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

• 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

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

• 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

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

• 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