Learning to learn Artificial Intelligence

In traditional Machine Learning domains, we usually take a huge dataset which is specific to a particular task and wish to train a model for regression/classification purposes using this dataset. That's radically far from how humans take advantage of their past experiences to learn very quickly a new task from only a handset of examples. Meta-Learning is essentially learning to learn. Formally, it can be defined as using metadata of an algorithm or a model to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself. Each learning algorithm is based on a set of assumptions about the data, which is called its inductive bias.