Raw LLM Responses

I personally really liked [this explanation](https://www.reddit.com/r/worldnews/comments/4a7pew/go_champion_lee_sedol_strikes_back_to_beat/d0y3m7y) by /u/MUWN: > AlphaGo made one vital mistake really, which was readable, but still in a complicated situation and pretty difficult to see. It's not too surprising that it was missed, I think, although I can't really comment on that. > > After AlphaGo made that mistake, it shortly after realized it was suddenly very far behind. All of the "nonsense" moves after that were standard Monte-Carlo approaches. i.e., trying desperate moves that have a low probability of working, but which would reverse the game back to AlphaGo's favor if they did. It's very strange to see that sort of play between two pro-level players, but it is what you would expect from an AI that uses (in part) Monte-Carlo algorithms. And [the subsequent analogy](https://www.reddit.com/r/worldnews/comments/4a7pew/go_champion_lee_sedol_strikes_back_to_beat/d0y4m5n) by /u/terryspeed: > It's kind of similar to a sport game where there is only 2:00 left and one team is badly trailing behind. > > That team may try desperate moves as it's the only way it can win. If those moves fail, the gap between the teams will widen, meaning the losing team will have to make even more extreme moves, etc. It's a vicious circle.