The ability to recognize and identify aircraft on sight remains a skill that soldiers need to acquire even in a highly automated military, according to the U.S. Army.
“Soldiers must be knowledgeable in the identification of all types of aerial platforms ranging from fixed, tilt, and rotary wing aircraft and unmanned aircraft, in order to protect friendly forces and to prevent fratricide,” a newly updated Army manual said.
“There have been many arguments through the years that the military does not need VACR [visual aircraft recognition], because of the advancement of technology that identifies friendly or enemy aerial platforms. [But] VACR is a basic skill that every Soldier should know. Soldiers cannot blindly depend on automation to do their jobs for them.”
The manual provides reference information on “current operational aircraft that are observed worldwide or in the combat area” but “it is not all-inclusive because of some classification guidelines.”
Along with several new aircraft profiles, the updated manual now includes photographs of the referenced aircraft. See Visual Aircraft Recognition, US Army Training Circular 3-01.80, May 2017.
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FAS and FLI partnered to build a series of convenings and reports across the intersections of artificial intelligence (AI) with biosecurity, cybersecurity, nuclear command and control, military integration, and frontier AI governance. This project brought together leaders across these areas and created a space that was rigorous, transpartisan, and solutions-oriented to approach how we should think about how AI is rapidly changing global risks.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
AI is already consequential, but its future trajectory remains contested. Policymakers should make their assumptions explicit, focus on what can be shaped rather than what can be perfectly predicted, and build institutions that can learn and respond as evidence changes.