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The current revolution in the field of machine learning (ML) is leading tomany interesting developments in a wide range of areas, including fluidmechanics. Here we review recent and emerging possibilities in the context ofpredictions, simulations and control of fluid flows, focusing on wall-boundedturbulence. A number of important areas are benefiting from ML, and it isimportant to identify the synergies with the existing pillars of scientificdiscovery, i.e. theory, experiments and simulations. It is essential to adopt abalanced approach as a community in order to harness all the positive potentialof these novel methods.

Perspectives on predicting and controlling turbulent flows through deep learning