AI May Read Soldiers” Intents, Anticipate Their Needs

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April 17, 2019 | Originally published by Date Line: April 17 on

Scientists at the Army Research Lab are studying how brain activity could be harnessed to optimize a performance of a human-machine team.

By feeding artificial intelligence systems information about a soldier”s intent, advanced systems “could dynamically respond and adapt to assist the soldier in completing the task,” said Jean Vettel, a senior neuroscientist at ARL”s Combat Capabilities Development Command and co-author of a recent paper on the topic.

On the battlefield, soldiers perform many tasks at once — surveying and navigating the landscape, listening to communications, assessing threats — each of which is carried out in different regions of the brain. Researchers want to be able to analyze brain data in the moment so they can tell what tasks soldiers are performing.  Learning how areas of the brain work together to accomplish a task will help scientists build AI systems that can anticipate how a task should be accomplished and where they can assist.  

Related Paper

Cognitive Chimera States In Human Brain Networks, American Association for the Advancement of Science, 2019

Abstract: The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.