These are excerpts and elaborations from my book "The Nature of Consciousness"
Parallel Distributed
Computing One can view a connectionist
structure as a new form of computing, a different way of finding a solution to
a problem (than searching a solution space). Traditionally, we think of problem
solving as an activity in which a set of axioms (of things we know for sure)
helps us figure out whether something else is true or false. We derive the
"theorem" from the premises through a sequence of logical steps.
There is one, well-defined stream of information that flows from the premises
to the demonstration of the theorem. This is the approach that mathematicians
have refined over the centuries. On the contrary, a
connectionist structure such as our brain works in a non-sequential way: many
"nodes" of the network can be triggered at the same time by another
node. The result of the “computation” is a product of the parallel processing
of many streams of information. There are no axioms and no rules of inference.
There are just nodes that exchange messages all the time and adjust their
connections depending on the frequency of those messages. No logic whatsoever,
no reasoning, no “intelligence” is required. Information does not flow: it gets
propagated. Computing (if it can still be called “computing”) occurs everywhere
in the network, and it occurs all the time. The obvious reason to be
intrigued by connectionist (or “neural”) computing is that our brain does it,
and, if our brain does it, there must be a reason. Another reason is that this
form of computing does have advantages over the logical approach. There are
many tasks that would be extremely difficult to handle with Logic, but are
quite naturally handled by neural computation. For example, what our brain does
best: recognizing patterns (whether a face or a sound). It has been proven that
everything that knowledge-based systems do can be done as well with neural
networks. The idea of connectionism,
of computing in a network rather than in a formal system, basically revolutionized the very concept of problem
solving. After all, very few real-world problems can be solved in the vacuum of
pure logic. From weather forecast to finance, most situations involve countless
factors that interact with each other at the same time. One can predict the
future only if one knows all the possible interactions. Back to the beginning of the chapter "Connectionism and Neural Machines" | Back to the index of all chapters |