AI: Its Nature and Future by Margaret A. Boden

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 artwork galleries and - no longer least - on the web. the result of man made Intelligence were worthy to biologists, psychologists, and linguists in supporting to appreciate the methods of reminiscence, studying, and language from a clean angle.

As an idea, synthetic Intelligence has fuelled and sharpened the philosophical debates about the nature of the brain, intelligence, and the individuality of humans. Margaret A. Boden experiences the philosophical and technological demanding situations raised via synthetic Intelligence, contemplating even if courses may ever be quite clever, inventive or perhaps wakeful, and indicates how the pursuit of man-made Intelligence has helped us to understand how human and animal minds are possible.

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In some 44 AI BOX Figure 1 Monkey and bananas problem: how does the monkey get the bananas? (The usual approach to this problem assumes, though doesn’t explicitly state, that the relevant “world” is that shown inside the dotted-line frame. ) Reprinted from M. A. Boden, Artificial Intelligence and Natural Man (1977: 387). technical/scientific areas, that’s so. 19) In general, however, it isn’t. That’s a major reason why AI systems lack common sense. In brief, the frame problem lurks all around us—and is a major obstacle in the quest for AGI.

Nevertheless, such systems can be used to do planning. If they couldn’t, they would be of limited use for AI. But they do it differently from how it’s done in the oldest, most familiar, form of programming (sometimes called “executive control”). In programs with executive control (like GPS and the Logic Theory Machine: see Chapter 1), planning is represented explicitly. ” Sometimes, the “this” or the “so-and-so” is an explicit instruction to set up a goal or sub-goal. For instance, a robot with the goal of leaving the room may be instructed [sic] to set up the sub-goal of opening the door; next, if examining the current state of the door shows it to be closed, set up the sub-sub-goal of grasping the door handle.

Besides requiring huge engineering advances in hardware and communications infrastructure, this ambitious project (directed by Sir Tim Berners-Lee) needs to deepen the Web-roaming programs’ understanding of what they’re doing. Search engines like Google’s, and NLP programs in general, can find associations between words and/or texts—but there’s no understanding there. Here, this isn’t a philosophical point (for General Intelligence as the Holy Grail 39 that, see Chapter 6), but an empirical one—and a further obstacle to achieving AGI.

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