Statistical Relational AI---the science and engineering of making intelligent machines acting in noisy worlds composed of objects and relations among the objects---is currently motivating a lot of new AI research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools enable new applications in several large, complex real-world domains including those involving big data, natural text, social networks, the web, medicine and robotics, among others. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to faithfully model the former while probability helps to effectively manage the latter. Our intention here is to give a brief (and necessarily incomplete) overview and invitation to the emerging field of Statistical Relational AI from the perspective of acting optimally and learning to act.