So this remembering nowhow about science till win every battle, but
religion wan the way before it even began. Wold you agree MATT DAMON? DON"T
BLOW THE MEET WITH MATSUI) :)
On Fri, Oct 25, 2013 at 3:10 AM, Telmo Menezes <te...@telmomenezes.com>wrote:
> On Fri, Oct 25, 2013 at 12:08 PM, Telmo Menezes <te...@telmomenezes.com>
> > On Thu, Oct 24, 2013 at 11:05 PM, meekerdb <meeke...@verizon.net> wrote:
> >> On 10/24/2013 12:08 PM, John Mikes wrote:
> >> Craig and Telmo:
> >> Is "anticipation" involved at all? Deep Blue anticipated hundreds of
> >> in advance (and evaluated a potential outcome before accepting, or
> >> rejecting).
> >> What else is in "thinking" involved? I would like to know, because I
> have no
> >> idea.
> >> John Mikes
> >> Learning from experience. Actually I think Deep Blue could do some
> >> by analyzing games and adjusting the values it gave to positions. But
> >> reason it seems so unintelligent is that its scope of perception is very
> >> narrow (i.e. chess games) and so it can't learn some things a human
> >> can. For example Deep Blue couldn't see Kasparov look nervous, ask for
> >> changes in the lighting, hesitate slightly before moving a piece,...
> > Bret,
> Sorry I misspelled your name! A quick google search shows me that it's
> not something offensive, just another name. Uff... :)
> > Even in the narrow domain of chess this sort of limitation still
> > applies. Part of it comes from the "divide and conquer" approach
> > followed by conventional engineering. Let's consider a simplification
> > of what the Deep Blue architecture looks like:
> > - Pieces have some values, this is probably sophisticated and the
> > values can be influenced by overall board structure;
> > - Some function can evaluate the utility of a board configuration;
> > - A search tree is used to explore the space of possible plays,
> > counter-plays, counter-counter-plays and so on;
> > - The previous tree can be pruned using some heuristics, but it's
> > still gigantic;
> > - The more computational power you have, the deeper you can go in the
> > search tree;
> > - There is an enormous database of openings and endings that the
> > algorithm can fallback to, if early or late enough in the game.
> > Defeating a grand master was mostly achieved by increasing the
> > computational power available to this algorithm.
> > Now take the game of go: human beings can still easily beat machines,
> > even the most powerful computer currently available. Go is much more
> > combinatorially explosive than chess, so it breaks the search tree
> > approach. This is strong empirical evidence that Deep Blue
> > accomplished nothing in the field of AI -- it did did accomplish
> > something remarkable in the field of computer engineering or maybe
> > even computer science, but it completely side-stepped the
> > "intelligence" part. It cheated, in a sense.
> > How do humans play games? I suspect the same way we navigate cities
> > and manage businesses: we map the problem to a better internal
> > representation. This representation is both less combinatorially
> > explosive and more expressive.
> > My home town is relatively small, population is about 150K. If we were
> > all teleported to Coimbra and I was to give you guys a tour, I could
> > drive from any place to any place without thinking twice. I couldn't
> > draw an accurate map of the city if my life depended on it. I go to
> > google maps and I'm still surprised to find out how the city is
> > objectively organised.
> > If Kasparov were to try and explain us how he plays chess, something
> > similar would happen. But most AI research has been ignoring all this
> > and insisting on reasoning based on objective, 3rd person view
> > representations.
> > My intuition is that we don't spend a lot of time exploring search
> > trees, we spend most of our time perfecting the external/internal
> > representation mappings. "I though he was a nice guy but now I'm not
> > so sure" and so on...
> > Cheers,
> > Telmo.
> >> Brent
> >> --
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