Leonid Perlovsky

Leonid Perlovsky.

Dr. Leonid Perlovsky is a Visiting Scholar at Harvard University, Principal Research Physicist and Technical Advisor at the United States Air Force Research Laboratory, Hanscom Air Force Base.^[1] He is the Program Manager for United States Department of Defense (DOD) Semantic Web program and leads several research projects, including cognitive algorithms, modeling of evolution of languages and cultures. From 1985 to 1999, he served as Chief Scientist at Nichols Research, a $0.5B high-tech DOD contractor leading the corporate research in intelligent systems, neural networks, sensor fusion, and automatic target recognition.

He served as professor at Novosibirsk State University and New York University, and participated as a principal in commercial startups developing tools for text understanding, Biotechnology , and financial predictions. His company predicted the market crash following September 11, 2001 attacks a week before the event, detecting activities of Al-Qaeda traders, and later helped SEC tracking the perpetrators.^[2] He is invited as a keynote plenary speaker and tutorial lectures worldwide, and has published more than 320 papers and 10 book chapters and authored three books, including “Neural Networks and Intellect,” Oxford University Press, 2000 (currently in the 3rd printing) ^[3] and two books with Springer in 2007,.^[4][5] Dr. Perlovsky organizes conferences on Computational intelligence,^[6][7] Chairs IEEE Boston Computational Intelligence Chapter.^[8] He serves as Associate Editor for IEEE Transactions on Neural Networks,^[9] , Editor-at-Large for "New Mathematics and Natural Computation"^[10] and Editor-in-Chief for “Physics of Life Reviews.” ^[11] He received National and International awards including the IEEE Distinguished Member of Boston Section Award 2005; the US AFRL Charles Ryan Memorial Award for Basic Research, 2007; Gabor Award 2007, the top engineering award from the International Neural Network Society;^[12] and John McLucas Award 2007, the highest US Air Force Award for science.

His current research interests include modeling mechanisms of the mind: neural modeling fields, knowledge instinct, aesthetic emotions, emotions of beautiful and sublime, language, language evolution, emotionality of languages, language and cognition, evolution of languages and cultures, symbols as psychological processes, evolution of consciousness, languages, and cultures, mathematical intelligence and art, role of music in evolution of consciousness and cultures, science and religion, including scientific explanations for the role of sacred values in the workings of the mind, and why religious teleology is equivalent to scientific causality.

Knowledge instinct

The term knowledge instinct is used by Leonid Perlovsky in his book Neural Networks and Intellect: Using Model-Based Concepts and other publications.^[13][14] In his works the knowledge instinct is recognized on a par with other ‘basic instincts’ such as instincts for food and procreation as a fundamental mechanism of human functioning.

The existence of knowledge instinct follows from the cognitive and neural evidence about the brain mechanisms of perception and cognition, as well as mathematical modeling of these mechanisms. The conclusion that that humans and higher animals have a special instinct responsible for cognition is supported by several other researchers. Harry Harlow discovered that monkeys as well as humans have the drive for positive stimulation,^[15] regardless of satisfaction of drives such as hunger; David Berlyne discussed curiosity^[16] in this regard; Leon Festinger, introduced the notion of cognitive dissonance^[17] and described many experiments on the drive of humans to reduce dissonance; John Cacioppo discussed the need for cognition.^[18]

The fundamental nature of this mechanism is related to the fact that our knowledge always has to be modified to fit the current situations. One rarely sees exactly the same object: illumination, angles, surrounding objects are usually different; therefore, adaptation-learning is required. A mathematical formulation of the mind mechanisms (Perlovsky 2006) emphasizes the fundamental nature of our desire for knowledge. Virtually all learning and adaptive algorithms (tens of thousands of publications) maximize correspondence between the algorithm internal structure (knowledge in a wide sense) and objects of recognition. Concept-models that our mind uses for understanding the world are in a constant need of adaptation. Knowledge is not just a static state; it is in a constant process of adaptation and learning. Without adaptation of concept-models humans and animals will not be able to understand the ever-changing surrounding world. They will not be able to orient ourselves or satisfy any of the bodily needs. Therefore, animals must have an inborn need, a drive, an instinct to improve knowledge, called the knowledge instinct. Mathematically it is described as a maximization of a similarity measure between the knowledge stored in mind concepts and the world as it is sensed by sensory organs.

Dynamic logic (neural)

In Perlovsky's works , the term Dynamic logic refers to a logic-process describing mathematically a fundamental mind mechanism of interactions between bottom-up signals and top-down signals as a process of adaptaton from vague to crisp concepts. Dynamic logic is a mathematical description of the Knowledge instinct. This mathematical formulation results in practically usable algorithms used within the framework of Neural modeling fields. It resulted in significant improvement in solving classical problems (such as detection, recognition, tracking, fusion) and new approaches to recently emerging problems (such as text search engines and cultural prediction models).

Physics of the mind

Physics occupies itself with the search for basic laws, a few universal “first principles” describing a wealth of observed phenomena. Perlovsky aims to develop a physical theory of the mind and suggests the first principles that need to be included in such theory. Among “the first principles” of the mind are interactions between bottom-up signals and top-down signals, which is the essence of perception, cognition, and concept formation; mechanisms of instincts and emotions, and their interaction with cognition; the knowledge instinct driving cognition, higher mental abilities, and related aesthetic emotions. Dynamic logic mathematically describes these mechanisms.

See also

• Neural modeling fields

References

1. ^ [1]: L. Perlovsky, Personal web page www.leonid-perlovsky.com
2. ^ [2]: Ascent Capital Management
3. ^ [3]: Perlovsky, L.I, "Neural Networks and Intellect: using model based concepts", 2001, New York: Oxford University Press.
4. ^ [4]: Perlovsky, Leonid I.; Kozma, Robert (Eds.), "Neurodynamics of Cognition and Consciousness",2007, ISBN 978-3-540-73266-2
5. ^ [5]: Mayorga, Rene V.; Perlovsky, Leonid (Eds.), "Toward Artificial Sapience, Principles and Methods for Wise Systems",2008,ISBN 978-1-84628-998-9
6. ^ [6]: International Joint Conference on Neural Networks (IJCNN 2009), official web site
7. ^ [7]: International COnference: Integration of Knowledge Intensive Multi-Agent Systems (KIMAS) Home Page
8. ^ [8]: The IEEE Boston Section home page
9. ^ [9]: IEEE Transactions on Neural Networks, Journal home page
10. ^ [10]: New Mathematics and Natural Computation, journal home page
11. ^ [11]: PHYSICS OF LIFE REVIEWS home page
12. ^ [12]: IJCNN award recipients
13. ^ Perlovsky, L.I. (2006). Toward Physics of the Mind: Concepts, Emotions, Consciousness, and Symbols. Phys. Life Rev. 3(1), pp.22-55.
14. ^ Perlovsky, L.I. & McManus, M.M. (1991). Maximum Likelihood Neural Networks for Sensor Fusion and Adaptive Classification. Neural Networks 4 (1), pp. 89-102; see also (Perlovsky 2001, 2006).
15. ^ Harlow, H.F., & Mears, C. (1979). The Human Model: Primate Perspectives, Washington, DC: V. H. Winston and Sons.
16. ^ Berlyne, D. E. (1960). Conflict, Arousal, And Curiosity, McGraw-Hill, New York, NY; Berlyne, D. E. (1973). Pleasure, Reward, Preference: Their Nature, Determinants, And Role In Behavior, Academic Press, New York, NY.
17. ^ Festinger, L. (1957). A Theory of Cognitive Dissonance, Stanford, CA: Stanford University Press.
18. ^ Cacioppo, J. T, & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116-131

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