AlphaGo beats Lee Sedol 3-0 [video]
My (long) commentary here:<p><a href="https:&#x2F;&#x2F;www.facebook.com&#x2F;yudkowsky&#x2F;posts&#x2F;10154018209759228" rel="nofollow">https:&#x2F;&#x2F;www.facebook.com&#x2F;yudkowsky&#x2F;posts&#x2F;10154018209759228</a><p>Sample:<p>At this point it seems likely that Sedol is actually far outclassed by a superhuman player. The suspicion is that since AlphaGo plays purely for <i>probability of long-term victory</i> rather than playing for points, the fight against Sedol generates boards that can falsely appear to a human to be balanced even as Sedol&#x27;s probability of victory diminishes. The 8p and 9p pros who analyzed games 1 and 2 and thought the flow of a seemingly Sedol-favoring game &#x27;eventually&#x27; shifted to AlphaGo later, may simply have failed to read the board&#x27;s true state. The reality may be a slow, steady diminishment of Sedol&#x27;s win probability as the game goes on and Sedol makes subtly imperfect moves that <i>humans</i> think result in even-looking boards...<p>The case of AlphaGo is a helpful concrete illustration of these concepts [from AI alignment theory]...<p>Edge instantiation.  Extremely optimized strategies often look to us like &#x27;weird&#x27; edges of the possibility space, and may throw away what we think of as &#x27;typical&#x27; features of a solution. In many different kinds of optimization problem, the maximizing solution will lie at a vertex of the possibility space (a corner, an edge-case).  In the case of AlphaGo, an extremely optimized strategy seems to have thrown away the &#x27;typical&#x27; production of a visible point lead that characterizes human play...