By James McAllister
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These heuristics assume that such problems can be solved by applying sequences of formal rules for symbol manipulation. This is called "symbolic processing," and digital computers do it well. A number of high-level programming languages like Lisp and Prolog have been developed to make this job simpler, and AI has been successful in areas that would require a human to use symbolic of purely cognitive skills. Neural networks attempt to solve the same kinds of problems but at a much lower levelthat of the structure of the machine itself.
Under any circumstance, there are unquestionably complementary uses for both disciplines. We can envision a large hybrid system in the not-too-distant future composed of a combination of neural, AI, and digital systems. It might have a number of neural networks at the low end to combine the outputs of several types of sensors, to reduce the amount of information that later stages of the system must handle, and other so-called low-level tasks. It could have larger neural networks at intermediate and higher processing levels for integrating visual, auditory, and other sensory information and for adding contextual information.
A large number of choices exist for this operation, and designers are usually free to choose the one that works best for their application. The exact form chosen generally depends on design decisions made on other levels. 0. Except for the very simplest networks we discuss in part II, the activation function is a nonlinear one; it does not correspond to a straight line plot. For a number of reasons we will discuss later in the book, including compatibility with biological systems, this is generally the best choice.
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