The Defense Department is continuing its push to reduce human thought and human action to a few lines of code. The latest effort comes from the Air Force Office of Scientific Research, which is looking to build "mathematical or computational models of human attention, memory, categorization, reasoning, problem solving, learning and motivation, and decision making." The ultimate goal, according to a recent request for research proposals, is to "elucidate core computational algorithms of the mind and brain." Good luck with that, guys.

It's one in a heap of different Office projects to try to teach machines to act more like living things. "Nature has used evolution to build materials and sensors that outperform current sensors (for example, a spider’s haircells can detect air flow at low levels even in a noisy background)," the Office writes. So it's got a second program, to not only "mimic existing natural sensory systems, but also add existing capabilities to these organisms" so they can more "precise[ly] control" their God-given gifts.

For example, maybe the military can develop better "active and passive camouflage" by learning from creatures who are able to change color, to hide from their predators. Maybe the armed forces can improve on eznymes which would eat away at an enemy's gear. Maybe the military can bioengineer the organisms living in extreme heat, or extreme acidity, to make our equipment stronger.

The Office also wants to know what makes collections of living creatures tick. So the Office is looking to assemble a "fundamental understanding of the interactions between demographic groups... to explain and predict outcomes between competing factions within geographic regions." It wants to "identify and quantify cultural variability" to model the effects of an "info warfare campaign" online.

Once that's done, it's back to digitizing brainwork. "New computational and mathematical principles of cognition are

needed to form a symbiosis between human and machine systems," the Office says.

[Photo: Lawrence Berkeley Lab]

ALSO: