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Generalization is a more fundamental problem, and (imho) should be tackled first at a more fundamental level.

For example, if you have a classifier that can recognize cats, it doesn't mean it will work for cartoon cats. You'd have to train the system all over again with cartoon cats. Instead, you want the system to learn more like humans, where only a small number of examples is necessary to make the connection between real and cartoon cats.

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It is possible that the problems are related—-it may be that, to achieve human-like generalization, neural nets need to learn in a human-like environment, instead of from a folder full of images. But time will tell.

This has been said many times in different ways over the years. To achieve human-like intelligence one needs a human-like body operating in a human-like environment. It's the first of the E's in: embodied emergent extended enactive. https://plato.stanford.edu/entries/embodied-cognition/

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