Steven Pinker -- How the Mind Works - Hampshire High Italian ...

Thinking Machines 109inputs in the future. A whole series of inputs and their outputs is presentedto the network, over and over, causing waves of little adjustmentsof the connection weights, until it gets every output right for every input,at least as well as it can manage to.A pattern associator equipped with this learning technique is called aperceptron. Perceptrons are interesting but have a big flaw. They are likethe chef from hell: they think that if a little of each ingredient is good, alot of everything must be better. In deciding whether a set of inputs justifiesturning on an output, the perceptron weights them and adds themup. Often that gives the wrong answer, even on very simple problems. Atextbook example of this flaw is the perceptrons handling of the simplelogical operation called exclusive-or ("xor"), which means "A or B, butnot both."When A is on, the network should turn A-xor-B on. When B is on, thenetwork should turn A-xor-B on. These facts will coax the network intoincreasing the weight for the connection from A (say, to .6) and increasingthe weight for the connection from B (say, to .6), making each onehigh enough to overcome the output unit's threshold (say, .5). But whenA and B are both on, we have too much of a good thing—A-xor-B isscreaming its head off just when we want it to shut up. If we try smallerweights or a higher threshold, we can keep it quiet when A and B areboth on, but then, unfortunately, it will be quiet when just A or just B ison. You can experiment with your own weights and you will see thatnothing works. Exclusive-or is just one of many demons that cannot bebuilt out of perceptrons; others include demons to determine whether aneven or an odd number of units are on, to determine whether a string ofactive units is symmetrical, and to get the answer to a simple additionproblem.The solution is to make the network less of a stimulus-response creatureand give it an internal representation between the input and outputlayers. It needs a representation that makes the crucial kinds of informationabout the inputs explicit, so that each output unit really can just add