If people could communicate with and interactively modify the behavior of AI systems, both people and machines could behave more intelligently. Unfortunately, most AI systems are black boxes designed to solve a single narrowly defined problem, such as chess or face recognition or click prediction, and adjusting their behavior requires deep technical expertise. In this talk, I will describe progress towards more transparent and flexible AI systems capable of augmenting rather than just replacing human intelligence, building on the emerging field of probabilistic programming. Probabilistic programming draws on probability theory, programming languages, and system software to provide concise, expressive languages for modeling and general-purpose inference engines that both humans and machines can use. This talk focuses on BayesDB and Picture, domain-specific probabilistic programming platforms being developed by my research group, aimed at augmenting intelligence in the fields of data science and computer vision, respectively. BayesDB, which is open source and in use by organizations like the Bill & Melinda Gates Foundation and JPMorgan, lets users who lack statistics training understand the probable implications of data by writing queries in a simple, SQL-like language. Picture, a probabilistic language being developed in collaboration with Microsoft, lets users solve hard computer vision problems such as inferring 3D models of faces, human bodies and novel generic objects from single images by writing short (<50 line) computer graphics programs that generate and render random scenes. Unlike bottom-up vision algorithms, Picture programs build on prior knowledge about scene structure and produce complete 3D wireframes that people can manipulate using ordinary graphics software. This talk will also briefly illustrate the fundamentals of probabilistic programming using Venture, an interactive platform suitable for teaching and applications in fields ranging from statistics to robotics, and concludes with a summary of current and future research directions.