This post is also available in: heעברית (Hebrew)

2019 has been undoubtedly the year of artificial intelligence in the US military. Military leaders recognize AI’s potentially seismic impact on their mission and operations, and they expect practical applications to proliferate, from threat monitoring to asset tracking to predictive maintenance. 

The accelerated delivery of AI-enabled capabilities and the cultivation of a much-needed tech workforce in the US evolved as result of both the Department of Defense’s AI strategy launched in February followed by the White House’s executive order on “Maintaining American Leadership in Artificial Intelligence”.

Russia, China and other countries make substantial investments in AI capabilities, posing threats to US capabilities in this field. 

The traditional US military decision-making cycle OODA (observe, orient, decide, and act) paired with AI can provide commanders and executives a practical, high-level framework to consider potential military and national security applications for the technology, according to defensesystems.com. 

Observing (sensing) – Every soldier is a potential sensor for internet-of-things application. So too are military assets such as ships, battlefield equipment and aircraft. 

Orienting (sense-making) – Digital data becomes valuable when it can be understood. While hype continues to build around the potential for massive general AI deployment, narrow AI applications represent a sweet spot for the technology today. Industries such as trucking and railroading already use machine learning models extensively for predictive maintenance to anticipate and determine the root causes of failures. 

Deciding and acting – AI holds potential for massive application in long-term decision-making and operationalization of chosen action plans. At the same time, significant near-term impact is possible through narrow and practical decide-and-act initiatives, such as the use of robotic process automation to streamline financial management and other labor intensive processes. A major trucking company, for example, used AI technology for sensing and sense-making. Over time, it began to integrate AI into new vehicles as an IoT application. 

Broadly speaking, all major combat weapon systems — surface, sea, air and space — can benefit from AI. One such example is the US Navy’s F/A-18 E/F Super Hornet fighter jet fleet, which lost effectiveness due to insufficient mission-capable aircraft. Using machine learning, Navy leaders are gaining insights into how they can address underlying readiness problems and increase the availability of mission-capable strike fighters. 

Aviation fuel quality is another example. Machine learning is enabling examination of fuel composition down to molecular levels to identify chemical compositions that could trigger operational failures. These insights are dramatically reducing the time it takes to determine whether suspect fuels can be burned — directly improving operational readiness. 

Looking to the future, human-machine collaboration will become more possible and important to fighting and winning. The time will come when both battle networks and weapon systems employ AI to make some decisions and take actions on their own.