As in many other industries, the use and spending of machine learning (ML) technologies has drastically increased for the Department of Defense. Contract spending for 2019 yielded $973 million for ML related projects and is projected to rise to $2.8 billion by 2023. ML methods and technologies have existed for many years but have quickly become critical in fields such as engineering, medicine, and consumer services. Recently, ML has found enormous benefits in XR-enabled environments used for a variety of purposes such as product and process design as well as training. Understanding the vast field of ML and its specific application to training systems can be extremely challenging. Miscomprehension can lead to poor management and development activities that will result in more costly and disappointing training solutions. Understanding the fundamentals of ML, and its application to Extended Reality (XR), will empower managers to make appropriate strategic and costing decisions and allow designers, developers, and engineers to successfully implement effective training systems.
This tutorial provides an overview of ML technologies from early research to today’s modern algorithms. This tutorial will include how ML can be combined with XR environments to fundamentally change how humans interact with training systems. The presentation will review how specific ML and XR tools can produce more immersive training solutions while providing deeper insights from a variety of data that can be collected and analyzed about trainee performance. This tutorial will also present examples demonstrating ML’s use in designing, testing, and optimizing XR training systems and evaluate the efficacy of incorporating this technology to aide in warfighter training to improve efficiency, reduce costs and training time.
This tutorial is for a wide range of stakeholders from those interested in gaining a basic understanding of ML for administrative level decision making to those who want detailed methods and integrations within XR-enabled training environments to gain specific performance improvements.