The attitude that "deep learning solves everything and we shouldn't bother with other techniques" is primarily one of laziness. There are many types of problems out there that call for many different types of approaches, but it's easier to just declare your favorite is best than it is to continue one's education and development.
I can think of reams of problems that convex or heavily-priored approaches are typically used for that are yet not even possible to connect to the machine-learning structure, yet you would claim that somehow deep learning has superseded fields it's not even connected to? This is unbridled arrogance.
Agreed, though sometimes "the man with a hammer" has just run out of ideas and then DL gives you an inefficient and expensive half-solution (which is better than nothing). Same thing happened with genetic algorithms etc., nothing new under the sun.
I can think of reams of problems that convex or heavily-priored approaches are typically used for that are yet not even possible to connect to the machine-learning structure, yet you would claim that somehow deep learning has superseded fields it's not even connected to? This is unbridled arrogance.