Automatic Detection of Generated Texts and Energy: Exploring the Relationship
The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/49/e3sconf_icies2023_01101.pdf |
| _version_ | 1797737683117670400 |
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| author | Al Karkouri Adnane Ghanimi Fadoua Bourekkadi Salmane |
| author_facet | Al Karkouri Adnane Ghanimi Fadoua Bourekkadi Salmane |
| author_sort | Al Karkouri Adnane |
| collection | DOAJ |
| description | The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection. |
| first_indexed | 2024-03-12T13:32:57Z |
| format | Article |
| id | doaj.art-fd84b80dc5934fd6812b997e4217619a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T13:32:57Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-fd84b80dc5934fd6812b997e4217619a2023-08-24T08:21:14ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014120110110.1051/e3sconf/202341201101e3sconf_icies2023_01101Automatic Detection of Generated Texts and Energy: Exploring the RelationshipAl Karkouri Adnane0Ghanimi Fadoua1Bourekkadi Salmane2Ibn Tofail UniversityIbn Tofail UniversityUniversity Of PoitiersThe proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/49/e3sconf_icies2023_01101.pdfautomatic detectiongenerated textsenergy consumptionenergy efficiencyai systemsnatural language processing (nlp)machine learningdeep learningmodel compressionalgorithmic optimizationsupervised learningunsupervised learningdeep neural networksmodel architecturecomputational resourcesenvironmental impactsustainabilitytrustworthy aiethical considerationsinterdisciplinary research |
| spellingShingle | Al Karkouri Adnane Ghanimi Fadoua Bourekkadi Salmane Automatic Detection of Generated Texts and Energy: Exploring the Relationship E3S Web of Conferences automatic detection generated texts energy consumption energy efficiency ai systems natural language processing (nlp) machine learning deep learning model compression algorithmic optimization supervised learning unsupervised learning deep neural networks model architecture computational resources environmental impact sustainability trustworthy ai ethical considerations interdisciplinary research |
| title | Automatic Detection of Generated Texts and Energy: Exploring the Relationship |
| title_full | Automatic Detection of Generated Texts and Energy: Exploring the Relationship |
| title_fullStr | Automatic Detection of Generated Texts and Energy: Exploring the Relationship |
| title_full_unstemmed | Automatic Detection of Generated Texts and Energy: Exploring the Relationship |
| title_short | Automatic Detection of Generated Texts and Energy: Exploring the Relationship |
| title_sort | automatic detection of generated texts and energy exploring the relationship |
| topic | automatic detection generated texts energy consumption energy efficiency ai systems natural language processing (nlp) machine learning deep learning model compression algorithmic optimization supervised learning unsupervised learning deep neural networks model architecture computational resources environmental impact sustainability trustworthy ai ethical considerations interdisciplinary research |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/49/e3sconf_icies2023_01101.pdf |
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