Machine understanding and deep learning representation.
Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent...
| Publicado en: | Synthese Vol. 201; no. 2; pp. 1 - 47 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Feb2023
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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=161613552&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 161613552 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Feb2023 vid: 201 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 161613552 10.1007/s11229-022-03999-y ppf: 1 ppct: 46 formats: fmt: – @attributes: type: T – @attributes: type: P size: 751KB tig: atl: Machine understanding and deep learning representation. aug: au: Tamir, Michael Shech, Elay affil: University of California, Berkeley, California, USA Auburn University, Auburn, Alabama, USA sug: keyword: Artificial intelligence Deep learning Information theory Machine learning Representation Understanding ab: Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2023. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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