US armed forces are looking to close the gap between the laboratory and the battlefield, in terms of developing and fielding artificial intelligence (AI) and machine learning (ML) capabilities, by rapidly integrating Al-enabled technologies into combat platforms and pushing algorithm development, testing, and training down to the tactical level. AI development at the tactical and operational level has focused on two major initiatives. One has been advancing computing hardware and applying Al-enabled capabilities to address rear echelon operations from maintenance and supply logistics to processing. The other has been implementing AI technologies for battlefield support operations, such as exploitation and dissemination of actionable intelligence to combat units."We started with predictive maintenance, humanitarian assistance [and] disaster relief, and some elements of defensive cyber [operations]" to build those AI and ML capabilities within the US armed forces, said US Air Force Lieutenant General Jack Shanahan, the inaugural director of the US Department of Defense's (DoD's) Joint AI Center, in June 2020. "We will start with some smaller use cases to learn what right' looks like [because] we are not jumping into Al-enabled autonomous weapons," he said, adding the centre's near-term AI strategy would focus on "going with lower risk, lower consequence missions".However, certain semi-autonomous combat technologies, such as Al-enabled optic systems aboard tactical vehicles and Al-centric battlefield communication platforms to reduce latency and improve data transmission speeds, have also begun to emerge within the US armed forces and their allies. Dramatic advances in computer servers, routers, processors, and other types of networked communication hardware have spurred military and industry engineers to push the boundaries of what is possible for AI at the tactical edge.
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