Big Data and Deep Learning Models
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Principia (Florianópolis. Online) |
Texto Completo: | https://periodicos.ufsc.br/index.php/principia/article/view/84419 |
Resumo: | Although deep learning has historically deep roots, with regard to the vast area of? artificial intelligence and, more specifically, to the study of machine learning and artificial neural networks, it is only recently that this line of investigation has developed fruits with great commercial value, starting to have thus a significant impact on society. It is precisely because of the wide applicability of this technology nowadays that we must be alert, in order to be able to foresee the negative implications of its indiscriminate uses. Of fundamental importance, in this context, are the risks associated with collecting large amounts of data for training neural networks (and for other purposes too), the dilemma of the strong opacity of these systems, and issues related to the misuse of already trained neural networks, as exemplified by the recent proliferation of deepfakes. This text introduces and discusses these issues with a pedagogical bias, thus aiming to make the topic accessible to new researchers interested in this area of? application of scientific models. |
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Big Data and Deep Learning ModelsArtificial IntelligenceArtificial Neural NetworksBig DataBlack BoxesDeepfakesDeep LearningAlthough deep learning has historically deep roots, with regard to the vast area of? artificial intelligence and, more specifically, to the study of machine learning and artificial neural networks, it is only recently that this line of investigation has developed fruits with great commercial value, starting to have thus a significant impact on society. It is precisely because of the wide applicability of this technology nowadays that we must be alert, in order to be able to foresee the negative implications of its indiscriminate uses. Of fundamental importance, in this context, are the risks associated with collecting large amounts of data for training neural networks (and for other purposes too), the dilemma of the strong opacity of these systems, and issues related to the misuse of already trained neural networks, as exemplified by the recent proliferation of deepfakes. This text introduces and discusses these issues with a pedagogical bias, thus aiming to make the topic accessible to new researchers interested in this area of? application of scientific models.Federal University of Santa Catarina – UFSC2022-12-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsc.br/index.php/principia/article/view/8441910.5007/1808-1711.2022.e84419Principia: an international journal of epistemology; Vol. 26 No. 3 (2022); 597–614Principia: an international journal of epistemology; Vol. 26 Núm. 3 (2022); 597–614Principia: an international journal of epistemology; v. 26 n. 3 (2022); 597–6141808-17111414-4247reponame:Principia (Florianópolis. Online)instname:Universidade Federal de Santa Catarina (UFSC)instacron:UFSCenghttps://periodicos.ufsc.br/index.php/principia/article/view/84419/52281Copyright (c) 2022 Daniel Sander Hoffmannhttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessHoffmann, Daniel Sander2022-12-13T17:42:20Zoai:periodicos.ufsc.br:article/84419Revistahttps://periodicos.ufsc.br/index.php/principiaPUBhttps://periodicos.ufsc.br/index.php/principia/oaiprincipia@contato.ufsc.br||principia@contato.ufsc.br1808-17111414-4247opendoar:2022-12-13T17:42:20Principia (Florianópolis. Online) - Universidade Federal de Santa Catarina (UFSC)false |
dc.title.none.fl_str_mv |
Big Data and Deep Learning Models |
title |
Big Data and Deep Learning Models |
spellingShingle |
Big Data and Deep Learning Models Hoffmann, Daniel Sander Artificial Intelligence Artificial Neural Networks Big Data Black Boxes Deepfakes Deep Learning |
title_short |
Big Data and Deep Learning Models |
title_full |
Big Data and Deep Learning Models |
title_fullStr |
Big Data and Deep Learning Models |
title_full_unstemmed |
Big Data and Deep Learning Models |
title_sort |
Big Data and Deep Learning Models |
author |
Hoffmann, Daniel Sander |
author_facet |
Hoffmann, Daniel Sander |
author_role |
author |
dc.contributor.author.fl_str_mv |
Hoffmann, Daniel Sander |
dc.subject.por.fl_str_mv |
Artificial Intelligence Artificial Neural Networks Big Data Black Boxes Deepfakes Deep Learning |
topic |
Artificial Intelligence Artificial Neural Networks Big Data Black Boxes Deepfakes Deep Learning |
description |
Although deep learning has historically deep roots, with regard to the vast area of? artificial intelligence and, more specifically, to the study of machine learning and artificial neural networks, it is only recently that this line of investigation has developed fruits with great commercial value, starting to have thus a significant impact on society. It is precisely because of the wide applicability of this technology nowadays that we must be alert, in order to be able to foresee the negative implications of its indiscriminate uses. Of fundamental importance, in this context, are the risks associated with collecting large amounts of data for training neural networks (and for other purposes too), the dilemma of the strong opacity of these systems, and issues related to the misuse of already trained neural networks, as exemplified by the recent proliferation of deepfakes. This text introduces and discusses these issues with a pedagogical bias, thus aiming to make the topic accessible to new researchers interested in this area of? application of scientific models. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufsc.br/index.php/principia/article/view/84419 10.5007/1808-1711.2022.e84419 |
url |
https://periodicos.ufsc.br/index.php/principia/article/view/84419 |
identifier_str_mv |
10.5007/1808-1711.2022.e84419 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufsc.br/index.php/principia/article/view/84419/52281 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Daniel Sander Hoffmann http://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Daniel Sander Hoffmann http://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Federal University of Santa Catarina – UFSC |
publisher.none.fl_str_mv |
Federal University of Santa Catarina – UFSC |
dc.source.none.fl_str_mv |
Principia: an international journal of epistemology; Vol. 26 No. 3 (2022); 597–614 Principia: an international journal of epistemology; Vol. 26 Núm. 3 (2022); 597–614 Principia: an international journal of epistemology; v. 26 n. 3 (2022); 597–614 1808-1711 1414-4247 reponame:Principia (Florianópolis. Online) instname:Universidade Federal de Santa Catarina (UFSC) instacron:UFSC |
instname_str |
Universidade Federal de Santa Catarina (UFSC) |
instacron_str |
UFSC |
institution |
UFSC |
reponame_str |
Principia (Florianópolis. Online) |
collection |
Principia (Florianópolis. Online) |
repository.name.fl_str_mv |
Principia (Florianópolis. Online) - Universidade Federal de Santa Catarina (UFSC) |
repository.mail.fl_str_mv |
principia@contato.ufsc.br||principia@contato.ufsc.br |
_version_ |
1799875201194786816 |