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Neural Networks and Intellect

Using Model-Based Concepts

Leonid I. Perlovsky

Publication Date - October 2000

ISBN: 9780195111620

496 pages
7-1/2 x 9-1/4 inches

Retail Price to Students: $244.99


Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique text discusses deep philosophical questions in detail and relates them to mathematics and the engineering of intelligence. Ideal for courses in neural networks, modern pattern recognition, and mathematical concepts of intelligence, it will also be of interest to anyone working in a variety of fields including neural networks, AI, cognitive science, fuzzy systems, pattern recognition and machine/computer vision, data mining, robotics, target tracking, and financial forecasting.
Neural Networks and Intellect describes model-based neural networks that utilize the intriguing concept of an internal "world" model, an idea that originated in artificial intelligence and cognitive psychology but whose roots date back to Plato and Aristotle. Combining the a priori knowledge with adaptive learning, the new mathematical concept addresses the most perplexing problems in the field of neural networks: fast learning and robust generalization. The author provides an overview of computational intelligence and neural networks, relating hundreds of seemingly disparate techniques to several fundamental mathematical concepts, which are in turn linked to concepts of mind in philosophy, psychology, and linguistics. Topics covered include the hierarchical and heterarchical organization of intelligent systems, statistical learning theory, genetic algorithms, complex adaptive systems, mathematical semiotics, the dynamic nature of symbols, Godel theorems and intelligence, emotions and thinking, the mathematics of emotional intellect, consciousness, and more. Perlovsky's remarkable conclusion is that the work of ancient philosophers came closer to the computational concepts emerging today than that of pattern recognition and AI experts of just a few years ago.
The following website contains information about Dr. Perlovsky's current research related to the theory developed in the book and about available funding opportunities under a Research Associateship Program: to find it search for Perlovsky on http://www4/nationalacademies.org/pga/rap.nsf. Other sources of funding might be available for US-based and international researchers.

Table of Contents

    Chapters 1-7, 9, and 10 end with Notes, Bibliographical Notes, and Problems
    Chapter 8 ends with Bibliographical Notes and Problems
    Chapters 11 and 12 end with Notes and Bibliographical Notes
    1. Introduction: Concepts of Intelligence
    1.1. Concepts of Intelligence in Mathematics, Psychology, and Philosophy
    1.2. Probability, Hypothesis Choice, Pattern Recognition, and Complexity
    1.3. Prediction, Tracking, and Dynamic Models
    1.4. Preview: Intelligence, Internal Model, Symbol, Emotions, and Consciousness
    2. Mathematical Concepts of Mind
    2.1. Complexity, Aristotle, and Fuzzy Logic
    2.2. Nearest Neighbors and Degenerate Geometries
    2.3. Gradient Learning, Back Propagation, and Feedforward Neural Networks
    2.4. Rule-Based Artificial Intelligence
    2.5. Concept of Internal Model
    2.6. Abductive Reasoning
    2.7. Statistical Learning Theory and Support Vector Machines
    2.8. AI Debates Past and Future
    2.9. Society of Mind
    2.10. Sensor Fusion and JDL Model
    2.11. Hierarchical Organization
    2.12. Semiotics
    2.13. Evolutionary Computation, Genetic Algorithms, and CAS
    2.14. Neural Field Theories
    2.15. Intelligence, Learning, and Computability
    3. Mathematical versus Metaphysical Concepts of Mind
    3.1. Prolegomenon: Plato, Antisthenes, and Artifical Intelligence
    3.2. Learning from Aristotle to Maimonides
    3.3. Heresy of Occam and Scientific Method
    3.4. Mathematics vs. Physics
    3.5. Kant: Pure Spirit and Psychology
    3.6. Freud vs. Jung: Psychology of Philosophy
    3.7. Wither We Go From Here?
    4. Modeling Field Theory
    4.1. Internal Models, Uncertainties, and Similarities
    4.2. Modeling Field Theory Dynamics
    4.3. Bayesian MFT
    4.4. Shannon-Einsteinian MFT
    4.5. Modeling Field Theory Neural Architecture
    4.6. Convergence
    4.7. Learning of Structures, AIC, and SLT
    4.8. Instinct of World Modeling: Knowledge Instinct
    5. MLANS: Maximum Likelihood Adaptive Neural System for Grouping and Recognition
    5.1. Grouping, Classification, and Models
    5.2. Gaussian Mixture Model: Unsupervised Learning or Grouping
    5.3. Combined Supervised and Unsupervised Learning
    5.4. Structure Estimation
    5.5. Wishart and Rician Mixture Models for Radar Image Classification
    5.6. Convergence
    5.7. MLANS, Physics, Biology, and Other Neural Networks
    6. Einsteinian Neural Network
    6.1. Images, Signals, and Spectra
    6.2. Spectral Models
    6.3. Neural Dynamics of ENN
    6.4. Applications to Acoustic Transient Signals and Speech Recognition
    6.5. Applications to Electromagnetic Wave Propagation in the Ionosphere
    6.6. Summary
    6.7. Appendix
    7. Prediction, Tracking, and Dynamic Models
    7.1. Prediction, Association, and Nonlinear Regression
    7.2. Association and Tracking Using Bayesian MFT
    7.3. Association and Tracking Using Shannon-Einsteinian MFT (SE-CAT)
    7.4. Sensor Fusion MFT
    7.5. Attention
    8. Quantum Modeling Field Theory (QMFT)
    8.1. Quantum Computing and Quantum Physics Notations
    8.2. Gibbs Quantum Modeling Field System
    8.3. Hamiltonian Quantum Modeling Field System
    9. Fundamental Limitations on Learning
    9.1. The Cramer-Rao Bound on Speed of Learning
    9.2. Overlap Between Classes
    9.3. CRB for MLANS
    9.4. CRB for Concurrent Association and Tracking (CAT)
    9.5. Summary: CRB for Intellect and Evolution?
    9.6. Appendix: CRB Rule of Thumb for Tracking
    10. Intelligent Systems Organization: MFT, Genetic Algorithms, and Kant
    10.1. Kant, MFT, and Intelligent Systems
    10.2. Emotional Machine (Toward Mathematics of Beauty)
    10.3. Learning: Genetic Algorithms, MFT, and Semiosis
    11. Godel's Theorems, Mind, and Machine
    11.1. Penrose and Computability of Mathematical Understanding
    11.2. Logic and Mind
    11.3. Godel, Turing, Penrose, and Putnam
    11.4. Godel Theorem vs. Physics of Mind
    12. Toward Physics of Consciousness
    12.1. Phenomenology of Consciousness
    12.2. Physics of Spiritual Substance: Future Directions
    12.3. Epilogue
    List of Symbols

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