Defense in the Age of Artificial Intelligence
While most artificial intelligence (AI) is happening in the consumer sector, it is also becoming a necessary component of modern combat, presenting significant opportunities to improve mission effectiveness with AI for defense organizations.
Investment in AI for defense is the largest in some of the most developed nations, with a significant portion geared toward rigorous research and development in current technologies, such as AI applications for a range of military functions. These include intelligence collection and analysis, logistics, cyber operations, information operations, command and control, and usage in semiautonomous and autonomous vehicles. As a matter of fact, AI for defense has already been incorporated into real-world military operations globally, including in Afghanistan, Iraq, and Syria.
How exactly will this technology shape the industry and military operations going forward? Let’s explore some key areas where AI-enabled technology can be used to maximize assets, augment resources, and provide the most value in military defense.
Enabling Machines to “See”
How tank crews operate their machine has gone essentially unchanged over the past four decades. The process of detecting, identifying, and engaging targets relies entirely on manual inputs from highly trained operators. Tank commanders and gunners manually detect targets using their sensors. Once they come across a target, they must manually select the ammunition they will use to service that target, lase the target to get an accurate range, and a few other factors. The process is repeated for every target, compromising human efficiency and accuracy, especially as the soldier experiences fatigue.
Implementing computer vision allows machines to see as humans do and process and identify objects — and even activities — within a still frame or a video sequence. Because the computer is powered by AI for defense, it would understand what it is seeing, assess the situation, identify suspicious or out-of-the-ordinary sequences, and advise users of necessary actions. The specifics around computer vision include object detection, which combines object classification or object identification with object location. Other techniques include motion analysis, 3D scene reconstruction, and image segmentation. Object tracking is another capability receiving increased attention, wherein the computer picks up an entity and follows it through a scene. An AI-powered remote weapons system significantly enhances object tracking and monitoring capabilities, thus empowering humans by reducing fatigue and ensuring better security.
Maintaining military equipment requires a lot of manual inspection by a maintenance crew, which can be quite and leave massive room for human error. Further, a technical failure in a hostile environment could put soldiers’ lives at risk. Good thing, AI for defense has the potential to enhance the safety of operating aircraft, ships, and vehicles in complex, rapidly changing situations by alerting operators to hidden dangers and enabling proper intervention before a breakdown occurs.
Through computer-vision-based analysis, algorithms can detect damage or wear marks, predict the failure of critical parts, automate diagnostics, and plan maintenance based on data and equipment conditions. Similar technology can be used to guide the provisioning of spare parts and optimize inventory levels. These advances will ensure appropriate inventory levels, assist in troubleshooting, and enable more quickly deployable and adaptable forces at a reduced cost (lower staff requirements).
Machine Learning for Optimized Mission Planning
Traditionally, the success of Intelligence, Surveillance, and Reconnaissance (ISR) depends on human expertise and control. However, speed and accuracy can be an issue, not to mention the risk that manned missions pose to human lives, especially in multi-domain operations. Operators, who must make critical decisions quickly, such as protecting ships in crowded sea lanes, are challenged by vast amounts of data that they must sift through manually.
By implementing AI and machine learning into next-generation ISR capabilities, operators can rapidly make the right decisions in any threat environment. The approach is to synthesize reams of data into actionable intelligence and accurate targeting information at speed and scale in high-risk environments. That way, operators can work above the tediousness of data processing and instead focus on decision-making, resulting in better ISR outcomes, exponentially faster threat response, and lower operations costs. These results are possible even with autonomous operations. Unmanned aerial vehicles (UAVs) will have the necessary sensors and flight control systems to embed the software to generate autonomous missions from takeoff to landing while completing missions, including the collection and dissemination of ISR data. The increased use of UAVs and drones in ISR could also mean lower risk to personnel.
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