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You're describing unsupervised training. It works with computers too, as in this article on using Google Brain to build an unsupervised image classifier. http://www.wired.com/2012/06/google-x-neural-network

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Unsupervised training is the deep learning equal of context clues. It sees people talking about cats, sees an image guesses cat. Sees similar images + similar words, eventually builds a cat type prototype.

What I'm talking about is a child can see a photo of an unknown animal, I can show that child a cartoon elephant (which is the original animal). I then ask what the original animal is, the child likely responds correctly.

Reprocessing of already learned data as the scheme of the world changes based on new information.

This is the ability to abstract concepts and then recognize them in different settings (for instance, the idea of a child being a miniature version of a given animal, with less pronounced traits). In order to understand the clues you are talking about, an AI has first to be familiar with the terms used in the discussed topic, so as to be able to construct a definition by itself (what is "miniature", "traits", "pronounced"). These terms' definition must be synthetized somehow before hand, or perhaps as the discussion goes, but then the amount of necessary information in that discussion must be much larger, for the AI to untangle them properly.

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