Valentina Poletti <[EMAIL PROTECTED]> wrote:
> I was wondering why no-one had brought up the information-theoretic aspect of
> this yet.
It has been studied. For example, Hutter proved that the optimal strategy of a
rational goal seeking agent in an unknown computable environment is AIXI: to
guess that the environment is simulated by the shortest program consistent with
observation so far [1]. Legg and Hutter also propose as a measure of universal
intelligence the expected reward over a Solomonoff distribution of environments
[2].
These have profound impacts on AGI design. First, AIXI is (provably) not
computable, which means there is no easy shortcut to AGI. Second, universal
intelligence is not computable because it requires testing in an infinite
number of environments. Since there is no other well accepted test of
intelligence above human level, it casts doubt on the main premise of the
singularity: that if humans can create agents with greater than human
intelligence, then so can they.
Prediction is central to intelligence, as I argue in [3]. Legg proved in [4]
that there is no elegant theory of prediction. Predicting all environments up
to a given level of Kolmogorov complexity requires a predictor with at least
the same level of complexity. Furthermore, above a small level of complexity,
such predictors cannot be proven because of Godel incompleteness. Prediction
must therefore be an experimental science.
There is currently no software or mathematical model of non-evolutionary
recursive self improvement, even for very restricted or simple definitions of
intelligence. Without a model you don't have friendly AI; you have accelerated
evolution with AIs competing for resources.
References
1. Hutter, Marcus (2003), "A Gentle Introduction to The Universal Algorithmic
Agent {AIXI}",
in Artificial General Intelligence, B. Goertzel and C. Pennachin eds.,
Springer. http://www.idsia.ch/~marcus/ai/aixigentle.htm
2. Legg, Shane, and Marcus Hutter (2006),
A Formal Measure of Machine Intelligence, Proc. Annual machine
learning conference of Belgium and The Netherlands (Benelearn-2006).
Ghent, 2006. http://www.vetta.org/documents/ui_benelearn.pdf
3. http://cs.fit.edu/~mmahoney/compression/rationale.html
4. Legg, Shane, (2006), Is There an Elegant Universal Theory of Prediction?,
Technical Report IDSIA-12-06, IDSIA / USI-SUPSI,
Dalle Molle Institute for Artificial Intelligence, Galleria 2, 6928 Manno,
Switzerland.
http://www.vetta.org/documents/IDSIA-12-06-1.pdf
-- Matt Mahoney, [EMAIL PROTECTED]
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agi
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