This Air Force Targeting AI Thought It Had a 90% Success Rate. It Was More Like 25%
Too little of the right kind of data can throw off target algorithms. But try telling the algorithm that.
By Patrick Tucker
Technology Editor
December 9, 2021
If the Pentagon is going to rely on algorithms and artificial intelligence, it’s got to solve the problem of “brittle AI.” A top Air Force official recently illustrated just how far there is to go.
In a recent test, an experimental target recognition program performed well when all of the conditions were perfect, but a subtle tweak sent its performance into a dramatic nosedive,
Maj. Gen. Daniel Simpson, assistant deputy chief of staff for intelligence, surveillance, and reconnaissance, said on Monday.
Initially, the AI was fed data from a sensor that looked for a single surface-to-surface missile at an oblique angle, Simpson said. Then it was fed data from another sensor that looked for multiple missiles at a near-vertical angle.
https://www.defenseone.com/technology/2021/12/air-force-targeting-ai-thought-it-had-90-success-rate-it-was-more-25/187437/
