Story
June 21, 2024
Integrating artificial intelligence (AI) into military applications poses complex, multifaceted challenges that include technological advances, policy frameworks, strategic considerations, and ethical concerns. To keep pace with the rapid improvements and evolution of AI technologies, the U.S. Department of Defense (DoD) has established a unified adoption strategy aimed at improving the organizational environment to empower DoD leaders and warfighters to make faster, more informed decisions by skillfully leveraging high-quality data, advanced analytics, and AI to gain a persistent decision advantage.
While the use of AI in the military and on the battlefield is a relatively new phenomenon, limited use of the technology is already benefiting warfighters. In this early form, AI is primarily used for intelligence gathering and processing functions. For example, AI is much more efficient than humans at sifting through large amounts of data and imagery, and searching for meaningful information from feeds.
Today, one of the key applications is threat identification and recognition, especially in air combat. This can be achieved by locating the unique radio or radar signals emanating from individual aircraft or aircraft types. In the past, this was a huge task, requiring the operation of various sensors to record information from different sources and frequency ranges. This sensor data is then analyzed by experts to locate and identify the various signals associated with individual aircraft or aircraft types. Today, an identification task that would take many individuals tens or hundreds of hours to perform can be performed by AI systems in milliseconds to seconds.
The hardware that powers military AI
Military AI applications use a variety of hardware components.
High Performance Computing (HPC): Most military AI applications require powerful computing resources to process large amounts of data and perform complex calculations in real time. HPC systems, such as supercomputers and clusters of high-end servers, provide the necessary computing power.
There has been a great deal of debate and research on the placement of these HPC resources. One school of thought argues that HPC components are better placed in a central area away from the battlefield. Another view is that all computation should be pushed to the edge.
Performing most of the intensive computations in a central location allows for the use of much more and more diverse types of equipment and components, but it also makes the network or “pipe” a more critical component for AI applications.
On the other hand, field-deployed edge hardware has tighter size constraints than externally located hardware: edge hardware is limited by size, whereas external hardware is limited by the security and strength of the pipes.
Graphics Processing Unit (GPU): While GPUs are not strictly required, they are often used to accelerate AI computations, especially when leveraging machine learning and deep learning algorithms. GPUs offer significant benefits in applications that rely on parallel processing. Military AI systems often use GPUs for tasks such as image recognition, object detection, and autonomous navigation.
AI algorithm and UI software
AI algorithms and models: Military AI applications rely on advanced algorithms and models to perform tasks such as image recognition, natural language processing, decision-making, and predictive analytics.
Simulation/Training Software with Large Datasets: To train AI systems and simulate different scenarios, specialized software platforms are used that allow realistic simulation of military environments, tactics, and equipment. To optimally train military AI, huge data sets must be applied to these simulations. The more data, the better.
Integration software: Military AI systems need to integrate with existing infrastructure and interact with other systems seamlessly and intuitively — soldiers in the field cannot be expected to navigate difficult user interfaces within a software platform.
US Department of Defense AI Policy
The US Department of Defense (DoD) has been strategically embracing AI and machine learning (ML) technologies through various policies and strategic documents over the past few years. The 2018 DoD Artificial Intelligence Strategy released by DoD laid the foundation for developing a centralized infrastructure, integrating new technologies, and achieving international leadership in AI ethics and safety. Subsequent strategies such as the 2020 DoD Data Strategy and the establishment of the Chief Digital and Artificial Intelligence Office (CDAO) further emphasized the importance of a data-centric approach and optimizing AI capabilities across the DoD.
The current guiding policy outlined in the 2023 DoD Data, Analytics, and AI Adoption Strategy builds on previous policy documents with an emphasis on speed, agility, learning, and accountability. It emphasizes decentralizing authority and creating tighter feedback loops between developers and end users with the goal of strengthening the decision-making process within the DoD. The 2023 strategy outlines a foundational guiding approach to AI rather than a step-by-step guide.
Key components of the 2023 strategy include an AI hierarchy of needs (Figure 1) that prioritizes high-quality data as the foundation for insightful analytics and responsible AI development. The strategy also promotes the need for user-friendly infrastructure and continuous refinement of policies and processes to ensure responsible AI implementation.
[Figure 1 ǀ The DoD AI Hierarchy of Needs prioritizes high-quality data. Image courtesy U.S. Department of Defense.]
Implemented AI solutions
A wide range of manufacturers and contractors are currently incorporating AI into military applications, from large corporations such as Boeing, General Dynamics, Lockheed Martin, Raytheon and Northrop Grumman to startups such as Anduril.
Shooter Detection Systems: While not strictly a military application, shooter detection systems have evolved into AI-integrated solutions to help first responders pinpoint the exact location of a gunshot. The systems use a series of acoustic and infrared flash detection sensors integrated with video, access control, and mass notification systems. Data collected by the sensor system is fed through an I/O module directly into an AI-powered software platform that can determine if and when a gunshot occurred, pinpoint its exact location, notify authorities, and send mass notification, all in under half a second.
Tactical Intelligence Targeting Access Node (TITAN): The Tactical Intelligence Targeting Access Node (TITAN) is a scalable, expeditionary intelligence ground station that accelerates and streamlines the Army's ability to access and process large amounts of intelligence, surveillance, and reconnaissance (ISR) data.
Physically, TITAN (Figure 2) is a mobile data center with integrated power, heating and cooling, redundant communications, and computing platforms, all built into a large truck-based platform. The vehicle-mounted expeditionary ground station uses AI to provide deep sensing capabilities that enable long-range precision fires on the modern battlefield. Using AI, TITAN performs data integration, fusion, processing, and analysis functions using AI and ML to automate and help the Army shorten the sensor-to-fire timeline.
[Figure 2 ǀ Shown is the TITAN ALPHA working concept vehicle. Photo credit: Palantir.]
The Sealevel Relio R1 Rugged embedded computer is the core of the TITAN system. The Relio R1 Rugged monitors the overall health and performance of the TITAN. This small form factor computer hosts multiple software applications and interprets data from various internal sensors.
The Future of AI
Integrating AI into military applications represents a major advancement in modern warfare, enhancing capabilities in information processing, threat identification, and decision-making processes. The evolution of AI technology is coupled with the development of a robust technology infrastructure guided by strategic initiatives and policy frameworks developed by organizations such as CDAO. AI holds great potential to improve military effectiveness, but it also raises important considerations regarding the responsible development, deployment, and impact of autonomous systems in conflict scenarios. Continued collaboration between manufacturers and developers, policymakers, and warfighters is essential to ensure military AI applications enhance operational capabilities and contribute responsibly to global security.
Drew Thompson is a technical writer and marketing specialist for SeaLevel Systems, a leading designer and manufacturer of embedded computers, industrial I/O, and critical communications software. He holds a Master's in International Relations and Global Studies from Northeastern University. Thompson can be contacted at: [email protected].
Sea Level Systems • https://www.sealevel.com/
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