Keywords
AI;AUGMENTED AND VIRTUAL REALITY (AR/VR);CONTENT GENERATION;MACHINE LEARNING;MODELING;VISUALIZATIONAdditional Keywords not in the list above
GenAI, GPTLearning Objective 1
Comprehend the fundamental capabilities, limitations, and applications of applied Generative AI.Learning Objective 2
Learn specific methodologies and practices to leverage Generative AI in today's M&S practices.Learning Objective 3
Understand the future trajectory and anticipated disruption of Generative AI for asset and scenario pipelines.Abstract
Simulation environments, virtual and otherwise, are often bottlenecked by laborious art design, graphical modeling, and data integration. This asset development pipeline is slow and costly, requiring specialized labor that can complicate the logistics or expose the operational security of a training system. However, the emerging field of generative AI allows one individual to direct a single secure computer to build libraries of relevant, usable materials through simple voice or text prompts. Once these processes have fully matured, the net increase in productivity will likely be measured in orders of magnitude.
This tutorial will review and critically analyze modern modeling and simulation production workflows against the next-generation approach of directed GenAI, exploring possible trajectories of highly disruptive new tech. The audience will gain a deep understanding of the current generative AI methodologies with a particular emphasis on applied utility rather than theoretical potential. Together, we will walk through common simulation challenges and dissect the corresponding AI prompts that generate passable solutions within seconds or minutes. The audience will learn how to begin testing these new tools and be given recommendations on how to use them effectively and responsibly.