Re:Today’s dominant approach to A.I. has not worked out
by PeterisP on Saturday May 19, 2018 @06:22PM
(#17110034)
Are we?
It seems quite the opposite, that the vast research reserves of AI, now heavily funded and employed by industry, are applied to pathways on hills that credibly seem high enough. Current deep learning approaches are sufficient for systems that work well enough to make profitable products, and that's it, there's no claim, expectation or necessity for that to be even a local optimum, much less a global one.
Re:Today’s dominant approach to A.I. has not worked out
by jhall1468 on Saturday May 19, 2018 @08:11PM
(#17110457)
> but little thought has been given if our AI evolution has been trending toward one or not.
Wait, you think people aren't thinking about that all the time? They absolutely are. And there's very little obvious ways to determine if this is a local or global optima. Resources are limited and everyone wants to follow the current best results, because that's necessary for business success.
Re:Today’s dominant approach to A.I. has not worked out
by lostcolony on Monday May 21, 2018 @02:29PM
(#17120559)
One would expect a competitive advantage, then, in funding research to try something else. Exclusively following the current best results just means equivalency, and provides no competitive edge.
We are completely aware of the issues of finding a local optima in our AI (especially ML) algorithms, but little thought has been given if our AI evolution has been trending toward one or not. We're pouring vast research reserves along a few pathways, and hoping that our hill climbing leads to a global maxima (or at least a local one sufficiently high as to be 'human level intelligence')