The SP theory of intelligence: distinctive features and advantages
| Autor: | James Gerard Wolff |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
cognition
FOS: Computer and information sciences information compression Theoretical computer science General Computer Science Computer science Computer Science - Artificial Intelligence Competitive learning Big data 02 engineering and technology perception 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science Artificial neural network Unified Theories of Cognition business.industry Deep learning General Engineering multiple alignment Cognition artificial intelligence neural networks Artificial Intelligence (cs.AI) Pattern recognition (psychology) Unsupervised learning 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 Natural language |
| Zdroj: | IEEE Access, Vol 4, Pp 216-246 (2016) |
| Popis: | This paper aims to highlight distinctive features of the SP theory of intelligence, realized in the SP computer model, and its apparent advantages compared with some AI-related alternatives. Perhaps most importantly, the theory simplifies and integrates observations and concepts in AI-related areas, and has potential to simplify and integrate of structures and processes in computing systems. Unlike most other AI-related theories, the SP theory is itself a theory of computing, which can be the basis for new architectures for computers. Fundamental in the theory is information compression via the matching and unification of patterns and, more specifically, via a concept of multiple alignment. The theory promotes transparency in the representation and processing of knowledge, and unsupervised learning of natural structures via information compression. It provides an interpretation of aspects of mathematics and an interpretation of phenomena in human perception and cognition. concepts in the theory may be realized in terms of neurons and their inter-connections (SP-neural). These features and advantages of the SP system are discussed in relation to AI-related alternatives: the concept of minimum length encoding and related concepts, how computational and energy efficiency in computing may be achieved, deep learning in neural networks, unified theories of cognition and related research, universal search, Bayesian networks and some other models for AI, IBM's Watson, solving problems associated with big data and in the development of intelligence in autonomous robots, pattern recognition and vision, the learning and processing of natural language, exact and inexact forms of reasoning, representation and processing of diverse forms of knowledge, and software engineering. In conclusion, the SP system can provide a firm foundation for the long-term development of AI and related areas, and at the same time, it may deliver useful results on relatively short timescales. |
| Databáze: | OpenAIRE |
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