Dr. Leonid Perlovsky is Professor of Psychology at Northeastern University and CEO LP Information Technology. In the past, Visiting Scholar at Harvard University, School of Engineering and Applied Sciences and Medical School Athinoula Martinos Brain Imaging Center, Technical Advisor and Principal Research Scientist at the AF Research Lab. He created a new area of cognitive mathematical engineering, dynamic logic, which solved a number of problems unsolvable for decades. He leads research projects on mathematical models of the mind mechanisms including higher cognitive functions, language, emotions of the beautiful, music, cognitive algorithms for various applications. He develops physics of the mind. In leading brain imaging labs around the world his models have been proven experimentally to explain the mind mechanisms not understood previously. As Chief Scientist at Nichols Research, a $500mm high-tech DOD contractor, he led the corporate research in intelligent systems. He served as professor at Novosibirsk University and New York University; as a principal in commercial startups developing tools for biotechnology, text understanding, and financial predictions. He is invited as a keynote plenary speaker and tutorial lecturer worldwide, including most prestigious venues such as the Nobel Forum at Karolinska Institutet Stockholm; published more than 500 papers, 17 book chapters, and 6 books including"Neural Networks and Intellect," Oxford University Press, 2001 (currently in the 3rd printing) and"Emotional, Cognitive, Neural Algorithms with Engineering Applications," Springer 2011; awarded 2 patents. Dr. Perlovsky participates in organizing conferences on Computational Intelligence, Chair for the IEEE Task Force on The Mind and Brain, on the International Neural Network Society (INNS) Board of Governors, past Chair for the IEEE Boston Computational Intelligence Chapter, Chair of The INNS Award Committee. He has founded and serves as Editor-in-Chief for"Physics of Life Reviews" the IF=9.5 ranked #4 in the world by Thomson Reuters. He received National and International awards including The Best Paper Award at Russian most prestigious magazine, Zvezda; the Gabor Award, the top engineering award from the INNS; and the John McLucas Award, the highest US Air Force Award for basic research.
Mathematical modeling of the mind has been attempted since the 1950s, yet existing mathematical methods could not model the mind mechanisms. Even algorithms for "simple" perception have met computational complexity. Simple cases such as perception of isolated objects could be modeled, but algorithms for perception of multiple objects in presence of noise required the number of operations larger than all interactions of elementary particles in the Universe. This problem also interfered with solving complex engineering problems "artificial intelligence."
Fundamental reasons for this complexity have been related to logic. Gödelian arguments leading to fundamental limitations of logic, when applied to finite systems have led to complexity. Even algorithms specifically designed to overcome limitations of logic, such as neural networks, and fuzzy systems, used logic at some steps and inexorably encountered complexity that could not have been overcome.
A new mathematical theory dynamic logic, DL, has been designed to overcome this fundamental limitation. Instead of static states of classical logic DL is a process logic. DL processes evolve "from vague to crisp," they explain that logical states in the mind appear at the end of vague thinking processes and for this reason do not lead to complexity. DL has overcome computational complexity and makes possible to model the mind. An understanding of the mind becomes possible. A fundamental aspect of the working of the mind is that representations are vague.
Mind processes of cognition and emotions are organized into a hierarchy. Cognition works by matching contents of mental representations to patterns in the world. This matching requires improvement of representations, in other words improvement of knowledge. The mind constantly improves representations motivated by emotions; these special emotions related to knowledge are aesthetic emotions. Kant was the first to suggest that aesthetic emotions are related to knowledge. Representations at higher levels of the hierarchy are built on lower levels of vague representations, and they are vaguer and less accessible to consciousness. This theoretical conclusion has been proved in the mind imaging experiments.
What exactly are emotions and contents of representations at the "top" of the mental hierarchy? At every level of the hierarchy representations unify lower level representations into more general and abstract concepts. The highest representations attempt to unify knowledge at all lower levels. Contents of these most general representations unifying our entire life are experienced as the meaning of life. When we understand complex abstract ideas we may experience pleasant aesthetic emotions. The highest aesthetic emotions experienced when understanding the most general representations, when understanding of the meaning of life improves, these highest aesthetic emotions are the emotions of the beautiful.
I will tell about recent experiments confirming these theoretical predictions. Of course experimental confirmations of predictions of a theory are the foundations of science from Newton to Einstein. A new area of science, physics of the mind emerges.
