It seems that AI does well when the problem and the performance metrics are well defined: chess, Go, various scheduling problems, pattern recognition, etc. At t

It seems that AI does well when the problem and the performance metrics are well defined: chess, Go, various scheduling problems, pattern recognition, etc. At the very least we can track, quantitatively, how far off we are from a satisfactory solution, and we know we can only ever get closer. "True", or general-purpose AI, is harder to pin down, and thus harder to define well. I'd argue that the moment we have define it formally (and thus provided the relevant performance metrics) is the moment we have reduced it to a specialized AI problem.