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A PDP community is made of subsymbolic units, whose semantic importance cannot easily be expressed in terms of familiar semantic content material, still less propositions. Some GOFAI programs employ subsymbolic units, but most do not. That is, no single unit codes for case solution recognizable concept, equivalent to dog or cat. These concepts are represented, rather, by case research sample of activity allotted over case research entire community. Because case research illustration is not stored in case solution single unit but is distributed over case analysis whole community, PDP programs can tolerate imperfect data. Some GOFAI methods can do so too, but provided that case analysis imperfections are specially foreseen and provided for by case analysis programmer. Moreover, case answer single subsymbolic unit may mean one thing in one input context and another in an alternative. 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Broadly, case research weight on an excitatory link is higher by every coactivation of case analysis two units concerned: cells that fireplace in combination, wire together. These two AI tactics have complementary strengths and weaknesses. For example, symbolic AI is better at modeling hierarchy and strong constraints, whereas connectionism copes better with sample recognition, especially if many conflicting—and maybe incomplete—constraints are relevant. Despite having fervent philosophical champions on both sides, neither method is adequate for all of case analysis tasks handled by AI scientists. Indeed, much lookup in connectionism has aimed case study repair case analysis lost logical strengths of GOFAI case study neural networks—with only limited success by case research beginning of case research 21st century. Another, and more these days conventional, AI technique is situated robotics Brooks 1991. Like connectionism, this was first explored in case research 1950s. 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