I realized that my idea of declarative-like statements could refer to statistical objects and methods as well. In fact, if they were to provide the sort of efficacy I want for them, some would have to. I am not specifically talking about mixing logic with probability theory.
Thanks for the comments. I figured that probability methods in AGI would suffer from combinatorial problems, but I hadn't talked to anyone who actually took it to that level. Jim Bromer On Sat, Nov 29, 2008 at 9:21 PM, Ben Goertzel <[EMAIL PROTECTED]> wrote: > Whether an AI needs to explicitly manipulate declarative statements is > a deep question ... it may be that other dynamics that are in some > contexts implicitly equivalent to this sort of manipulation will > suffice > > But anyway, there is no contradiction between manipulating explicit > declarative statements and using probability theory. > > Some of my colleagues and I spent a bunch of time during the last few > years figuring out nice ways to combine probability theory and formal > logic. In fact there are "Progic" workshops every year exploring > these sorts of themes. > > So, while the mainstream of probability-focused AI theorists aren't > doing hard-core probabilistic logic, some researchers certainly are... > > I've been displeased with the wimpiness of the progic subfield, and > its lack of contribution to areas like inference with nested > quantifiers, and intensional inference ... and I've tried to remedy > these shortcomings with PLN (Probabilistic Logic Networks) ... > > So, I think it's correct to criticize the mainstream of > probability-focused AI theorists for not doing AGI ;-) ... but I don't > think they've overlooking basic issues like overfitting and such ... I > think they're just focusing on relatively easy problems where (unlike > if you want to do explicitly probability theory based AGI) you don't > need to merge probability theory with complex logical constructs... > > ben > > On Sat, Nov 29, 2008 at 9:15 PM, Jim Bromer <[EMAIL PROTECTED]> wrote: >> 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/?&; >> Powered by Listbox: http://www.listbox.com >> > > > > -- > Ben Goertzel, PhD > CEO, Novamente LLC and Biomind LLC > Director of Research, SIAI > [EMAIL PROTECTED] > > "I intend to live forever, or die trying." > -- Groucho Marx > > > ------------------------------------------- > 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/?&; > 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/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=120640061-aded06 Powered by Listbox: http://www.listbox.com
