Modern sensors can see farther than humans. Electronic circuits can shoot faster than nerves and muscles can pull a trigger. Humans still outperform armed robots in knowing what to shoot at — but new research funded in part by the Army may soon narrow that gap.
Researchers from DCS Corp and the Army Research Lab fed datasets of human brain waves into a neural network — a type of artificial intelligence — which learned to recognize when a human is making a targeting decision. They presented their paper on it at the annual Intelligent User Interface conference in Cyprus in March.
Why is this a big deal? Machine learning relies on highly structured data, numbers in rows that software can read. But identifying a target in the chaotic real world is incredibly difficult for computers. The human brain does it easily, structuring data in the form of memories, but not in a language machines can understand. It’s a problem that the military has been grappling with for years.
“We often talk about deep learning. The challenge there for the military is that that involves huge datasets and a well-defined problem,” Thomas Russell, the chief scientist for the Army, said at a recent National Defense Industrial Association event. “Like Google just solved the Go game problem.”