A Reply to “Parallel Computation and the Mind-Body
Problem”
Publihsed as Krellenstein, M. (1987). A reply to "Parallel Computation and the Mind-Body
Problem". Cognitive Science, 11, 155-157.
Marc Krellenstein
New School for Social Research
Access Technology
The following is in reply to some points made in Paul
Thagard’s “Parallel Computation and the Mind-Body
Problem” (1986).
(1) Thagard argues that
increased speed has metaphysical importance because only intelligent creatures
(or machines) quick enough to adequately deal with the demands of their
environment will survive. Thus, the best possible serial simulation of a
parallel algorithm, while strictly possible, may be hopelessly slow. Lest we
consider this conceptually irrelevant, Thagard
admonishes us not to ignore such real-world limitations, observing that our
theories relating matter and intelligence need no more account for the merely
conceivable than Newtonian mechanics need explain worlds with negative
gravitation.
But this still shows only that the hardware must be
able to run the software fast enough to be useful/survive. A program running on
a parallel machine that produced some sort of intelligence will also run on a
serial machine, and this is enough to show the hardware irrelevant for
explaining the nature, if not the evolution, of that particular intelligence.
If the program runs too slowly on the serial machine to be useful we would not
say that it no longer demonstrates intelligence but only that it is too slow,
or that the particular approach, though successful, is impractical. (Some kind
of practicality test is relevant to determining whether we have produced an
intelligence that works in the same way as, as opposed to being functionally
equivalent to, some aspect of human intelligence, but Thagard
does not so constrain his position).
The analogy to negative gravitation is to instruct us
not to muddy our thinking or burden any non-functionalist position with a
purely theoretical multiple instantiation hypothesis and speculation about
exotic serial machines. But the serial simulation of parallel processes and the
portability of software are computational principles and everyday empirical
realities, and they are deducible from, rather than premises of or motivation
for, the computational paradigm embraced both by Thagard
and the functionalists he attacks. These principles may prove less interesting
if we find it impossible for intelligence to exist without massively parallel
architectures very similar to the brain, but that will hardly make them less
true or confer more than contingent importance on the particular hardware
needed to achieve the requisite level of computational power.
(2) Thagard contrasts the
functionalist’s “sharp distinction between hardware and software” with the
fuzzier separation of the two in current computers as evidenced by
special-purpose computers with hard-wired software, or general-purpose
computers with microprogrammed hardware. But
functionalism is not concerned as much with different means of physical
encoding as with the distinction between (virtual) machine and program, between
interpretive mechanism and symbolic codes that are interpreted-for which
“hardware” and “software” are a convenient shorthand.
A given system may be viewed as consisting of multiple such virtual machines, and the point (if any) at which there is
sufficient “hardness” as to render one essentially unmodifiable
is rightly regarded as arbitrary.
At any given level, however, the line between virtual
machine and program in a computer system is quite clear; it is what allows us
to discuss the algorithms “followed” by a hard-wired chess machine, or to view
the microprogramming level as part of the hardware virtual machine. What is
important for the functionalist position is the equivalence between cognition
and a program executed by some virtual machine.
(3) Thagard attempts to
show that parallel hardware architectures offer not merely increased speed of
processing but suggest qualitatively different programming approaches. One
approach cited is being able to pursue multiple, sometimes improbable
hypotheses simultaneously, causing Thagard to say
that parallelism “lends itself to audacity.”
But the position is overstated. A program running on
a serial architecture limited to a set of heuristics for solving a problem
might well adopt such an “audacious” approach given sufficient time to solve
the problem relative to pursuing any one path. More likely, several paths might
be pursued at different points in the process, foregoing the need and
inefficiency (due to commonalities in approach) to pursue every approach from
start to finish. Any of these paths might themselves represent improbable
hypotheses if there is time to pursue all of the probable ones. This is a limitation
shared by the parallel architecture: The ability to pursue the improbable is
there only if there is sufficient processing power (sufficient processors) to
pursue the probable.
Such an approach may in fact be more natural on a
parallel architecture -- but probably because it is a simple answer to the
difficult question of how to take advantage of multiple processors given an
essentially serial approach to a problem for which there is not, or is not time
for, a single algorithm certain of success. If such an approach turns out to be
inadequate (perhaps because of number of likely and unlikely hypotheses) or if
the goal is to harness the power of multiple processors to execute a complex
algorithm guaranteed to produce the answer then there will be a need to more
subtly distribute processing; and this is far more difficult.
Nor is it the case that pursuing multiple hypotheses
is just a poor example of the extent of the differences suggested by parallel
hardware. Rather, algorithms for parallel machines are often modified versions
of (and more similar in essentials to than different from) serial algorithms,
or directly exploit the presence of certain order-independent steps in such
algorithms, performing in parallel several steps that would otherwise be arbitrarily
performed serially (see, for example, the discussion of algorithms for the
radically parallel “connection machine” in Hillis,
1985). Conversely, traditional serial programs (e.g., compilers) are
increasingly realized to contain sections that are perhaps most naturally
implemented as parallel communicating tasks, perhaps in one of the several
programming languages running on serial machines that support such concurrency.
Such reformulations are only loosely (if at all) tied to the current or future
existence of parallel architectures for running them.
REFERENCES
Churchland, P. (1984). Matter and
consciousness.
Hillis, D. (1985). The connection machine.
Pylyshyn, Z. (1984). Computation and cognition.
Tanenbaum, A. (1976). Structured computer organization.
Thagard, P. (1986). Parallel computation and the mind-body
problem. Cognitive Science, 10, 301-318.