Meta-learning in natural and artificial intelligence

Humans are remarkable for continuously learning throughout the entirety of their lives, from acquiring physical reasoning and language skills at a young age [64, 43], to the ability to reason about the detailed complexities inherent in everyday adult life. One key quality of this learning is that it happens at multiple scales, both in terms of time and abstraction, in a process termed meta-learning or learning to learn. The fundamental principle of meta-learning is that learning proceeds faster with more experience, via the acquisition of inductive biases or knowledge that allows for more efficient learning in the future [66, 59, 57]. These favorable properties of meta-learning have recently gained it considerable renewed interest within the deep learning/artificial intelligence community. Despite their tremendous successes in recent years [46, 61], deep learning systems still require many orders of magnitude of data than humans [40, 12]. Although early work demonstrated the feasibility for neural networks to discover their own learning rules [10, 58], it was only recently that the field has experienced a resurgence of new research in meta-learning using deep neural networks. This has demonstrated the wide-ranging potential of neural networks to meta-learn all aspects of the learning process. Deep neural networks are typically trained via backpropagation, which adjusts the weights of the neural network so that given a set of input data, the network outputs match some desired target outputs (e.g., classification labels).