Sustainable

This influential paper introduces the concept of Green AI, which encourages AI research that yields better results while consuming less computing power and thus lower environmental impact. The authors contrast Green AI with what they call Red AI: research that seeks to improve accuracy through massive computational power, regardless of the environmental cost. The paper proposes new evaluation criteria for AI research that include computational efficiency alongside accuracy, encouraging more sustainable approaches to AI development.

This research presents a comprehensive framework for developing environmentally sustainable software, with a particular focus on AI systems and applications. The study identifies key principles and practices for green software engineering, including energy-aware design patterns, efficient coding practices, and sustainability metrics. The authors provide concrete guidelines and case studies demonstrating how to implement sustainable software development practices throughout the entire software lifecycle.

This research investigates techniques for making the training of large language models more environmentally sustainable without compromising model performance. The authors propose novel methods for reducing energy consumption during training, including adaptive batch sizing, efficient model architectures, and intelligent resource allocation. The study provides extensive empirical analysis of different training strategies and their impact on both model quality and environmental footprint.

Ce rapport intermédiaire du Shift Project examine les implications environnementales des technologies d’intelligence artificielle. L’étude analyse la consommation d’énergie, les émissions de carbone et les ressources nécessaires à l’entraînement et au déploiement des modèles d’IA. Le rapport formule des recommandations pour développer et utiliser l’IA en accord avec les objectifs de durabilité écologique et les principes de sobriété numérique.

This paper presents a methodology for accurately measuring the carbon emissions of AI workloads running in cloud environments. The research provides detailed measurements across different cloud providers and regions, showing how carbon intensity can vary significantly based on location and time of day. The authors also release tools and best practices for researchers and practitioners to measure and reduce the carbon footprint of their AI applications.

Cette étude de Boavizta explore les indicateurs et les ressources nécessaires pour évaluer le retour sur investissement (ROI) des démarches de numérique responsable. Le rapport analyse comment les organisations peuvent justifier économiquement leurs actions en faveur d’un numérique plus durable, en identifiant des métriques pertinentes qui allient performance environnementale et bénéfices financiers. L’étude fournit un cadre méthodologique et des recommandations concrètes permettant aux décideurs de construire un argumentaire solide en faveur des initiatives de numérique responsable, facilitant ainsi leur adoption à grande échelle.

This paper provides a comprehensive analysis of the environmental impact of AI systems throughout their lifecycle, from development to deployment and maintenance. The authors examine various strategies for reducing the carbon footprint of AI, including efficient model architectures, green computing practices, and renewable energy usage. The research also presents concrete recommendations for developing and deploying AI systems in an environmentally responsible manner.

This comprehensive survey examines the environmental impact of artificial intelligence throughout its lifecycle, from development to deployment and maintenance. The paper provides a systematic analysis of the challenges in making AI more sustainable, including hardware efficiency, algorithm design, and operational practices. The authors identify key opportunities for reducing AI’s environmental footprint and propose a research agenda for sustainable AI development.

This practical guide provides concrete recommendations for implementing sustainable computing practices in AI research and development. The research outlines specific strategies for reducing energy consumption and carbon emissions throughout the AI development lifecycle, from experiment design to deployment. The authors present case studies and empirical evidence demonstrating the effectiveness of various sustainability practices in real-world AI projects.

This research investigates methods for developing environmentally sustainable natural language processing systems, focusing on reducing computational costs and energy consumption. The study analyzes various efficiency techniques specific to NLP tasks, including model compression, efficient attention mechanisms, and task-specific optimizations. The authors provide empirical evidence of energy savings and performance trade-offs across different NLP tasks and model architectures.