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Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI

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Artificial General Intelligence (AGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13154))

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Abstract

Statistical artificial intelligence based upon machine learning is facing major challenges such as machine bias, explainability, and verifiability problems. Resolving them would be of the utmost importance for ethical, safe, and responsible AI for social good. In this paper we propose to address these problems with the (recently rapidly developing) methodology of category theory, a powerful integrative scientific language from pure mathematics, and discuss, in particular, the possibility of the categorical integration of statistical (inductive) with symbolic (deductive) artificial intelligence. Categorical artificial intelligence arguably has the potential to resolve the aforementioned urgent problems, thus being beneficial to make artificial intelligence more ethical, verifiable, responsible, and so more human, which would be crucial for AI-pervasive society.

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Acknowledgements

This work was supported by the Moonshot R&D Programme (JST; JPMJMS2033). Special thanks to Samson Abramsky and Bob Coecke.

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Authors and Affiliations

  1. School of Computing, The Australian National University, Canberra, Australia

    Yoshihiro Maruyama

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  1. Yoshihiro Maruyama
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Correspondence to Yoshihiro Maruyama .

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Editors and Affiliations

  1. SingularityNET, Amsterdam, The Netherlands

    Ben Goertzel

  2. SingularityNET, Amsterdam, The Netherlands

    Matthew Iklé

  3. SingularityNET, Amsterdam, The Netherlands

    Alexey Potapov

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Maruyama, Y. (2022). Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI. In: Goertzel, B., Iklé, M., Potapov, A. (eds) Artificial General Intelligence. AGI 2021. Lecture Notes in Computer Science(), vol 13154. Springer, Cham. https://doi.org/10.1007/978-3-030-93758-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-93758-4_14

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