AlphaGo represents a general approach to AI, but its instantiation on the specific problem of Go tightly constrains the problem domain and solution space ..

Sure, but that wasn't my point. I was addressing key questions of training data size, sample efficiency, and learning speed. At least for Go, vision, and related domains, the sample efficiency of DL based systems appears to be approaching that of humans. The net learning efficiency of the brain is far beyond current DL systems in terms of learning per joule, but the gap in terms of learning per dollar is less, and closing quickly. Machine DL systems also easily and typically run 10x or more faster than the brain, and thus learn/train 10x faster.