Petr,
{repeat from a different thread slightly modified for this one}
What I was addressing was more around what Robert Jasiek is describing in
his joseki books and other materials he's produced. And it is exactly why I
think the "explanation of the suggested moves" requires a much deeper
baking into the participating ANN's (bottom up approach). And given what I
have read thus far (including your above information), I am still seeing
the risk extraordinarily high and the payoff exceedingly low, outside an
academic context.
However, if someone was to do all the dirty work setting up all the
infrastructure, hunt down the training data and then financially facilitate
the thousands of hours of human work and the tens to hundreds of thousands
of hours of automated learning work, I would become substantially more
interested...and think a high quality desired outcome remains a low
probability.
That said, I wish whomever takes on this project the very best of luck
because I will very much enjoy being wrong about this...at someone else's
expense. :)
Jim
On Thu, Mar 31, 2016 at 6:04 AM, Petr Baudis <pa...@ucw.cz> wrote:
> On Wed, Mar 30, 2016 at 09:58:48AM -0500, Jim O'Flaherty wrote:
> > My own study says that we cannot top down include "English explanations"
> of
> > how the ANNs (Artificial Neural Networks, of which DCNN is just one type)
> > arrive a conclusions.
>
> I don't think that's obvious at all. My current avenue of research
> is using neural models for text comprehension (in particular
> https://github.com/brmson/dataset-sts) and the intersect with DCNNs is
> for example the work on automatic image captioning:
>
> http://cs.stanford.edu/people/karpathy/sfmltalk.pdf
> https://www.captionbot.ai/ (most recent example)
>
> One of my project ideas that I'm quite convinced could provide some
> interesting results would be training a neural network to caption
> Go positions based on game commentary. You strip the final "move
> selection" layer from the network and use the previous fully-connected
> layer output as rich "semantic representation" of the board and train
> another network to turn that into words (+ coordinate references etc).
>
> The challenges are getting a large+good dataset of commented positions,
> producing negative training samples, and representing sequences (or just
> coordinate points). But I think there's definitely a path forward
> possible here to train another neural network that provides explanations
> based on what the "move prediction" network sees.
>
> It could make a great undergraduate thesis or similar.
>
> (My original idea was simpler, a "smarter bluetable" chatbot that'd just
> generate "position-informed kibitz" - not necessarily *informative*
> kibitz. Plenty of data for that, probably. ;-)
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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