--- On Wed, 10/15/08, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote:

> My intention is not to define intelligence. I choose
> mathematics just as a
> test domain for first AGI algorithms.
> 
> The reasons:
> 1. The domain is well understood.
> 2. The domain has regularities. Therefore a high
> intelligent algorithm has a
> chance to outperform less intelligent algorithms
> 3. The domain can be modeled easily by software.
> 4. The domain is non-trivial. Current algorithms fail for
> hard problems in
> this domain because of the exponential growing complexity.
> 5. The domain allows a comparison with performance of human
> intelligence.

Would you consider Gelernter's theorem prover [1] an example of AGI? It proved 
geometry theorems by drawing diagrams that helped it heuristically trim the 
search space.

The problem with well understood problems is that they are well understood. 
Thus, there has been little progress since the pioneering work in AI done 
before 1965.

Computers can do many tasks better than humans. The areas where computers are 
weak are language, vision, and motor coordination. I think a lot of the 
intuition that mathematicians use in solving difficult proofs is really a 
language modeling problem. Mathematicians can read proofs done by others and 
apply similar techniques.

Likewise, writing software has to be understood in terms of natural language 
learning and modeling. A programming language is a compromise between what 
humans can understand and what machines can understand. Humans learn C++ 
grammar and semantics by induction, from lots of examples. Machines know C++ 
from an explicit specification.

Natural language is poorly understood, which is exactly why we need to study it.

1. Gelernter, H., Realization of a Geometry-Theorem Proving Machine, 
Proceedings of an International Conference on Information Processing, Paris: 
UNESCO House, pp. 273-282, 1959.

-- Matt Mahoney, [EMAIL PROTECTED]



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