On Fri, Aug 18, 2017 at 09:06:41AM +0200, Gian-Carlo Pascutto wrote:
> On 17-08-17 21:35, Darren Cook wrote:
> > "I'm sure some things were learned about parallel processing... but the
> > real science was known by the 1997 rematch... but AlphaGo is an entirely
> > different thing. Deep Blue's chess algorithms were good for playing
> > chess very well. The machine-learning methods AlphaGo uses are
> > applicable to practically anything."
> >
> > Agree or disagree?
>
> Deep Thought (the predecessor of Deep Blue) used a Supervised Learning
> approach to set the initial evaluation weights. The details might be
> lost in time but it's reasonable to assume some were carried over to
> Deep Blue. Deep Blue itself used hill-climbing to find evaluation
> features that did not seem to correlate with strength much, and improve
> them.
>
> A lot of the strength of AlphaGo comes from a fast, parallelized tree
> search.
>
> Uh, what was the argument again?
Well, unrelated to what you wrote :-) - that Deep Blue implemented
existing methods in a cool application, while AlphaGo introduced
some very new methods (perhaps not entirely fundamentally, but still
definitely a ground-breaking work).
And I completely agree with that argument. Nonwithstanding, it's
clear that AlphaGo's methods take advantage of many convenient
properties of Go and there's still a lot to do. I liked Andrej
Karpathy's summary on this:
https://medium.com/@karpathy/alphago-in-context-c47718cb95a5
--
Petr Baudis, Rossum
Run before you walk! Fly before you crawl! Keep moving forward!
If we fail, I'd rather fail really hugely. -- Moist von Lipwig
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