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
Preface
PART ONE: OVERVIEW: 2300 YEARS OF PHILOSOPHY, 100 YEARS OF MATHEMATICAL LOGIC, AND 50 YEARS OF COMPUTATIONAL INTELLIGENCE
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?
PART II: MODELING FIELD THEORY: NEW MATHEMATICAL THEORY OF INTELLIGENCE WITH EXAMPLES OF ENGINEERING APPLICATIONS
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
PART THREE: FUTURISTIC DIRECTIONS: FUN STUFF: MIND--PHYSICS + MATHEMATICS + CONJECTURES
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
Definitions
Bibliography
Index

Reviews

Advance praise: "Neural Networks and Intellect is like Kant's famous 'Critique of Pure Reason' with mathematical equations between the lines."--Dr. L. Levitin, Distinguished Professor of Engineering Science, Boston University, Fellow of IEEE