Despite enormous progress in machine learning, artificial neural networks still lag behind brains in their ability to generalize to new situations. Given identical training data, differences in generalization are caused by many defining features of a learning algorithm, such as network architecture and learning rule. Their joint effect - called inductive bias - determines how well any learning algorithm - or brain - generalizes: Robust generalization needs good inductive biases. Artificial networks use rather nonspecific biases and often latch onto patterns that are only informative about the statistics of the training data but may not generalize to different scenarios. Brains, on the other hand, generalize across comparatively drastic changes in the sensory input all the time. I will highlight recent research on the differences in ‘perception’ in humans and artificial networks and discuss some ideas how biases built into brains could improve artificial algorithms.