It’s no secret that the US military is very interested in both artificial intelligence and robotics. Boston Dynamics has been developing robots for possible use in combat situations for years, though only in support capacities. Now, all of that may be about to change.

Researchers from the DCS Corp engineering firm and the Army Research Lab are trying to teach AI to shoot — specifically, when to shoot and when not to. Artificial Intelligence wired with electronic sensors have already proven they’re faster and more efficient than humans at a variety of tasks, from avoiding driving accidents to even playing board games. However, when applied to a theoretical combat bot, the science can get murky.

Human soldier are better able to judge on the fly, whether they need to pull the trigger or stand down. This is possible thanks to our ability to draw on memories and experience to augment the situational data we’re taking in and put it into context. AI, on the other hand has to be trained to deal with tasks within a particular stipulation. This lack of dynamic adaptability makes it ill-suited to for the responsibility that comes with carrying lethal force. Now, the Army Research Lab has presented a paper where it tries to teach a neural network how to think like a human, by feeding it datasets of human brainwaves.

“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 Army’s chief scientist, said at a recent event. “Like Google just solved the Go game problem.”

In 2016, Google’s DeepMind lab proved that its AI, AlphaGO, was able to beat the world champion at Go, a board game considered to be significantly harder than competitive 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,” Russell added. “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.”

Teaching AI an important lesson in human perceptive skills

The Army Research Lab’s project is studying how to do just that, by feeding their neural network datasets of brainwaves. When a person receives a contextual cue or has to make a snap decision, their brain fires off a burst of electricity called a P300 response. The team intends to have their bot study these brainwaves, as well as eye movements, in order to learn when a person is making a decision. The idea is that, instead of training an AI based on each soldier’s sensor input data, the army can instead train their AI to learn directly from human technique.

Think of like sitting in a Mathematics class. You can train a student to solve a particular type of equation through repetition. However, you could instead teach him how to process the equation on a basic level, allowing him to successfully tackle different variations of the same problem. That’s what this research is all about. Teaching the neural network to understand how an experienced soldier thinks, can let it learn different tasks just by observing a squad it’s monitoring. At least, that’s how the researchers hope it’ll work.