I personally prefer 'generic hashing/parsing'; deep learning excels at the automatic creation of a mapping of unstructured information to structured information, after a sufficient period of training.

Hmm... but isn't that what our brains do as well? Unstructured intensities of light bouncing off our retinas which becomes a structured recognized object.

It definitely seems to be part of what our brain does. The visual cortex is an apt comparison since that's where a lot of the structural inspiration for modern ANNs comes from. But, there does seem to be a little more than that too; it's not clear whether all the brain does is reducible to a hash function (reducible in any useful sense, at least; a very very very big, very very very sparse hash function, perhaps).

Our brain can understand that a cartoon-picture of a cat is a cat. Also, our brain can understand that a picture of a cat taken from a hugely different angle than seen before is a cat. Deep learning cannot do those kind of tricks.

A related problem is "one shot learning", [1].

[1] https://en.wikipedia.org/wiki/One-shot_learning

Maybe something like subtle memorization?

It's obfuscated memorization though. Otherwise wouldn't youtube search on my "smart" TV yield videos that contain the search term?

I searched for "face detection" and got "face recognition" videos. I felt like a linear model would have been more useful.