Abstract
Artificial Intelligence systems cannot yet match human abilities to apply knowledge to situations that vary from what they have been programmed for, or trained for. In visual object recognition, methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Here a component of this type of inference, called 'projection', is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations, across a range of AI domains, such as vision, robotics, or language. Finally, the relevance of projection to tackling the commonsense knowledge problem is discussed.