The Turing Test is not good enough to tell us if a particular AGI program is feasible. We also need to find some way to simplify the idea of AGI in order to start somewhere. A particular model of AGI sub-programs may not turn out to be a good indicator of potential scalability but I feel that I have to try to devise some kind of test and then test the test out as long as near-human-like AGI is out of reach.
There are some things that a human being can't figure out, so AGI is not going to be perfectly general. But an AGI program also has to be able to deal with a great variety of situations similar to the way human beings can. And an AGI program also has to be able to come up with some pretty good insights about a variety of problems or situations. The idea of insight extends beyond a narrow solution to variations of one kind of problem, but it also extends beyond the narrow idea of one kind of response. So not only does an AGI method have to give appropriate responses to a variety of problems it also has to be able to fit that analysis and response into a greater variety of relations. (This may go without saying, but my point is that it leads to a model for more fundamental AGI methods.) An AGI program (or sub-program) has to have some kind of meta awareness. A method of meta awareness does not have to be (in itself) complicated. This ability to have sufficient awareness to connect an analysis and appropriate response into other knowledge constitutes a kind of judgment. And I have mentioned other qualifications that I think are seen in most human judgment. The feeling that once someone figures it out it will be as obvious as the Turing Test is not valid. There is no reason for an AGI program to act human without a lot of additional programming. So tying some kinds of sub-programs with simple tests for judgment relative to various domains makes perfect sense. The test won't be tied to one domain but will be tried with different domains. Although there is evidence that general knowledge is necessary for specialized knowledge, my theory which states that a great many relations related to a subject are necessary to know one simple thing about the subject does not preclude this condition from occurring in tests for domain knowledge. - Jim Bromer On Wed, Aug 14, 2013 at 3:06 PM, Mike Archbold <[email protected]> wrote: > I think I got the gist of what you are saying here... It looks > interesting; maybe just a couple of comments. > > I am inclined to think that it depends upon perspective as far as if > we consider some component narrow- vs strong-AI. I think the > distinction is helpful, but not necessarily at some level such as > function vs. subfunction vs. program vs. entire system etc. It > strikes me that it is the behavior of the program, software, entity, > as a *whole* that is in the determining factor of narrow- vs strong-. > It is hard to imagine as subroutine that has some specific task as > being general. Perhaps I missed the point above (?). The > distinguishing characteristic of narrow-ai seems to be that it 1) has > nothing like a common sense understanding of human consensus reality > and 2) can only function in a well described domain and often gets > tangled up on cases which veer to far from the domain. If it isn't > that, it's strong/general AI (ie., we don't have it ;) > > The German philosophers that I've studied distinguish sharply between > judgement and understanding. This is a good distrinction. The > hallmark of a judgement is that it could be other than what it is. > Understanding we've talked about on this list a lot. They both happen > at once, of course.... > > conceptual typing... can you point to a thread where you explain? > > Mike > > On 8/12/13, Jim Bromer <[email protected]> wrote: >> My idea of judgment does rely on reason based reasoning. This >> definition would seem to favor explicit representation. However, I do >> recognize that we make some decisions that are not based on explicit >> reasons so I do include implicit or hidden reasons in my definition of >> judgment. And the fact that we can use poor judgment or poor reasons >> for making a decision does seem to weaken the theory. But by making >> AGI learning partially dependent on previous judgment-mediated >> learning, the idea does hold together even if it cannot be pinned down >> to an absolute computational definition. >> >> How does this idea of judgment-mediated-learning tie into a definition >> of an AGI function that can be differentiated from a Narrow AI >> function? My idea of conceptual typing, dynamic creative and rational >> creative functions and trial and error methods can be combined to >> explain how novel conceptual typing might be developed as the program >> is running. So that means that I have an explicit way of dynamically >> developing new ways of looking at the data as the program is running. >> In most contemporary AGI models this is not detailed. So, artificial >> judgment can examine the presumptions behind the conceptual structures >> that are running as well as develop results that are dependent on >> them. - Jim Bromer >> >> >> ------------------------------------------- >> 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/303/24379807-f5817f28 > 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
