Big Data and Deep Learning Models
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,...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Santa Catarina
2022-12-01
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| Series: | Principia: An International Journal of Epistemology |
| Subjects: | |
| Online Access: | https://periodicos.ufsc.br/index.php/principia/article/view/84419 |
| _version_ | 1811293609366913024 |
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| author | Daniel Sander Hoffmann |
| author_facet | Daniel Sander Hoffmann |
| author_sort | Daniel Sander Hoffmann |
| collection | DOAJ |
| 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.
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| first_indexed | 2024-04-13T05:04:00Z |
| format | Article |
| id | doaj.art-d8d8152990c441b998d56964d52b7c5c |
| institution | Directory Open Access Journal |
| issn | 1414-4247 1808-1711 |
| language | English |
| last_indexed | 2024-04-13T05:04:00Z |
| publishDate | 2022-12-01 |
| publisher | Universidade Federal de Santa Catarina |
| record_format | Article |
| series | Principia: An International Journal of Epistemology |
| spelling | doaj.art-d8d8152990c441b998d56964d52b7c5c2022-12-22T03:01:14ZengUniversidade Federal de Santa CatarinaPrincipia: An International Journal of Epistemology1414-42471808-17112022-12-0126310.5007/1808-1711.2022.e84419Big Data and Deep Learning ModelsDaniel Sander Hoffmann0Universidade Estadual do Rio Grande do Sul (UERGS)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. https://periodicos.ufsc.br/index.php/principia/article/view/84419Artificial IntelligenceArtificial Neural NetworksBig DataBlack BoxesDeepfakesDeep Learning |
| spellingShingle | Daniel Sander Hoffmann Big Data and Deep Learning Models Principia: An International Journal of Epistemology Artificial Intelligence Artificial Neural Networks Big Data Black Boxes Deepfakes Deep Learning |
| title | 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_short | Big Data and Deep Learning Models |
| title_sort | big data and deep learning models |
| topic | Artificial Intelligence Artificial Neural Networks Big Data Black Boxes Deepfakes Deep Learning |
| url | https://periodicos.ufsc.br/index.php/principia/article/view/84419 |
| work_keys_str_mv | AT danielsanderhoffmann bigdataanddeeplearningmodels |