How does one arrive at scientifically proven knowledge? This question has accompanied research from the very beginning. Depending on the scientific context, claims to the degree of truth, and on scientific methodology, different answers to this question have been given throughout the ages. More recently, a new scientific methodology has emerged that can best be characterized as "probabilistic rationalism." In collaboration between computer science and physics, methods have been developed in recent decades and years that allow large amounts of data collected in modern experiments to be analyzed in terms of their probabilistic properties. Artificial intelligence or machine learning are the methods of the moment.

This is the subject of a new Techreport entitled "On Probabilistic Rationalism" by Prof. Dr. Dr. Wolfgang Rhode. It does not deal with individual aspects of statistical analysis, but rather with the entire evolutionary process of knowledge expansion. Interdisciplinary aspects of epistemology from the perspectives of physics, computer science and philosophy are brought together to form an up-to-date and consistent model of knowledge acquisition. This model can be used to overcome some well-known problems of existing epistemological approaches. In particular, interesting parallels have been identified between the functioning of machine learning and biological-neural learning processes.

The report can be found here.