The key to attaining general artificial intelligence is to develop architectures that are capable of learning complex algorithmic behaviors modeled as programs. The ability to learn programs can allow these architectures to learn to compose high-level abstractions that can lead to many benefits: i) enable neural architectures to perform more complex tasks, ii) learn interpretable representations (programs which can be analyzed, debugged, or modified), and iii) better generalization to new inputs (like algorithms). In this talk, I will present some of our recent work in developing neural architectures for learning programs from examples, and also briefly discuss other applications such as program repair and fuzzing that can benefit from such neural program representations.