Abstract

In the past decade artificial intelligence research has achieved impressive results, mostly due to the creation of efficient machine learning algorithms. One of the most promising approaches is constituted by deep learning, which allows to build multi-layer artificial neural networks that can autonomously extract knowledge from large-scale data sets. In this review we will discuss the main theoretical and technological progresses underlying these achievements, also focusing on their relevance for psychology and cognitive neuroscience. We will also highlight some of the limits of deep learning models and possible research directions to overcome them.