In response to my message, where I said, "What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences." Abram noted, "The "AI-probability group" definitely considers such problems. There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand."
Suppose I responded with a remark like, 6341/6344 wrong Abram... A remark like this would be absurd because it lacks reference, explanation and validity while also presenting a comically false numerical precision for its otherwise inherent meaninglessness. Where does the ratio 6341/6344 come from? I did a search in ListBox of all references to the word "overfitting" made in 2008 and found that out of 6344 messages only 3 actually involved the discussion of the word before Abram mentioned it today. (I don't know how good ListBox is for this sort of thing). So what is wrong with my conclusion that Abram was 6341/6344 wrong? Lots of things and they can all be described using declarative statements. First of all the idea that the conversations in this newsgroup represent an adequate sampling of all ai-probability enthusiasts is totally ridiculous. Secondly, Abram's mention of overfitting was just one example of how the general ai-probability community is aware of the problem that I mentioned. So while my statistical finding may be tangentially relevant to the discussion, the presumption that it can serve as a numerical evaluation of Abram's 'wrongness' in his response is so absurd that it does not merit serious consideration. My skepticism then concerns the question of just how would a fully automated AGI program that relied fully on probability methods be able to avoid getting sucked into the vortex of such absurd mushy reasoning if it wasn't also able to analyze the declarative inferences of its application of statistical methods? I believe that an AI program that is to be capable of advanced AGI has to be capable of declarative assessment to work with any other mathematical methods of reasoning it is programmed with. The ability to reason about declarative knowledge does not necessarily have to be done in text or something like that. That is not what I mean. What I really mean is that an effective AI program is going to have to be capable of some kind of referential analysis of events in the IO data environment using methods other than probability. But if it is to attain higher intellectual functions it has to be done in a creative and imaginative way. Just as human statisticians have to be able to express and analyze the application of their statistical methods using declarative statements that refer to the data subject fields and the methods used, an AI program that is designed to utilize automated probability reasoning to attain greater general success is going to have to be able to express and analyze its statistical assessments in terms of some kind of declarative methods as well. Jim Bromer ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=120640061-aded06 Powered by Listbox: http://www.listbox.com
