A new deep-learning algorithm has been developed that can trigger an early warning system ‘against runaway climate change’.
The research, led by Chris Bauch, a professor of applied mathematics at the University of Waterloo, explores the tipping points ‘beyond which rapid or irreversible change happens in a system’.
I just hope that these new findings will lead to equitable, positive change.Madhur Anand
“We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point,” Bauch said. “Many of these tipping points are undesirable, and we’d like to prevent them if we can.”
Invisible methane eruptions caused by melting arctic permafrost; rapid weather changes due to ocean current breakdown; or rapid sea rises on the back of ice sheet disintegration, are among the thresholds beyond which run-away climate change could occur.
The researchers say their new AI tool is unique in its ability to forewarn against the biggest threats because it has been trained to understand the characteristics of tipping points in general, rather than just one. Taught to comprehend a ‘universe of possible tipping points’ incorporating half a million models, the AI has been successfully tested on real-world examples of different systems, as well as historical climate scenarios.
The ability to detect patterns well in advance of possible tipping points will allow actions to be taken that can avert the worst outcomes.
“Our improved method could raise red flags when we’re close to a dangerous tipping point,” explained Timothy Lenton, director of the Global Systems Institute at the University of Exeter and one of the study’s co-authors. “Providing improved early warning of climate tipping points could help societies adapt and reduce their vulnerability to what is coming, even if they cannot avoid it.”
Which makes the algorithm a “game-changer for its ability to anticipate big shifts, including those associated with climate change,” said Madhur Anand, another of the researchers on the project and director of the Guelph Institute for Environmental Research.
She added: “It definitely gives us a leg up. But of course, it’s up to humanity in terms of what we do with this knowledge. I just hope that these new findings will lead to equitable, positive change.”
AI causes emissions
AI also causes emissions, a fact explored in a recent study by researchers from Lancaster University, who found that ICT’s share of emissions globally could be between 2.1-3.9%, more than aviation at 2%, and higher than previously thought. A percentage they say will rise due to the growth of frontier technologies like AI, IoT, and big data. The report sets out a range of actions for reversing this trend, including legally binding net zero targets on ICT companies that covers their supply chain emissions; prioritising some ICT uses above others; and the creation of a plan to achieve net zero in the ICT industry by 2050.
“Much more needs to be done by the ICT sector to understand and mitigate its footprint, beyond focusing on a transition to renewables and voluntary carbon reduction targets,” said Dr Kelly Widdicks, co-author of the study from Lancaster University. “We need a comprehensive evidence base of ICT’s environmental impacts as well as mechanisms to ensure the responsible design of technology that is in-line with the Paris Agreement.”
If these obstacles can be overcome, the Boston Consulting Group believes AI can contribute to a 10% reduction in global emissions by 2030. Solving the energy puzzle will be key though, and innovation can help clean up this problem for frontier technologies. Take CodeCarbon, it calculates emissions created by AI and gives developers information on coding to reduce it, as well as insight on where to locate infrastructure geographically so it can easily access clean energy sources; or the collaboration between Google and AI-platform electrictyMap, which shows the search engine giant when clean electricity is being supplied to the grid, allowing it to carry out computing processes at the best times – these are the types of solutions that can drastically reduce the energy burden of tech.