While growing up in Normandy in northern France, Yannis Swami became fascinated by the sea. One of his earliest memories, he says, was “going to the ocean, snorkeling and being amazed at the fish and wildlife.”
And, today, the 42-year-old entrepreneur runs Sinai, a French startup that uses artificial intelligence to analyze ocean data — on shipping movements, weather patterns, and air and water pollution — To help the maritime industry streamline its operations. and reduce its environmental impact. One of its services enables users to monitor both underwater noise, from sources such as shipping or pile driving, and the presence of marine life that could be adversely affected by it.
“Human business has a huge impact on marine biodiversity,” says Swamy. “We can combine biodiversity conservation with business efficiency using the best available information, thanks in part to AI.” He points out that protecting the world's oceans – which produce about half of the Earth's oxygen – is an important part of the fight against climate change.
Sinai is part of a constellation of European startups that are using AI and machine learning to help businesses both tackle climate change and minimize their contribution to it.
Applications such as emissions monitoring, recycling management, and predictive infrastructure maintenance are gaining significant interest from large companies and investors. Last week, for example, Danish startup Electricity Maps, which tracks the carbon intensity of corporate electricity use and counts Google and Samsung among its customers, closed a €5 million financing round. gave
Power maps, Sinai and his colleagues are benefiting from a vast increase in funding for so-called climate tech. According to data platform Dealroom, European climate tech startups – in subsectors as diverse as electric mobility, nuclear fashion and alternative proteins – raised $20.2 billion last year, down slightly from the $20.4 billion they will raise in 2022. Is. Overall, they account for 43 percent of global venture capital investment in climate tech, up from 29 percent in 2022.
When it comes to AI, Lynn Kaack, who leads the AI and Climate Technology Policy Group at Berlin's Herti School, a graduate university for governance, says it pays to be skilled. “AI is not a silver bullet against climate change,” she says. “Successful startups are using machine learning as a factor in elegant technological applications, such as predicting the evolution of clouds to better operate a power grid with a high share of solar photovoltaics.”
One such company is Dutch startup Overstory, whose technology analyzes satellite images and other remote sensing data to detect when vegetation has encroached on power lines. The information enables power utilities to cut down invasive vegetation before it causes power outages or wildfires.
When it was founded, in 2018, Overstory aimed to use machine learning to detect deforestation. But, after two years, he decided to focus on serving utilities, which already spent heavily on plant monitoring. “We give them a network-wide view of the risks posed by plants,” says chief executive Fiona Sproul. “Companies never had the technology. They had to have someone walk the lines and understand the risks.”
By contrast, satellite imagery allows them to “target hazards faster and hopefully more cost-effectively,” Sproul says — adding that those risks are increasing because “utilities have Experiencing more extreme weather events”.
However, Sproul notes that AI is only as good as its training data. The company relies on experts to help label images, with visits to specialized sites still sometimes required.
“We've been powered by AI from the beginning so we're not jumping on the bandwagon,” says Sproul. “We have six years under our belts, but it's hard to identify a single tree species from space.”
Swami of Sinai also speaks similarly. Training AI models to recognize marine life, such as dolphins, turtles and seabirds, requires “less technology,” he says. “You need species identification experts to physically tag and then train the machine. It's low-tech.”
Sometimes, Swamy added, analyzing historical data is more valuable to his clients than AI-powered predictions of weather patterns. “We need to focus on where we deliver value and then choose the best tool to deliver it,” he says.
Other AI entrepreneurs are similarly modest about the technology. Alex Marti, chief executive of Mitiga, a Spanish startup that uses high-performance computing to assess the risk of natural disasters, warns that AI systems can embed biases in data that But they are trained. “It's very important to be transparent about how these models are built,” he says. “If the information isn't there, you can't use AI to build it.”
While some concerns about AI are rampant — such as “if you let it run wild, it'll turn into the Terminator” — Marty says the real danger is overextending it. “AI will help you, but it has limitations,” he says.
Some researchers, though, believe AI has environmental downsides — including huge energy and water consumption by data centers, and the potential to accelerate the spread of climate misinformation.
At the Herty School, Kock says that evaluating technology requires a broader perspective. “It's important to look at the impact of AI applications not just in the climate space,” she says. “Instead, we need to look at every place where AI is being applied, and ask: In what way is this application reducing or increasing emissions? . . . It's a multi-purpose tool, and Can also be used to harm the climate.”