I like your perspective, Adrian. It is more inline with the fractal nature of knowledge itself. And the idea that computers might be able to computationally explore deeper iterations in the fractal space than are currently possible within human neural cognition is quite exciting.
On Fri, Feb 10, 2017 at 5:56 AM, <adrian.b.rob...@gmail.com> wrote: > Richard J Lorentz <lore...@csun.edu> writes: > > > Thanks for the interesting link. Indeed, some good reading there. > > > > One quote that I've seen various versions of a number of times now: " > > More interesting for the rest of us, AlphaGo is playing moves and > > styles that all human masters had dismissed as stupid centuries ago." > > Related to this, the idea was mentioned that maybe AlphaGo is > beating humans partially because the strategies employed have > gotten trapped in a local minimum due to historical factors. > > It might not necessarily be historical factors, but simply that > the techniques that AlphaGo has been finding are just too hard > for humans to learn or use effectively. For example, in > mathematics, different techniques have been developed for both > symbolic and numerical manipulation for use in computer programs, > but humans don't use these because they are tedious or difficult > to error check, place a heavy load on memory, or don't provide > intuitive insight. Likewise, it could be that certain strategies > work well in Go but require keeping track of details and playing > out more precisely than human abilities typically allow. > > It is nice to hope that we could learn something about Go from > AlphaGo, but we may learn little more than what mathematicians > learn when a computer-assisted proof consisting of several > hundred pages is generated for a conjecture like Fermat's last > theorem. > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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