Owing to high-precision photometry from space missions such as Kepler and K2 over the past decade, asteroseismology, the study of stellar oscillations, has shifted its scope dramatically from the ‘retail’ studies of individual stars to the ‘wholesale’ analysis of large stellar populations. This transition of asteroseismology to a survey-era science has enabled the masses, radii, and ages of many distant stars to be measured with unprecedented precision and has thus driven the field’s growing involvement in Galactic archaeology studies. Consequently, there is now a pressing need for data-driven methods to extend asteroseismology to large photometric surveys like NASA’s Transiting Exoplanet Survey Satellite mission. In this talk, I will review how this need is addressed through pioneering the use of deep learning in asteroseismology. As a modern approach to artificial intelligence (AI), deep learning algorithms can perform highly complex tasks and have been integrated across a wide range of disciplines. I will discuss how the application of deep learning to asteroseismic data can expedite the seismic analyses of evolved Sun-like stars across a vast volume of our Galaxy.