The Move Toward Green Machine Learning - insideBIGDATA

A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model's carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers. The authors examined the total energy used and carbon emitted by five NLP models: GPT-3, GShard, Meena, Switch Transformer, and T5. They reported separate figures for training and inference. The authors joined the Allen Institute and others in calling for greener AI.