With every spectacular achievement of a machine learning system, the long-elusive AI breakthrough is popularly proclaimed to be just around the corner. Most recent
successes have been due in large part to massive data and computation, in particular using deep artificial neural networks. But can artificial cognition really be achieved just by further scaling up existing machine-learning techniques? I discuss examples of simple, perceptual problems that are easily solved by humans but very difficult for today's machine learning methods. These problems reflect how humans conceptualize their world. Their mastery is thus likely to be an essential prerequisite for autonomous robots to attain higher levels of cognitive abilities. To get there, a few core issues can be identified that should drive research in cognitive robotics.