Scientists developed a neural network deep learning technique to extract hidden turbulent motion information from observations of the sun. Tests on three different sets of simulation data showed that it is possible to infer the horizontal motion from data for the temperature and vertical motion. This technique will benefit solar astronomy and other fields such as plasma physics, fusion science, and fluid dynamics.