Understanding spatial environments from images

The ability to understand spatial environments based on visual perception arguably is a key function of the cognitive system of many animals, including mammalians and others. A common presumption about artificial intelligence is that its goal is to build machines with a similar capacity of "understanding." The research community in artificial intelligence, however, has settled on a more pragmatic approach. Instead of attempting to model or quantify understanding directly, the objective is to construct machines that merely solve tasks that seem to require understanding. Understanding can only be measured indirectly, for example, by analyzing the ability of a system to generalize the solving of new tasks, which is sometimes called transfer learning (1).