How ensembles can reduce machine learning's carbon footprint - Dataconomy

Commercial and industrial applications of artificial intelligence and machine learning are unlocking economic opportunities, transforming the way we do business, and even helping to solve complex social and environmental problems. In fact, generative applications of this technology have become tools for environmental sustainability. With machine learning's capability to analyze and make predictions using massive pools of data, these applications are now able to accurately model climate change and fluctuations, so that energy infrastructures and energy consumption can be re-engineered accordingly. Ironically, training large-scale models via deep neural networks requires vast computational power. It also produces a great deal of thermal energy from each of the associated graphics processing units (GPUs) or tensor processing units (TPUs) used.