From: Eliezer S. Yudkowsky (email@example.com)
Date: Sun Mar 05 2006 - 16:55:37 MST
Peter Voss wrote:
> Have you seriously considered putting focused effort into proving that
> practical self-modifying systems can *not* have predictably stable goal
> I don't recall specific discussion on that point.
I often consider that problem; if I could prove the problem impossible
in theory I would probably be quite close to solving it in practice. My
attention tends to focus on Godelian concerns, though I think of them as
"Lobian" after Lob's Theorem.
> I strongly suspect that such a proof would be relatively simple.
> (Obviously, at this stage you don't agree with this sentiment).
> Naturally the implication for SIAI (and the FAI/AGI community in
> general) would be substantial.
Please go right ahead and do it; don't let me stop you!
> - Any practical high-level AGI has to use its knowledge to interpret
> (and question?) its given goals
Any AGI must use its model of the real world to decide which real-world
actions lead to which real-world consequences, and evaluate its utility
function or other decision system against the model's predicted
This does *not* necessarily involve changing the utility function.
> - Such a system would gain improved knowledge from interactions with the
> real world. The content of this knowledge and conclusions reached by the
> AGI are not predictable.
> - By the nature of its source of information, much knowledge would be
> based on induction and/or statistics, and be inherently fallible.
Please note however, that accidentally killing a human is not a
catastrophic failure. One evil deed does not turn the FAI evil, like a
character in a bad movie. *Catastrophic* failures, which change what
the FAI is *trying* to do, require that the FAI fail on the task of
So an impossibility proof would have to say:
1) The AI cannot reproduce onto new hardware, or modify itself on
current hardware, with knowable stability of the decision system (that
which determines what the AI is *trying* to accomplish in the external
world) and bounded low cumulative failure probability over many rounds
2) The AI's decision function (as it exists in abstract form across
self-modifications) cannot be knowably stably bound with bounded low
cumulative failure probability to programmer-targeted consequences as
represented within the AI's changing, inductive world-model.
If I could rigorously prove such an impossibility, my understanding
would probably have advanced to the point where I could also go ahead
and pull it off in practice.
-- Eliezer S. Yudkowsky http://intelligence.org/ Research Fellow, Singularity Institute for Artificial Intelligence
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