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[timelets]

https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/

Most neural networks lack this ability to go backward. It’s a hard trait to engineer. One advantage of feed-forward networks is that they’re relatively straightforward to train — process an image through these six layers and get an answer. But if neural networks are to have license to do a double take, they’ll need a sophisticated understanding of when to draw on this new capacity (when to look twice) and when to plow ahead in a feed-forward way. Human brains switch between these different processes seamlessly; neural networks will need a new theoretical framework before they can do the same.
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Earlier this month, Google AI announced a contest to crowdsource image classifiers that can see their way through adversarial attacks.

As a separate thought, it shows the value of truth: one can afford efficient feed-forward strategy in high-trust situations.