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

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130 I HOW THE MIND WORKSthat makes it impossible to think clearly. Certainly learning plays anenormous role in connectionist modeling. Often a modeler, sent back to<strong>the</strong> drawing board by <strong>the</strong> problems I have mentioned, will take advantageof a hidden-layer network's ability to learn a set of inputs and outputsand generalize <strong>the</strong>m to new, similar ones. By training <strong>the</strong> livingdaylights out of a generic hidden-layer network, one can sometimes get itto do approximately <strong>the</strong> right thing. But heroic training regimes cannot,by <strong>the</strong>mselves, be <strong>the</strong> salvation of connectoplasm. That is not because<strong>the</strong> networks have too little innate structure and too much environmentalinput. It is because raw connectoplasm is so underpowered that networksmust often be built with <strong>the</strong> worst combination: too much innatestructure combined with too much environmental input.For example, Hinton devised a three-layer network to compute familyrelationships. (He intended it as a demonstration of how networks work,but o<strong>the</strong>r connectionists have treated it as a real <strong>the</strong>ory of psychology.)The input layer had units for a name and units for a relationship, such as"Colin" and "mo<strong>the</strong>r." The output layer had units for <strong>the</strong> name of <strong>the</strong> personso related, such as "Victoria." Since <strong>the</strong> units and connections are<strong>the</strong> innate structure of a network, and only <strong>the</strong> connection weights arelearned, taken literally <strong>the</strong> network corresponds to an innate module in<strong>the</strong> brain just for spitting out answers to questions about who is relatedto a named person in a given way. It is not a system for reasoning aboutkinship in general, because <strong>the</strong> knowledge is smeared across <strong>the</strong> connectionweights linking <strong>the</strong> question layer to <strong>the</strong> answer layer, ra<strong>the</strong>r thanbeing stored in a database that can be accessed by different retrievalprocesses. So <strong>the</strong> knowledge is useless if <strong>the</strong> question is changed slightly,such as asking how two people are related or asking for <strong>the</strong> names andrelationships in a person's family. In this sense, <strong>the</strong> model has too muchinnate structure; it is tailored to a specific quiz.After training <strong>the</strong> model to reproduce <strong>the</strong> relationships in a small,made-up family, Hinton called attention to its ability to generalize to newpairs of kin. But in <strong>the</strong> fine print we learn that <strong>the</strong> network had to betrained on 100 of <strong>the</strong> 104 possible pairs in order to generalize to <strong>the</strong>remaining 4. And each of <strong>the</strong> 100 pairs in <strong>the</strong> training regime had to befed into <strong>the</strong> network 1,500 times (150,000 lessons in all)! Obviouslychildren do not learn family relationships in a manner even remotely likethis. The numbers are typical of connectionist networks, because <strong>the</strong>y donot cut to <strong>the</strong> solution by means of rules but need to have most of <strong>the</strong>examples pounded into <strong>the</strong>m and merely interpolate between <strong>the</strong> exam-

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