Stefan Mileschin

But thinks it is brilliant

US Air Force Major General Daniel Simpson has said that an experimental AI-based target recognition program turned out to be absolutely rubbish but had supplied data which implied it was better than it appeared.

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.

However the algorithm did not perform well. It actually was accurate maybe about 25 percent of the time, Simpson said.

Simpson said the AI was a good example of brittle AI, which "occurs when any algorithm cannot generalize or adapt to conditions outside a narrow set of assumptions".

A 2020 report by researcher and former Navy aviator Missy Cummings [no really.ed] identified the issue when the data used to train the algorithm consists of too much of one type of image or sensor data from a unique vantage point, and not enough from other vantages, distances, or conditions, you get brittleness, Cummings said. In settings like driverless-car experiments, researchers will just collect more data for training.

This is tricky in military settings where there might be a whole lot of data of one type — say overhead satellite or drone imagery — but very little of any other type because it wasn't useful on the battlefield.

https://fudzilla.com/news/ai/54044-a...killing-people