In
discussing Artificial Intelligence Steven
Pinker, in The
Language Instinct
writes (my highlighting):
Since
innate similarity spaces are inherent to the logic of learning, it is
not surprising that human-engineered learning systems in artificial
intelligence are always innately designed to exploit the
constraints in some domain of knowledge. A computer program intended
to learn the rules of baseball is pre-programmed with the assumptions
under-lying competitive sports, so that it will not interpret
players' motions as a choreographed dance or a religious ritual. A
program designed to learn the past tense of English verbs is given
only the verb's sound as its input; a program designed to learn a
verb's dictionary entry is given only its meaning. This requirement
is apparent in what the designers do, though not always in what they
say. Working within the assumptions of the Standard Social Science
Model, the computer scientists often hype their programs as mere
demos of powerful general-purpose learning systems. But
because no one would be so foolhardy as to try to model the entire
human mind, the researchers can take advantage of this allegedly
practical limitation. They are free to
hand-tailor their demo program to the kind of problem it is charged
with solving, and they can be a deus ex
machina funnelling just the right inputs to
the program at just the right time. Which is not a criticism; that's
the way learning systems have to work!
In
various past and future postings on this blog I am arguing that
CODIL is a model of at least some aspects of how the brain works and
in making wide claims it is important that I ask myself whether I am
being foolhardy, and whether I am making extravagant claims for a
model general purpose learning system for the brain.
