Mitchell Porter wrote:
The idea is that a superintelligence would have a 'computational core' which
spends its time approximating Omega, and modules which take general
problems, encode them as halting problems, and look them up in Approximate
Omega.
Anders Sandberg wrote:
Ah, that's where the rub is: how do you convert a general problem into a
halting problem in an efficient way? <snip>
I would guess that the sum of work often remains constant in the general
case: the amount of work needed to encode a problem into a form solvable by
an algorithm and the amount of work in using the algorithm tend to be fairly
constant. Intelligence is about finding ways of getting around this by
exploiting patterns that make the problem non-general, such as in
mathematical tricks where a simple transformation makes a hard problem
simple.
D. T. Spreng, a swiss physicist, wrote (1) that to produce output Q, in a
time = T, using an energy = E, and some information = I
f (T) f (E) f (I) = C = constant.
In other words to carry out a given task, you can save information by using
more time and/or energy, or you can save time by using more energy and/or
information, or you can save time and information by using more energy. Now
wich is the goal of "take general problems, encode them as halting problems,
and look them up in Approximate Omega"? Save information, save time, or save
both?
(1): D. T. Spreng, ³On time, information and energy conservation²,
ORAU/IEAo78o22(R), Inst. For Energy Analysis, Oak Ridge Assoc. Universities,
Oak Ridge, Tennessee, 1978
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