Tag : deep learning

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 hardware requirements are increasingly generating e-waste, which contains hazardous materials and resource depletion. This requires better recycling, sustainable design and a circular economy approach.

The huge energy consumption of AI comes from the intense computing power required to train and run complex models, especially in data centers. This results in a large carbon footprint and resource depletion, requiring urgent solutions such as efficient hardware, model optimization, and the use of renewable energy.

AI literacy is the ability to understand, use, and critically evaluate artificial intelligence systems, including their benefits, risks, and ethical implications.