Re: [Computer-go] Replicating AlphaGo results

> Even if you have a lot of hardware, it's *hard* to make it add value, as anyone who tried to run MCTS on a cluster could testify - it's not just a matter of throwing it at the problem, and the challenges aren't just engineering-related either. For those of us who don't know, could you talk a lit

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

> One thing that is not explained is how to determine that a game is over You'll find that very little of the literature explicitly covers this. When I asked this question I had to search a lot of papers on MCTS which mentioned "terminal states" before finding one which defined them. Let me see i

Re: [Computer-go] Neural Net move prediction

Sounds similar to adversarial networks On Thu, Feb 4, 2016, 04:50 Huazuo Gao wrote: > Sounds like some kind of boosting, I suppose? > > On Thu, Feb 4, 2016 at 7:52 PM Marc Landgraf wrote: > >> Hi, >> >> lately a friend and me wondered about the following idea. >> >> Let's assume you have a reas

Re: [Computer-go] AlphaGo & DCNN: Handling long-range dependency

> not because of a new better algorithm but because the Deep Blue's 11.38 GFLOP power is available on desktop from about 2006F This isn't true, modern chess engines look at far fewer positions than Deep Blue did. >From wikipedia : "Chess engines cont

Re: [Computer-go] agz -- meditations

Well, if you have both, why not use both :) On Thu, Oct 19, 2017 at 11:51 AM Richard Lorentz wrote: > An interesting juxtaposition. > > Silver said "algorithms matter much more than ... computing". > > Hassabis estimated they used US$25 million of hardware. >