These simple changes can make AI research much more energy efficient

Since the first paper studying this technology's impact on the environment was published three years ago, a movement has grown among researchers to self-report the energy consumed and emissions generated from their work. Having accurate numbers is an important step toward making changes, but actually gathering those numbers can be a challenge. "You can't improve what you can't measure," says Jesse Dodge, a research scientist at the Allen Institute for AI in Seattle. "The first step for us, if we want to make progress on reducing emissions, is we have to get a good measurement." To that end, the Allen Institute recently collaborated with Microsoft, the AI company Hugging Face, and three universities to create a tool that measures the electricity usage of any machine-learning program that runs on Azure, Microsoft's cloud service.