Casacuberta, David and Estany, Anna (2019) Convergence between experiment and theory in the processes of invention and innovation. THEORIA. An International Journal for Theory, History and Foundations of Science, 34 (3). pp. 373-387. ISSN 2171-679X
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Abstract
This article starts from the debate in philosophy of science between the theoretical and the experimental traditions, and it aims to show its relation with the study of innovation and invention processes in science, thus crossing the most theoretical approaches of the philosophy of science with issues more related to the philosophy of technology and applied science. In this way we analyze the interrelation between experiment and theory in the processes of invention and innovation and connect the fields of theoretical and applied science, thus showing the continuity between them. That way, we can also show how in science there is always mutual dependence on theory and experimentation, and how that dependence can also be extrapolated to the processes of innovation and invention.
Taking as starting point the debate around the theoretical and experimental traditions, we will see to what extent the arguments that question the theoretical traditions and opt for the experimental ones fit with the phenomena of invention and innovation. The case that we are going to take as a reference to apply this analysis is that of «machine learning», as a branch of computational algorithms designed to emulate human intelligence by learning from the environment. This field is relevant because, in spite of its eminently theoretical nature –in substance it is applied mathematics–, it presents a whole series of characteristics that makes it very similar to the analysis from the experimental traditions.
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| Item Type: | Published Article or Volume | |||||||||
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| Additional Information: | ISSN: 0495-4548 (print) | |||||||||
| Keywords: | Experimental tradition, epistemological innovation, invention, machine learning | |||||||||
| Subjects: | General Issues > Experimentation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Unnamed user with email theoria@ehu.es | |||||||||
| Date Deposited: | 12 Dec 2019 00:27 | |||||||||
| Last Modified: | 12 Dec 2019 00:27 | |||||||||
| Item ID: | 16707 | |||||||||
| Journal or Publication Title: | THEORIA. An International Journal for Theory, History and Foundations of Science | |||||||||
| Publisher: | Euskal Herriko Unibertsitatea / Universidad del País Vasco | |||||||||
| Official URL: | https://www.ehu.eus/ojs/index.php/THEORIA/article/... | |||||||||
| DOI or Unique Handle: | 10.1387/theoria.17921 | |||||||||
| Subjects: | General Issues > Experimentation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | September 2019 | |||||||||
| Page Range: | pp. 373-387 | |||||||||
| Volume: | 34 | |||||||||
| Number: | 3 | |||||||||
| ISSN: | 2171-679X | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/16707 |
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