In the field of Deep Learning, highly complex models are designed to maximize performance metrics, and little importance is often assigned to the issue of interpreting their results, or reasoning about them. Consequently, several models tend to be extremely reliable when faced with a scenario they have repeatedly experienced in the past but generalize poorly to new quests. On the contrary, humans can leverage logical reasoning to make guesses about a new circumstance and are able to infer knowledge from few to zero examples. To cope with this fundamental issue, novel research areas are emerging. Among them, Neural-Symbolic Integration, involved inter alia in the assimilation of logic into deep architectures, and Few-Shot Learning, extending the traditional classification problem to settings affected by scarcity or lack of labelled examples, are some of the most dynamical.