Artificial intelligence has been around for many decades, and as such, is not a new concept to the government, military, and defense industries. In fact, many of the first early advances in AI came out of military applications and defense funding, with funding originating from Defense and Intelligence communities to support initiatives around machine translation, computer vision, robotics, and many of the concepts around machine learning that we still use to this day. However, many of these early AI efforts were limited in scope, and wide-spread adoption of AI stagnated across the public and private sectors. With the recent resurgence of interest and funding in AI, military branches such as the Navy are now taking a closer look at how AI and machine learning approaches can help with a wide variety of problems such as predictive maintenance, personnel and logistics management, and data integration and visibility challenges.
The use of effective predictive analytics can help more efficiently deliver goods, staff appropriately, and proactively re-route, re-direct, and re-arrange various cargo and equipment as needed. The use of AI-based predictive analytics helps predict asset shortages, anticipate machine failure, predict and optimize maintenance, optimize logistics and personnel, and simplify access to disparate data in the organization. In this manner, AI is playing an augmentative role, not replacing humans but helping humans do their job better and eliminating routine, shifting the labor force to higher-value work.
The U.S. Navy can potentially use AI-based predictive analytics to eliminate unplanned downtime for critical shipboard systems, manage materiel and personnel, and simplify the connection of data across the organization. In this whitepaper, we outline current pain points for the Navy, how organizations with problems similar to the Navy have addressed these problems, and how the Navy can use AI to help with their mission and defense of the nation.
White Paper sponsored by MarkLogic
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- January 4, 2020 Create Date
- January 4, 2020 Last Updated