I'm still not quite sure if what I said came across clearly, because
some of what you just said is so far away from what I intended that I
have to make some kind of response.
For example, it looks like I've got to add "Seed AI" to the list of dumb
approaches that I do NOT want to be identified with! At least if you
define Seed AI the way you do: trying to bootstrap the whole AI from a
small core, without any big effort to encode some structure.
I thought I did deny that approach already: I explained that I was
doing a huge reengineering of the existing body of knowledge in
cognitive science. Can you imagine how much structure there is in such
a thing? There are roughly 1,000 human experiments or AI simulations
accounted for in that structure, and all integrated in such a way that
it implies one over system framework (at least, that is the goal of the
project). That doesn't sound like Seed AI to me: it has both structure
in its architecture, and it also allows for some priming of its
knowledge base with 'hand-built' knowledge.
As for your comment about "would Google ever have worked if they used
things like foo_A1 and foo_A27 for all their variable names?"
Huh?
That sounds like, after all, I communicated nothing whatsoever. I don't
know if that is supposed to be a serious point or not. I will assume not.
Richard Loosemore.
Russell Wallace wrote:
Ah! That makes your position much clearer, thanks. To paraphrase to make
sure I understand you, the reason you don't regard human readability as
a critical feature is that you're of the "seed AI" school of thought
that says we don't need to do large-scale engineering, we just need to
solve the scientific problem of how to create a small core that can then
auto-learn the large body of required knowledge.
I spent a lot of time on every known variant of that idea and some AFAIK
hitherto unknown ones, before coming to the conclusion that I had been
simply fooling myself with wishful thinking; it's the perpetual motion
machine of our field. Admittedly biology did it, but even with a whole
planet for workspace it took four billion years and "I don't know about
you gentlemen, but that's more time than I'm prepared to devote to this
enterprise". When we try to program that way, we find there's an awful
lot of prep work to generate a very small special-purpose program A to
do one task, then to generate small program B for another task is a
whole new project in its own right, and A and B can never be
subsequently integrated or even substantially upgraded, so there's a
hard threshold on the amount of complexity that can be produced this
way, and that threshold is tiny compared to the complexity of Word or
Firefox let alone Google let alone anything with even a glimmer of
general intelligence.
One of the arguments against this position, of course, is that We Don't
Care, because if we went to enough trouble we could 'hand-build' a
complete system, or get it up above some threshold of completeness
beyond which it would have enough intelligence to be able pick up the
learning ball and go on to build new knowledge in a viable way (Doug
Lenat said this explicitly in his Google lecture, IIRC).
Oh no, I don't believe that. I don't believe a complete system can be
hand-built; Google wasn't, after all, most of what it knows was
auto-learned (admittedly from other human-generated material, but not as
part of the same project or organization). Conversely (depending on how
you look at it) either there is no completeness threshold, or it's so
far beyond anything we can coherently imagine today that there might as
well not be one, so the seed AI approach can't work either.
In reality, both software engineering and (above a minimum adequacy
threshold) auto-learning are both going to stay important all the way up
so we have to cater for both. And from the software engineering
viewpoint (which is what I'm talking about here)... well, would Google
ever have worked if they used things like foo_A1 and foo_A27 for all
their variable names? No. QED :)
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