ULTIMATE COMPUTING - Quantum Consciousness Studies

Toward Ultimate Computing 15
1.3.2 Connectionism
The Mind’s Eye may be the apex of a collective hierarchy of parallel systems
in which the cytoskeleton and related structures are the ground floor. Parallel
systems in both computers and biological systems rely on lateral connections and
networks to provide the richness and complexity required for sophisticated
information processing. Computer simulations of parallel connected networks of
relatively simple switches (“neural nets”) develop “cognitive-like functions” at
sufficient levels of connectedness complexity-a “collective phenomenon”
(Huberman and Hogg, 1985). Philosopher John Searle (Pagels, 1984), who has an
understandable bias against the notion that computer systems can attain human
consciousness equivalence, points out that computers can do enormously complex
tasks without appreciating the essence of their situation. Searle likens this to an
individual sorting out Chinese characters into specific categories without
understanding their meaning, being unable to speak Chinese. He likens the
computer to the individual sorting out information without comprehending its
essence.
It would be difficult to prove that human beings comprehend the essence of
anything. Nevertheless, even the simulation of cognitive-like events is interesting.
Neural net models and connectionist networks (described further in Chapter 4)
have been characterized mathematically by Cal Tech’s John Hopfield (1982) and
others. His work suggests that solutions to a problem can be understood in terms
of minimizing an associated energy function and that isolated errors or incomplete
data can, within limits, be tolerated. Hopfield describes neural net energy
functions as having contours like hills and valleys in a landscape. By minimizing
energy functions, information (metaphorically) flows like rain falling on the
landscape, forming streams and rivers until stable states (“lakes”) occur. A new
concept in connectionist neural net theory has emerged with the use of multilevel
networks. Geoffrey Hinton (1985) of Carnegie-Mellon University and Terry
Sejnowski of Johns Hopkins University have worked on allowing neural nets to
find optimal solutions, like finding the lowest particular lake in an entire
landscape. According to Sejnowski (Allman, 1986; Hinton, Sejnowski and
Ackley, 1984) the trick is to avoid getting stuck in a tiny depression between two
mountains:
Imagine you have a model of a landscape in a big box and you want
to find a lowest point on the terrain. If you drop a marble into the
box, it will roll around for a while and come to a stop. But it may not
be the lowest point, so you shake the box. After enough shaking you
usually find it.