Artificial Intelligence has recently shown great success at language translation, computer vision and many other sensory perception tasks. However, it still requires further improvements to address problems that involve higher order cognitive behaviors, such as reasoning. The human brain relies on multiple memory systems for intelligent behavior. Working memory is an essential component for high order cognitive tasks ranging from language, planning and reasoning to decision making. In this thesis, I introduce a new model called Differentiable Working Memory (DWM), which emulates the human working memory. As it shows the same functional characteristics as working memory, the model robustly learns psychology-inspired tasks and converges faster than comparable state-of-the-art models.