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.”
Last year, Google’s DeepMind lab showed that an AI could beat the world’s top player in the game of Go, a game considered exponentially harder than chess. “You can train the system to do deep learning in a [highly structured] environment but if the Go game board changed dynamically over time, the AI would never be able to solve that problem. You have to figure out…in that dynamic environment we have in the military world, how do we retrain this learning process from a systems perspective? Right now, I don’t think there’s any way to do that without having the humans train those systems.”
Their research branched out of a multi-year, multi-pronged program called the Cognition and Neuroergonomics Collaborative Technology Alliance.
SOURCE: Patrick Tucker