Skip to main content
SpringerLink
Log in

Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model

  • Chapter
  • First Online:
Interpretable Artificial Intelligence: A Perspective of Granular Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 937))

  • 746 Accesses

Abstract

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 chunking-with-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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See, for example, Agisoft (www.agisoft.com/), All3DP (all3dp.com/), Sculpteo (www.sculpteo.com), and more.

References

  1. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R.: Intriguing properties of neural networks. Technical report, Google Inc. and others (2014). arXiv:1312.6199v4 [cs.CV] 19 Feb 2014, http://bit.ly/1elzRGM (PDF)

  2. Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Confernce on Computer Vision and Pattern Recognition (CVPR 2015), pp. 427–436 (2015). https://doi.org/10.1109/CVPR.2015.7298640

  3. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)

    Article  MathSciNet  Google Scholar 

  4. Pedrycz, W.: Granular computing for data analytics: a manifesto of human-centric computing. IEEE/CAA J. Automatica Sinica 5(6), 1025–1034 (2018)

    Article  MathSciNet  Google Scholar 

  5. Wolff, J.G.: The SP theory of intelligence: an overview. Information 4(3), 283–341 (2013). https://doi.org/10.3390/info4030283. arXiv:1306.3888 [cs.AI], http://bit.ly/1NOMJ6l

  6. Wolff, J.G.: Unifying Computing and Cognition: The SP Theory and Its Applications. CognitionResearch.org, Menai Bridge (2006). ISBNs: 0-9550726-0-3 (ebook edition), 0-9550726-1-1 (print edition). Distributors, including Amazon.com, are detailed on http://bit.ly/WmB1rs

  7. Kuhn, T.S.: The Structure of Scientific Revolutions. University of Chicago Press, Chicago and London, fourth, Kindle edition (2012)

    Book  Google Scholar 

  8. Attneave, F.: Some informational aspects of visual perception. Psychol. Rev. 61, 183–193 (1954)

    Article  Google Scholar 

  9. Barlow, H.B.: Sensory mechanisms, the reduction of redundancy, and intelligence. In: HMSO, editor, The Mechanisation of Thought Processes, pp. 535–559. Her Majesty’s Stationery Office, London (1959)

    Google Scholar 

  10. Barlow, H.B.: Trigger features, adaptation and economy of impulses. In: Leibovic, K.N. (ed.) Information Processes in the Nervous System, pp. 209–230. Springer, New York (1969)

    Google Scholar 

  11. Wolff, J.G.: Information compression as a unifying principle in human learning, perception, and cognition. Complexity 38 (2019). https://doi.org/10.1155/2019/1879746. Article ID 1879746. viXra:1707.0161v3, hal-01624595 v2

  12. Wolff, J.G.: Information compression, multiple alignment, and the representation and processing of knowledge in the brain. Front. Psychol. 7, 1584 (2016). ISSN 1664-1078. https://doi.org/10.3389/fpsyg.2016.01584. arXiv:1604.05535 [cs.AI], http://bit.ly/2esmYyt

  13. Wolff, J.G.: Mathematics as information compression via the matching and unification of patterns. Complexity 25 (2019). https://doi.org/10.1155/2019/6427493. Article ID 6427493, Archives: http://vixra.org/abs/1912.0100, http://hal.archives-ouvertes.fr/hal-02395680

  14. Ford, M.: Architects of Intelligence: The Truth About AI From the People Building It. Packt Publishing, Birmingham, UK, Kindle edition (2018)

    Google Scholar 

  15. Wolff, J.G.: Problems in AI research and how the SP System may help to solve them (2020). Download: http://tinyurl.com/y48m84t5, submitted for publication

  16. Wolff, J.G.: Big data and the SP Theory of Intelligence. IEEE Access 2, 301–315 (2014). https://doi.org/10.1109/ACCESS.2014.2315297. arXiv:1306.3890 [cs.DB], http://bit.ly/2qfSR3G. This paper, with minor revisions, is reproduced in Fei Hu (Ed.), Big Data: Storage, Sharing, and Security, Taylor & Francis LLC, CRC Press, 2016, Chapter 6, pp. 143–170

  17. Wolff, J.G.: Application of the SP Theory of Intelligence to the understanding of natural vision and the development of computer vision. SpringerPlus 3(1), 552–570 (2014). https://doi.org/10.1186/2193-1801-3-552. arXiv:1303.2071 [cs.CV], http://bit.ly/2oIpZB6

  18. Wolff, J.G.: Towards an intelligent database system founded on the SP theory of computing and cognition. Data Knowl. Eng. 60, 596–624 (2007). https://doi.org/10.1016/j.datak.2006.04.003. arXiv:cs/0311031 [cs.DB], http://bit.ly/1CUldR6

  19. Wolff, J.G.: Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search. Decis. Support Syst. 42, 608–625 (2006). https://doi.org/10.1016/j.dss.2005.02.005. arXiv:1409.8053 [cs.AI], http://bit.ly/1F366o7

  20. Palade, V., Wolff, J.G.: A roadmap for the development of the ‘SP Machine’ for artificial intelligence. The Comput. J. 62, 1584–1604 (2019). https://doi.org/10.1093/comjnl/bxy126. https://doi.org/10.1093/comjnl/bxy126 , arXiv:1707.00614, http://bit.ly/2tWb88M

  21. Wolff, J.G.: Learning syntax and meanings through optimization and distributional analysis. In: Levy, Y., Schlesinger, I.M., Braine, M.D.S. (eds.) Categories and Processes in Language Acquisition, pp. 179–215. Lawrence Erlbaum, Hillsdale, NJ (1988). http://bit.ly/ZIGjyc

  22. Lenneberg, E.H.: Understanding language without the ability to speak: a case report. J. Abnorm. Soc. Psychol. 65, 419–425 (1962)

    Article  Google Scholar 

  23. Brown, C.: My Left Foot. Vintage Digital, London, Kindle edition (2014). First published in 1954

    Google Scholar 

  24. Chater, N., Vitányi, P.: ‘Ideal learning’ of natural language: positive results about learning from positive evidence. J. Math. Psychol. 51(3), 135–163 (2007). https://doi.org/10.1016/j.jmp.2006.10.002

    Article  MathSciNet  MATH  Google Scholar 

  25. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)

    Article  Google Scholar 

  26. Fujita, H., Gaeta, A., Loia, V., Orciuoli, F.: Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Trans. Cybern. 49(5), 1835–1848 (2018). https://doi.org/10.1109/TCYB.2018.2815178

    Article  Google Scholar 

  27. Birtwistle, G.M., Dahl, O.-J., Myhrhaug, B., Nygaard, K.: Simula Begin. Studentlitteratur, Lund (1973)

    Google Scholar 

  28. Lee, S.-Y., Liou, R.-L.: A multi-granularity locking model for concurrency control in object-oriented database systems. IEEE Trans. Knowl. Data Eng. 8(1), 144–156 (1996)

    Article  Google Scholar 

  29. Wolff, J.G.: Software engineering and the SP Theory of Intelligence. Technical report, CognitionResearch.org (2017). Submitted for publication. arXiv:1708.06665 [cs.SE], http://bit.ly/2w99Wzq

  30. Zhang, Q., Zhu, S.-C.: Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19, 27–39 (2018)

    Article  Google Scholar 

  31. Alvarez-Melis, D., Jaakkola, T.S.: Towards robust interpretabilitywith self-explaining neural networks. In: Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018). Montréal, Canada (2018)

    Google Scholar 

  32. Arrieta, A.B., Díaz-Rodríguez, N., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., Herrera, F.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)

    Article  Google Scholar 

  33. Byrne, R.M.J.: Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, pp. 6276–6282 (2019)

    Google Scholar 

  34. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z.: XAI—explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019). https://doi.org/10.1126/scirobotics.aay7120

  35. Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., Holzinger, A.: Explainable AI: The New 42? In: CD-MAKE 2018, 27-30 Aug 2018, Hamburg, Germany, Lecture Notes in Computer Science, volume 11015, pp. 295–303 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CognitionResearch.org, Menai Bridge, UK

    J. Gerard Wolff

Authors
  1. J. Gerard Wolff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Gerard Wolff .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering Edmonton, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wolff, J.G. (2021). Transparency and Granularity in the SP Theory of Intelligence and Its Realisation in the SP Computer Model. In: Pedrycz, W., Chen, SM. (eds) Interpretable Artificial Intelligence: A Perspective of Granular Computing. Studies in Computational Intelligence, vol 937. Springer, Cham. https://doi.org/10.1007/978-3-030-64949-4_7

Download citation

Publish with us

Policies and ethics

Search

Navigation