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The hard problem of meta-learning is what-to-learn

Published online by Cambridge University Press:  23 September 2024

Yosef Prat Open the ORCID record for Yosef Prat [Opens in a new window]  and
Ehud Lamm Open the ORCID record for Ehud Lamm [Opens in a new window]
Yosef Prat*
Affiliation:
The Cohn Institute for History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel yosefprat@gmail.com ehudlamm@post.tau.ac.il https://www.ehudlamm.com
Ehud Lamm
Affiliation:
The Cohn Institute for History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel yosefprat@gmail.com ehudlamm@post.tau.ac.il https://www.ehudlamm.com
*
*Corresponding author.
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Abstract

Binz et al. highlight the potential of meta-learning to greatly enhance the flexibility of AI algorithms, as well as to approximate human behavior more accurately than traditional learning methods. We wish to emphasize a basic problem that lies underneath these two objectives, and in turn suggest another perspective of the required notion of “meta” in meta-learning: knowing what to learn.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e161
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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