Throwing light on black boxes: emergence of visual categories from deep learning.
One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw senso...
| Published in: | Synthese Vol. 198; no. 10; pp. 10021 - 10042 |
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| Format: | Article |
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Springer Nature
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152559386&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152559386 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Oct2021 vid: 198 iid: 10 pid: 237 pub: Springer Nature artinfo: ui: 152559386 10.1007/s11229-020-02700-5 ppf: 10021 ppct: 21 formats: fmt: @attributes: type: P size: 428KB tig: atl: Throwing light on black boxes: emergence of visual categories from deep learning. aug: au: López-Rubio, Ezequiel affil: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga (UMA), Bulevar Louis Pasteur 35, 29071, Málaga, Spain Departamento de Lógica, Historia y Filosofía de la Ciencia, Universidad Nacional de Educación a Distancia (UNED), Paseo de Senda del Rey 7, 28040, Madrid, Spain su: Artificial intelligence Deep learning Generative adversarial networks Convolutional neural networks Cognitive science Cognition sug: subj: Artificial intelligence Deep learning Generative adversarial networks Convolutional neural networks Cognitive science Cognition keyword: Machine learning Visual categories ab: One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial deep learning networks, namely the convolutional neural network and the generative adversarial network have been found to possess the capability to build internal states that are interpreted by humans as complex visual categories, without any specific hints or any grammatical processing. This emergent ability suggests that those categories do not depend on human knowledge or the syntactic structure of language, while they do rely on their visual context. This supports a mild form of empiricism, while it does not assume that computational functionalism is true. Some consequences are extracted regarding the debate about amodal and grounded representations in the human brain. Furthermore, new avenues for research on cognitive science are open. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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