There is a divide in the domain of artificial intelligence. On the one end of this divide are the various sub-symbolic, or signal-based systems that are able to distill stable representations from a potentially noisy signal. Pattern recognition and classification are typical uses of such signal-based systems. On the other side of the divide are various symbol-based systems. In these systems, the lowest-level of representation is that of the a priori determined symbol, which can denote something as high-level as a person, place, or thing. Such symbolic systems are used to model and reason over some domain of discourse given prescribed rules of inference. An example of the unification of this divide is the human. The human perceptual system performs signal processing to yield the rich symbolic models that form the majority of our interpretation of and reasoning about the world. This presentation will provide an introduction to different signal and symbol systems and discuss the unification of this divide.
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