智力的SP理论：特色与优势
The SP theory of intelligence: distinctive features and advantages
摘要：
本文强调了“SP智力理论”的鲜明特点，以及与一些与人工智能相关的替代方案相比的明显优势。独特的特征和优势是：观察和概念的简化和整合;计算系统中结构和过程的简化和整合;理论本身是计算理论;它可以成为计算机新体系结构的基础;通过模式的匹配和统一，更具体地说，通过多重对齐进行信息压缩是基本的;知识表示和处理的透明度;通过信息压缩发现“自然”结构（DONSVIC）;数学解释;人类感知和认知的解释;以及根据神经元及其相互连接实现抽象概念（“SP神经”）。这些事情与人工智能相关的替代方案有关：最小长度编码和相关概念;神经网络中的深度学习;认知和相关研究的统一理论;通用搜索;贝叶斯网络等;模式识别和视觉;自然语言的分析，生产和翻译;自然语言的无监督学习;精确和不精确的推理形式;表示和处理不同形式的知识; IBM的沃森;软件工程;解决与大数据相关的问题，以及自主机器人的智能开发。总之，SP系统可以为人工智能的长期发展提供坚实的基础，具有许多潜在的好处和应用。它还可以在相对较短的时间内提供有用的结果。从SP计算机模型衍生出的高并行、开源版本的SP机器，将成为各地研究人员探索该系统可以做什么并创建新版本的手段。
英文摘要：
This paper highlights distinctive features of the "SP theory of intelligence" and its apparent advantages compared with some AI-related alternatives. 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; it can be the basis for new architectures for computers; information compression via the matching and unification of patterns and, more specifically, via multiple alignment, is fundamental; 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 relate to AI-related alternatives: minimum length encoding and related concepts; deep learning in neural networks; unified theories of cognition and related research; universal search; Bayesian networks and more; 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
