On 20.10.2017 15:07, adrian.b.rob...@gmail.com wrote:
1) Where is the semantic translation of the neural net to human theory
knowledge?
As far as (1), if we could do it, it would mean we could relate the
structures embedded in the net's weight patterns to some other domain --
The other domain can be "human go theory". It has various forms, from
informal via textbook to mathematically proven. Sure, it is also
incomplete but it can cope with additions.
The neural net's weights and whatnot are given. This raw data can be
deciphered in principle. By humans, algorithms or a combination.
You do not know where to start? Why, that is easy: test! Modify ONE
weight and study its effect on ONE aspect of human go theory, such as
the occurrance (frequency) of independent life. No effect? Increase the
modification, test a different weight, test a subset of adjacent weights
etc. It has been possible to study semantics of parts of DNA, e.g., from
differences related to illnesses. Modifications on the weights is like
creating causes for illnesses (or improved health).
There is no "we cannot do it", but maybe there is too much required
effort for it to be financially worthwhile for the "too specialised"
case of Go? As I say, a mathematical proof of a complete solution of Go
will occur before AI playing perfectly;)
So far neural
nets have been trained and applied within single domains, and any
"generalization" means within that domain.
Yes.
--
robert jasiek
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