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Intelligence Is Wrong

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Amanda Pollock
11-02-15
U57476929

When Artificial Intelligence Is Wrong

When thinking about whether strong AI (artificial intelligence) as compatible source for understanding the way of thinking and let alone machines is practically irrelevant according to John R. Searle (Searle 372). In terms of thinking and the aspects of a machine is far more complex than that of an ordinary strong AI. Machines in general have a great deal more going on than just a symbol manipulation system such of that as a strong AI. Ultimately the strong AI would then be incapable of being able to help understand machines and the aspect of thinking.
Furthermore, the definition of “machine” needs to be reiterated in a simpler term to help explain this issue. …show more content…
Allen Newell and Herbert A. Simon are the basic instructors for the construction of strong AI systems. In “Computer Science as Empirical Inquiry: Symbols and Search”, Newell and Simon state “Symbols lie at the root of intelligent action, which of course, the primary topic of artificial intelligence” (Haugeland 83). A physical symbol system is in fact a type of system that is fed an input (types of symbols that mean various things) and then has an output (answers/responses to the symbols). Some other outside source (i.e. real brain/human of understanding) initially programs the physical symbol …show more content…
Furthermore in order for a machine to be more complex that of a strong AI it must in fact learn (behavior/think) mainly from environment, instead of using the physical symbol system as previously thought. According to David E. Rumelhart in “The Architecture of Mind: A Connectionist Approach”, “it is crucial in development of any model to have a clear representation of the environment in which the model is to exist” (Haugeland 215). This is also described in the experiments of Rodney A. Brooks creating his so-called “creatures” (not to be confused with AI, because they are without a central system). These “creatures” were able to get a perception of the environment by trial and error of exploring their surroundings, thus altering their behavior. In addition to further support this claim Paul M. Churchland says in “On the Nature of Theories: A Neurocomputational Perspective”, “living subjects have to learn to make the complex perceptual judgments possible”(Haugeland 253). He explains this with the concept of learning a language as a natural brain to that of an AI. Churchland basically states that nothing (not even the learning of an AI) can compare to that of a natural

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