[Computer-go] Mental Imagery in Go - playlist

Steven Clark steven.p.clark at gmail.com
Mon Aug 3 09:02:29 PDT 2015

RE: CNNs: They can be, and have been, successfully applied to "movies" as
well. See http://www.cs.cmu.edu/~rahuls/pub/cvpr2014-deepvideo-rahuls.pdf
Also, in the first .pdf I linked you, the input layer has a notion of "age"
of the stones. For example, this stone was played 5 moves ago, this one 3
moves ago, etc. So, it is not a strictly static snapshot of a board.
In any event, the best performance will probably not come ONLY from CNNs
(although its prediction accuracy is surprisingly high), but the marriage
of CNNs to monte-carlo tree search, etc.

My sense is that we will continue clinging to romantic notions of human
intelligence (shapes, proverbs, etc.) until we eventually get ground to
dust in a Deep-Blue style competition. Not too long now :)

On Sun, Aug 2, 2015 at 9:33 PM, djhbrown . <djhbrown at gmail.com> wrote:

> Thanks for the replies to my first message; i looked at the links you
> supplied and comment on them later in this email.
> I noticed that Google does not show you the playlist when you look at
> episode 1 of the series (of currently 3 videos), so you may have missed the
> second two episodes which are more significant than the first.  Here is a
> link to the playlist:
> https://www.youtube.com/playlist?list=PL4y5WtsvtduqNW0AKlSsOdea3Hl1X_v-S
> episode 2 introduces mental images and episode 3 is a conversation between
> Hajin Lee and me about her thoughts on a couple of moves early in one of
> her games.  It includes my first attempt at "picturing" her thoughts, both
> as symbolic information structures and as paint overlays on the game board.
> My hope is that the former might one day become the basis of symbolic
> generic heuristic rules that could be used to generate and evaluate move
> candidates and the latter could evolve into useful instructional materials
> for people learning the game - so that they can, so to speak, "look through
> the eyes" of an expert like Hajin.
> To these ends, i need the assistance of people with better skills than me
> at (a) drawing pictures, (b) software and (c) Go.  I think that programming
> is like gymnastics - best done by the young, with their abundance of
> enthusiasm and energy.  I enjoyed programming 50 years ago, but i'm too old
> in the tooth now to burn midnight oil.
> Now to your replies:
> Folkert: "Stop" is a good start but as you already know, there's a long
> way to go yet :)
> Steven:  I expect there is a future for CNN's in recognising static
> images, but my gut feel is that a position in a Go game is more like one
> frame of a movie; as such, it requires a technology that can interpret
> dynamic images - maybe work being done in automatous car driving can
> contribute something useful to Go playing?  Nevertheless, I was surprised
> by the many humanlike moves of DCNNigo on KGS (until it revealed its
> brittleness).  To be sure, drawing upon the moves of experts is one way of
> gaining expertise, but my feeling is that one should try to abstract the
> position - to generalise from the examples - so that general knowledge can
> be formed and applied to novel situations.  It may be that a CNN arguably
> does do some kind of generalisation - but can it, for example, characterise
> something as basic as "the waist of a keima"?
> Ingo:  Tanja may be the kind of artist who could produce nice drawings of
> Hajin's mental images, perhaps based on my own crude sketches?  It would be
> unpaid work though...  I liked Fuego's and Jonathan's territory pictures,
> which reminded me of Zobrist's early work on computing influence.  [Albert
> Zobrist (*1969*). *A Model of Visual Organisation for the Game of Go*.
> Proceedings of the Spring Joint Computer Conference, Vol. 34, pp. 103-112.]
> However, whereas being able to picture influence and territory is one of my
> objectives, i want to try to picture the richness of what Hajin (aka
> Haylee) sees rather than the result of a primitive computation.  For
> example, at 10:24 in episode 3, she points out that when black is on J4
> instead of K4, there is an opening in black's lower side for white to
> invade.  This tiny gap makes all the difference to the dynamic meaning of
> the position a few moves prior (ie whether it is sensible for white to
> approach Q3 at Q5).
> One of the major influences on my own thinking about Go programming is the
> seminal work "Thought and Choice in Chess" by Adriaan de Groot  which i
> reckon is well worth a read by anyone interested in programming Go
> https://books.google.com.au/books?id=b2G1CRfNqFYC&pg=PA99
> ---
> ​personal website <http://sites.google.com/site/djhbrown2/home>
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