Re: Re: [agi] A question on the symbol-system hypothesis

[Mark Waser]

>> Models that are simple enough to debug are too simple to scale.  >> The contents of a knowledge base for AGI will be beyond our ability to comprehend.       Given sufficient time, anything should be able to be understood and debugged.  Size alone does not make something incomprehensible and I defy you to point at *anything* that is truly incomprehensible to a smart human (for any reason other than we lack knowledge on it).  I've seen all the analogies with pets not understanding and the beliefs that AIs are going to have minds "immeasurably greater than our own" and I submit that it's all just speculation on your part.  My contention is that there is a threshold and that we are above it and that beyond that, it's just a matter of speed and how much you can hold in working memory at a time.  I certainly don't buy the "mystical" approach that says that sufficiently large neural nets will come up with sufficiently complex discoveries that we can't understand them.  I contend that if you can't explain it to a very smart human (given sufficient time), then you don't understand it.       Give me *one* counter-example to the above . . . .   ----- Original Message ----- From: Matt Mahoney To: agi@v2.listbox.com Sent: Monday, November 13, 2006 10:22 PM Subject: Re: Re: [agi] A question on the symbol-system hypothesis James Ratcliff <[EMAIL PROTECTED]> wrote: >Well, words and language based ideas/terms adequatly describe much of the upper levels of human interaction and see >appropriate in that case. > >It fails of course when it devolpes down to the physical level, ie vision or motor cortex skills, but other than that, using >language internaly would seem natural, and be much easier to look inside the box ,and see what is going on and correct the >system's behaviour. No, no, no, that is why AI failed.  You can't look inside the box because it's 10^9 bits.  Models that are simple enough to debug are too simple to scale.  How many times will we repeat this mistake?  The contents of a knowledge base for AGI will be beyond our ability to comprehend.  Get over it.  It will require a different approach. 1. Develop a quantifiable criteria for success, a test score. 2. Develop a theory of learning. 3. Develop a training and test set (about 10^9 bits compressed). 4. Tune the learning model to improve the score. Example: 1. Criteria: SAT analogy test score. 2. Theory: word associtation matrix reduced by singular value decomposition (SVD). 3. Data: 50M word corpus of news articles. 4. Results: http://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-48255.pdf An SVD factored word association matrix seems pretty opaque to me.  You can't point to which matrix elements represent associations like cat-dog, moon-star, etc, nor will you be inserting such knowledge for testing.  If you want to understand it, you have to look at the learning algorithm.  It turns out that there is an efficient neural model for SVD.  http://gen.gorrellville.com/gorrell06.pdf It should not take decades to develop a knowledge base like Cyc.  Statistical approaches can do this in a matter of minutes or hours.   -- Matt Mahoney, [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/?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