Earlier quoted context omitted.
I think this is wrong. Take for instance the task of classifying images. You can train a RBN with backprop (after contrastive divergence alg) to correctly classify images. In the process it has automagically determined properties of the image which allow it to perform the classification. These properties are combinations of pixel elements. So it has in effect determined how to solve a problem without your input. In a…
"In the process it has automagically determined properties of the image which allow it to perform the classification." (Emphasis mine.) But that's the point; it may be "auto", but if you understand how NNs work it's not magic . It's not even all that hard to understand (considered broadly), and once you understand how they work it is, for instance, easy to construct cases they fall flat on.... "So it has in effect de…
The classic example is facial recognition. Training a neural network for facial recognition will result in lots of neurons contributing a very small part of the whole, and only when all (or most) are involved is the answer correct.
To most people, this (emergent behavior) is "magic".