Modeling and simulation is a well-proven approach for conducting what-if analyses of complex scenarios. However, the current societal and technical challenges require increasingly complex models and larger simulation experiments, which calls for more intelligent approaches to simulation and modeling in all aspects of simulation studies. To this end, we believe simulation and modeling can benefit from the recent advancements in machine learning and artificial intelligence (AI) as well as the emerging powerful and pervasive hardware and computing paradigms and systems. In this article, we examine the existing AI techniques and emerging hardware platforms in the context of the modeling and simulation life cycle, broadly comprising the stages of model creation, calibration, and experimentation. We identify key challenges on the path to deeper integration between AI and simulation techniques and outline research directions towards the vision of a higher degree of automation in simulation-supported scientific discovery.