[tossaway322]

This.

Why the broadspread interest in a technology so unpredictable, whose payoff is so little and that requires so much investment in hardware/developer time? Wouldn't you be better off learning tools you can understand and that can be used to build reliable and predictable programs?

Why not warn graduate students:

"You'll be working on this project for about two years. We don't know what to tell you about how to solve the problems involved. We don't have any good general guidelines; this field is changing all the time; nobody knows how these things work except in very broad terms (there is no explanatory power). Sometimes it takes years to find something useful. Luck is the key - if you're unlucky, you're screwed."

"Nothing useful will come of your work when you're done. At completion you'll be dumber than when you started, because you will not have learned anything useful except possibly 'patience', a trait not valued in our field and sometimes viewed as equivalent to 'stupidity' or 'stubbornness'. You will, as a result of working with deep learning/NN, forget that sometimes you must cut your losses and quit exploring a particular solution path. At the end, you _may_ get a Master's degree, if you can show you made a good effort or even some progress. Three months after graduation, everything you have done will be obsolete and no firm will hire you: those familiar with NN because your knowledge is obsolete; those not in NN because they see no particular value in your training."