AbstractBy the end of the last decade, robotic telescopes were established as effective alternatives to the traditional role of astronomer in planning, conducting and reducing time-domain observations. By the end of this decade, machines will play a much more central role in the discovery and classification of time-domain events observed by such robots. While this abstraction of humans away from the real-time loop (and the nightly slog of the nominal scientific process) is inevitable, just how we will get there as a community is uncertain. I discuss the importance of machine learning in astronomy today, and project where we might consider heading in the future. I will also touch on the role of people and organisations in shaping and maximising the scientific returns of the coming data deluge.