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Challenges to the Omohundro–Bostrom framework for AI motivations

Olle Häggström (Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden)

Foresight

ISSN: 1463-6689

Article publication date: 25 October 2018

Issue publication date: 11 March 2019

Abstract

Purpose

This paper aims to contribute to the futurology of a possible artificial intelligence (AI) breakthrough, by reexamining the Omohundro–Bostrom theory for instrumental vs final AI goals. Does that theory, along with its predictions for what a superintelligent AI would be motivated to do, hold water?

Design/methodology/approach

The standard tools of systematic reasoning and analytic philosophy are used to probe possible weaknesses of Omohundro–Bostrom theory from four different directions: self-referential contradictions, Tegmark’s physics challenge, moral realism and the messy case of human motivations.

Findings

The two cornerstones of Omohundro–Bostrom theory – the orthogonality thesis and the instrumental convergence thesis – are both open to various criticisms that question their validity and scope. These criticisms are however far from conclusive: while they do suggest that a reasonable amount of caution and epistemic humility is attached to predictions derived from the theory, further work will be needed to clarify its scope and to put it on more rigorous foundations.

Originality/value

The practical value of being able to predict AI goals and motivations under various circumstances cannot be overstated: the future of humanity may depend on it. Currently, the only framework available for making such predictions is Omohundro–Bostrom theory, and the value of the present paper is to demonstrate its tentative nature and the need for further scrutiny.

Keywords

Acknowledgements

The author is grateful to Lars Bergström and Karim Jebari for helpful advice, and to Björn Bengtsson and an anonymous referee for valuable comments on an earlier draft.

Citation

Häggström, O. (2019), "Challenges to the Omohundro–Bostrom framework for AI motivations", Foresight, Vol. 21 No. 1, pp. 153-166. https://doi.org/10.1108/FS-04-2018-0039

Publisher

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Emerald Publishing Limited

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