From oceans to farms, AI-driven insights are helping people make informed decisions that promote sustainability, reduce waste and combat the effects of climate change.
“The ocean absorbs much more carbon than the rainforests do,” Kendra MacDonald, CEO of Canada’s Ocean Supercluster, told the audience during a panel on ClimateTech. “The North Atlantic in Canada has the most intense carbon sink in the world.”
But, as the ocean absorbs more and more heat, the effectiveness of that carbon sink is decreasing, which means there’s a risk that the ocean becomes an emitter of carbon versus an absorber of carbon.
The good news is that ocean-based climate solutions can deliver up to 35 per cent of annual greenhouse gas emission cuts needed to limit a 1.5°C rise in global temperature by 2050, according to research by the High Level Panel for a Sustainable Ocean Economy.
“The opportunities are endless for where artificial intelligence can play a role in being able to help,” said MacDonald. For example, AI can be used to monitor remote regions such as the Arctic, automate hydro dams to make them more efficient and track marine litter in an effort to clean up our oceans.
Novarium is bringing companies, investors and the scientific community together to create sustainable solutions for the blue economy—and many start-ups in its acceleration program are using AI to make oceans better, said Daniel Olivier, senior strategic advisor with Novarium.
Whale Seeker is one of those start-ups. Combining human expertise with AI, it uses satellite imagery to identify, locate and inventory marine mammals, such as whales, sea lions and polar bears. With its proprietary AI, Whale Seeker can perform tasks in less than an hour that normally take human beings 25 hours.
“You can imagine how important this is becoming as a decision-support tool for commercial navigation or even sustainable fishing,” said Olivier.
Another start-up, Blue Lion Labs, uses AI for a similar function—but to track and monitor harmful organisms such as algae blooms that cause losses in aquaculture and public health.
“So whether it’s maritime shipping and ports, whether it’s biotech, aquaculture, sustainable fishing, marine mammals, AI can save money, can save time, but it also can make the oceans better,” said Olivier.
On land, AI is being used to optimize the way we grow crops, both in fields and in vertical farms.
“For our industry, yield and quality is hugely important to the overall business,” said Juanita Moore, VP of corporate development with TruLeaf. The company has created vertical farms that grow leafy greens 365 days a year, without the need for pesticides, herbicides or fungicides, while reducing transportation costs and spoilage.
“We have a controlled environment where we’re not at the mercy of climate, so we can actually get repeatable harvests and figure out how to consistently grow better—and use AI and data insights to drive that optimization,” she said.
Open fields are less controlled, but here AI can be used as well.
Semios, for example, has developed a massive IoT network as part of a crop management platform for tree fruit, nuts and vines—providing insights into everything from weather to water management to pest pressure.
“That’s where AI can come in because we have this really vast data set that humans are not going to be able to work their way through. But through models, we can provide a value that’s simple to understand,” said Stuart Shiell, data insights lead for Semios.Artificial intelligence could play a leading role in the fight against climate change, from speeding up data collection and simulations, to accelerating scientific discovery. But David Rolnick, a core academic member at MILA and co-founder and chair of Climate Change AI, said that AI isn’t a silver bullet.
“It’s not the answer to climate change—there is no one answer to climate change,” said Rolnick during a panel at ALL IN about the challenges of measuring AI impacts on climate change. “We need lots and lots of different tools. AI is one of those tools.”
The challenges ahead are numerous. For example, it’s difficult to collect data that’s constantly changing or limited in scope, and not all of it is on the public record. Lack of transparency, and even greenwashing, means data could be imbalanced. That, in turn, means algorithms could be imbalanced.
Powering AI also has an environmental impact, from the emissions produced in data centres to the carbon required to make AI chips. That’s why companies need to start thinking about “the entire impact all the way down the line, so that we don’t have really misleading numbers,” said Sylvain Carle, partner at Innovobot Resonance Ventures.
It’s also critical that climate assessment metrics are standardized. “It doesn’t help if Europe has one set of standards, North America has another and Asia has another,” said Lee Tiedrich, professor at Duke University Science & Society.
Meeting these challenges will be critical in harnessing AI responsibly to accelerate positive environmental action.
