[Computer-go] Deep Blue the end, AlphaGo the beginning?

Petr Baudis pasky at ucw.cz
Fri Aug 18 07:56:10 PDT 2017

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:


					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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