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A circular economy for AI hardware aims to reduce waste and maximize resource use by designing durable, repairable, and renewable devices, promoting reuse and recycling, and recovering valuable materials.
AI model optimization makes AI models faster, smaller, and more efficient by improving performance and reducing resource requirements. Key techniques include pruning, quantization, knowledge distillation, and hyperparameter tuning. This enables deployment on resource-constrained devices and reduces costs.
AI is crucial for renewable energy to optimize resource utilization, improve forecasting, and enhance grid management, leading to a more sustainable and reliable energy system.
AI powers green computing by optimizing energy usage in data centers and devices, improving resource management, and promoting sustainable technology practices.
AI exacerbates inequality by benefiting privileged groups while marginalized communities face barriers to access, justice, and opportunity.
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