green ai

Introduction

Green AI is an emerging field focused on developing and deploying artificial intelligence (AI) systems that are environmentally friendly and sustainable. This research area addresses the significant carbon footprint and energy consumption associated with AI technologies, aiming to create more efficient and sustainable AI solutions.

Key Insights

Energy Efficiency and Carbon Footprint Reduction:
- Green AI aims to reduce the carbon footprint of AI models by optimizing hyperparameters, monitoring model footprints, and benchmarking models, with reported energy savings up to 115% .
- AI techniques, including machine learning (ML) and deep learning (DL), are being used to manage networks and improve energy efficiency in communication systems, particularly in the context of 5G and future 6G networks .
Applications of Green AI:
- Green AI can optimize operations in various sectors, such as smart grids, buildings, agriculture, public transportation, environmental monitoring, waste management, and manufacturing, thereby reducing greenhouse gas emissions and conserving natural resources .
- In smart cities, Green AI supports efficient, sustainable, and equitable urban transformations by addressing complex urbanization problems with a consolidated AI approach.
Green Learning and Edge AI:
- Green Learning (GL) is proposed as an alternative to traditional deep learning, characterized by low carbon footprints, small model sizes, low computational complexity, and logical transparency.
- Edge AI, which involves deploying AI models on wireless edge networks near end-user devices, requires energy-efficient design methodologies to ensure sustainable performance, especially in latency-sensitive applications.
Challenges and Future Directions:
- Despite the potential benefits, challenges such as the high energy consumption and carbon footprint of AI systems need to be addressed through effective policies and regulations .
- There is a need for more Green AI tools and solutions, particularly in the areas of electricity storage and energy-efficient computing systems .

Conclusion

Green AI represents a significant shift towards more sustainable and environmentally friendly AI technologies. By optimizing energy efficiency and reducing carbon footprints, Green AI can contribute to various sectors, including smart cities, communication systems, and edge computing. However, addressing the challenges associated with energy consumption and developing effective policies will be crucial for the widespread adoption and success of Green AI.

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Summary

Introduction

Key Insights

Conclusion

Sources and full results

A systematic review of Green AI

Green AI research has matured, with potential energy savings of up to 115%, and is ready for adoption and industrial implementation.

DEVELOPMENT AND DEPLOYMENT OF GREEN ARTIFICIAL INTELLIGENCE

Green AI can reduce environmental impact and improve society's sustainability by optimizing various sectors and addressing challenges like energy consumption and carbon footprint.

AI Models for Green Communications Towards 6G

AI-based green communications models can manage networks and improve energy efficiency in 6G, addressing challenges like quality of service, security, flexibility, and intelligence.

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Green AI is crucial for smart city transformation, addressing efficiency, sustainability, and equity issues while addressing shortfalls in mainstream AI systems.

Green Learning: Introduction, Examples and Outlook

Green learning (GL) offers low carbon footprints, small model sizes, low computational complexity, and logical transparency in artificial intelligence, offering energy-efficient solutions for cloud centers and mobile/edge devices.

Impact of Artificial Intelligence on Future Green Communication

AI technology can enhance green communication by reducing power consumption and improving network efficiency in various applications.

On the Opportunities of Green Computing: A Survey

Green Computing has the potential to address resource constraints and environmental impact in AI development, encouraging more researchers to focus on this research direction for more environmentally friendly AI.

Human-Centered AI: A Framework for Green and Sustainable AI

Human-centered AI approach can achieve green and sustainable AI by focusing on human needs and desires.

Green Edge AI: A Contemporary Survey

Green edge AI aims to optimize energy efficiency in deep learning tasks, addressing resource limitations in wireless edge networks and ensuring optimal performance for 6G networks.

A Review for Green Energy Machine Learning and AI Services

Advanced Green AI techniques, particularly LSTM, have significantly improved forecasting accuracy in the field of sustainable energy, but more solutions are needed for electricity storage challenges.

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