I'm hoping someone creates adversarial formations for AlphaGo if Google ever releases their model :) One thing I've been pondering is if many adversarial sampl

I'm hoping someone creates adversarial formations for AlphaGo if Google ever releases their model :) One thing I've been pondering is if many adversarial samples exist. The board is rather low dimensional (19 x 19) and discrete. While certainly a massive state space, one of the suggestions for why adversarial images work is that the real number line is incredibly dense. For example our possible Go board space is 2^(log2(3) * 19^2) for Go but 2^(24 * 28^2) for greyscale [0, 1] normalized single precision float imagery for MNIST. Thats an exponentially bigger space (I think something like 1e910 times bigger!), and gets only larger if you train with double precision, have larger images, add multiple color channels, add more nonlinear layers, etc.