Hey Aaron, Why don't you join us in OpenCog ... Ruiting Lian is currently attempting to do exactly the same thing in OpenCog, and could use some help ;)
See the PDF at http://wiki.opencog.org/w/Link2Atom for a general description of the approach... ben On Mon, Aug 27, 2012 at 7:10 PM, Aaron Hosford <[email protected]> wrote: > That's the base assumption for my current project. I'm starting from human > language and attempting to derive an internal representation that > corresponds roughly to that used by humans. It is my hope that once an > appropriate model of how humans internally represent knowledge is > available, the actual mental computations we perform to handle higher-level > rational thought should become much more amenable to understanding and > analysis. This seems self evident to me, since clearly human beings have > solved the GI problem, which means we probably have some sort of internal > representation that makes the sorts of representational gymnastics that are > necessary for GI much simpler to perform. This approach has the advantage, > as well, that its accuracy as a model of human internal knowledge > representation should be directly verifiable in experiments with human > subjects. > > The idea is that I can run a parser on a piece of natural language, > extract out the relationships between the words as a semantic net, convert > that format into another semantic net that represents the meaning of the > language sample, and then reverse the flow back to natural language which > is identical in meaning but may be stated differently. When the meaning has > been extracted and represented using the internal format, it can be linked > up with other semantic nets that represent the meanings of other > statements/questions. This combined net in turn can be analyzed directly as > a collection of logical predicates and queries in which the binding of two > symbols (word/phrase occurrences) to a common referent are directly > represented as links from those symbols to that referent's node. New > statements/queries can be generated via inference rules and other daemons, > and then converted to natural language using the parser, etc. in reverse. > > I have already built a small system as a proof of concept with Boolean > links -- either a semantic link exists or doesn't, rather than allowing > links to have real-valued strengths -- and was able to resolve anaphora > moderately well, given its toy nature. Since this initial implementation > left me unsatisfied with how uncertainty was handled, I'm working now on > rebuilding the system using real-valued links that represent > probability/uncertainty, similarly to the truth values used by the term > logic-based inference system of NARS ( > https://sites.google.com/site/narswang/). Adding in the ability to > represent uncertainty will allow the system to compare alternatives and > choose the most salient anaphoric referent *that makes sense*, taking > advantage of knowledge the system has already acquired to determine what > makes sense, rather than just taking the most salient/obvious choice in > terms of raw language structure independent of conceptual context. > > > > On Mon, Aug 27, 2012 at 4:55 PM, Anastasios Tsiolakidis < > [email protected]> wrote: > >> As I started reading I thought to myself "I told you 1000 times, it >> depends on the criteria". Reading on, I saw that it is precisely the >> criteria you use as a parameter. Well, I'd like to find out the >> programming language that makes the most money while giving >> immortality :) A little more seriously, if the criteria are cognitive, >> as they often are in the real world, you'd be digging yourself a hole >> too deep to get out of. On the other hand, if the criteria are >> domain-specific, relating to well-behaved domains, I am afraid we are >> heading towards tautologies and trivialities. Something like >> Mathematica would be optimal for algebra, analysis, gravity, mechanics >> etc (though what about instead of calculating a parachute drop >> actually measure a real parachute drop), for economics, psychology, >> necromancy most things would do equally badly, and for AGI all options >> have so far being worse than bad. Mind you, I am in the process of >> defining an AGI architecture not as a compression problem but as a >> distributed computation problem, and I would challenge you to answer >> the question: >> >> Which programming language/mechanism would be ideal for calculating X >> as quickly as possible. >> >> where X, for the sake of argument, is just a/any "heavy calculation" >> without necessarily any of the anomalies of chaotic behavior, pi's >> infinite series etc. It is not that I expect intelligence to arise out >> of PDEs and integrals, rather I am asking which is the "perfect" >> distributed system for calculus, as I am expecting your answer to take >> the form of multipliers and other exotic units all converging in an >> addition pipeline. I still can't help thinking that the fastest way >> for parallel computations is the actual experiment, after all we have >> the 3/n body problem and a ton of mathematics OR just an experiment >> with n bodies in a field. >> >> With regards to a possible language for AGI, I don't see how you can >> do much better than a human language. Never mind Turing completeness, >> we have GI completeness here (except for that part of human language, >> perhaps 100% of it, that gets its meaning from its grounding, its >> grounding from its embodiment, and its embodiment from - god?) >> >> AT >> >> On Mon, Aug 27, 2012 at 10:44 PM, Russell Wallace >> <[email protected]> wrote: >> > On Mon, Aug 27, 2012 at 9:12 PM, Ben Goertzel <[email protected]> wrote: >> >> For domains in which one is concerned with recognizing large ensembles >> >> of weak patterns, the language one uses to represent patterns can make >> >> a big difference... >> >> >> >> Image analysis, genetic data analysis and financial prediction are >> >> contexts in which I've found this to be the case >> >> >> >> In these settings, if one does pattern recognition via automated >> >> program learning with an Occam bias, >> >> the underlying language relative to which the Occam bias is expressed >> >> makes a big difference... >> > >> > Absolutely, but these overheads are not constants - the computational >> > cost of a poor choice of representation language is typically >> > exponential. >> > >> >> From a different direction, consider Hutter's proof that AIXI-tl is as >> >> good as any other reinforcement learning system ... up to an arbitrary >> >> constant. >> > >> > Well, much violence is being done to the word 'constant' in this case. >> > Sure, f(N) is constant for a given N, but... :) >> > >> > >> > ------------------------------------------- >> > AGI >> > Archives: https://www.listbox.com/member/archive/303/=now >> > RSS Feed: >> https://www.listbox.com/member/archive/rss/303/14050631-7d925eb1 >> > 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/303/23050605-bcb45fb4 >> >> 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> > <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD http://goertzel.org "My humanity is a constant self-overcoming" -- Friedrich Nietzsche ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
