Whats the difference between a neural net and a symbol system?
Neural nets are like the brain in that they have interconnected units
which pass activity between themselves. Some units are specific others
are part of a pattern. Neural nets do however differ from the brain in
other ways eg. they have no dendrites, axons, synapses, glia,
neurotransmitters or action potentials. How similar or different they
are to the brain doesn't really matter. It's how good they are at
resembling the output of the brain which is important. If the output is
the same then perhaps reverse engineering could be used to work out
'how' it occurs. The neural nets consist of either just an input and an
output layer or these plus other layers. Using supervised learning
which gives feedback to enable back propagation to strenghen
connections, neural nets are able to learn.
A symbol is an arbitrary shape which can be manipulated using
algorithms to achieve the correct answer. The algorithm is a formula
which can be followed machanically, no meaning is needed and therefore
no mind is needed. The symbols represent objects or words and can be
manipulated to produce variations of sentences, for example. This type
of manipulation is not possible with neural nets because the outputs
are held within a unit, or a pattern of units. They cannot be broken
down in order to construct a new sentence from the components, as you
can with symbols. The net would need to learn from scatch the new
association of words.
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