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I think the problem is still that there isn't really a clear definition of general intelligence or how it should be embodied on a computer. I agree that puttin
by machine 19y ago
I think the problem is still that there isn't really a clear definition of general intelligence or how it should be embodied on a computer. I agree that putting a bunch of different algorithms for different tasks in a box won't create something people would be willing to call general intelligence, but what do you expect a general purpose intelligence algorithm to do exactly? That is, if I handed you one, how would you test it? What are the inputs and outputs?
Don't get me wrong, I'd love to create a general artificial intelligence. It's just not clear to me what that means. In the absence of a good definition of the general problem, I think it's perfectly reasonable to pick an extremely hard specific problem (like visual object recognition), and focus on that, under the assumption that in order to completely solve this specific problem you will end up solving the (undefined) general problem.
"A "true AI" would discover regularities [2], or patterns, in any search space, and exploit these patterns to incrementally improve its ability to navigate the search space." -- Actually this sounds like Eurisko, a heuristic search program that modifies it's own heuristics http://en.wikipedia.org/wiki/Eurisko http://en.wikipedia.org/wiki/Eurisko
Also, it's worth pointing out that the different techniques used in different subfields of AI are not always that different. There has been some work in creating very general powerful frameworks that explain a lot of specific algorithms, like Markov logic networks http://en.wikipedia.org/wiki/Markov_logic_network http://en.wikipedia.org/wiki/Markov_logic_network which are sufficiently powerful that they subsume all of logic and statistical graphical models (which is to say maybe 90% of all recent machine learning algorithms). With these sorts of general frameworks new algorithms and approaches in one subfield get ported over to other subfields, and there is more interaction then you might think.
- jey 19y agoShane Legg and Marcus Hutter of IDSIA did some recent work on a metric for machine intelligence. ( http://www.vetta.org/documents/ui_benelearn.pdf http://www.vetta.org/documents/ui_benelearn.pdf ) Marcus Hutter's PhD thesis produced a theory of "Universal Artificial Intelligence" called "AIXI" based on Solomonoff induction. Sadly, AIXI is incomputable, and the time and space bounded version "AIXItl" is still impractical. http://www.hutter1.net/ai/aixigentle.htm http://www.hutter1.net/ai/aixigentle.htm But you're right: we don't know much about intelligence nor how it should be embodied on a computer. This is why we should encourage some really smart people to focus on the problem! I sincerely think this is an area that would yield to research. Might take a long time and a lot of brainpower though... Lenat's Eurisko is pretty cool, and I don't know much about it, but it doesn't sound like Eurisko is as general as is needed.