THE ARTIFICIAL BECOMES REAL


environments, such as the highways and streets of modern cities. Nothing could be further from the environments where the Army-specific AI will have to operate—unstructured, unstable, cha- otic, rubble-filled urban combat.


MAP TO THE FUTURE


Just 30 years ago, finding an efficient route on a complex map with current traffic conditions and road closures was considered cutting-edge AI research. Today, it’s commonplace—just one more app on your smartphone. (Photo by NEstudio/Shutterstock)


As another example, the recent explosion of successes in machine learning has been connected with availability of very large, accurate, well-labeled data sets, which can be used for training and validating machine learning algorithms and, given lengthy periods of time, for the learn- ing process. But Army-relevant machine learning must work with data sets that are dramatically different: often observed and learned in real time, under extreme time constraints, with only a few obser- vations (e.g., of the enemy techniques or materiel); potentially erroneous, of uncertain accuracy and meaning; or even intentionally misleading and deceptive. In other words, some of the very foun- dations of commercial AI algorithms diverge strongly from what the Army needs.


example, as is IBM’s TrueNorth chip,


which emulates brain neurons for power- efficient computations. For machine learning, Army S&T uses well-developed software tools such as TensorFlow.


At the same time, the focus of the Army S&T community is on problems that are quite distinct and are not going to be addressed by commercial applications. For example, much of Army research and development (R&D) investments in autonomy are focused mainly on autonomous convoys traveling in adver- sarial environments on terrain other than conventional roads; on robotics for manned-unmanned teams for reconnais- sance, surveillance and target acquisition and breaching; and on AI for military


92 Army AL&T Magazine January-March 2018


intelligence data analysis. Tese are not yet areas of significant interest to com- mercial developers, who focus on lucrative consumer markets.


Furthermore, there are deep, founda- tional differences in the scientific and technical challenges that Army-specific AI problems present, and which are not typical—or at least not a high priority— compared with the problems targeted by commercial investments. For example, AI and machine learning for self-driving cars, although initially spurred by the Defense Advanced Research Projects Agency’s Grand Challenge competi- tions, are currently being developed by industry and optimized for


relatively orderly, stable, rule-driven, predictable


MANNED-UNMANNED TEAMING Human-agent teams—Soldiers teamed with robots and other intelligent sys- tems operating with varying degrees of autonomy—will be ubiquitous on the future battlefield. Tese systems will selec- tively collect and process


information,


help Soldiers make sense of the environ- ment they’re in, and—with appropriate human oversight—undertake coordi- nated offensive and defensive actions.


Many will resemble more compact,


mobile and capable versions of current systems such as unattended ground sen- sors, unmanned aerial vehicles (drones) and fire-and-forget missiles. Such systems could carry out individual actions, either autonomously or under human control,


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