Nowadays, artificial neural networks have an impact on many areas of our day-to-day lives. They are used for a wide variety of complex tasks, such as driving cars, performing speech recognition (for example, Siri, Cortana, Alexa), suggesting shopping items and trends, or improving visual effects in movies (e.g., animated characters such as Thanos from the movie Infinity War by Marvel).

Traditionally, algorithms are handcrafted to solve complex tasks. This requires experts to spend a significant amount of time to identify the optimal strategies for various situations. Artificial neural networks — inspired by interconnected neurons in the brain — can automatically learn from data a close-to-optimal solution for the given objective. Often, the automated learning or “training” required to obtain these solutions is “supervised” through the use of supplementary information provided by an expert. Other approaches are “unsupervised” and can identify patterns in the data. The mathematical theory behind artificial neural networks has evolved over several decades, yet only recently have we developed our understanding of how to train them efficiently. The required calculations are very similar to those performed by standard video graphics cards (that contain a graphics processing unit or GPU) when rendering three-dimensional scenes in video games.