The SP theory of intelligence: distinctive features and advantages

This paper aims to highlight distinctive features of the "SP theory of intelligence" and its apparent advantages compared with some AI-related alternatives. In summary, distinctive features and advantages are: simplification and integration of observations and concepts; simplification and integration of structures and processes in computing systems; the theory is itself a theory of computing; the fundamental roles of information compression via the matching and unification of patterns and multiple alignment; transparency in the representation and processing of knowledge; the discovery of `natural' structures via information compression (DONSVIC); interpretations of mathematics; interpretations in human perception and cognition; and realisation of abstract concepts in terms of neurons and their inter-connections ("SP-neural"). These things are discussed in relation to AI-related alternatives: the concept of minimum length encoding and related concepts; deep learning in neural networks; unified theories of cognition and related research; universal search; Bayesian networks and some other models for AI; pattern recognition and vision; the analysis, production, and translation of natural language; Unsupervised learning of natural language; exact and inexact forms of reasoning; representation and processing of diverse forms of knowledge; IBM's Watson; software engineering; solving problems associated with big data, and in the development of intelligence in autonomous robots. In conclusion, the SP system can provide a firm foundation for the long-term development of AI, with many potential benefits and applications. It may also deliver useful results on relatively short timescales. A high-parallel, open-source version of the SP machine, derived from the SP computer model, would be a means for researchers everywhere to explore what can be done with the system, and to create new versions of it.