> 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 little about those challenges? On Thu, Jan 28, 2016 at 8:38 AM Petr Baudis <pa...@ucw.cz> wrote: > On Thu, Jan 28, 2016 at 10:29:29AM -0600, Jim O'Flaherty wrote: > > I think the first goal was and is to find a pathway that clearly works to > > reach into the upper echelons of human strength, even if the first > version > > used a huge amount of resources. Once found, then the approach can be > > explored for efficiencies from both directions, top down (take this away > > and see what we lose, if anything) and bottom up (efficiently > reoriginate a > > reflection of a larger pattern in a much more constrained environment). > > >From what I can see in the chess community, this is essentially what > > happened following Deep Blue's win against Kasperov. And now their are > > solutions on single desktops that can best what Deep Blue did with far > more > > computational resources. > > Certainly! > > Also, reflecting on what I just wrote, > > > On Thu, Jan 28, 2016 at 10:07 AM, Petr Baudis <pa...@ucw.cz> wrote: > > > > > > (I guess I'm a bit disappointed that no really new ML models had to > be > > > invented for this though, I was wondering e.g. about capsule networks > or > > > training simple iterative evaluation subroutines (for semeai etc.) by > > > NTM-based approaches. Just like everyone else, color me very awed by > > > such an astonishing result with just what was presented.) > > > > > > In summary, seems to me that the big part of why this approach was so > > > successful are the huge computational resources applied to this, which > > > is of course an obstacle (except the big IT companies). > > this is not meant at all as a criticism of AlphaGo, purely just > a discussion point! 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. > > So maybe I'd actually say that this was even understated in the paper > - that AlphaGo uses an approach which scales so well with available > computational power (at training time) compared to previous approaches. > > -- > Petr Baudis > If you have good ideas, good data and fast computers, > you can do almost anything. -- Geoffrey Hinton > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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