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Abstract
Can scientific evidence outstretch what scientists have mentally entertained, or could ever entertain? This article focuses on the plausibility and consequences of an affirmative answer in a special case. Specifically, it discusses how we may treat automated scientific data-gathering systems - especially AI systems used to make predictions or to generate novel theories - from the point of view of confirmation theory. It uses AlphaFold2 as a case study.
Original language | English |
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Article number | 11 |
Number of pages | 17 |
Journal | Asian Journal of Philosophy |
Volume | 2 |
Issue number | 1 |
Early online date | 4 Apr 2023 |
DOIs | |
Publication status | Published - Jun 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Funding
The work described in this paper was fully supported by a Senior Research Fellowship award from the Research Grants Council of the Hong Kong SAR, China (‘Philosophy of Contemporary and Future Science’, Project no. SRFS2122-3H01).
Keywords
- AlphaFold2
- Artificial intelligence
- Confirmation
- Deep neural networks
- Evidence
- Machine learning
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Philosophy of Contemporary and Future Science
ROWBOTTOM, D. P. (PI)
Research Grants Council (HKSAR)
1/01/22 → 31/12/26
Project: Grant Research