On 20.10.2017 21:12, uurtamo . wrote:
do something like really careful experimental design across many dimensions simultaneously (node weights) and several million experiments -- each of which will require hundreds if not tens of thousands of games to find the result of the change. Worse, there are probably tens of millions of neural nets of this size that will perform equally well (isomorphisms plus minor weight changes). So many changes will result in no change or a completely useless game model.
It is possible that things turn out as complex as you describe...
"modeling through human knowledge" neural nets doesn't sound like a sensible goal
...but I am not convinced. Researchers in the human brain's thinking keep their optimism, too.
Nevertheless, alternative approaches can be imagined. E.g., while building a neural net of eventually great strength also build in its own semantic interpretator, semantic verificator (including exclusion of errors as far as computationally possible) and translator between internal structure and human (or programming) language representation. I do not know if such dynamic self-representations of neural nets have already been described but if not this would be an interesting research topic.
-- robert jasiek _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
