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]
>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.
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