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By Margaret A. Boden

The purposes of man-made Intelligence lie throughout us; in our houses, colleges and places of work, in our cinemas, in paintings galleries and - no longer least - on the web. the result of synthetic Intelligence were necessary to biologists, psychologists, and linguists in supporting to appreciate the approaches of reminiscence, studying, and language from a clean angle.

As an idea, man made Intelligence has fuelled and sharpened the philosophical debates about the nature of the brain, intelligence, and the distinctiveness of people. Margaret A. Boden stories the philosophical and technological demanding situations raised by way of man made Intelligence, contemplating even if courses may possibly ever be particularly clever, inventive or perhaps wide awake, and exhibits how the pursuit of synthetic Intelligence has helped us to understand how human and animal minds are possible.

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The network may represent words as well as concepts, by adding links coding for syllables, initial letters, phonetics, and homonyms. Such a network is used by Kim Binsted’s JAPE and Graeme Ritchie’s STAND UP, which generate jokes (of nine different types) based on puns, alliteration, and syllable-switching. For example: Q: What do you call a depressed train? A: A low-comotive; Q: What do you get if you mix a sheep with a kangaroo? A: A woolly jumper. A caveat: semantic networks aren’t the same thing as neural networks.

For example, it doesn’t cope well with metaphor (although the database includes many dead metaphors, of course). It ignores various aspects of naïve physics. Its NLP, although constantly improving, is very limited. And it doesn’t yet include vision. In sum, despite its en-CYClopedic aims, it doesn’t really encompass human knowledge. The Dream Revitalized Newell, Anderson, and Lenat beavered away in the background for 30 years. Recently, however, interest in AGI has revived markedly. An annual conference was started in 2008, and SOAR, ACT-R, and CYC are being joined by other supposedly generalist systems.

The journalists, and the graduate students, followed. Symbolic AI was challenged yet again. In the twenty-first century, however, it has become clear that different questions require different types of answers—horses for courses. Although traces of the old animosities remain, there’s now room for respect, and even cooperation, between different 20 AI approaches. For instance, “deep learning” is sometimes used in powerful systems combining symbolic logic with multilayer prob­ abilistic networks; and other hybrid approaches include ambitious models of consciousness (see Chapter 6).

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