【Time】
Monday, May 11
9:00–11:00
【Location】
Room 716, Science Building
Personal Profile
Dr. Ibragim Suleimenov, Professor, Academician of the National Academy of Engineering of Kazakhstan, holds a Ph.D. in Physics and Mathematics (1990) from Leningrad State University and a Doctor of Chemical Sciences degree (2000). He has 40 years of research experience in fields such as physics, physical chemistry, information theory, neural networks, and artificial intelligence. He has served as a professor and chief scientist at several universities in Kazakhstan and is currently the Chief Scientist of the National Academy of Engineering of Kazakhstan. His research interests include the physics of complex systems, digital image and signal processing (based on Galois fields), multi-valued logic, artificial neural networks, information dialectics, and the philosophy of science. He has published numerous high-level papers and received the Kapitza Silver Medal (2008) and the Kapitza Gold Medal (2022). He also serves as a member of the National Scientific Committee on Electronics and Telecommunications of Kazakhstan.
Brief Introduction
The existing paradigm of scientific development, oriented toward a rigid disciplinary structure, became institutionally established in the first half of the twentieth century. By now, it has largely exhausted its potential. Today, it is necessary not only to create effective tools for overcoming interdisciplinary barriers, but also to ensure the formation of an “integral” understanding of reality. Among the ideas aimed at solving this task is the thesis on the convergence of natural-scientific, technical, and humanitarian knowledge.
Some of the most vivid — and at the same time fundamental — tasks corresponding to this thesis are connected with the creation of a general theory of complex systems, which is inseparably linked to the prospects for developing a theory of their evolution.
This lecture addresses these theses from the standpoint of a neural-network interpretation of the category of the “complex.” It is argued that a system should be treated as complex if, and only if, it becomes a physical analogue of a neural network. It is shown that only such an approach makes it possible to demonstrate that the nature of any complex system is inseparably connected with both its “material” and its “informational” components. It is argued that the evolution of a complex system of any kind proceeds in accordance with the dialectical law of the unity and struggle of opposites — namely, its “material” and “informational” components.
Examples from various branches of physics are provided to illustrate these propositions.
