  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 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.
