Or weiqi.
Peter Drake
http://www.lclark.edu/~drake/
On Oct 12, 2007, at 7:29 AM, steve uurtamo wrote:
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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