Some tipping points that are often associated with runaway climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.
The innovative approach with the AI, according to the research team from the University of Waterloo, Canada, is that it was programmed to learn about not just one type of tipping point but the characteristics of tipping points generally.
The researchers are looking at thresholds beyond which rapid or irreversible change happens in a system. Chris Bauch, a professor of applied mathematics at the University of Waterloo and co-author of the research paper reporting results on the new deep-learning algorithm, said: “We found that the new algorithm was able to not only predict the tipping points more...