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Rich out of equilibrium collective dynamics of strongly interacting largeassemblies emerge in many areas of science. Some intriguing and not fullyunderstood examples are the glassy arrest in atomic, molecular or colloidalsystems, flocking in natural or artificial active matter, and the organizationand subsistence of ecosystems. The learning process, and ensuing amazingperformance, of deep neural networks bears resemblance with some of thebefore-mentioned examples. Quantum mechanical extensions are also of interest.In exact or approximate manner the evolution of these systems can be expressedin terms of a dynamical mean-field theory which not only captures many of theirpeculiar effects but also has predictive power. This short review presents asummary of recent developments of this approach with emphasis on applicationson the examples mentioned above.

Recent Applications of Dynamical Mean-Field Methods