try baduk!

s.


----- Original Message ----
From: Chris Fant <[EMAIL PROTECTED]>
To: computer-go <computer-go@computer-go.org>
Sent: Friday, October 12, 2007 10:04:23 AM
Subject: Re: [computer-go] Former Deep Blue Research working on Go

Ho can I find Go vids on youtube?  Searching for "go" obviously does nothing.


On 10/12/07, steve uurtamo <[EMAIL PROTECTED]> wrote:
> > Hi Steve,
> >
> > So this doesn't get too lengthy I'll remove the stuff I'm not responding
> > to.
>
> no problem.
>
> > But why would it suddenly go "log" at some point nearby?   This is the
> > same superstition people had in computer chess for decades!   Everyone
> > had this gut feeling based on nothing whatsoever.
>
> well, every continuous function is well-approximated by a linear function
> at a small enough scale, right?  so we should expect to see linearity
> over a reasonably small range.  if we don't know the function and don't
> have datapoints from anywhere other than the beginning of the function,
> we can't really say much about datapoints at the end of the function, much
> less guess the function itself.
>
> having sparse datapoints from all over the function would give more 
> information
> than having really detailed datapoints at the "easy" end of the function.
> unfortunately, it's really difficult to get datapoints further down the 
> function.
> so i'm not sure that we can extrapolate from one end of the function to the
> other.  that's all.
>
> in a physics experiment you sample from all over the range where you think
> that your fitting function is appropriate.  it would be unreasonable to sample
> from one end and make claims about the other end.
>
> the number of doublings is relevant here as well -- the valid human ELO
> range in chess is quite a bit smaller than the same for go.  we can obtain
> datapoints from all over the chess ELO range.  we don't have the same for go.
>
> > What DID happen is that there were always some hills the computer
> > couldn't climb over and there still are, but it had nothing to do with
> > their improvement rate.    Your fallacy is that you believe the
> > landscape is relatively smooth, but with some monster unscaleable hill
> > just out of sight.   The truth is there are many different hills of all
> > different sizes.  Each improvement will enable the program to climb over
> > one or two it couldn't before.   That's really how you should be
> > thinking of this.   There is no wall around the corner.
>
> that's a good point -- any incremental gain in strength may be by
> having the ability to solve a completely different class of subproblems
> (described in a completely different way) in the game than the ones that
> humans try to solve.
>
> > I think professional play is a long way off too.   But I also believe
> > this is romanticized too much.   As I gradually became better at chess I
> > learned that a lot of concepts were just barely out of reach and not
> > really that big a deal.   With just a little extra understanding a
> > profound move becomes rather simple but if you don't understand it it
> > seems like magic.   Great players have a LOT of these and we look at
> > their games and imagine them to be gods.
>
> it's true that people are quite falliable -- i think that someone recently
> posted on the list (with youtube video) an example of a big group being in
> atari in a professional game and one of the two players not noticing.
> this is the kind of error that would simply be impossible for any program
> that can count liberties.
>
> s.
>
>
>
>
>
>
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