  We conduct the first study of its kind to generate and evaluate vector
representations for chess pieces. In particular, we uncover the latent
structure of chess pieces and moves, as well as predict chess moves from chess
positions. We share preliminary results which anticipate our ongoing work on a
neural network architecture that learns these embeddings directly from
supervised feedback.
