DeepMind’s work in 2016: a round-up
> In its ability to identify and share new insights about one of the most contemplated games of all time, AlphaGo offers a promising sign of the value AI may one day provide, and we're looking forward to playing more games in 2017.What I find fascinating is how different AlphaGo's impact was from the impact of early chess engines. Once chess engines became decent (not even good—before even Deep Blue), they identified tons of inaccuracies in published chess literature. These were missed tactics, hard-to-see moves, requiring only relatively shallow calculation but a computer's precision. These inaccuracies were found even in classical annotations of well-known chess games, as well as standard books about openings. I believe John Nunn was known for this kind of work.AlphaGo hasn't achieved nearly the same impact. Have they even tried to identify the same types of inaccuracies in classical Go books? Can you imagine how absolutely cool it would be for a go engine to find errors in Invincible? Maybe they tried, but didn't find any inaccuracies, so now this negative result is sitting in one of their file drawers? I really wish they were more active with this sort of thing.