Data-Intensive Science: Problems and Development of the Fourth Paradigm

Abstract

The article examines the evolution and current state of the data intensive sciences (DISs). The article focuses on approaches to methods of data mining generated by the development of artificial intelligence. It is noted that the rich opportunities of new approaches have caused unreasonable enthusiasm among scientists with respect to their capabilities, while the achieved level of knowledge is clearly ignored. It is shown how numerous facts of limited data processing potential have gradually accumulated without taking into account all previously established laws of nature and research methods. A significant role in the awareness of the real potential of working with data (including big data methods) was played by specialists in the field of methodology of science, who created a new direction, the epistemology of the DIS. Various ways and means of introducing expert knowledge at subsequent stages of analysis in the form of machine learning are listed. In sum, the appearance is noted of special algorithms for physically informed machine learning using data in combination with a traditional approach based on solving equations of mathematical physics.