Automatic Detection of Generated Texts and Energy: Exploring the Relationship

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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Bibliographic Details
Main Authors: Al Karkouri Adnane, Ghanimi Fadoua, Bourekkadi Salmane
Format: Article
Language:English
Published: EDP Sciences 2023-01-01
Series:E3S Web of Conferences
Subjects:
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
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/49/e3sconf_icies2023_01101.pdf
Description
Summary: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.
ISSN:2267-1242

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