A group of prominent scientists and engineers has released a sixty-page paper detailing how artificial intelligence can be used to fight climate change, as part of a broader initiative called Climate Change AI. The twenty-two person team includes some of the world’s leading AI and climate researchers, including Yoshua Bengio, winner of the 2019 Turing Award, and Felix Creutzig, a coordinating lead author of the United Nations’ IPCC Sixth Assessment Report. After a preliminary announcement in June, the newly released paper is accompanied by graphics, interactive summaries, digital resources, and a discussion forum aimed at bringing together experts in AI and areas such as energy, agriculture, and disaster response.
The paper focuses on machine learning, a powerful branch of artificial intelligence that can learn from data to find patterns and can optimize solutions much more quickly than humans. “Machine learning can be deployed to help reduce greenhouse gas emissions and build a society that is more resilient to climate change,” said David Rolnick, a postdoctoral fellow at the University of Pennsylvania and chair of Climate Change AI. “It can help design better batteries, control heating and cooling systems efficiently, track the effects of a changing climate using satellite imagery, and a lot more.”
The authors emphasize that machine learning is only one piece of the puzzle, and that all applications require cooperation between many stakeholders. “Machine learning is not a silver bullet, but it can facilitate many climate change strategies from policy and engineering,” said Lynn Kaack, a postdoctoral researcher at ETH Zürich and co-chair of Climate Change AI. “It is important that machine learning experts work together with those who deeply understand the problems of climate change to make sure machine learning is applied where it can make a difference.”
The Climate Change AI initiative was launched at a special workshop of the International Conference on Machine Learning, which brought together researchers, entrepreneurs, and investors from across the world. A series of similar events is planned, the next of which will be held in December at the leading AI conference Neural Information Processing Systems. “By serving as a nexus for collaboration between universities, companies, governments, and NGOs, we hope to empower meaningful work at the intersection of climate change and machine learning,” said Priya Donti, a PhD student at Carnegie Mellon University and co-chair of Climate Change AI. “We hope our work will inspire others to leverage their skills to tackle climate change — we need all hands on deck.”
The paper “Tackling Climate Change with Machine Learning” was co-authored by David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, and Yoshua Bengio, and is available online at https://arxiv.org/pdf/1906.05433.pdf.