>> It is up to humans to define the goals of an AGI, so that it will do what we
>> want it to do.
Why must we define the goals of an AGI? What would be wrong with setting it
off with strong incentives to be helpful, even stronger incentives to not be
harmful, and let it chart it's own course based upon the vagaries of the world?
Let it's only hard-coded goal be to keep it's satisfaction above a certain
level with helpful actions increasing satisfaction, harmful actions heavily
decreasing satisfaction; learning increasing satisfaction, and satisfaction
naturally decaying over time so as to promote action . . . .
Seems to me that humans are pretty much coded that way (with evolution's
additional incentives of self-defense and procreation). The real trick of the
matter is defining helpful and harmful clearly but everyone is still mired five
steps before that.
----- Original Message -----
From: Matt Mahoney
To: agi@v2.listbox.com
Sent: Wednesday, August 27, 2008 10:52 AM
Subject: AGI goals (was Re: Information theoretic approaches to AGI (was Re:
[agi] The Necessity of Embodiment))
An AGI will not design its goals. It is up to humans to define the goals of
an AGI, so that it will do what we want it to do.
Unfortunately, this is a problem. We may or may not be successful in
programming the goals of AGI to satisfy human goals. If we are not successful,
then AGI will be useless at best and dangerous at worst. If we are successful,
then we are doomed because human goals evolved in a primitive environment to
maximize reproductive success and not in an environment where advanced
technology can give us whatever we want. AGI will allow us to connect our
brains to simulated worlds with magic genies, or worse, allow us to directly
reprogram our brains to alter our memories, goals, and thought processes. All
rational goal-seeking agents must have a mental state of maximum utility where
any thought or perception would be unpleasant because it would result in a
different state.
-- Matt Mahoney, [EMAIL PROTECTED]
----- Original Message ----
From: Valentina Poletti <[EMAIL PROTECTED]>
To: agi@v2.listbox.com
Sent: Tuesday, August 26, 2008 11:34:56 AM
Subject: Re: Information theoretic approaches to AGI (was Re: [agi] The
Necessity of Embodiment)
Thanks very much for the info. I found those articles very interesting.
Actually though this is not quite what I had in mind with the term
information-theoretic approach. I wasn't very specific, my bad. What I am
looking for is a a theory behind the actual R itself. These approaches
(correnct me if I'm wrong) give an r-function for granted and work from that.
In real life that is not the case though. What I'm looking for is how the AGI
will create that function. Because the AGI is created by humans, some sort of
direction will be given by the humans creating them. What kind of direction, in
mathematical terms, is my question. In other words I'm looking for a way to
mathematically define how the AGI will mathematically define its goals.
Valentina
On 8/23/08, Matt Mahoney <[EMAIL PROTECTED]> wrote:
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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