Much of the conversation around artificial intelligence (AI) these days centers on whether it will eventually take your job, how it's trying to compete with humans in creative fields, or how it can be misused, say, as a writing tool. You can probably chalk this one-sidedness up to an all-too-human tendency to be suspicious of new tech that isn't well understood by the mainstream (yet). But AI isn't intrinsically evil or good: It's a tool, a vast technology with enormous potential, and there are myriad ways to implement it beyond the current discourse. One vitally important use case is helping us fight and survive the consequences of climate change. Whether it's mitigating the effects of disasters such as floods and fires more quickly or building a cleaner energy grid, the evidence is mounting that AI has an essential role to play in helping to protect us as the planet reacts to climate change.

The carbon footprints of data centres, which provide cloud-computing services, can range widely.Credit: Feature China/Future Publishing/Getty As machine-learning experiments get more sophisticated, their carbon footprints are ballooning. Now, researchers have calculated the carbon cost of training a range of models at cloud-computing data centres in various locations1. Their findings could help researchers to reduce the emissions created by work that relies on artificial intelligence (AI). The team found marked differences in emissions between geographical locations. For the same AI experiment, "the most efficient regions produced about a third of the emissions of the least efficient", says Jesse Dodge, a researcher in machine learning at the Allen Institute for AI in Seattle, Washington, who co-led the study.

As human-robot teamwork becomes increasingly common, a key challenge is to fluidly and intuitively coordinate team members' interactions. Our Productivity and Wellness Pal (PaWPal) and Coordinating Human-Robot Teamwork projects explore two modalities of human-robot coordination: active, where agents intentionally attempt to understand and influence the plans of human teammates, and passive, where agents simply react to their human teammates' varying behavior.

Five graduate students at Carnegie Mellon University's School of Computer Science have received Siebel Scholars awards for 2022. "Every year, the Siebel Scholars continue to impress me with their commitment to academics and influencing future society. This year's class is exceptional, and once again represents the best and brightest minds from around the globe who are advancing innovations in healthcare, artificial intelligence, financial services and more," said Thomas M. Siebel, chairman of the Siebel Scholars Foundation. "It is my distinct pleasure to welcome these students into this ever-growing, lifelong community, and I personally look forward to seeing their impact and contributions unfold." Ahuja is a Ph.D. candidate in the Human-Computer Interaction Institute (HCII) whose research focuses on machine learning and sensing.

The importance of start-ups and climate tech companies in advancing the use of machine learning to combat climate change was emphasized at a recent online workshop. May 6, 2020 pv magazineAcademics from a group devoted to considering how machine learning can help combat climate change have spoken of the response to a recent workshop which was moved online because of the Covid-19 crisis.The Climate Change AI group hosted a'tackling climate change with machine learning' workshop during this year's International Conference on Learning Representations (ICLR) event."We've "These forecasts can then be sold to electricity suppliers …

Two Department of Energy Computational Science Graduate Fellowship recipients are leading an effort to address global climate change effects with machine-learning techniques. Priya Donti, a third-year fellow in computer science and public policy at Carnegie Mellon University, and Kelly Kochanski, a fourth-year fellow in Earth surface processes at the University of Colorado Boulder, are on the steering committee (Donti is co-chair) for Climate Change AI. The group's website says it is a coalition of "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Machine learning algorithms identify patterns in known data and use that information to make predictions or to classify previously unseen data. Machine learning is a key component of artificial intelligence (AI).