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

Thinking Machines 103do it. Mechanically speaking, the more clue units there are that areturned on, the greater the activation of the conclusion unit. We couldimplement different statistical procedures in the network by designingthe conclusion unit to integrate its inputs in different ways. For example,the conclusion unit could be a threshold unit like the ones in crisp logicgates; that would implement a policy to put out a decision only if theweight of evidence exceeded a critical value (say, "beyond a reasonabledoubt"). Or the conclusion unit could increase its activity gradually; itsdegree of confidence could increase slowly with the first clues tricklingin, build quickly as more and more are amassed, and level off at a pointof diminishing returns. These are two of the kinds of unit that neuralnetworkmodelers like to use.We can get even more adventurous, and take inspiration from the factthat with neurons, unlike silicon chips, connections are cheap. Why notconnect every unit to every other unit? Such a network would embodynot only the knowledge that greenness predicts vegetablehood andcrunchiness predicts vegetablehood, but that greenness predicts crunchiness,crunchiness predicts leafiness, greenness predicts lack of mobility,and so on: