AI Federated Learning: Federated learning trains a shared machine learning model on multiple decentralized devices (such as smartphones), keeps the training data on the devices, and only shares model updates, thus preserving privacy.
AI transparency and reporting involves making AI systems understandable and accountable for how they work, what data they use, and their potential impacts.
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.
