As artificial intelligence technology rapidly evolves, it continues to provide opportunities to mitigate or eliminate human error and inefficiency in completing tasks or sorting overwhelming amounts of data. As the Department of Defense forms and refines its overall strategy regarding the technology and its applications, each branch of the Armed Services has applied AI in specific instances to relieve pain points, improve efficiency, and provide Commanders with reliable and poignant threat recognition and real-time intelligence.
The Department of the Navy has applied AI in both rudimentary and novel ways. The Navy has pursued using an AI chatbot, Amelia, to help conversational interaction in support of IT desks to free up technicians for more complex troubleshooting or mission-critical support. Chatbot functionality also presents opportunities to gain efficiency and accuracy through accessing voluminous repositories of policies, procedures, and techniques to enable the completion of tedious and time-consuming tasks, including writing subordinate policies, program requirements for acquisition purposes, and repolling existing databases for targeted analysis and report generation. Organic efforts are also underway to utilize a chatbot to help establish trends in assessment results, mishaps, and near-miss databases to enable Naval Safety Command to more precisely target their efforts in mishap prevention, fleet awareness, and training products. Both of these language recognitions use cases rely primarily on Navy-specific data, presenting significant opportunities for industry or other branches of the military to further aid in the development and advancement of the aforementioned tools. These tools will continue to enable more efficient use of human capital in the time staff need to complete routine tasks and develop relevant informational products, policies, reports, and orders. This will free up staff to engage in a higher volume of complex planning and problem-solving, which are often rushed by the consumption of limited time in the preparatory phases of various processes.
However, artificial intelligence goes far beyond language recognition in its ability to recognize, analyze, and disseminate actionable information that humans cannot. This includes sensor data on weapon systems, propulsion plants, electronic and electrical systems, and vital support equipment. AI is utilized for both the interpretation of massive amounts of data for threat recognition and to provide predictive analysis of data provided by equipment to conduct targeted, preventative maintenance to reduce equipment downtime and reduce or eliminate time-critical system downtime. The Naval Maintenance, Repair, and Overhaul (N-MRO) solution was awarded to Lockheed Martin in 2021 for continued development and is increasingly employed throughout the Navy and Marine Corps, particularly in aviation and submarine-related maintenance. By using predictive analytics and AI support, operators can often prevent unplanned system outages, preserve warfighting readiness, and provide commanders with confidence in the material readiness of assigned forces.
The machine learning aspects of AI have enabled the U.S. Navy to make leaps in updating and fielding threat recognition models to autonomous underwater vehicles, particularly as it applies to mine warfare. The Navy’s Expeditionary Missions Team partnered with industry by leveraging the Defense Innovation Unit (DIU) to form a team overseeing Automated Machine Learning for Mine Countermeasures Operations (AMMO). The Domino Enterprise AI platform, featuring modular and open architecture hosted in the AWS GovCloud, reduced the time to deploy AI models from six months to two weeks and the time to retrain models from twelve months to two weeks. As mine threats and counter-detection methods and technologies evolve, the ability to rapidly update AI models to provide near-real-time updates to forward-positioned unmanned systems is critical in keeping pace in the cat-and-mouse game of undersea warfare. AI provides a profound advantage in detecting underwater objects by analyzing sonar noise-return data, synthetic aperture sonar imaging, or other audio and visual data collection methods. Early and confident detection of these threats provides critical information to Commanders and operational staff in executing time-critical and crisis action planning, including rerouting forces or tasking high-value, short-supply assets to prosecute and neutralize confirmed threats. Reducing project timelines allows for the more frequent reassignment of limited subject matter experts to a wider range and volume of problem sets.
The US Navy is going all in on the application of AI and its uses in semi and fully autonomous systems, even leading to the creation of a new job specialty rating in 2024, Robotics Warfare Specialist. This is a critical realization and necessary investment in the ever-changing face of warfare which has only accelerated since the Russian invasion of Ukraine and there are no signs of it slowing down. Naval Sea Systems command was reorganized to establish a new Program Executive Office Unmanned and Small Combatants (PEO USC). This was born out of the success of unmanned experimentation in the Arabian Gulf under Task Force 59 and the establishment of Unmanned Underwater Squadron One and Unmanned Surface Vessel Squadron. The efforts developed in support of Project Overmatch under former Chief of Naval operations, Admiral Mike Gilday, were the catalyst to the current CNO’s principal line of effort, Project 33, which aims to continue to focus on fleet modernization through enhanced use of unmanned systems and platforms to maintain critical sea control.
AI can also collate numerous streams of sensor and intelligence data and analyze and provide actionable information in a fraction of human collection and analysis time. Efforts to improve the speed of decision-making by interconnecting networks of various information sources under what the Navy has termed network-centric warfare. Under this approach, codified as Project Overmatch, the U.S. Navy aims to interconnect disaggregated forces to provide track data and close kill chains faster than the adversary. These techniques have been trained to, certified and tested during the advanced training phases for three carrier strike groups on the West Coast. Additionally, AI and other non-kinetic means of conducting operations have led to the establishment of Information Warfare as a principal warfare area with growing importance. Elevating tactical-level data from numerous sensors allows commanders to have operational and strategic views at the 30,000-foot level. This will enable quicker and well-informed decision-making even in the heaviest fog of war.
The focus of AI is to enhance the effectiveness of human control of the kill chain, not to remove humans ‘from the loop.’ Although autonomous platforms continue to grow in quantity and quality, the goal of the U.S. Navy in the near term is to enhance warfighter awareness and survivability through intelligence, surveillance, and reconnaissance (ISR) and electronic warfare (EW) payloads. These mission sets will enable theater commands to close kill chains more quickly and effectively while disrupting and delaying the adversary from closing their kill chains. The speed of decision-making and enablement of action can level the playing field when a technological or force composition disadvantage exists.
Unit-level and theater-level commands will need to become more aware of the various AI applications relevant to their level of command. It will also be critical to understand what the applications can’t do, how to augment their processes and experience, and how to allocate their staff and other resources to battle management. Unit-level commands, including pilots and ground combat officers, must be familiar with the added capability of automated processing, task assistance (including precision carrier deck landings), and autonomous support for increased munitions or non-kinetic effects. As comfort with AI applications grows, use and function will likely expand to the lower execution levels. Enhancing the information presented to watchstanders at shipboard consoles and further automating track identification and classification will continue to increase the efficiency of watchstanders and enable them to match up against advanced threats, including advanced mines and supersonic and hypersonic missiles. As tactical-level improvements are realized and tested, the confidence and effectiveness of operational commanders will continue to increase and bridge the divide across services in Joint operations. The Navy is going flank speed to catch up and overtake its adversaries.
