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AI beats humans again: Drone racing champion defeated by intelligent machine
AI beats humans again: Drone racing champion defeated by intelligent machine
News on August 31, 2021 shows that artificial intelligence (AI) has successfully defeated humans in chess, video games and other fields, and now for the first time, AI has defeated humans in a Defeat human champions in real sports designed and participated by humans
The latest humans to be defeated by AI are three world champions in drone racing, who lost to a drone that can speed around a three-dimensional track. Flying AI drone. The AI drone is called Swift and was developed by researchers at the University of Zurich. The Swift is able to fly at extremely fast speeds while avoiding or minimizing collisions and crashes. The researchers published their results in the journal Nature and described the Swift competing with three drone racing champions. A series of competitions between them. This site notes that
Swift won 15 of the 25 races and set the fastest lap record. Swift uses a technique called deep reinforcement learning to find the best instructions for flying around the track through trial and error. During training, Swift crashed hundreds of times in the simulated environment, but because it was a simulation, the researchers could simply start over. During the race, Swift sends video from a camera on the drone to a neural network that detects gates on the track. This information is combined with readings from inertial sensors to estimate the drone's position, direction and speed. These estimates were then fed into another neural network that was able to calculate the instructions to send to the drone.
The Swift was faster at the start of the race and took tighter turns than the human competitors.
Swift’s fastest lap time was 17.47 seconds, half a second faster than the fastest human competitor. But the Swift isn't invincible, losing 40 percent of its races against humans and having several crashes. An important advance for Swift is its ability to cope with real-world challenges such as aerodynamic turbulence, camera blur, and lighting changes that can cause systems to make errors when trying to follow precomputed trajectories. Swift's developers say the technology could also be used in other areas, such as fire search and rescue or inspection of large structures such as ships
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