[tlarkworthy]

Cool, but convo nets are not the right tool though. Neural networks have an implicit smoothness and locality prior (which is what back propagation exploits) which this game does not really posses. Decision trees (e.g. random forests), are able to deal with sharp discontinuities better and would be more appropriate. I would expect less training, and perfect results.

[azakai]

I agree decision trees are more natural here, but even they are overkill, I think: all that needs to be learned is a function from the 8 neighbors to the center, which means from 8 bits to 2 bits, or in other words, there are just 256 values to be learned.

Literally any reasonable learning algorithm, even nearest neighbor, will learn that fairly quickly. The article had to use millions of samples, which is extremely excessive - a more efficient algorithm should only require thousands.

What might be interesting could be to see whether the neural network learns the underlying rule, that placement does not matter, only the sum matters, and that the crucial value is "3". That would be cool to see.

[mdpopescu]

... there are just 256 values to be learned

This is a very good point and it's the reason why I don't find much value in GAs. There is an inherent conflict between "an algorithm for a specific problem" (which can be optimized to run in a short time - the above problem can be reduced to a lookup in a 256 x 2 bit matrix and thus run in O(1)) and "an algorithm for any problem (in a class)" which, while theoretically capable of finding a solution, is prohibitive in time and/or space.

As far as I can tell, GAs are barely any better than a random walk and thus not useful for any non-trivial problems. I am not yet sure if NNs are in the same category, though I suspect they are.