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A natural phenomenon consisting of polka-dot-style formations has been cropping up around the world, and scientists are baffled as to why. The circular-shaped patches of ground have been seen in deserts in Australia and Namibia but now experts believe they are more widespread than originally thought. Known as “fairy circles”, there are now 263 known sites across the globe where they can be found, according to new research published in the journal Proceedings of the National Academy of Sciences (PNAS). They have been documented in 15 countries, across three continents, including the Sahel region of Africa, Madagascar, and in Middle-West Asia. And yet, despite the spread of these anomalies, scientists are still none the wiser about how they actually form. A team led by environmental scientist Emilio Guirado, of the University of Alicante in Spain, explained in their paper on the "intriguing" phenomenon: “We conducted a global and systematic assessment of fairy circle-like vegetation patterns and discovered hundreds of [fairy-circle]-like locations on three continents. “Our study provides insights into the ecology and biogeography of these fascinating vegetation patterns and the first atlas of their global distribution.” The mysterious circles appear in desert regions and can be as wide as 12 metres (39 feet) in diameter. They are almost always spaced out and rarely connect or overlap with one another. Several theories have been put forward as to what causes them, including, tiny insects, termites, and plant toxins. But, none have been accompanied by any significant evidence and some have been debunked completely. One significant factor limiting their study is they are often found in places that are difficult to access and are inhospitable. Locating the 263 different sites of “fairy circles” involved analysing high-resolution satellite imagery. Guirado and his team wrote in their paper: “[The sites] include those already identified in Namibia and Western Australia, as well as areas never described before, including the Sahel, Western Sahara, Horn of Africa, Madagascar, Southwest Asia, or Central and Southwest Australia. “By doing so, our study provides a global atlas of areas showing FC-like vegetation patterns and expands the known existence of this vegetation type to new countries and continents.” The team hopes that locating new sites will enable them to find common traits that may point towards their cause. Sign up to our free Indy100 weekly newsletter Have your say in our news democracy. Click the upvote icon at the top of the page to help raise this article through the indy100 rankings.

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An artificial intelligence system has achieved a key milestone by winning multiple races against three world-class drone-racing champions, marking the first time AI has beat humans at a physical sport. The AI system called Swift, developed by researchers from the University of Zurich in Germany and Intel, could prove its mettle in first-person view (FPV) drone racing – a sport in which pilots fly quadcopters at speeds exceeding 100kmph. Until now AI systems have achieved a number of remarkable victories over humans in a range of strategy-based games, including IBM’s Deep Blue winning at chess against Gary Kasparov in 1996 and Google’s AlphaGo defeating top Go champion Lee Sedol in 2016. However, physical sports are more challenging for AI as they are less predictable than board or video games. “We don’t have a perfect knowledge of the drone and environment models, so the AI needs to learn them by interacting with the physical world,” Davide Scaramuzza, one of the authors of the study from the University of Zurich, explained. AI-driven autonomous drones have until now taken twice as long as those piloted by humans to fly through racetracks unless an external position-tracking system was used to precisely control their trajectories. But the new Swift AI drone, described in a new study in the journal Nature, has demonstrated that it can react in real-time to the data collected by an onboard camera, just like the one used by human racers in the sport. Sensors on the drone measure acceleration and speed while the AI system uses data from the camera to locate the drone in space and detect the gates along the racetrack. A control unit in the drone, also based on AI, a control unit, then chooses the best action to finish the race circuit as fast as possible. Researchers say the Swift drone trained itself to fly in a simulated environment by trial and error. Using simulations, scientists could avoid destroying multiple drones in the early stages of learning when the system often crashes. “To make sure that the consequences of actions in the simulator were as close as possible to the ones in the real world, we designed a method to optimise the simulator with real data,” study first author Elia Kaufmann said. During the testing phase, the drone flew autonomously using very precise positions provided by an external position-tracking system, while also recording data from its camera. By comparing the two data sets, Swift could learn to autocorrect errors it made in interpreting information from the onboard sensors, scientists say. The AI was soon ready to challenge some of the world’s top human drone pilots – the 2019 Drone Racing League champion Alex Vanover, the 2019 MultiGP Drone Racing champion Thomas Bitmatta, and three-time Swiss champion Marvin Schaepper. In races that took place between 5 and 13 June 2022, on a special track designed in a hangar of the Dübendorf Airport near Zurich, Swift achieved the fastest lap, with a half-second lead over the best lap by a human pilot. The special track – about 25 by 25 meters in dimension – was built with seven square gates that had to be passed in the right order to complete a lap. Drones had to execute challenging maneuvers to successfully finish the track, including an acrobatic “Split-S” feature that involves half-rolling the drone and executing a descending half-loop at full speed. While Swift could record the fastest lap, humans are more adaptable than the AI drone, which failed when the conditions were different from what it was trained for, scientists say. However, they add that the new breakthrough in AI flight is an important way beyond drone racing. “Drones have a limited battery capacity; they need most of their energy just to stay airborne. Thus, by flying faster we increase their utility,” Dr Scaramuzza said. The new research, according to scientists, may lead to better drones for forest monitoring or space exploration, and in cases where flying fast is important to cover large spaces in a limited time. They say fast AI drones could also be used for shooting action scenes in movies and may also “make a huge difference” for rescue drones sent inside a burning building. Read More Ukraine launches ‘massive’ drone strikes on six regions of Russia – destroying war planes Ukraine-Russia war live: Kyiv’s huge drone attack as Putin floods frontline with ‘poorly trained troops’ To stop wildfires, residents in some Greek suburbs put their own money toward early warning drones Google launches AI to go to meetings for you Behind the AI boom, the armies of overseas workers in ‘digital sweatshops’ Nvidia sales hit record high as AI chip demand soars