A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence

Palade, Vasile; Wolff, J. Gerard

doi:10.1093/comjnl/bxy126

Cited by 12 publications

(11 citation statements)

References 45 publications

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“…Taking account of these features of the SP system, and others described in other publications, the SP system has potential as a foundation for the development of human-like general AI or "artificial general intelligence". The realisation of that potential (via a possible scenario outlined in [19]) is likely to yield further benefits in the recognition of entities, in scene analysis, and in the inference and recognition of three-dimensional structures. s".…”

Section: Discussionmentioning

confidence: 99%

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The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

Wolff¹

2021

BDCC

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This paper aims to describe how pattern recognition and scene analysis may with advantage be viewed from the perspective of the SP system (meaning the SP theory of intelligence and its realisation in the SP computer model (SPCM), both described in an appendix), and the strengths and potential of the system in those areas. In keeping with evidence for the importance of information compression (IC) in human learning, perception, and cognition, IC is central in the structure and workings of the SPCM. Most of that IC is achieved via the powerful concept of SP-multiple-alignment, which is largely responsible for the AI-related versatility of the system. With examples from the SPCM, the paper describes: how syntactic parsing and pattern recognition may be achieved, with corresponding potential for visual parsing and scene analysis; how those processes are robust in the face of errors in input data; how in keeping with what people do, the SP system can “see” things in its data that are not objectively present; the system can recognise things at multiple levels of abstraction and via part-whole hierarchies, and via an integration of the two; the system also has potential for the creation of a 3D construct from pictures of a 3D object from different viewpoints, and for the recognition of 3D entities.

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Section: Discussionmentioning

confidence: 99%

“…Current thinking in that area is described in [26]. In view of the strengths and potential of the SP system (Appendix A.6), the SPCM appears to have promise as the foundation for the development of an SP machine, as mentioned in Appendix A.2, and described in [19].…”

Section: Appendix a Outline Of The Sp Systemmentioning

confidence: 99%

The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

Wolff¹

2021

BDCC

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“…In the SP programme of research, unsupervised grammatical inference is regarded as a paradigm or framework for other kinds of unsupervised learning, not merely the learning of syntax. In addition to the learning of syntactic structures [16, Section 12.3], it may, for example, provide a model for the unsupervised learning of non-syntactic semantic structures [16, Section 12.1], and for learning the integration of syntax with semantics [16,Section 12.3]. Unsupervised learning in the SP System may itself be a good foundation for 'supervised' kinds of learning, such as learning by being told, learning by imitation, learning via rewards and punishments, and so on.…”

Section: The Challenges Of Unsupervised Learningmentioning

confidence: 99%

Problems in AI research and how the SP System may help to solve them

Wolff

2020

Preprint

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This paper describes problems in AI research and how the SP System (described in sources referenced in the paper) may help to solve them. Most of the problems considered in the paper are described by leading researchers in AI in interviews with science writer Martin Ford, and reported by him in his book Architects of Intelligence. These problems, each with potential solutions via SP, are: the divide between symbolic and non-symbolic kinds of knowledge and processing, and how the SP System may bridge the divide; the tendency of deep neural networks (DNNs) to make large and unexpected errors in recognition, something that does not happen with the SP System; in most AI research, unsupervised learning is regarded as a challenge, but unsupervised learning is central in how SP learns; in other AI research, generalisation, with under-and over-generalisation is seen as a problem, but it is a

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“…It is envisaged that the SP Theory of Intelligence and the SP Computer Model will be developed into a highly parallel "SP-Machine", as described in [20], and shown schematically in Figure 4.…”

Section: Future Developmentsmentioning

confidence: 99%

Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model

Wolff¹

2020

Preprint

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This chapter describes how the SP System, meaning the SP Theory of Intelligence, and its realisation as the SP Computer Model, may promote transparency and granularity in AI, and some other areas of application. The chapter describes how transparency in the workings and output of the SP Computer Model may be achieved via three routes: 1) the program provides a very full audit trail for such processes as recognition, reasoning, analysis of language, and so on. There is also an explicit audit trail for the unsupervised learning of new knowledge; 2) knowledge from the system is likely to be granular and easy for people to understand; and 3) there are seven principles for the organisation of knowledge which are central in the workings of the SP System and also very familiar to people (eg chunkingwith-codes, part-whole hierarchies, and class-inclusion hierarchies), and that kind of familiarity in the way knowledge is structured by the system, is likely to be important in the interpretability, explainability, and transparency of that knowledge. Examples from the SP Computer Model are shown throughout the chapter.

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A Roadmap for the Development of the ‘SP Machine’ for Artificial Intelligence

Cited by 12 publications

References 45 publications

The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

Problems in AI research and how the SP System may help to solve them

Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model

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