Our own intelligence is defined by our abilities to predict and generalize. As Jeff Hawkins points out, we live our lives constantly predicting what will happen to us in the next few seconds. (See Numenta.com - Hawkin's company - for the source code to NTA hierarchical temporal memory system.)

We also generalize by learning to recognize patterns and ignore noise.

AI systems need to implement prediction and generalization, and do this in a way that scales so that we can move past small toy problems. Scalability is most important in prediction because of the size of data required to model the environment that an AI will live in and the real time requirements (prediction does us little good if the calculation takes too long).

Knowledge Engineering is not AI, it is the engineering discipline for the understanding and re-implementation in software of human level expertise in narrow problem domains.