The paper is here... http://arxiv.org/abs/1506.03229
Sensationalist media article here: http://www.iflscience.com/technology/scientists-create-artificial-system-capable-learning-human-language This is from Angelo Cangelosi (among others), who works with the iCub robot and gave a keynote at AGI-12 at Oxford... It's very good stuff, but unlike what that news article says, this is not the first time automated response-generation has been done w/ neural nets.... I recall a paper by some Russian dude giving similar results in the "Artificial Brains" special issue of Neurocomputing that Hugo DeGaris and I co-edited some years ago... What distinguishes this work is more the sophistication of the underlying cognitive architecture ... maybe it works better than prior NNs trained for dialogue-response or maybe it doesn't; careful comparison isn't given (understandably -- there is no standard test corpus for this stuff, and prior researchers mostly didn't open their code).... But the cognitive architecture is very carefully constructed in a psychologically realistic way; combined with the interesting practical results, this is pretty nifty... The training method is interesting, incrementally feeding the system facts with increasing complexity, while interacting with it along the way, and letting it build up its knowledge bit by bit. A couple weeks ago I talked to a Russian company at RobotWorld in Korea who was training a Russian NLP dialogue system in a similar way.... (again with those Russians!!) Note that with this method, the system can respond to questions involving the word "dad" without really knowing what a "dad" is (e.g. without knowing that a dad is a human or is older than a child, etc.). This is just fine, and people can do this too. But we should avoid assuming that just because it gives responses that, if heard from a human, would result from a certain sort of understanding, the system is demonstrating that same sort of understanding. This system is building up question-response patterns from the data fed into it, and then performing some generalization. The AI question is whether the kind of generalization it is performing is really the right kind to support generally intelligent cognition. My thought is that the kind of processing their network is doing, actually plays only a minor supporting rule in human question-answering and dialogue behavior. They are using a somewhat realistic cognitive architecture for reactive processing, and a somewhat realistic neural learning mechanism -- but the way the learning mechanism is used within the architecture for processing language, is not very much like the way the brain processes language. The consequence of this difference is that their system is not really forming the kinds of abstractions that a human mind (even a child's mind) automatically forms when processing this kind of linguistic information.... The result of this is that the kinds of question-answering, question-asking, concept formation etc. their system can do will not actually resemble that of a human child, even though their system's answer-generation process may, under certain restrictions, give results resembling those you get from a human child... These observations do not really contradict anything they say in the paper, at least upon my quick read.... An interesting step, anyway... -- Ben Goertzel, PhD http://goertzel.org "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." -- George Bernard Shaw ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
