[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
mahrgell87 at gmail.com
Tue Feb 2 12:10:55 PST 2016
What? You have mixed up things.
2016-02-02 20:21 GMT+01:00 Olivier Teytaud <olivier.teytaud at inria.fr>:
>>> If AlphaGo had lost at least one game, I'd understand how people can have
>>> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
>>> have an upper bound on his level. After all, AlphaGo might just have played
>>> well enough for crushing Fan Hui, and a weak move while the position is
>>> still in favor of AlphaGo is not really a weak move (at least in a
>>> game-theoretic point of view...).
>> I just want to point that according to Myungwan Kim 9p (video referenced
>> in this thread) on the first game, Alpha Go did some mistake early in the
>> game and was behind during nearly the whole game so some of his moves should
>> be weak in game-theoric point of view.
> Thanks, this point is interesting - that's really an argument limiting the
> strength of AlphaGo.
> On the other hand, they have super strong people in the team (at the pro
> level, maybe ? if Aja has pro level...),
> and one of the guys said he is "quietly confident", which suggests they have
> strong reasons for believing they have a big chance :-)
> Good luck AlphaGo :-) I'm grateful because since this happened many more
> doors are opened for people
> working with these tools, even if they don't touch games, and this is really
> useful for the world :-)
> "I will never sign a document with logos in black & white." A. Einstein
> Olivier Teytaud, olivier.teytaud at inria.fr, http://www.slideshare.net/teytaud
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> Computer-go at computer-go.org
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