JAR/Russell:
This seems to be an example of what I was talking about in the other
thread - AI-ers starting with the set of sign systems and tools - and here
the kinds of intelligence - they know of personally, professionally, and
assume that they are the only kind, and encompass all types of intelligence
and problemsolving.
Your ideas of spatial intelligence seem to be mainly mathematical and
logical - do you think, for example, they include broadly artistic
intelligence and creativity?
http://www.balloonbedandbreakfast.com/images/Norman_Rockwell_Self-portrait.jpg
http://www.artquotes.net/masters/picasso/picasso_lesdemoiselles1907.jpg
http://www.bidoun.com/images/press/Vogue/Vogue-Cover.jpg
And can you explain how these kinds of spatial intelligence can be subsumed
into "deliberative"?
Bear in mind that the numbers of people engaged in broadly artistic and
design professions solving these kinds of problems of intelligence are huge
and may well considerably outnumber the scientific ones from which you take
your examples of intelligence - and these are more formal expressions of
everyday problemsolving that we all engage in.
These are also kinds of problems that a general intelligence must be able to
solve.
J ANdrew Rogers>:, Russell Wallace wrote:
Suppose we say there are two types of intelligence (not in any
rigorous sense, just in broad classification):
Deliberative. Able to prove theorems, solve the Busy Beaver problem
for small N, write and prove properties of small functions, construct
cellular automata computers for small functions, derive small
functions from specifications, notice what it's doing, accept symbolic
heuristics to improve its efficiency, think about said heuristics etc.
Symbolic intelligence that can, in some crude sense, copy some of the
things humans can symbolically do.
Spatial. Able to perceive patterns in two or three dimensions. Can be
used, with mods, for a robot visual cortex; image recognition; given a
series of photographs of a landmark from varying viewpoints, can
derive a 3d model and backtrack that to the 2d image visible from any
other viewpoint; can pathfind units around a map in a video game; can
make much better than random guesses as to likely folds of a new
protein chain; can animate a cartoon from the description "cat sits on
mat".
I will take a third position and point out that there is no real
distinction between these two categories, or at least if there is you are
doing it wrong. One of the amusing and fruitless patterns of behavior in
the AI community is the incessant categorization of various processes
into nominally distinct buckets in the absence of a theoretically
justifiable reason for doing so. The above is such an example.
As a general comment, the computer science literature on the D- oriented
side of things is *much* deeper than the S-oriented side of things, and
the literature that theoretically integrates the two is thin on the
ground indeed. This is probably a reflection of the observation that
competency at D is far more widely distributed than S, or at least that
far more competent people have worked on D than on S.
When I originally switched to the "Spatial" side of things, one of the
first things I noticed was that the backing theory and literature was
medieval compared to what you call the "Deliberative" side. On the
downside that meant that there was not a lot to work from, but on the
upside that also meant there was still a fair amount of low-hanging fruit
left to be picked. Spatial can scale extremely well in a very general
sense, but you'll have to do some original work to get there because your
off-the-shelf computer science will leave you wanting.
It should actually be pretty obvious, even without really hammering out
the theory, how the "Deliberative" part can be trivially expressed in a
"Spatial" solution -- the former can be correctly viewed as a narrow
instance of the latter.
J. Andrew Rogers
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agi
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agi
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