Beisbart, Claus (2022). Machine learning in physics – philosophical perspectives on new tools (In Press). SPG Mitteilungen, 68, pp. 41-47.
Full text not available from this repository.In most fields of physics, machine learning (ML) is all the rage. Physicists use ML algorithms to analyze data, to cast predictions or to emulate computer simulations. But what exactly is machine learning? How trustworthy are its results? What are possible pitfalls? And how does ML impact on the methodology of physics? The aim of this article is to discuss initial answers to these questions on the basis of recent philosophical work.
Item Type: |
Newspaper or Magazine Article |
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Division/Institute: |
06 Faculty of Humanities > Department of Art and Cultural Studies > Institute of Philosophy 06 Faculty of Humanities > Department of Art and Cultural Studies > Institute of Philosophy > Theoretical Philosophy |
UniBE Contributor: |
Beisbart, Claus |
Subjects: |
100 Philosophy 100 Philosophy > 120 Epistemology |
Language: |
German |
Submitter: |
Claus Beisbart |
Date Deposited: |
03 Nov 2022 09:57 |
Last Modified: |
09 Jul 2024 09:30 |
URI: |
https://boris.unibe.ch/id/eprint/174358 |
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