>So sure, backprop (the credit assignment scheme that gives us a good search direction in program space, one of multiple techniques that could do so) is pervasi

>So sure, backprop (the credit assignment scheme that gives us a good search direction in program space, one of multiple techniques that could do so) is pervasive, but AI is starting to work primarily as a result of a deeper epiphany - that we are not very good at all at writing code. Isn't it applicable to a class of programs only? Best example of which is Computer Vision. Or do you imply your argument to hold for a wider set of programs. I can think of a large set of programs in which direct coding of logic, rather than discovery, is more suited. For example take sorting. I guess, sorting could also be taught to the machine, by having a training set. But what about the latency of the discovered program. Also what about the proof of such a sorting program, which is discovered by Machine learning? Must add, that I largely agree to your excellent point regarding discovery of programs. But I am not sure about its wide applicability. In fact, I contend that it applies only to a subset of all programs. Particulary those which have been traditionally difficult to code. So in that sense, now making a tangential point here, it is good that more complex applications are now possible, by combining both kind of programs. And there will be more programming work in the future. Edit: minor