I don't agree with Mark Waser that we can "engineer the complexity out
of intelligence."

I agree with Richard Loosemore that intelligent systems are
intrinsically complex systems in the Santa Fe Institute type sense

I hate to do this but . . . .

Richard's definition of complexity is *NOT* the standard Santa Fe Institute type sense.

Richard: A system is deemed "complex" if the smallest size of a theory that will explain that system is so large that, for today's human minds, the discovery of that theory is simply not practical. Notice that this definition does not imply that there any such systems in the real world, it just says that *if* the theory size were ever to go off the scale *then* the system would (by definition) be complex.

Santa Fe is quite sure that there are complex systems out there and by their examples, since there is no one acknowledged definition, I would say that intelligence is necessarily complex in their sense.

I said and repeat that we can "engineer the complexity out of intelligence" in the Richard Loosemore sense. I did not say and do not believe that we can "engineer the complexity out of intelligence" in the Santa Fe Institute sense.

However, I don't agree with Richard as to the *extent* of the
complexity problem.  I think he overestimates how hard it will be to
roughly estimate the behavior of AGI systems based on their designs
and measurement of their components.  I think it will be easier to do
this with AGI systems than with natural systems, not because we can
engineer the complexity out of the systems, but because we (as the
designers) understand the systems better, and can measure the systems
more thoroughly...

According to Richard, if the system is complex then, by his definition, you can't understand it. You then arguing that you *can* understand AGI systems better is nonsensical since it is totally contrary to his stated definition.



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agi
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