> 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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