Artificial intelligence (AI) is transforming military training and system design by integrating advanced modeling and simulation (M&S) capabilities. Companies like CAE and General Atomics are at the forefront of this evolution, utilizing AI to enhance training scenarios and refine operational systems, including drones used in actual combat environments. This shift is providing military personnel with unprecedented opportunities to engage in realistic training exercises, ultimately improving their readiness for real-world missions.
Enhanced Training through AI Integration
The application of AI and machine learning to M&S allows for the analysis of vast datasets generated during training simulations. This analysis can yield critical insights, helping to identify trends and lessons learned. For organizations like CAE, which provides training to the US Air Force, Army, and Navy, AI-enhanced training means delivering more impactful learning experiences both during and after training events.
For General Atomics Aeronautical Systems, known for its Reaper drones and recent projects involving Collaborative Combat Aircraft, leveraging AI offers engineers valuable insights. These insights help in refining their product offerings to meet customer needs more effectively. By incorporating advanced AI techniques, training scenarios can now include automated computer-generated forces that behave in unpredictable and challenging ways, enhancing the realism of the exercises.
Previously, military training required the presence of actual personnel to fulfill roles in training scenarios, a costly and logistically challenging endeavor. Now, AI can create sophisticated simulations that emulate well-trained teammates and opponents, significantly reducing the need for large numbers of participants. “The promise of AI is that I can use that to build models of behavior to serve as opponent forces and teammates at varying levels of complexity,” said Brian Stensrud, technical fellow for artificial intelligence at CAE USA Defense & Security.
Data-Driven Insights and Continuous Improvement
As AI continues to shape military training, one emerging application is the development of embedded proficiency analysis tools. During tabletop exercises and field drills, the demand on trainers to manage events effectively often leads to missed opportunities for in-depth observations. To address this, CAE is working on creating an “omnipresent” AI observer that can monitor and assess actions throughout training events. This technology aims to provide instructors with valuable feedback, fostering improved proficiency for both trainees and their educators.
Such advancements depend heavily on data quality and availability. CAE possesses extensive datasets that enable the identification of persistent proficiency challenges across various training platforms. If a significant number of students struggle with a specific skill, it could indicate flaws in the curriculum or teaching methods. Utilizing machine learning algorithms, CAE can pinpoint these issues effectively.
Understanding the context behind the data is crucial. For instance, telemetry data collected during training—such as speed, location, and bank angle—must be analyzed in light of the specific mission objectives. Without this context, the data could misrepresent a trainee’s performance. As Anastacia MacAllister, technical director for autonomy and artificial intelligence at General Atomics, notes, it is essential for organizations to cultivate a data-centric mindset to leverage these tools effectively.
By emphasizing good data practices, military training can evolve to better prepare servicemembers for real-world operations. As CAE and General Atomics continue to innovate, they aim to enhance both warfighter skills and operational capabilities to meet current and future threats.