Linas, Yes, you probably can code all the patterns you need. But it's only the tip of the iceberg: problem is that for those 1M rules there are also thousands that are being constantly generated, assessed and discarded. Knowledge formation happens all the time and adapts those 1M rules to gazillion of real-world situations. You can consider those additional rules 'inference', but then again, if you can do your inference that good, you can do without 1M of hand-coded rules, allowing system to learn them from the ground up. If your inference is not good enough, it's not clear how many rules you'd need to code in manually, it may be 10^6 or 10^12, or 10^30, because you'd also need to code _potential_ rules which are not normally stored in human brain, but generated on the fly.
I plan to support recent context through a combination of stable activation patterns (which is analogous to constant reciting of certain phrase, only on lower level) and temporary induction (roughly, cooccurrence of concepts in near past leads to them activating each other in the present, and similarly there are temporary concepts being formed all the time, of which only those which get repeatedly used in their short lifetime are retained for longer and longer). On 11/2/07, Linas Vepstas <[EMAIL PROTECTED]> wrote: > On Thu, Nov 01, 2007 at 02:58:07PM -0700, Matt Mahoney wrote: > > --- Linas Vepstas <[EMAIL PROTECTED]> wrote: > > > > > Thus, I find that my interests are now turning to representing > > > conversational state. How does novamente deal with it? What > > > about Pei Wang's NARS? It seems that NARS is a reasoning system; > > > great; but what is holding me back right now is not an ability > > > to reason per-se, but the ability to maintain a conversational > > > state. > > > > If the matrix is sparse it can be compressed using singular value > > decomposition (SVD). > > [...] > > > This is not a model you can tack onto a structured knowledge base. > > Why not? Bot NARS and novamente have real-number-valued associative > deduction abilities. I see no reason why simple matrix or nerual net > algo's couldn't be layered on top of it. > > > Your approach has been tried > > hundreds of times. > > Yes, I figured as much. I haven't yet seen a cogent explanation of > why folks gave up. For shrdlu, sure .. compute power was limited. > There's discussion about grounding, and folks wander off into the weeds. > > > There is a great temptation to insert knowledge directly, > > but the result is always the same. Natural language is a complicated beast. > > You cannot hand code all the language rules. After 23 years of developing > > the > > Cyc database, Doug Lenat guesses it is between 0.1% and 10% finished. > > And hasn't stopped trying. We also have Wordnet, and assorted ontology > projects. > > How many english words are there? About 250K, but this hasn't stopped > classical dictionary authors, nor wordnet, nor Lenat. > > How many sentence parse patterns are there? 10K? 100K? 1M? Its not > infinite, even though it can feel that way sometimes. Just because > you personally don't feel like trying to hand-build an association > matrix between sentence parse patterns and a semantic "current topic > of conversation" dataset doesn't mean its unbuildable. > > I didn't claim the approach I described as being "good"; its not; > and I can see its limitations already. I did claim that its "practical", > and after half-a-dozen weekends coding, I have a demo. I'm trying to > understand just how far the approach can be pushed. I get the impression > that it hasn't been pushed very far at all, before people give up. > > --linas > > > ----- > 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/?&; > -- Vladimir Nesov mailto:[EMAIL PROTECTED] ----- 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/?member_id=8660244&id_secret=60236054-ec756f
