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Published on: June 30, 2020
The hard problem of meta-learning is what-to-learn
1The Cohn Institute for History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel yosefprat@gmail.com ehudlamm@post.tau.ac.ilhttps://www.ehudlamm.com.
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.
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