AI can significantly reduce costs in a number of business areas: Automated work: Reduces labor costs and improves efficiency. Predictive maintenance: Prevents costly breakdowns. Resource optimization: Streamlines supply chains and inventory. Improve customer service: Handles basic queries, freeing up human agents. Improves R&D and production: Speeds up processes and reduces errors.
The growing environmental impact of AI, mainly in terms of energy-related calculations, can be mitigated by improving model performance, using renewable energy, optimizing hardware, and promoting responsible practices.
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.
