AI’s Carbon Footprint Problem
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions.
A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions – about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. That’s twice as much as the average American exhales over an entire lifetime.
But there are ways to make machine learning cleaner and greener, a movement that has been called “Green AI.” Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
The key, however, is for AI developers and companies to know how much their machine learning experiments are spewing and how much those volumes could be reduced.
Now, a team of researchers from Stanford, Facebook AI Research, and McGill University has come up with an easy-to-use tool that quickly measures both how much electricity a machine learning project will use and how much that means in carbon emissions.
“As machine learning systems become more ubiquitous and more resource intensive, they have the potential to significantly contribute to carbon emissions,” says Peter Henderson, a PhD student at Stanford in computer science and the lead author. “But you can’t solve a problem if you can’t measure it. Our system can help researchers and industry engineers understand how carbon-efficient their work is, and perhaps prompt ideas about how to reduce their carbon footprint.”
Henderson teamed up on the “experiment impact tracker” with Dan Jurafsky, chair of linguistics and professor of computer science at Stanford; Emma Brunskill, an assistant professor of computer science at Stanford; Jieru Hu, a software engineer at Facebook AI Research; Joelle Pineau, a professor of computer science at McGill and co-managing director of Facebook AI Research; and Joshua Romoff, a PhD candidate at McGill.
“There’s a big push to scale up machine learning to solve bigger and bigger problems, using more compute power and more data,” says Jurafsky. “As that happens, we have to mindful of whether the benefits of these heavy-compute models are worth the cost of the impact on the environment.”
Machine learning systems build their skills by running millions of statistical experiments around the clock, steadily refining their models to carry out tasks. Those training sessions, which can last weeks or even months, are increasingly power-hungry. And because the costs have plunged for both computing power and massive datasets, machine learning is increasingly pervasive in business, government, academia, and personal life.
To get an accurate measure of what that means for carbon emissions, the researchers began by measuring the power consumption of a particular AI model. That’s more complicated than it sounds, because a single machine often trains several models at the same time, so each training session has to be untangled from the others.
