Synergies between machine learning and quantum physics are increasing in numbers. Initially, quantum-enhanced learning protocols and some sporadic applications of classical reinforcement learning in quantum control were developed. Today we have a much broader scope: the latest developments include representing quantum states by a machine learning-inspired ansatz, establishing connections between tensor networks and deep learning, applying machine learning on quantum many-body physics problems, as well as the appearance of a series of no-go results in the high-level theory of quantum-enhanced machine learning. In this talk, we give a quick overview of where the field is, and we highlight some interesting open questions.