License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.10.143
URN: urn:nbn:de:0030-drops-178247
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17824/
Go back to Dagstuhl Reports

Rehg, James M. ; Oudeyer, Pierre-Yves ; Smith, Linda B. ; Tsuji, Sho ; Stojanov, Stefan ; Thai, Ngoc Anh
Weitere Beteiligte (Hrsg. etc.): James M. Rehg and Pierre-Yves Oudeyer and Linda B. Smith and Sho Tsuji and Stefan Stojanov and Ngoc Anh Thai

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

pdf-format:
dagrep_v012_i010_p143_22422.pdf (2 MB)

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.

BibTeX - Entry

@Article{rehg_et_al:DagRep.12.10.143,
  author =	{Rehg, James M. and Oudeyer, Pierre-Yves and Smith, Linda B. and Tsuji, Sho and Stojanov, Stefan and Thai, Ngoc Anh},
  title =	{{Developmental Machine Learning: From Human Learning to Machines and Back (Dagstuhl Seminar 22422)}},
  pages =	{143--165},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{10},
  editor =	{Rehg, James M. and Oudeyer, Pierre-Yves and Smith, Linda B. and Tsuji, Sho and Stojanov, Stefan and Thai, Ngoc Anh},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17824},
  URN =		{urn:nbn:de:0030-drops-178247},
  doi =		{10.4230/DagRep.12.10.143},
  annote =	{Keywords: developmental psychology, human learning, machine learning, computer vision, language learning}
}

Keywords: developmental psychology, human learning, machine learning, computer vision, language learning
Collection: DagRep, Volume 12, Issue 10
Issue Date: 2023
Date of publication: 03.05.2023

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI