Artificial intelligence-driven algorithms controlled sensor and navigation systems on a U.S. Air Force U-2S Dragon Lady spy plane in a flight test yesterday. The service says that this is the first time that artificial intelligence has been “safely” put in charge of any U.S. military system and appears to be the first time it has been utilized on a military aircraft anywhere in the world, at least publicly.
The test, which took place on Dec. 15, 2020, involved a U-2S from the 9th Reconnaissance Wing at Beale Air Force Base in California. The Air Force has dubbed the artificial intelligence (AI) software package as ARTUµ, the latest in a string of references to the iconic droid from the Star Wars universe, who serves as a sort of robotic flight engineer and navigator, in recent Air Force projects having to do with developments in AI and autonomous flight.
“ARTUµ’s groundbreaking flight culminates our three-year journey to becoming a digital force,” Will Roper, Assistant Secretary of the Air Force for Acquisition, Technology, and Logistics, an outspoken advocate of incorporating this kind of technology across the service, said in a statement. “Putting AI safely in command of a U.S. military system for the first time ushers in a new age of human-machine teaming and algorithmic competition. Failing to realize AI’s full potential will mean ceding decision advantage to our adversaries.”
“[The AI’s] role was very narrow … but, for the tasks the AI was presented with, it performed well,” the pilot of the U-2 in the test yesterday, who the service has only identified by their callsign, Vudu, said, according to The Washington Post. “For the most part I was still very much the pilot in command.”
Still, the delegation of these tasks to an artificial intelligence-driven “copilot,” as Roper referred to ARTUµ, even in a test, represents a significant milestone for the Air Force, as well as the U.S. military as a whole. The Assistant Secretary of the Air Force highlighted how AI will be critical to helping pilots, as well as the operators of various other systems, manage ever-increasing and more complex workloads, and speed up decision-making cycles. At the same time, the humans in the chain will need to be able to trust in their machines to reliably make the right calls or suggest the best courses of action.