Prediction problems are among the most common learning problems for neural networks (e.g., in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classifications are predictable while still being as specific as possible. The approach can be related to the IMAX method of Becker and Hinton (1989) and Zemel and Hinton (1991). Experiments include a binary stereo task proposed by Becker and Hinton, which can be solved more readily by our system.