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Meta-learning: Data, architecture, and both

Published online by Cambridge University Press:  23 September 2024

Marcel Binz Open the ORCID record for Marcel Binz [Opens in a new window] ,
Ishita Dasgupta ,
Akshay Jagadish ,
Matthew Botvinick ,
Jane X. Wang  and
Eric Schulz
Marcel Binz*
Affiliation:
Max Planck Institute for Biological Cybernetics, Tübingen, Germany Helmholtz Institute for Human-Centered AI, Munich, Germany [email protected] [email protected] [email protected]
Ishita Dasgupta
Affiliation:
Google DeepMind, London, UK [email protected] [email protected] [email protected]
Akshay Jagadish
Affiliation:
Max Planck Institute for Biological Cybernetics, Tübingen, Germany Helmholtz Institute for Human-Centered AI, Munich, Germany [email protected] [email protected] [email protected]
Matthew Botvinick
Affiliation:
Google DeepMind, London, UK [email protected] [email protected] [email protected]
Jane X. Wang
Affiliation:
Google DeepMind, London, UK [email protected] [email protected] [email protected]
Eric Schulz
Affiliation:
Max Planck Institute for Biological Cybernetics, Tübingen, Germany Helmholtz Institute for Human-Centered AI, Munich, Germany [email protected] [email protected] [email protected]
*
*Corresponding author.
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Abstract

We are encouraged by the many positive commentaries on our target article. In this response, we recapitulate some of the points raised and identify synergies between them. We have arranged our response based on the tension between data and architecture that arises in the meta-learning framework. We additionally provide a short discussion that touches upon connections to foundation models.

Type
Authors' Response
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e170
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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