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%smolen[e87,jmc]		On Smolensky's "Proper Treatment of Connectionism"

\title{Epistemological Challenges for Connectionism}

1. I find the notion that there is a subsymbolic level of cognition
between a symbolic level and the neural level plausible enough to be worth
exploring.  Even more worth exploring is Smolensky's further conjecture that
the symbolic level is not self-sufficient, especially where intuition
plays an important role, and the causes of some symbolic events must be
explained at some subsymbolic level.  The possibility that present day
connectionism will model this subsymbolic level is also worth exploring
although I find it somewhat implausible.

	An example of Smolensky's proposal is that the content of some new
idea may be interpretable symbolically but how it came to be thought of
may require a subsymbolic explanation.  A further conjecture, not explicit
in the paper, is that an AI system capable of coming up with new ideas may
require a subsymbolic level.  My own work explores the contrary conjecture
--- that even creativeness is programmable at the symbolic level.
Smolensky doesn't argue for the connectionist conjectures in the paper,
and I won't argue for the logic version of the ``physical symbol
hypothesis'' in this commentary.  I'll merely state some aspects of it.


2. The paper looks at the symbolic level from a certain distance
that does not make certain distinctions --- most important being
the distinction between programs and propositions and the different
varieties of proposition.

3. My comments on connectionism will entirely concern epistemology ---
not heuristics.  Thus I will be concerned with what the system finally
learns --- not how it learns it.  From this point of view, the examples
I have seen suffer from what might be called the unary and even propositional
fixation of 1950s pattern recognition.   The basic predicates are all
unary and are even applied to a fixed object, and a concept is
a propositional function of these predicates.  The room classification
problem solved by Rumelhart, Smolensky, McClellan and Hinton (1986)
is based on unary predicates of rooms, e.g. whether a room contains
a stove.  However, suppose we would like the system to learn that the
butler's pantry is the room between the kitchen and the dining room
or that a small room adjoining only a bedroom and without windows is
a closet.  As far as I can see the RSMH system is not ``elaboration
tolerant'' in this direction, because its inputs are all unary
predicates on single rooms.  To handle the butler's pantry, one
might have to build an entirely different connectionist network,
with the RSMH network having no salvage value.

The 1960s vision programs were partly motivated by a desire to get away
from the unary bias of the 1950s.  The slogan was ``description, not mere
discrimination''.  Indeed one of the motivations for starting on robotics
was to illustrate and explore the fact that to pick up a connecting rod a
robot requires more than just identifying the scene as containing a connecting
rod; it requires a description of the rod and its location and
orientation.  Perhaps connectionist models can do this, and it seems to me
very likely that it can be done subsymbolically.  I hope that Smolensky
will address this question in his response to the commentaries.
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Notes:

pandemonium, broadcast
combination of facts
why does AI need connectionism
q in Chinese
misprint on p. 19 l. -8 : contrued → construed
need more than modus ponens
How does it get a goal?  It looks like a lot of human setup of
the problem has been required so far.
Boltzmannize circumscription
p.27 - The system's knowledge of Ohm's law is distributed ...
Smolensky's performance-competence distinction doesn't seem to
correspond to that of the linguist.  Rather two levels of performance
description seem to be in question.

The effect of blood chemistry on ideas.
This would be worth a formal psychological experiment.  Matched groups
are asked to discuss the same topic before and after a fatiguing
experience or before and after drinking or taking various drugs.