Automatic Detection of Generated Texts and Energy: Exploring the Relationship

Abstract

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.

Keywords

• 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