I agree with Ben's post that this kind a system has been tried many times and produced very little. How can a collection of "Cats have claws; Kitty is a cat; therefore Kitty has claws." relate cat and kitty and that kitty is slang and normally used for a young cat. A database of this type seems to be like the Chinese room dilemma where even if you got something that looked intelligent out of the system, you know for a fact that no intelligence exists. To know that a cat is a mammal as are people and dogs can only be had by a huge collection of interrelated models that show the relationships, properties, abilities etc of all of these things. Such models could be automatically created (probably) by using this kind of information tidbits that you suggest but the process would be very messy and the size of database would be enormous. It would be like the AI trying to find the rules and relations of things out of a huge pile of word facts. Why not just build the rules and relationships into the AI from the beginning, populating the models with relevant facts as you go. This could be done with much less labor by using the AI itself to build the models by using higher and higher levels of teaching methods by multiple individuals.
Computer languages use a strict subset of English to populate their syntax. People use English to communicate with each other. Why would we want to use a new language like Lojban when we already use subsets of English with computers? Why does an arbitrary English sentence have to be unambiguous? Most of the time this isn't a problem for English language people and where it might be a problem why couldn't it just be clarified the same as we humans do all the time? The teachers of the AI could intentionally use an unambiguous subset of English and gradually use more and more sophisticated sentences as the intelligence of the AI progressed. Isn't this what we do with children as they grow up? Most people verify they understand instructions given to them before them actually act on those instructions and potential misunderstandings are normally avoided. Why can't we do the same with an AI? Adding an additional language won't eliminate the need for the humans using English or the computer using it's English subset language. Whatever the ambiguity problem is between humans and computers, will only be transported to between the human and the new language for no net benefit. David Clark ----- Original Message ----- From: "Benjamin Goertzel" <[EMAIL PROTECTED]> To: <agi@v2.listbox.com> Sent: Thursday, January 18, 2007 1:28 PM Subject: Re: [agi] Project proposal: MindPixel 2 > YKY, this kind of thing has been tried many dozens of times in the > history of AI. > > It does not lead to interesting results! Alas... > > The key problem is that you can't feasibly encode enough facts to > allow interesting commonsense inferences -- commonsense inference > seems to require a very massive store of highly uncertain > knowledge-items, rather than a small store of certain ones. > > BTW the rule > > "if X is-a Y and Z(Y), then Z(X)". > > exists (in a slightly different form) in Novamente and many other > inference systems... > > I feel like you are personally rediscovering GOFAI, the kind of AI > that I read about in textbooks when I first started exploring the > field in the early 1980's!!!! > > Ben G > > > Thanks for the tips. My idea is quite simple, slightly innovative, but not > > groundbreaking. Basically, I want to collect a knowledgebase of facts as > > well as rules. Facts are like "water is wet" etc. The rules I explain > > with this example: "Cats have claws; Kitty is a cat; therefore Kitty has > > claws." Here is an implicit rule that says "if X is-a Y and Z(Y), then > > Z(X)". I call rules like this the "Rules of Thought". They are not logical > > tautologies but they express some common thought patterns. > > > > My theory is that if we collect a bunch of these rules, add a database > > of common sense facts, and add a rule-based FOPL inference engine (which may > > be enhanced with eg Pei Wang's numerical logic), then we have a common sense > > reasoner. That's what I'm trying to build as a first-stage AGI. > > > > If it does work, there may be some commercial applications for such a > > reasoner. Also it would serve as the base to build a full AGI capable of > > machine learning etc (I have crudely worked out the long-term plan). > > > > So, is this a good business idea? > > > > YKY ________________________________ > > This list is sponsored by AGIRI: http://www.agiri.org/email > > > > To unsubscribe or change your options, please go to: > > http://v2.listbox.com/member/?list_id=303 > > ----- > This list is sponsored by AGIRI: http://www.agiri.org/email > To unsubscribe or change your options, please go to: > http://v2.listbox.com/member/?list_id=303 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
