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

Thinking Machines 111We have reached what many psychologists treat as the height of theneural-network modeler's art. In a way, we have come full circle,because a hidden-layer network is like the arbitrary road map of logicgates that McCulloch and Pitts proposed as their neuro-logical computer.Conceptually speaking, a hidden-layer network is a way to composea set of propositions, which can be true or false, into acomplicated logical function held together by ands, ors, and nots—though with two twists. One is that the values can be continuousrather than on or off, and hence they can represent the degree of truthor the probability of truth of some statement rather than dealing onlywith statements that are absolutely true or absolutely false. The secondtwist is that the network can, in many cases, be trained to take on theright weights by being fed with inputs and their correct outputs. Ontop of these twists there is an attitude: to take inspiration from themany connections among neurons in the brain and feel no guilt aboutgoing crazy with the number of gates and connections put into a network.That ethic allows one to design networks that compute manyprobabilities and hence that exploit the statistical redundancies amongthe features of the world. And that, in turn, allows neural networks togeneralize from one input to similar inputs without further training, aslong as the problem is one in which similar inputs yield similar outputs.Those are a few ideas on how to implement our smallest demonsand their bulletin boards as vaguely neural machines. The ideas serveas a bridge, rickety for now, along the path of explanation that beginsin the conceptual realm (Grandmas intuitive psychology and the varietiesof knowledge, logic, and probability theory that underlie it), continueson to rules and representations (demons and symbols), andeventually arrives at real neurons. Neural networks also offer somepleasant surprises. In figuring out the mind's software, ultimately wemay use only demons stupid enough to be replaced by machines. If weseem to need a smarter demon, someone has to figure out how tobuild him out of stupider ones. It all goes faster, and sometimes goesdifferently, when neural-network modelers working from the neuronsupward can build an inventory of stock demons that do handy things,like a content-addressable memory or an automatically generalizingpattern associator. The mental software engineers (actually, reverseengineers)have a good parts catalogue from which they can ordersmart demons.