Computer science has advanced rapidly in the internet era, primarily due to the availability of very large data sets coupled with deep learning algorithms. The results have been impressive: autonomous vehicles, targeted advertisements, and computers chess champions. However, although there are significant opportunities to apply machine intelligence (MI) to materials problems, materials science has not yet capitalized on these advances. In this presentation, we will discuss various roles for MI – from black boxes to full physics predictors – using case studies that involve computer science approaches such as computer vision, data science, and machine learning applied to materials science and engineering objectives in interface science, micromechanics, microstructural characterization, and additive manufacturing. Based on our experience, we will suggest strategies for developing an MI ecosystem that combines advanced computational methods with well-curated data sets in order to realize the potential of these powerful tools.