I think of machine learning research as building two different types of entities: Turing’s Child Machine and H.G. Wells’ World Brain. The former is a machine that learns incrementally by receiving instruction from a trainer or by its own trial-and-error. The latter is a permanent repository that makes all human knowledge accessible to anyone in the world. While machine learning began following the Child Machine model, recent research has been more focussed on “organising the world’s information”. Both are important endeavours, however, incremental learning has been neglected and, we argue, should be revived.