The Importance of Understanding Deep Learning.
Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understan...
| Publicado en: | Erkenntnis Vol. 89; no. 5; pp. 1823 - 1841 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177192671&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177192671 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Jun2024 vid: 89 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 177192671 10.1007/s10670-022-00605-y ppf: 1823 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 794KB tig: atl: The Importance of Understanding Deep Learning. aug: au: Räz, Tim Beisbart, Claus affil: https://ror.org/02k7v4d05 University of Bern, Institute of Philosophy, Länggassstrasse 49a, 3012, Bern, Switzerland https://ror.org/02k7v4d05 Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland su: Artificial neural networks Deep learning Machine learning sug: subj: Artificial neural networks Deep learning Machine learning ab: Some machine learning models, in particular deep neural networks (DNNs), are not very well understood; nevertheless, they are frequently used in science. Does this lack of understanding pose a problem for using DNNs to understand empirical phenomena? Emily Sullivan has recently argued that understanding with DNNs is not limited by our lack of understanding of DNNs themselves. In the present paper, we will argue, contra Sullivan, that our current lack of understanding of DNNs does limit our ability to understand with DNNs. Sullivan's claim hinges on which notion of understanding is at play. If we employ a weak notion of understanding, then her claim is tenable, but rather weak. If, however, we employ a strong notion of understanding, particularly explanatory understanding, then her claim is not tenable. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2024. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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