To me this is an illustration of one reason we are still some way off from a truly general system. Applying these "filters" to sensor data is enormously helpful, but only in certain applications. For a system giving GPS driving directions, you may be able to linearize and smooth movement, or constrain it to the nearest road. But you have to know that those assumptions don't apply to other domains that could use GPS data (e.g. surveying, wilderness navigation, animal tracking), and you also have to know when the situation calls for just going out with a tape measure, or installing a camera and referencing landmarks.<p>Probabilistic reasoning is useful when the problem space has been constrained to admit probabilistic solutions. But the real world is not <i>just</i> analog and continuous, it is also <i>discrete</i>, and it may require fine discrimination between two states that are covered by the same distribution. Effective operation as a general intelligence in the world requires the ability to tell when the stakes or details of an event require a different approach. Determining when and how to make those decisions is still a largely unsolved problem.