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[Smarter than Humans?] Why We Can Be Confident of Turing Test Capability Within a Quarter Century by Ray Kurzweil

Original, July 13, 2006 http://www.kurzweilai.net/articles/art0683.html?printable=1

Of the three primary revolutions underlying the Singularity (G, N, and R), the most profound is R, which refers to the creation of nonbiological intelligence that exceeds that of unenhanced humans. A more intelligent process will inherently outcompete one that is less intelligent, making intelligence the most powerful force in the universe.

While the “R” in GNR stands for robotics, the real issue involved here is strong AI (artificial intelligence that exceedshuman intelligence). The standard reason for emphasizing robotics in this formulation is that intelligence needs an embodiment, a physical presence, to affect the world. I disagree with the emphasis on physical presence, however, for I believe that the central concern is intelligence. Intelligence will inherently find a way to influence the world, including creating its own means for embodiment and physical manipulation. Furthermore, we can include physical skills as a fundamental part of intelligence; a large portion of the human brain (the cerebellum, comprising more than half our neurons), for example, is devoted to coordinating our skills and muscles.

Artificial intelligence at human levels will necessarily greatly exceed human intelligence for several reasons. As I pointed out earlier machines can readily share their knowledge. As unenhanced humans we do not have the means of sharing the vast patterns of interneuronal connections andneurotransmitter-concentration levels that comprise ourlearning, knowledge, and skills, other than through slow,language-based communication. Of course, even this methodof communication has been very beneficial, as it has distinguished us from other animals and has been an enabling factor in the creation of technology.

Human skills are able to develop only in ways that have beenevolutionarily encouraged. Those skills, which are primarily based on massively parallel pattern recognition, provide proficiency for certain tasks, such as distinguishing faces, identifying objects, and recognizing language sounds. But they’re not suited for many others, such as determining patterns in financial data. Once we fully master pattern-recognition paradigms, machine methods can apply these techniques to any type of pattern.2

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