I actually disagree that the ability to self-modify an AI architecture
at some fundamental level is even important, at least in the sense that
I get the strong impression people are using it.

The value of deep self-modifying code is apparently premised on the
first implementations of AGI being duct-taped architectures that barely
function, due in no small part to their extreme complexity.  My own take
is that if AGI is actually as complicated as most believe, then the only
likely and plausible human engineered implementations will almost have
to be very close approximations of "optimal intelligence" (read: elegant
and properly generalized models) as a consequence. Compounding this is
the probable fragility of AGI architectures with respect to the various
forms of computational complexity, meaning that most design vectors that
stray away from optimal architectures may not be able to reach seed AI
level due to tractability problems.  From this perspective, I don't
think it is unreasonable to assert that architectural self-modification
is an unnecessary capability as all likely human implementations of an
AI will almost have to be optimal (or close approximations) to even be
practical.

If this is the case and the first "real" AGI architecture is a close
approximation of optimal, then the qualitative bootstrap process will
essentially be hardware limited no matter how intelligent the AGI
actually is.  Obviously there has to be some self-modification at higher
abstractions or a system couldn't learn, but that doesn't need to impact
the underlying architecture (and is essentially orthogonal to the
question in any case).

-James Rogers
 jamesr@best.com