From: Robin Hanson (hanson@econ.berkeley.edu)
Date: Wed Sep 23 1998 - 17:16:19 MDT
Eliezer S. Yudkowsky writes:
>> and the key assumption is that "the phenotypic optimum changes suddenly and
>> then remains fixed during the bout of adaptation studied." ...
>
>Punctuated equilibrium, as the fossil-record phenomenon, neither requires nor
>prohibits single big-win mutations. ...
Yes, and this theory paper doesn't help settle the question. If the enviroment
changes very very quickly, then the first few changes can be very big wins.
But if the environment just changes a hundred times as fast some times as others,
then you just get a distrubution of small vs. itsy-bitsy wins, with no big wins.
>I noticed that paper because it explained punctuated equilibrium in a way
>that exported fairly well to breakthrough/bottleneck AI trajectories.
>It was the first explanation I had seen with that property.
I can describe my sense of AI progress in terms of this model as well. Early
on in AI research people came across the big win concepts, and the rate of
discovery of such big wins then declined with time. The main way in which the
environment for AI programs is changing now is hardware improvement. Some big
wins have to await enough compute power to verify/study them, and these sort
continue to show themselves more steadly with time. But none of these are so
huge as to create an average factor of ten productivity win for AI programs.
As best I can tell, your reason for expecting big future wins seems to be that
you, Eliezer, have personally come up with great (largely untested) designs
for AI programs, and you're sure they're enough to change everything. Are you
aware of how stereotypical this is of young people when they first get into AI?
Robin Hanson
hanson@econ.berkeley.edu http://hanson.berkeley.edu/
RWJF Health Policy Scholar, Sch. of Public Health 510-643-1884
140 Warren Hall, UC Berkeley, CA 94720-7360 FAX: 510-643-8614
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