The Air Force is on its third of a series of sprint exercises intended to show how artificial intelligence can supercharge human decision-making. And while officials are raving about the results, they also demonstrate that the algorithms can still propose bad or nonsensical options that need to be babysat.
Maj. Gen. Robert Claude, Space Force representative to the Air Force’s Advanced Battle Management Cross-Functional Team, said participating in the Decision Advantage Sprint for Human-Machine Teaming (DASH) series, led by his team, was an “eye-opening experience,” though it proved the limitations of AI processing as well.
The DASH-2 sprint, held at Shadow Operations Center-Nellis (SHOC-N), the USAF’s premier tactical command and control battle lab, outside of Las Vegas earlier this summer focused on a decision-intensive process: matching the right platform and weapon to a desired military target, Claude told The War Zone at the U.S. Air Force Association’s Air, Space & Cyber Conference.
According to a release, six industry teams and one SHOC-N innovation team participated in the exercise, attacking the challenge of designing AI-enabled microservices that could help operators select a weapon to destroy an identified target. The kinds of targets identified in the scenario were not described. Developers watched human-only battle-management crews and designed their microservices based on their observed needs and processes. Finally, human-only teams went head to head in the weapon-matching exercise against human-machine teams.
In terms of generating courses of action – or COAs – the machines easily had it over their human counterparts on speed and quantity.
“I think it was roughly eight seconds [for the algorithm] to generate COAs, as opposed to 16 minutes for the operators,” Claude said, adding that the machine generated 10 different COAs to the human team’s three.
But AI-generated slop continues to be a problem.
“While it’s much more timely and more COAs generated, they weren’t necessarily completely viable COAs,” Claude said. “So what is going to be important going forward is, while we’re getting faster results and we’re getting more results, there’s still going to have to be a human in the loop for the foreseeable future to make sure that, yes, it’s a viable COA, or just a little bit more of this to make a COA viable, to make decisions.”
Claude clarified in response to another question the kinds of mismatches the AI was creating.
“If you’re trying to identify a targeting package with a particular weapon against a particular target, but it didn’t factor in, it’s an [infrared] target, or it’s an IR-sensor weapon, but it’s cloudy and [bad] weather conditions,” Claude said. “So that’s just as an example, those fine-tuned types of things that they found these COAs weren’t where they needed to be. But as we build this out, theoretically into the future … those sorts of things will be factored in.”
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