Go, Marvin Minsky, and the Chasm That AI Hasn’t yet Crossed
Interesting article! However, Deep Blue, Watson, and AlphaGo are very different from one another. I don't think anyone deemed beating humans at chess or jeopardy impossible at the time Deep Blue and Watson were built. On AlphaGo the point about generalizing AI is valid, yet I think the author doesn't fully appreciate the novelty of approach described in the AlphaGo paper. Their work advances the field, has more general utility than Deep Blue's chess or Watson's jeopardy programs. AlphaGo paper specifically represents an advance in machine learning algorithms for games in general. As I understand, Watson's new NLP algorithm is PRISMATIC [1]. PRISMATIC is a rule-based NLP system and AlphaGo is more statistical inference/neural networks. Even if AlphaGo's 'policy-network- value-network' framework is not too generalizable, the philosophical implication is that we can build AIs that can mimic 'human intuition'. Jeopardy and chess have lesser components of 'human intuition' than Go. They are apples and oranges. So, I wonder if the author errs in bringing Deep Blue and Watson-Jeopardy into the picture.<p>In my opinion while Watson-Jeopardy and Deep Blue were 'over-fitting' for Jeopardy and chess respectively, AlphaGo algorithms are more general and over-fits for the larger category of 'games'.<p>[1] <a href="http://brenocon.com/watson_special_issue/05%20automatic%20knowledge%20extration.pdf" rel="nofollow">http://brenocon.com/watson_special_issue/05%20automatic%20kn...</a>