We're investigating if deep learning can help scientists exploring fundamental physics with ultra-cold atoms. The Nobel prize was awarded to scientists who first discovered how to cool atoms to near absolute zero to create a special phase of matter called a Bose-Einstein Condensate (BEC). In a BEC all atoms are in the same quantum state, meaning they move together as if they are one super atom. We can use BECs to make ultra-precise measurements of gravity, potentially allowing us to make gravitational images to see hidden features in the world around us. BECs are made using a process of evaporative cooling, where the boundaries that trap the atoms are changed over time to let the hotter atoms escape. This approach has hit a limit, and BECs have remained around the same size for the last 10 years. We are handing over control of our ultra-cold atom experiment to a deep-learning algorithm, and investigating if it can find entirely new ways to make BECs. In particular we let the deep learning algorithm take control of not only the boundaries of the atoms but the interactions between the atoms as well.