Jim: Even though Watson-Jeopardy did not use Neural Networks or something that was intuitively similar to them, I believe it was an example of deep learning. But the question that many of us are more interested in is was it an example of Narrow AI? My first response is that it is not because it can be applied to such a wide range of problems (even out of the box-or out of the virtual box). So then, why isn't it AGI? Why can't it think outside the box? Why does it not demonstrate the traits of what I call semi-strong AI? This question bothered me but I think I finally have figured it out.
Channelling Mike Tintner: "It's not AGI because it isn't creative. It's just answering questions. To be human-like AGI IT HAS TO BE CREATIVE. Meaning given some novel situation, it comes up with a solution that was not preprogrammed." The answer to that criticism is yes, Watson wouldn't be creative in that right, but maybe they built in or will build in some evolutionary functions to approximate "creativity." Anyway, I like Ben's list of required attributes to be AGI, which I don't have at hand. I am pretty sure creativity is on the list. A lot of this discussion is around if some system is or is not AGI, is it narrow-AI, or the like. I think we have to allow that "less narrow" is a possibility. I know AGI is qualitatively different, but still some approaches seem to push beyond narrow AI. Implementing less narrow AI is obviously within the realm of possibility. Watson sure looks less narrow!!! But, then again, we are left with reclassifying "less narrow AI" as narrow AI, moving the goal posts.... this has been going on for a long time. But, I think a crucial question is if you could implement a great less narrow AI, could that serve as a basis and nucleus for authentic AGI later on down the line? Or do you have to throw out your less narrow AI nucleus? Mike A On 1/14/16, Jim Bromer <[email protected]> wrote: > I realized that Deep NLP that Watson probably referred to Deep Search > NLP because there was something about examining text to find relations > between words in specialized cases as well as the most general > relationships that are found by taking only the most frequent > relations. (My paraphrasing is terrible but that is the idea that I > came across In one of the IBM texts about Watson.) However, there is > more to it than that of course. And the discovery of specialized > relations is a kind of learning. > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/11943661-d9279dae > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: 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
