From: Ben Goertzel (firstname.lastname@example.org)
Date: Wed May 22 2002 - 17:58:45 MDT
I got the book yesterday. I skimmed it and read a few chapters carefully
(Chapters 1 and 2, and the chapters on Physics and Perception/Analysis).
Over the next few weeks I'll read the whole thing carefully. Now I just
have a few preliminary observations:
1) There seemed to be nothing in the book fundamentally conceptually new to
me. However, there is much in the book that has been conceptually treated
only in various "marginal" places rather than in the scientific mainstream.
2) What he has done, from my point of view, is to go through a LOT of ideas
from the complex systems literature and place them all within a common
framework of cellular-automaton-ish systems. (Not all his dynamical systems
are strictly CA's, but most of them are either CA's or very similar). This
allows a single book to discuss what otherwise would take 10 different
books, each involving a different notation and mathematical formalism.
3) His physics approach is cool, but in my view no better (or worse) than
some other discrete physics approaches, such as Tony Smith's (even fringier)
"Vodou Physics" model [There is a lot of cool stuff on Tony's website, see
4) His treatment of perception and analysis is conceptually fine, but very
limited and does not even come close to being pragmatically useful for AI
5) His discussion of biological pattern-formation is a repeat, in somewhat
more elegant form, of a lot of old ideas from the systems theory literature.
For instance, A. Lima de Faria's old book "Evolution without Selection"
makes the same conceptual points (along with other more controversial ones)
but without the numerous CA and quasi-CA examples.
Is it progress? Yeah. It's nice to have a common formalism (CA's and
quasi-CA's) for dealing with all these diverse complex-systems phenomena.
Is it a new kind of science? Maybe. But it's not *that* new -- it's
"complex systems science" which has been gathering steam for a while.
MAYBE his CA-ish formalism will prove powerful enough to make complex
systems science into a "real science", but this book does not demonstrate
that, because it doesn't really come to any dramatic and definitive
scientific conclusions in any particular branch of science.
He admits that the truly revolutionary nature of his approach will only be
seen in 10-20 years. Maybe so.
I think that maybe the reason he doesn't agree that most of his *concepts*
are old ones, is that he thinks he's gotten them "right". Whereas to me it
just seems like he's said familiar things a little *differently*.
The big problem is, given a complex system you want to build, or a complex
phenomenon you want to analyze, how do you go about constructing a CA-ish
model of it? (By intuition based on other examples, is his method, it
seems.) And if the CA-ish model you find just *qualitatively* but not
exactly models the phenomenon or realizes the behavior, how useful is it?
Finding a CA-ish model to *exactly* model a given collection of data about a
given system or set of systems, is a problem of function learning
approachable via GP-type methods, but he doesn't go there...
I certainly look forward to reading the book more fully, as there is much to
be learned from the details in the other chapters I haven't yet read
carefully I'm sure...
-- Ben G
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