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Chapters 1-7, 9, and 10 end with Notes, Bibliographical Notes, and Problems | |
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Chapter 8 ends with Bibliographical Notes and Problems | |
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Chapters 11 and 12 end with Notes and Bibliographical Notes | |
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Preface | |
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Overview: 2300 Years of Philosophy, 100 Years of Mathematical Logic, and 50 Years of Computational Intelligence | |
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Introduction: Concepts of Intelligence | |
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Concepts of Intelligence in Mathematics, Psychology, and Philosophy | |
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Probability, Hypothesis Choice, Pattern Recognition, and Complexity | |
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Prediction, Tracking, and Dynamic Models | |
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Preview: Intelligence, Internal Model, Symbol, Emotions, and Consciousness | |
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Mathematical Concepts of Mind | |
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Complexity, Aristotle, and Fuzzy Logic | |
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Nearest Neighbors and Degenerate Geometries | |
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Gradient Learning, Back Propagation, and Feedforward Neural Networks | |
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Rule-Based Artificial Intelligence | |
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Concept of Internal Model | |
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Abductive Reasoning | |
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Statistical Learning Theory and Support Vector Machines | |
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AI Debates Past and Future | |
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Society of Mind | |
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Sensor Fusion and JDL Model | |
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Hierarchical Organization | |
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Semiotics | |
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Evolutionary Computation, Genetic Algorithms, and CAS | |
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Neural Field Theories | |
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Intelligence, Learning, and Computability | |
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Mathematical versus Metaphysical Concepts of Mind | |
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Prolegomenon: Plato, Antisthenes, and Artifical Intelligence | |
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Learning from Aristotle to Maimonides | |
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Heresy of Occam and Scientific Method | |
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Mathematics vs Physics | |
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Kant: Pure Spirit and Psychology | |
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Freud vs Jung: Psychology of Philosophy | |
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Wither We Go From Here? | |
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Modeling field Theory: New Mathematical Theory of Intelligence with Examples of Engineering Applications | |
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Modeling Field Theory | |
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Internal Models, Uncertainties, and Similarities | |
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Modeling Field Theory Dynamics | |
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Bayesian MFT | |
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Shannon-Einsteinian MFT | |
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Modeling Field Theory Neural Architecture | |
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Convergence | |
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Learning of Structures, AIC, and SLT | |
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Instinct of World Modeling: Knowledge Instinct | |
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MLANS: Maximum Likelihood Adaptive Neural System for Grouping and Recognition | |
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Grouping, Classification, and Models | |
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Gaussian Mixture Model: Unsupervised Learning or Grouping | |
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Combined Supervised and Unsupervised Learning | |
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Structure Estimation | |
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Wishart and Rician Mixture Models for Radar Image Classification | |
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Convergence | |
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MLANS, Physics, Biology, and Other Neural Networks | |
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Einsteinian Neural Network | |
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Images, Signals, and Spectra | |
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Spectral Models | |
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Neural Dynamics of ENN | |
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Applications to Acoustic Transient Signals and Speech Recognition | |
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Applications to Electromagnetic Wave Propagation in the Ionosphere | |
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Summary | |
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Appendix | |
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Prediction, Tracking, and Dynamic Models | |
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Prediction, Association, and Nonlinear Regression | |
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Association and Tracking Using Bayesian MFT | |
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Association and Tracking Using Shannon-Einsteinian MFT (SE-CAT) | |
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Sensor Fusion MFT | |
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Attention | |
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Quantum Modeling Field Theory (QMFT) | |
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Quantum Computing and Quantum Physics Notations | |
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Gibbs Quantum Modeling Field System | |
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Hamiltonian Quantum Modeling Field System | |
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Fundamental Limitations on Learning | |
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The Cramer-Rao Bound on Speed of Learning | |
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Overlap Between Classes | |
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CRB for MLANS | |
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CRB for Concurrent Association and Tracking (CAT) | |
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Summary: CRB for Intellect and Evolution? | |
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Appendix: CRB Rule of Thumb for Tracking | |
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Intelligent Systems Organization: MFT, Genetic Algorithms, and Kant | |
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Kant, MFT, and Intelligent Systems | |
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Emotional Machine (Toward Mathematics of Beauty) | |
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Learning: Genetic Algorithms, MFT, and Semiosis | |
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Futuristic Directions: Fun Stuff: Mind--Physics + Mathematics + Conjectures | |
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Godel''s Theorems, Mind, and Machine | |
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Penrose and Computability of Mathematical Understanding | |
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Logic and Mind | |
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Godel, Turing, Penrose, and Putnam | |
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Godel Theorem vs Physics of Mind | |
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Toward Physics of Consciousness | |
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Phenomenology of Consciousness | |
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Physics of Spiritual Substance: Future Direct | |