But her partner convinced her not to give up her career entirely, but to apply her knowledge of artificial intelligence to some of the challenges posed by climate change. “You don’t have to quit your job in artificial intelligence to help fight the climate crisis,” he said. “There are ways that almost any artificial intelligence technique can be applied to different parts of climate change.” He joined the Montreal-based artificial intelligence research center Mila and became a founding member of Climate Change AI, a volunteer organization of academics advocating the use of artificial intelligence to solve problems related to climate change. Sasha Luccioni, a founding member of the non-profit group Climate Change AI, decided to apply her knowledge of computer science to problems related to climate change. (Camille Rochefort-Boulanger) Luccioni is part of a growing community of researchers in Canada using artificial intelligence in this way. In 2019, he authored a report arguing that machine learning can be a useful tool for mitigating and adapting to the effects of climate change. Computer scientists define machine learning as a form of artificial intelligence that allows computers to use historical data and statistical methods to make predictions and decisions without having to be programmed to do so. Common applications of machine learning include predictive text, spam filters, language translation applications, streaming content recommendations, malware and fraud detection, and social media algorithms. Applications for machine learning in climate research include climate prediction and optimization of electricity, transportation and energy systems, according to the 2019 report.
Preparation for crop diseases
Researchers at the University of Prince Edward Island (UPEI) are using artificial intelligence models to warn farmers about risks to their crops as the weather becomes more unpredictable. “If you have a dry year, you see very little disease, but with a wet year, you can get quite a lot of disease around the plants,” said Aitazaz Farooq, interim associate dean of UPEI’s School of Climate Change and Adaptation. Aitazaz Farooque is the interim associate dean of the UPEI School of Climate Change and Adaptation, which is piloting a project aimed at using weather forecasting to predict crop diseases. (Jane Robertson/CBC) Researchers can plug weather data from previous years into an artificial intelligence model to predict the type of diseases that could endanger crops at different times of the year, Farooq said. “Then the grower can be a little more proactive and understand what’s going in,” he said. WATCHES | Check out UPEI’s School of Climate Change and Adaptation:
Tour of the new climate change laboratory in St. Peter’s Bay
From drones to dormitories, the state-of-the-art research facility in St. Peter’s Bay will have world-class students and researchers studying the many facets of climate change. PEI’s agriculture is primarily rain-fed, and providing farmers with more accurate rainfall forecasts can also help them have more successful crop yields, Farooque said. “With climate change, we’re seeing different trends where total cumulative precipitation doesn’t change much, but timing matters,” he said. “If it doesn’t happen at the right time, then the sustainability of our agriculture could be at stake.”
A study of behavior around disruptive weather conditions
Another application of artificial intelligence is being studied at McGill University, where researchers are using historical and recent weather data to predict the social impacts of extreme weather events affected by climate change, such as heat waves, droughts and floods. According to Renee Sieber, an associate professor in McGill’s geography department, the researchers hope to learn how people have responded to disruptive weather events in the past and whether that can teach us anything about how resilient we will be in the future. The McGill Observatory contains weather records dating back to 1863 that will be used in an AI project that will analyze people’s reactions to extreme weather events. (McGill University Archives) The team will use a form of artificial intelligence called natural language processing to analyze weather-related social narratives in newspapers and other media. “Artificial intelligence is very good at organizing, synthesizing, finding trends or some sentiment from huge amounts of unstructured text,” Sieber said. “Basically, what you do is throw magazine articles into a bucket and see what comes out.” Sieber said her team will take the findings from past articles and current social media and compare them to corresponding weather records to trace people’s reactions to weather events over time. The records from McGill Observatory are the longest and most detailed non-stop written records of weather events in Canada and contain a huge amount of information, Sieber said. Weather recording there began in 1863 and continued into the 1950s. “This data is the only direct measure of climate change we have [in Canada]” said Sieber.
Optimization of energy use
Some Canadian companies are using artificial intelligence to minimize waste and create more energy-efficient infrastructure. Scale AI, a Montreal-based investor group that funds projects related to supply chains, has worked with grocery chains such as Loblaws and Save-on-Foods to identify shopping patterns. Through AI, companies are able to better predict demand and less food will be wasted, said Scale AI CEO Julien Billot. “Every optimization we can achieve improves the resilience of supply chains and helps use fewer resources,” he said. Another Montreal company, BrainBox Al, focuses on improving energy efficiency by optimizing HVAC systems in commercial buildings. The machine learning technology is contained in a 30 cm wide box that connects to a building’s HVAC system. Raises or lowers temperatures based on data inputs such as weather forecasts, utility rates and carbon emissions calculations. BrainBox AI technology optimizes a building’s HVAC system using data such as weather forecasts and utility rates. (BrainBox AI) The system has been able to reduce the energy consumed by some HVAC systems by 25 percent, said BrainBox CEO Sam Ramadori, and for two years, the company has installed the technology in 350 buildings in 18 countries. “The same kind of intelligence that we bring to buildings has potentially an infinite number of applications. Just pick a domain,” Ramadori said. “How we make cement, how we ship goods — all of that has to become more efficient over time as part of the fight against climate change.” According to Ramadori, BrainBox AI is working on technology that will allow buildings to connect to each other and communicate with energy networks through the company’s cloud server. Researchers work in the BrainBox AI office. (BrainBox AI) This has the potential to minimize energy waste on a city scale, as energy networks more accurately identify where and when energy is needed, he said. “The utility can say, ‘Hey, the next two hours are going to be busy. I want you to find a way to reduce consumption.” And with the AI brain on top, it can say, “Okay, I can reduce a little bit here and a little bit there. I’ve got you covered,” Ramadori said.
Equity Limits in AI
Access to the kind of artificial intelligence that can help solve climate-related problems is not equal around the world. Wildfires in North America, for example, tend to get more attention from developers than locust infestations in East Africa, said David Rolnick, an assistant professor of computer science at McGill and a member of Mila. “How climate change affects a community varies greatly between different geographies,” said Rolnick, who is also the president of Climate Change AI. David Rolnick, an assistant professor in the School of Computer Science at McGill University and a member of Mila, said relying on artificial intelligence to solve climate-related issues raises some concerns about fairness. (Guillaume Simoneau) AI technology relies on datasets, and many communities don’t have access to strong enough data needed to build machine learning algorithms, Rolnick said. In Canada, some Indigenous and remote northern communities still face significant digital divides compared to other parts of the country, he said. “Democratization work is fundamentally important,” Rolnick said. Rolnick co-authored a study last year outlining several limitations to applying artificial intelligence to climate change solutions in Canada. He called for increased funding for AI research and more AI education in primary and secondary education, as well as standards and protocols for sharing data related to climate projects. Rapid implementation of large-scale AI programs for politicians and leaders in climate-related industries could help “demystify” AI, the report says. “We often see a lack of knowledge, and training programs can help people understand what these tools can and can’t do,” Rolnick said.
