Data-Driven Granular Cognitive Computing

Many artificial intelligence (AI) theoretical models are inspired by human/natural/social intelligence mechanisms. Three main schools of artificial intelligence have been formed, that is, symbolism, connectionism and behaviorism. Cognitive computing is one of the key fields of AI. It is a critical task for AI researchers to develop advanced cognitive computing models. Cognitive computing is the third and the most transformational phase in computing’s evolution, after the Tabulating Era and Programming Era. Inspired by human’s granularity thinking based problem solving mechanism and the cognition law of “global precedence”, a data-driven granular cognitive computing model (DGCC) is proposed in this paper. It integrates two contradictory mechanisms, namely, human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”. According to DGCC, deep learning is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper.

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