Definition
This term refers to the significant resource requirements associated with AI technologies, particularly during the training phase of large models. It encompasses electricity usage for data centers, water consumption for cooling systems, and the carbon footprint generated by hardware manufacturing. As AI models grow larger and more complex, their environmental cost increases, prompting the field of Green AI to focus on creating more energy-efficient algorithms and sustainable computing practices to mitigate these negative ecological effects.
Summary
The ecological consequences of developing, training, and deploying AI models, including energy consumption and carbon emissions.
Key Concepts
- Carbon Footprint
- Energy Efficiency
- Data Center Consumption
- Green AI
Use Cases
- Auditing AI model sustainability
- Optimizing training algorithms for lower power
- Policy making for tech infrastructure