Just as important to survival as generalization is the ability to adapt so as to respond correctly to a changing world. So we must value history as an indicator of the future but allow new understandings to replace old ways when appropriate. In Figure 1, the purple line is a linear fit now based on data from 1850 to 1940. Compare the purple line to the red line, which forecasts one period ahead at a time on a rolling basis using a polynomial chosen so as to best fit the previous four data points. In certain circumstances, a regularly updated and flexible model can provide many advantages.
All models connect the dots, but only some are responsive enough to forecast a changing world.
This proposal runs counter to beliefs that are still held in cognitive science and AI, wherein it is often thought that an information processing architecture can be designed first, and that learning can be tacked on later. Many examples now show that learning laws that might be suitable in one type of architecture might be the wrong laws for a different type of architecture. As our data-mining software gets more complex, we often forget that selective learning laws are hidden in the algorithms and software we choose to use, such as the type of logistics function used in a neural network or the nature of the probability distribution used to calculate the value of derivatives. In any knowledge management problem, retrieval is the easiest part. Queries, such as key-word searches on the Internet, often bring unmanageably large results.