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.

Researchers at The University of Manchester are using computer simulations of chimpanzees to improve not only our understanding of how the animals walk, but also the technology we use to do it. The research, being published by the Royal Society Open Science Journal, shows how simple changes to'machine learning' algorithms can produce better looking, more accurate computer-generated animal simulations. It will also help researchers investigate the'curious way' that all primates walk and how this might be linked to stability whilst moving through the trees. Professor Bill Sellers, from the School of Earth and Environmental Sciences, says: "Starting from an animal's skeleton, computers using machine learning can now reconstruct how the animal could have moved. However, they don't always do a good job. "But with some simple changes to the machine learning goals we can now create much more accurate simulations.