Abstract: Formulating quantum gravity is one of the final goals of fundamental physics. Recent progress in string theory brought a concrete formulation called AdS/CFT correspondence, in which a gravitational spacetime emerges from lower-dimensional non gravitational quantum systems, but we still lack in understanding how the correspondence works. I discuss similarities between the quantum gravity and deep learning architecture, by regarding the neural network as a discretized spacetime. In particular, the questions such as, when, why and how a neural network can be a space or a spacetime, may lead to a novel way to look at machine learning. I implement concretely the AdS/CFT framework into a deep learning architecture, and show the emergence of a curved spacetime as a neural network, from a given teacher data of quantum systems.