SOCOM Is Using AI to Search for the Ideal MARSOC Raider

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A U.S. Marine Corps Forces Special Operations Command multipurpose canine handler assists a military working dog handler with 1st Law Enforcement Battalion, I Marine Expeditionary Force Information Group, in fast-roping from a U.S. Navy MH-60 Seahawk helicopter onto a building during a multipurpose canine subject matter expert exchange hosted by 1st Marine Raider Battalion, MARSOC, on Marine Corps Base Camp Pendleton, California, July 24, 2019. (U.S Marine Corps photo/Drake Nickels)
A U.S. Marine Corps Forces Special Operations Command multipurpose canine handler assists a military working dog handler with 1st Law Enforcement Battalion, I Marine Expeditionary Force Information Group, in fast-roping from a U.S. Navy MH-60 Seahawk helicopter onto a building during a multipurpose canine subject matter expert exchange hosted by 1st Marine Raider Battalion, MARSOC, on Marine Corps Base Camp Pendleton, California, July 24, 2019. (U.S Marine Corps photo/Drake Nickels)

In a not-too-distant future, recruiting for military special operations could focus less on aptitude scores and physical fitness and more on a computer algorithm that can predict success based on past candidates.

At least, that's the theory.

Officials with U.S. Special Operations Command are experimenting with artificial intelligence and machine learning to determine whether it's possible to highlight the qualities that distinguish an ideal operator. This summer, officials began collecting and assessing a wide swath of data regarding candidates for Marine Corps Forces Special Operations Command, said David Spirk, SOCOM's chief data officer.

"It's really going to be our first experiment. It's exciting," he said at a Special Operations Policy Forum hosted this month by the New America think tank.

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Spirk told Military.com that the data being collected covers as wide a range as possible, excluding only information that is protected from a privacy standpoint.

"Outside of that, we want the machine to do the regression backward for us, and so it might pick up something extremely unique, [such as the made-up example] that we didn't realize every Marine Raider is from Arkansas," he said. "What it would allow us to do is allow the machine to say, this is actually the series of things, that, when they come together, make this Marine Raider who made it all the way through successful."

Information is collected from candidates, who are then tracked through MARSOC's grueling two-phase assessment and selection process and onward.

"In, I think, the next six months, we'll have a minimal viable product that we can begin using to use machine learning to identify who would be a good Marine Raider," Spirk said.

The high levels of attrition in special operations and certain other military specialties have long had officials searching for efficiencies and early success indicators that go beyond selection criteria. The Marine Corps has studied non-cognitive testing, which purports to measure qualities such as impulse control and "grit," as a way to discover officer candidates who have what it takes to make it through the training pipeline and on to a successful career.

"Using a machine to [predict success], rather than us showing up at a gym with a bunch of pamphlets and saying, 'Who wants to be a Raider today' -- I think we can get a lot more efficient," Spirk said.

If the concept proves out, he said, it will transform every part of the process to enter SOF -- and even flag troops in the conventional forces for special training and preparation.

"If I can bring more people in who are likely to pass the assessment and selection process, I don't have to generate the level of individuals to go through training, so every time we take a turn, we save money by using these technologies," he said.

Similar testing is now underway at U.S. Army Special Operations Command, Spirk said.

-- Hope Hodge Seck can be reached at hope.seck@military.com. Follow her on Twitter at @HopeSeck.

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