In 2019, as a postdoctoral researcher at the University of Pennsylvania, David Rolnick was lead author of an influential report that described various ways machine learning could reduce greenhouse-gas emissions and help society adapt to climate change, from predicting energy needs to managing forests to modeling planet-scale weather systems. His coauthors included DeepMind cofounder Demis Hassabis and Turing Award winner Yoshua Bengio. That year, Rolnick was a lead organizer for the first workshops on climate change at three leading AI conferences, and lead organizer of an event on AI at the United Nations Climate Change Conference.
“David Rolnick has been hugely influential in convening AI practitioners to work on climate change,” says Andrew Ng, a cofounder of Google Brain and former chief scientist at Baidu. “By helping shape a vision of how AI could help climate change and tirelessly organizing a community around it, he has catalyzed a significant amount of activity on this important topic.”
Rolnick now leads a group at McGill University that uses different AI techniques to attack problems related to climate.
For example, data relevant to climate change—records of infrastructure spending or greenhouse-gas emissions or simply weather patterns—varies enormously between countries. And yet climate needs to be understood at a global level.
“In the Global South there can be less information on infrastructure,” says Rolnick. “So policymakers may have less to go on when it comes to making decisions about energy requirements or managing coastal flood risk.” Countries also have different regulations about what does and does not get recorded. Germany gathers information on where its solar panels are, for example, but the US does not, so researchers are using machine learning to identify solar panels in the US from satellite imagery. Machine learning can also be used to forecast energy demand more accurately than is possible with existing techniques, Rolnick says. This allows energy providers to manage their electricity grids more efficiently.
Rolnick and his colleagues are trying to come up with new machine-learning techniques that could be applied to the study of climate change as well.
For instance, they are building algorithms for transfer learning, which involves training an AI on one set of examples and then transferring what it’s learned to new situations. They are also researching meta-learning, a set of techniques that make AI better at learning from small or incomplete data sets. Rolnick thinks these methods are especially useful for modeling biodiversity because sources of real-world data are so patchy.
Rolnick is also involved in projects that combine machine learning with climate models to simulate complex physical and atmospheric processes like cloud formation. The precise means by which clouds form, and how much they reflect or absorb sunlight, is one of the largest sources of uncertainty in existing climate models—partly because simulating clouds in climate models is computationally intensive. Using machine learning to find patterns in when and where clouds form and how reflective they tend to be—without trying to understand the underlying atmospheric chemistry—allows scientists to run models more quickly.
Rolnick and his collaborators are convinced AI will be a crucial tool in fighting climate change. All the same, there are growing concerns that machine learning itself is part of the problem. He acknowledges that training today’s largest AI models consumes large amounts of energy, but he points out that this contributes a tiny fraction of global emissions—and that the real climate risks from AI arguably have more to do with its uses in areas such as oil and gas exploration. “I’m much more worried about negative applications of machine learning than I am about its energy use,” he says.