Abstract: We begin with a broad overview of binary hyperdimensional computing. We discuss the basic ideas behind hyperdimensional computing, including how data are represented and how inferences about those data are made. We further discuss recent research by the speaker involving hyperdimensional computing, including results which indicate limitations on the accuracy of these models, and their strengths, such as robustness to post-encoding noise in the data. No prior knowledge of hyperdimensional computing or machine learning is required.