(Government Executive) 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.
The researchers hope their new neural net will enable experiments in which a computer can easily understand when a soldier is evaluating targets in a virtual scenario, as opposed to having to spend lots of time teaching the system to understand how to structure different individuals’ data, eye movements, their P300 responses, etc. The goal, one day, is a neural net that can learn instantaneously, continuously, and in real-time, by observing the brainwaves and eye movement of highly trained soldiers doing their jobs.