Intelligence Semantics

Artificial Intelligence and Software Engineering: by Derek Partridge

By Derek Partridge

During this literate and easy-to-read dialogue, Derek Partridge is helping us comprehend what AI can and can't do. themes mentioned comprise strengths and weaknesses of software program improvement and engineering, the guarantees and difficulties of computer studying, professional platforms and luck tales, functional software program via man made intelligence, synthetic intelligence and standard software program engineering difficulties, software program engineering technique, new paradigms for method engineering, what the long run holds, and extra.

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Additional resources for Artificial Intelligence and Software Engineering: Understanding the Promise of the Future

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How good a static, modular, and well-circumscribed approximation we can eventually develop remains an interesting open question. But there is every indication that it will have to be treated as an AI problem—perhaps open-ended and dynamic—before we'll see computers with anything like a sophisticated ability to process English. In order to emphasize this important element of distinction, I'll provide you with examples from a totally different domain: the plant world. e. cacti. The mesquite tree is a system that is closely coupled to its context, while the cactus tends to be quite loosely coupled, almost context free.

They just become somewhat less obvious. So now you know what's meant by an incomplete performance-mode definition—a partial non-RFS, might be another way to describe it—and that's the sort of specification that we must deal with in AI. Before we leave this subtopic I should draw your attention to the use of inductive generalization in expert systems' technology (described in Chapter 7). This approach to knowledge elicitation can be viewed as the production of a generalized 'specification' from a partial performance-mode one.

Some maintain that the proofs promise to be more complicated than the programs, and so mechanical proof checking (which is a distant possibility, at best) is essential. Others (such as Dijkstra, 1989, p. 1414) counter this with the assertion that "the presumed dogma that calculational proofs are an order of magnitude too long to be practical has not been confirmed by my experience. " A more sweeping line of attack challenges the foundations of the verificationists' programme: it claims that "The differences between a program and a proof are so many and so profound...

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