Cognition and cognitive processes can be both natural and artificial. Cognitive machines designed on computational backbone do not have any physical limits of time and space to learn, memorize and analyze data and information, contrary to the human mind which have certain physical limits of processing data. But what lacks in the artificial systems of thought is the pure conceptual understanding of the theoretical contents of human thought and levels of abstraction. Thinking the modern artificial cognitive architecture as an emulator of the human mind, more as cognitive assistive systems designed to mimic human thinking, the ultimate convergence of cognitive science, physical science and computational sciences into a more formidable, powerful thinking entity is what that remains as a tough challenge to both cognitive systems analyst and computer scientists. The evolution of mind in this parlor is layered on integrating disparate pieces of information in a more compatible manner to seek for a universal theory capable of modeling the human mind. This proposed unified model of an artificial mind is aimed to explain the working of different elements of the mind encompassing techniques like reasoning with datasets that are ambiguous and inconsistent to derive something meaningful out of such endeavors. Modeling thought in physical forms lies at the backdrop of such techniques such as these models of thought would mimic more or less human patterns of thought. One such theory related to Jean Piaget's cognitive development in children where they develop abstract thought and retain those thoughts which can be well extended in machine thinking (learning by doing, seeing, simulating and emulating how children learn to develop thoughts) as simulation of machine thought. Yet, there are problems in designing human-like mind in machines. This topic forum is dedicated to such discussions which would likely open to some new directions in thought simulation in machines.
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