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From Symbols To Signals: Getting Closer To Machine Intelligence

Until recently, most artificial-intelligence researchers accepted the view that thinking consists of the manipulation of discrete symbols, such as the written or spoken language. With this understanding, they achieved a degree of progress, notably in machine understanding and generation of natural-language communication, in symbolic mathematics programs, and in the automatic proving of theorems and assertions by machines. Groups at MIT, Carnegie-Meilon, and Stanford dramatically extended theore

Joe Bosurgi
Until recently, most artificial-intelligence researchers accepted the view that thinking consists of the manipulation of discrete symbols, such as the written or spoken language. With this understanding, they achieved a degree of progress, notably in machine understanding and generation of natural-language communication, in symbolic mathematics programs, and in the automatic proving of theorems and assertions by machines. Groups at MIT, Carnegie-Meilon, and Stanford dramatically extended theorem-proving work by symbolically encoding large amounts of knowledge into “knowledge bases,” which led to the development of the first expert systems.

Despite these successes, the symbolic approach has proven insufficient. It has not adequately modeled intelligent behavior such as scene analysis or comprehensive speech recognition. Morever, even the so-called successful applications could not achieve a desirable characteristic known as graceful degradation, which is the ability to know when the limits of knowledge are reached and thereby to give up or fall hack on “common...

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