🌿 Green AI: Designing Energy-Efficient AI Systems

What is Green AI?

• Defining Green AI and its importance in sustainable technology development.

Carbon Footprint of AI

• Analyzing the energy consumption and carbon emissions associated with AI technologies, with a global context.

Challenges and Opportunities

• Identifying the challenges and opportunities in reducing AI’s environmental impact across industries.

Ethical and Policy Considerations

• Exploring the ethical considerations and policy frameworks supporting sustainable AI practices.

Efficient Algorithm Design

• Techniques for designing algorithms that minimize energy consumption without sacrificing performance.

Edge AI and Low-Carbon Computing

• The role of Edge AI in reducing carbon emissions by decentralizing computation and enabling low-carbon solutions.

Case Studies

• Real-world applications of energy-efficient AI across various industries.

Designing for Sustainability

• Approaches to incorporating sustainability from the initial design phase through deployment.

Introduction to Evaluation Tools

• Overview of tools and methods for evaluating AI’s environmental impact and sustainability.

Using CodeCarbon

• Practical guidance on integrating CodeCarbon to track and optimize AI’s carbon footprint.

MLCO2 and Other Tools

• Utilizing MLCO2 and other platforms to assess and reduce the carbon emissions of AI systems.

Dashboard Reporting

• Setting up dashboards to monitor and visualize AI sustainability metrics in real time.

Continuous Monitoring

• Best practices for continuous monitoring and ongoing optimization of AI sustainability efforts.