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MrMind comments on AIFoom Debate - conclusion? - Less Wrong Discussion
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That's a terrible argument. AlphaGo represents a general approach to AI, but its instantiation on the specific problem of Go tightly constrains the problem domain and solution space. Real life is far more combinatorial still, and an AGI requires much more expensive meta-level repeated cognition as well. You don't just solve one problem, you also look at all past solved problems and think about his you could have solved those better. That's quadratic blowup.
Tl;Dr speed of narrow AI != speed of general AI.
Although I disagree that fooming will be slow, from what I've learned studying it I would say that its approach is not easy to generalize.
AlphaGo draws its power partly due to the step where an 'intuitive' neural net is created, using millions of self-play from another already supervisedly trained net. But the training can be accurate because the end positions and the winning player are clearly defined once the game is over. This allows a precise calculation of the outcome function that the intuitive neural net is trying to learn.
Unsupervised learners interacting with an environment that has open ontologies will have a much harder time to come up with this kind of intuition-building step.