What is Pylyshyn's critique of neural nets ?
Firstly let me describe the basic qualities and
abilities of neural nets. A neural net was an attempt to
take artificial intelligence a stage further and the
approach taken was to try and resemble nature more closely
than before. A basic net or Perceptron consists of two
layers of neurodes with each and every neurode connected to
all the others. The net is organized into two layers, the
input laywer and the output layer. However, these basic
nets were grounded immediately by not being able to solve
problems like "exclusive or". However when another or
multiple layers were added between the input and output
layers then these problems were quickly solved.
With neurodes representing neurons and wire being
analogous to the conecting materials in nature it seemed as
though neural nets were destined to be a success in
The main ability that nets have is to separate and find
pattens in information and, if you like, to answer questions
on that data. There are two types of net: the standard net
that is able to do very well the basic tasks outlined above.
The other type is those that have either internally or
xternally a source of feedback which enables the net to
alter the bias of it's connections via back propagation.
This results in a net that as it gains more "experience"
it's probablity of reaching the desired answer is increased.
Eventually given finite information and long enough the net
should reach the correct response every time.
This is where I believe that Pylyshyn's critique really
comes in. Phylyshyn's angle on neural nets is not one of
caring at all about the physical make-up of the net but of
How the net arrives at the right answer. The back
propagating nets for example can be seen almost as symbol
systems. The input makes up an arbitary on/off series along
the input layer and is associated with an output that the
net has been taught. This I believe is the critique, if the
input has no meaning it must be arbitary, therefore there
is no meaning held in the net and as it "lights" up the
correct output then Pylyshyn's argument that a neural net is
just another irrelavent piece of hardware for running a
symbol system on is therefore correct. For without meaning,
the nature of the input is arbitary and it is just the
manipulation or association that is required to get the
right answer. This is in essence computation and as such
Pylyshyn's belief that neural nets are irrelevant to how we
actually do the task and their only relavence is in what we
use in the process of carring out that task.
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