Convolutional Neural Networks (CNNs) are a form of bio-inspired deep learning in artificial intelligence. The interaction of thousands of'neurons' mimics the way our brain learns to recognize images. 'These CNNs are successful, but we don't fully understand how they work', says Estefanía Talavera Martinez, lecturer and researcher at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence of the University of Groningen in the Netherlands. She has made use of CNNs herself to analyse images made by wearable cameras in the study of human behaviour. Among other things, Talavera Martinez has been studying our interactions with food, so she wanted the system to recognize the different settings in which people encounter food.

Dutch and Spanish computer scientists have discovered how systems that use artificial intelligence (AI) learn in practice. In many systems that are based on so-called'deep learning', it was not clear how that learning process actually took place. The researchers have now managed to figure out how an image recognition system learns about its environment. Then they simplified that learning system by forcing it to focus on less important information as well. AI systems for image recognition are of great importance for autonomous driving cars, for a start.