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 _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
