  We recount recent history behind building compact models of nonlinear,
complex processes and identifying their relevant macroscopic patterns or
"macrostates". We give a synopsis of computational mechanics, predictive
rate-distortion theory, and the role of information measures in monitoring
model complexity and predictive performance. Computational mechanics provides a
method to extract the optimal minimal predictive model for a given process.
Rate-distortion theory provides methods for systematically approximating such
models. We end by commenting on future prospects for developing a general
framework that automatically discovers optimal compact models. As a response to
the manuscript cited in the title above, this brief commentary corrects
potentially misleading claims about its state space compression method and
places it in a broader historical setting.
