De Toffoli, Silvia and Tanswell, Fenner Stanley (2026) The Technological Turn in Mathematics. [Preprint]
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The Technological Turn in Mathematics, 2.04.2026.pdf Download (274kB) |
Abstract
Quickly evolving technologies, such as Interactive Theorem Provers (ITPs), Automated Theorem Provers (ATPs), and Large Language Models (LLMs), all falling under the general heading ‘AI for mathematics,’ are transforming mathematical practice in profound ways. This chapter explores the implications of these innovations, focusing on their impact on how mathematical knowledge is created and shared. It also discusses how they are reshaping the social dimension of mathematics, altering collaboration dynamics, trust
relationships, and the collective production of knowledge. For instance, tools like ITPs facilitate large-scale collaborations and make new types of teamwork possible, where trust is not a necessary ingredient. ITPs also help us mitigate our human fallibility, yet they raise questions about the nature of formalization and the relationship between traditional and formal mathematics. Technologies such as LLMs are reshaping the division of epistemic labour between humans and machines and urge philosophers of mathematics to ask questions about the value of their work.
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| Item Type: | Preprint | |||||||||
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| Keywords: | Mathematical practice, interactive theorem provers, AI for mathematics, trust, mathematical knowledge, proof, social epistemology, collaboration | |||||||||
| Subjects: | Specific Sciences > Mathematics > Epistemology Specific Sciences > Mathematics > Methodology Specific Sciences > Mathematics > Practice Specific Sciences > Mathematics > Proof Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Dr Fenner Tanswell | |||||||||
| Date Deposited: | 05 Apr 2026 17:06 | |||||||||
| Last Modified: | 05 Apr 2026 17:06 | |||||||||
| Item ID: | 28906 | |||||||||
| Subjects: | Specific Sciences > Mathematics > Epistemology Specific Sciences > Mathematics > Methodology Specific Sciences > Mathematics > Practice Specific Sciences > Mathematics > Proof Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 2026 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/28906 |
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