[joe_the_user]

Modern machine-learning/deep-learning takes a bunch of data and uses high-dimension, more-or-less brute-force methods to approximate that data with a curve. It works good often (seldom works "great" 'cause the data can't fully capture the situation).

The appealing thing about this is the programmer doesn't have to understand anything. If you have little data, approximation just isn't going to capture the situation. Either the programmer gets an understanding of the system (extremely costs and time-consuming) or we create systems that are themselves capable of this understanding. But no one knows how to do this, all the "artificial intelligence" victories anyone has observed have come from throwing computing power at a problem. Maybe someone will figure out how to throw computing power at the general problem of understanding but I'm doubtful.