Yes, I've always thought of the neuro-net/evolutionary approaches to be the "meat" of AGI or soft-AI, whereas the structures/classical-AI as the "bones" of AGI or hard-AI.
Worms and mollusks are alive and are largely boneless, though vertebrate sophistication does need bones. Anyways my wonder is how to integrate this with OpenCog, or SPEL. SPEL is a very hard system at the moment, which makes it finicky like a compiler, so I think it would be great to have soft-AI at the edges where interacting with humans, That way it can make assumptions and self-corrections for those who wish to interact in a "softer" more ambiguous fashion. Another place for this soft-AI that learns gradually would be inside a companion-AGI, which I believe will be one of the first kinds of widely-used AGI's, as they are a natural extension of smart-phones. The companion-AGI would at first know little about it's user, but gradually learn more, and adapt to a users quirks. Thus their own phone after awhile of use would be easier to use than a brand new one of the same type, even if it had all the same apps and settings. So forwards compatibility would be necessary so that users don't have to retrain new companion-AI's each time they get a new smart-phone, which companies love. It will provide for more ways of extracting money from users, for instance cloud extensions and backups of the phone AI. Some thoughts, Logan On Sat, Nov 14, 2015 at 9:57 PM, Ben Goertzel <[email protected]> wrote: > 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> > <https://www.listbox.com/member/archive/rss/303/5037279-a88c7a6d> | Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > ------------------------------------------- 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
