Sustainable

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.

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.