Automated Discovery Systems, part 2

New developments, current issues, and philosophical lessons in machine learning and data science

Bibliographic Data

ID7956949
AuthorsPiotr Giza (0000-0002-4193-0795, Medical University of Lublin, corresponding author)
Year2022
Volume17
Issue1
Publication date2022-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePhilosophy Compass (JOURNAL)
Journal identifiersISSN: 1747-9991 • E-ISSN: 1747-9991
PublisherWiley (PUBLISHER • GB)
DOI10.1111/phc3.12802
OpenAlexW4200014423
LanguageEN
Citations received2
References cited26

Automated scientific discovery is a discipline which lies at the boarder of artificial intelligence, natural sciences and philosophy of science, and deals with the application of artificial intelligence methods to scientific discovery. Historically, its origins go back to the 1960s and there have been at least three major research programs in the field, each of them having different objectives, concerns, and methodology: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group, and the programs called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett, and P. Thagard. In the paper I briefly describe new developments in the field, recent issues in machine learning and data science applications to scientific discovery, and explore lessons for the philosophy of science that can be drawn

Cognitive science · Data science · Engineering ethics · Epistemology · Normative · Philosophy of science · Scientific discovery · Turing · Computer Science · Engineering · Genetics, Bioinformatics, and Biomedical Research · Mathematics · Philosophy · Philosophy and History of Science · Psychology · Scientific Computing and Data Management · Artificial Intelligence

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