When children develop into adults, how they learn changes a lot. While children show a lot of random behaviour, adults perform more goal-directed actions. An influential theory describes these changes as being similar to the behaviour of an optimisation algorithm commonly used in machine learning. This empirical test shows that there are striking similarities but also important differences between human development and machine learning algorithms.

Skepticism about the use of AI systems is widespread. Many say the systems are too opaque. Professor for Explainable Machine Learning Zeynep Akata wants to change that - and has made the user’s perspective the focal point of her research.