Still, the general ideas of supervised learning followed by reinforcement learning, training multiple models of varying complexities from the same dataset, and combining tree search with learned models as they did are useful general ideas. Hybrid methods as a whole will become increasingly common, I think (no doubt self driving cars already are very complicated hybrid models).
My take is that the approaches of AlphaGo are more applicable to other problems than DeepBlue, but not by much. Rigid rules make tree search and reinforcement learning easily applicable to Go, but not so much for many real life problems. I made a small diagram to illustrate this point (http://www.andreykurenkov.com/writing/images/2016-4-15-a-bri...) as part of a series of posts about Game AI (http://www.andreykurenkov.com/writing/a-brief-history-of-gam...).
Still, the general ideas of supervised learning followed by reinforcement learning, training multiple models of varying complexities from the same dataset, and combining tree search with learned models as they did are useful general ideas. Hybrid methods as a whole will become increasingly common, I think (no doubt self driving cars already are very complicated hybrid models).