Can a machine think (anything new)? Automation beyond simulation
Open accessAbstract
This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand (e.g. Leibniz’s mathesis universalis and Turing’s algorithmic method of computation), and the philosophical and cultural critique of these attempts on the other, I will argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us.
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LLMs play a sort of imitation game, without a focus on computational novelty.
“LLMs still play a sort of imitation game , without a focus on (computational) novelty (Fazi, 2019).”
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Gaie, C. (2026) · Advances in sustainability science and technology · doi:10.1007/978-981-92-0458-8_1
Huang, K., Liu, C., & Yang, J. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14090218
“These failures suggest that LLMs are best viewed as partial models of the associative component of creativity, not comprehensive models of the creative process (Beaty & Kenett, 2023; Mahowald et al., 2024; Fazi, 2018).”
- Authors
- Fazi, M. B.
- Affiliations
- University of Sussex (GB)
- Corresponding author
- Fazi, M. Beatrice
- ORCID
- Fazi, M. Beatrice
- Venue
- AI & SOCIETY
- Published
- 2018-02-12
- Volume
- 34
- Issue
- 4
- Pages
- 813-824
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W2790258664
- Cited by
- 62
- References
- 51
- Citations by year
- 2026: 10 · 2025: 6 · 2024: 14 · 2023: 6 · 2022: 13 · 2021: 5 · 2020: 7 · 2019: 1
- FWCI
- 15.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- AI & Society (publishedVersion, cc-by)
- Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- automation, prediction, decision-making, Leibniz, mathesis universalis, Turing, computation, axiomatic systems, epistemic autonomy, simulation, human cognition, lived experience, onto-epistemology
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