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.


To decide, whether you have an AGI or not, you also have to evaluate the
proofs and not only the fact whether it could prove something or not.

For example, the formula 1+2+3+...+n = 0.5*n*(n+1) can be proven by seeing a
pattern:

1     +  2    +   3   + ... + n-2   +  n-1   +   n +

n     + (n-1) + (n-2) + ..  +  3    +   2    +   1 =

(n+1) + (n+1) + (n+1) + ... +(n+1)  +  (n+1) +  (n+1)

= n*(n+1)

AGI will differ from AI by often using such pattern based proofs.
An AGI based theorem prover represents expressions by patterns. When it
comes to prove a certain formula, patterns of known expressions and rules
become active or inactive. 

-Matthias

Matt Mahoney wrote:

Goedel and Turing showed that theorem proving is equivalent to solving the
halting problem. So a simple measure of intelligence might be to count the
number of programs that can be decided. But where does that get us? Either
way (as as set of axioms, or a description of a universal Turing machine),
the problem is algorithmically simple to describe. Therefore (by AIXI) any
solution will be algorithmically simple too.

If you defined AGI this way, what would be your approach?

-- Matt Mahoney, [EMAIL PROTECTED]



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