With the dangers of continued fossil fuel use and environmental mismanagement unfolding before our eyes in the form of intense heat waves, droughts, and wildfires, it’s obvious that dramatic, transformative action must be taken.

Throughout the pessimistic debate about the effectiveness of climate change policy and methods of pollution mitigation, almost every solution under the sun has been proposed. Some have suggested the widespread use of carbon capture technology, while others, like Boyan Slat, have developed ways to remove garbage from our oceans. But one technology has the potential to revolutionize climate action: Artificial intelligence (AI).

In a recent paper spearheaded by professor David Rolnick of the Department of Computer Science, researchers studied the application of machine learning to climate science in great detail. Each section of the article explored a specific sector—including electricity, industry, or infrastructure—and explained the ways machine learning could be used to reduce the sector’s impact on the climate. 

Machine learning is an offshoot of AI. While the aim of AI is to develop computers that can “think” like a human, machine learning is more about training computers on experiences and data to recognize patterns and make decisions.  

“Machine learning is looking at large amounts of data, finding the patterns that are common across that data and linking those to what the algorithm is asked to do,” Rolnick said in an interview with The McGill Tribune.

Uses for machine learning fall into a few categories, according to Rolnick: Monitoring, optimization, simulation, and forecasting. Take, for example, how forecasting can be applied to the study of electricity.

“Machine learning is used to predict the amount of electricity that will be in demand at a given point in time so there is enough supply to meet that but not more than there needs to be,” Rolnick explained. “Understanding how much power is needed and how much power is available is important to make sure the grid is running effectively and without waste.”

Since AI cannot plant trees or pass legislation, its practical application may seem abstract. However, its effects are tangible: AI has been used to increase crop yield in India, improve electricity efficiency on wind farms by planning for weather, and improve data centres’ efficiency.

“Most of the technologies that I am talking about are at some level of deployment. For example, the U.K.’s national grid has already integrated deep learning models into forecasting supply and demand of electricity and has greatly increased efficiency as a result,” Rolnick said. “The UN uses AI to guide interventions in flooded areas [….] These are not just research projects and it’s fundamentally important.”

Although AI is an incredibly promising technology, there are a couple of drawbacks to be addressed. One of these drawbacks is human bias—since humans write the algorithms and supply the human-collected data to train machine learning, these tools can replicate human biases. To prevent these biases, then, human bias needs to be corrected—there is no software fix. 

“We cannot technology our way out of most biases,” Rolnick said. “The solutions to biases in technology are the same as solutions to biases in any other part of human endeavour. That means they are hard, but they are solvable via human choices.” 

This technology also requires enormous quantities of energy for algorithms to be trained and maintained, but the energy can be minimized by designing efficient algorithms and planning applications carefully. 

“It’s also worth noting that most of the negative climate impacts of AI globally come from how it is used, not the direct energy consumption,” Rolnick wrote in a follow-up email.

Although machine learning models can be quite energy hungry, the models Rolnick uses are not exceedingly energy-intensive. With careful planning, scientists hope that the emissions benefits from these models outweigh their energy consumption.