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
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