstracts in this report



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Author Details

James M. Rehg
  • Georgia Institute of Technology - Atlanta, US
Pierre-Yves Oudeyer
  • INRIA - Bordeaux, FR
Linda B. Smith
  • Indiana University - Bloomington, US
Sho Tsuji
  • University of Tokyo, JP
Stefan Stojanov
  • Georgia Institute of Technology - Atlanta, US
Ngoc Anh Thai
  • Georgia Institute of Technology - Atlanta, US
and all authors of the abstracts in this report

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James M. Rehg, Pierre-Yves Oudeyer, Linda B. Smith, Sho Tsuji, Stefan Stojanov, and Ngoc Anh Thai. Developmental Machine Learning: From Human Learning to Machines and Back (Dagstuhl Seminar 22422). In Dagstuhl Reports, Volume 12, Issue 10, pp. 143-165, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.12.10.143

Abstract

This interdisciplinary seminar brought together 18 academic and industry computer science researchers in artificial intelligence, computer vision and machine learning with 19 researchers from developmental psychology, neuroscience and linguistics. The objective was to catalyze connections between these communities, through discussions on both how the use of developmental insights can spur advances in machine learning, and how computational models and data-driven learning can lead to novel tools and insights for studying child development. The seminar consisted of tutorials, working groups, and a series of talks and discussion sessions. The main outcomes of this seminar were 1) The founding of DevelopmentalAI (http://www.developmentalai.com), an online research community to serve as a venue for communication and collaboration between develpomental and machine learning researchers, as well as a place collect and organize relevant research papers and talks; 2) Working group outputs - summaries of in-depth discussions on research questions at the intersection of developmental and machine learning, including the role of information bottlenecks and multimodality, as well as proposals for novel developmentally motivated benchmarks.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
Keywords
  • developmental psychology
  • human learning
  • machine learning
  • computer vision
  • language learning

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