Artificial Intelligence Can Make Companies Greener, but It Also Guzzles Energy

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For many, using AI has both positive and negative effects on their carbon emissions and water use.

Google and American Airlines used AI to help planes create fewer vapor trails, which contribute to global warming. Google also uses it to forecast river floods and to recommend eco-friendly routes on its maps service. San Francisco-based startup Verse is using AI to simplify the process for companies to get clean power. AI is even being used to generate images of what a warmer world will look like, from depicting ocean encroachment on coastal cities to mapping arid lands prone to wildfires.

For instance, manufacturing a graphics-processing unit—a piece of hardware that speeds up computations in deep learning—involves pure water and rare metals. That adds to climate cost. Bloom used more than a thousand of these GPUs in its training, and that is just one of many external factors Luccioni’s group evaluated in their research.

In the U.S., where there is no central electric grid, training models in one state versus another can have a significant impact on carbon emissions. In California, where there are large amounts of wind power, emissions could be lower than if the exact same model were trained using energy in a state like Virginia, which is reliant mostly on fossil fuels for power.

His group’s estimates for one of Google’s large-language models, known as LaMDA, had water use on the order of a million liters for training alone. Google’s on-site data center water consumption overall in 2022 increased by roughly 20% compared with 2021. that its Asian data centers’ actual water-use effectiveness was three times worse than that of the company’s locations in the Americas, meaning water use for identical AI training could triple based on location. That is because it is typically warmer in Asia, which necessitates water-cooled chillers.

Google, for instance, says it is using AI to accelerate climate action by arming individuals and organizations with better information to make choices. “We have used tested practices to reduce the carbon footprint of workloads by large margins, helping reduce the energy of training a model by up to 100x and emissions by up to 1,000x.

 

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