For avoiding nonsense in general, we might accumulate millions of censors. For all we know, this "negative meta-knowledge" — about patterns of though… - Marvin Minsky

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For avoiding nonsense in general, we might accumulate millions of censors. For all we know, this "negative meta-knowledge" — about patterns of thought and inference that have been found defective or harmful — may be a large portion of all we know.

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About Marvin Minsky

Marvin Lee Minsky (August 9, 1927 - January 24, 2016) was an American scientist in the field of artificial intelligence (AI), co-founder of MIT's AI laboratory, author of several texts on AI and philosophy, and winner of the 1969 Turing Award.

Also Known As

Native Name: Marvin Lee Minsky
Alternative Names: Marvin L. Minsky
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Questions about arts, traits, and styles of life are actually quite technical. They ask us to explain what happens among the agents of our minds. But this is a subject about which we have never learned very much... Such questions will be answered in time. But it will just prolong the wait if we keep using pseudo-explanation words like "holistic" and "gestalt." …It's harmful, when naming leads the mind to think that names alone bring meaning close.

Innate sentic detectors could help by teaching children about their own affective states. For if distinct signals arouse specific states, the child can associate those signals with those states. Just knowing that such states exist, that is, having symbols for them, is half the battle.

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More concretely, we would call the student's atten­tion to the following considerations: 1. Multilayer machines with loops clearly open all the questions of the general theory of automata. 2. A system with no loops but with an order restriction at each layer can compute only predicates of finite order. 3. On the other hand, if there is no restriction except for the absence of loops, the monster of vacuous generality once more raises its head. The perceptron has shown itself worthy of study despite (and even because of!) its severe limitations. It has many features to attract attention: its linearity; its intriguing learning theorem; its clear paradigmatic simplicity as a kind of parallel computation. There is no reason to suppose that any of these virtues carry over to the many-layered version. Nevertheless, we consider it to be an important research problem to elucidate (or reject) our intuitive judgment that the extension is sterile. Per­haps some powerful convergence theorem will be discovered, or some profound reason for the failure to produce an interesting “learning theorem” for the multilayered machine will be found.

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