  Foreseeing the future is one of the key factors of intelligence. It involves
understanding of the past and current environment as well as decent experience
of its possible dynamics. In this work, we address future prediction at the
abstract level of activities. We propose a network module for learning
embeddings of the environment's dynamics in a self-supervised way. To take the
ambiguities and high variances in the future activities into account, we use a
multi-hypotheses scheme that can represent multiple futures. We demonstrate the
approach by classifying future activities on the Epic-Kitchens and Breakfast
datasets. Moreover, we generate captions that describe the future activities
