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The U.S. military has stepped up its use of artificial intelligence tools after the Oct. 7 Hamas attack on Israel, according to a new report. bloomberg. Skylar Moore, chief technology officer for U.S. Central Command, told news outlets that machine learning algorithms helped the Pentagon identify targets for more than 85 airstrikes in the Middle East this month.
US bombers and fighter jets conducted airstrikes on seven facilities in Iraq and Syria on February 2, completely destroying or at least damaging rockets, missiles, drone storage facilities, and militia operations centers. Gave. The Pentagon also used AI systems to detect and destroy a rocket launcher in Yemen and a surface fighter jet in the Red Sea in multiple airstrikes in the same month.
The machine learning algorithms used to narrow down the targets were developed under Project Maven, a now-defunct partnership between Google and the Department of Defense. Precisely, the project involved the US military using Google’s artificial intelligence technology to analyze drone footage and flag images, with further human review. This caused an uproar among Google employees. Thousands of people petitioned the company to end its partnership with the Department of Defense, and some even quit altogether over the involvement. Months after that employee’s outcry, Google decided not to renew the contract, which ended in 2019.
Moore said. bloomberg U.S. military forces in the Middle East have continued to experiment with algorithms that use drone and satellite imagery to identify potential targets, even after Google ended its involvement. He said the military had been testing its use in digital exercises over the past year, but began using the targeting algorithm in actual operations after the Oct. 7 Hamas attack. However, she revealed that human workers constantly check and verify her AI system’s goal recommendations. She also had human personnel suggest how to conduct the attack and what weapons to use. “There’s never an algorithm that you just run, come to a conclusion, and move on to the next step,” she says. “For every step that involves AI, there is a human check-in at the end.”