Objectives: This course covers the rapidly evolving fields of Bio-Inspired and Cognitive computing.

The course focuses on the fundamental principles of cognition, their computational implementations, and practical applications. The course discusses the mind mechanisms, including concepts, emotions, instincts, behavior, language, cognition, understanding, thinking, intuitions, conscious and unconscious, abilities for symbols, role of music in cognition and evolution, the beautiful and spiritually sublime. Relations between science and religion are discussed along with evolution of cultures, emotionality of languages, joint evolution of languages and cognition, and role of language differences in current terrorism. Computational techniques are given for these mechanisms and abilities. New cognitive and mathematical principles are discussed, the knowledge instinct and dynamic logic. A number of applications are discussed; presented algorithms significantly outperform previous state of the art (often by orders of magnitude). The goals of the course are: First, to provide a basic mathematical understanding of the working of the mind and computational cognition. Second, to demonstrate practical applications of these mechanisms to a number of engineering applications: pattern recognition, data mining, tracking, fusion, web search engines, integrated systems combining cognition and language, cultural diagnostic models, and for financial engineering. Third, to outline future research directions in cognitive computation, language understanding, evolution of consciousness and cultures, financial market predictions. Historical and current difficulties in developing intelligent systems and applications will be discussed along with how cognition and new computational techniques overcome these difficulties. By the end of the course, students will be familiar with mathematical approaches to modeling the mind and cultural evolution, applications addressed by computational intelligence, computational difficulties encountered over fifty years, and basic novel approaches to overcoming these difficulties.

Targeted for: Individuals interested in working of the mind, evolution of languages and cultures, cognitive computing, the development and application of intelligent systems and intelligent signal processing.

Handouts: Copies of the course outline slides.

For more information: email Leonid.Perlovsky@hanscom.af.mil or see the website http://www.leonid-perlovsky.com/

Course Outline:

1.     Cognition – integration of real-time data and existing knowledge

1.1.   physics and mathematics of the mind

1.2.   genetic argument for the first principles

1.3.   the nature of understanding

1.4.   combinatorial complexity (CC) – a fundamental problem?

1.5.   CC since 1950s

1.6.   logic, mind, and CC

1.7.   mathematics vs. mind

1.8.   structure of the mind: concepts, instincts, emotions, behavior

1.9.   instinct for knowledge and aesthetic emotion

2.     Neural Modeling Field (NMF) and Dynamic Logic of cognition

2.1.   Formulation. Basic two-layer mechanism: data-concepts

2.2.   Instinct for knowledge = maximize similarity

2.3.   Dynamic Logic

2.4.   Block-Diagrams

2.5.   Hierarchical structure

2.6.   Applications: data mining, pattern recognition, tracking, fusion

3.     Language - integration of language data and language models

3.1.   Language: what is inborn?

3.2.   NMF of language

3.3.   Applications: web search engines

4.     Integration of cognition and language

4.1.   Language vs. thinking

4.2.   Chomskyan linguistics, cognitive linguistics, evolutionary linguistics

4.3.   Integrated models

4.4.   Integrated hierarchies of language and cognition

4.5.   Humboldt’s inner linguistic form

4.6.   Applications: integrated systems

5.     Prolegomena to a theory of the mind

5.1.   Why mind and emotions?

5.2.   From Plato to Kant, Jung, and Grossberg

5.3.   NMF vs. Buddhism

5.4.   Mind vs. brain

5.5.   NMF dynamics: thought process

5.6.   Consciousness and unconscious

5.7.   Understanding

5.8.   Models-concepts-agents

5.9.   Symbols, signs, and semiotics

5.10.   Aesthetic emotion and beauty

5.11.   Intuition: art, mathematics, physics

5.12.   List of applications

6.     Diagnostic and predictive cultural models

6.1.   Differentiation and synthesis, mechanisms of cultural evolution

6.2.   Language and differentiation

6.3.   Hierarchy of values and synthesis

6.4.   Cultural models: mathematics and measurements

6.5.   Evolving and stagnating cultures

6.6.   Terrorist’s consciousness

6.7.   Music, its role in cognition, cultural evolution, and synthesis

7.     Future directions

7.1.   Evolving integrated systems

7.2.   Evolution of languages and cultures

7.3.   Rrole of music in cognition and in evolution of cultures

7.4.   Future tests of the theory

Course Summary and Conclusion

Lecturer’s Biography: Dr. Leonid Perlovsky is Visiting Scholar at Harvard University, Principal Research Physicist and Technical Advisor at the Air Force Research Laboratory. He leads Semantic Web Program and other research projects. From 1985 to 1999, he served as Chief Scientist at Nichols Research, a $0.5B high-tech organization, leading the corporate research in information science, intelligent systems, neural networks, sensor fusion, signal processing, and algorithms. He served as professor at Novosibirsk University and New York University. He participated as a principal in commercial startups developing tools for text understanding, biotechnology, and financial predictions. He published about 60 papers in refereed scientific journals and 250 papers in conferences, delivered invited keynote plenary talks; authored 9 book chapters, a monograph “Neural Networks and Intellect: model-based concepts”, Oxford University Press, 2001 (currently in the 3rd printing) and two books with Springer in 2007.  He serves on IEEE Committees, as a leader of Task Force on the Mind and Brain, as Chair IEEE Boston Computational Intelligence Chapter, Organizing Committee Member IEEE World Congress on Computation Intelligence, Program Chair for IEEE International Conference on Computational Intelligence Measurement, General Chair for IEEE KIMAS conference, Associate Editor for IEEE Transactions on Neural Networks, Editor-at-large for New Math. and Natural Computations, and as Editor-in-Chief for “Physics of Life Reviews”. He received several best paper awards, Distinguished Member of the IEEE Boston Section Award, 2005; Air Force Research Lab Charles Ryan Memorial Award for Basic Research, 2007; International Neural Network Society Gabor Award for Engineering, 2007.

Registration includes course handouts and text book.

Decision (Run/Cancel) Date for  this Courses is Tuesday, October 30, 2007

Course Fee Schedule:

On-line Registration and Payment

On-line registration is closed for this course, but registration is still available on-site between 6:30PM – 7:00PM, Thursday, November 8, 2007 at the Holiday Inn Select, 15 Middlesex Canal Park, Woburn or by contacting the IEEE office at 781-245-5405.

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Updated Tuesday November 06, 2007