In the early years of the 17th century questions were different. People looked for ways to understand nature in order to better handle it.. Scientist devoted many years of empiric as well as scholastic study so they can come up with new theories. The IT revolution has changed all that. The way we experience the world now is different, the questions are different and researchers need tools to cope with the flood of information. We need a massive model for finding hidden cause-effect explanations, a hypotheses search machine, the kind that goes beyond existing expert knowledge and the limited human ability to conceptualize multi-variant mixed-phenomena events.
Can human create a machine that will be smarter than human, and by what means? The answer is positive, if using using self-clustering (grouping). Why grouping? - since it reduces the number of space dimensions, eliminates noise, and most importantly - creates new terminology that enables logical operations. Why self-grouping? In order to break the chicken-and-egg cycle. It is possible to sum up the endeavor of automating hypotheses as reverse-engineering of data or "formulas that turn given set of chaotic data to rationally explained events". For a number of reasons (e.g. as mentioned here earlier, the number of big data variables exceeds the number of instances), the task of reverse-engineering big data, is out of reach of mathematics (including Statistics). The solution hence is left for Search Algorithms. This is in essence what "GT data mining" Group Technology stands for.