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Comprehensive Overview of Machine Intelligence: Concepts, Techniques, and Frameworks

This course, led by Dr. Bo Yuan from Shanghai Jiaotong University, offers a deep dive into machine intelligence, focusing on key topics such as knowledge-based rules, symbolic representation, kernel-based heuristics, and nonlinear connections through neural networks. It explores inference methods like Bayesian and Markovian approaches, and addresses challenges in interactive and stochastic computing. The framework emphasizes top-down and bottom-up interactions for modeling complex systems, presenting low complexity solutions for high complexity problems. Ideal for those interested in AI's current state and future directions.

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Comprehensive Overview of Machine Intelligence: Concepts, Techniques, and Frameworks

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  1. Artificial Intelligence Bo Yuan, Ph.D. Professor Shanghai Jiaotong University

  2. Overview of Machine Intelligence • Knowledge-based rules (expert system, automata, …) • Symbolic representation in logics (Deep Blue) • Kernel-based heuristics (MDA, PCA, SVM, …) • Nonlinear connection for more representation (Neural Network) • Inference (Bayesian, Markovian, …) • To sparsely sample for convergence (GM) • Interactive and stochastic computing (uncertainty, heterogeneity) • To possibly overcome the limit of Turin Machine

  3. Top-Down Bottom-Up InteractionsThe Framework to Study a System

  4. How much can we represent and model a complex and evolving network ?

  5. Low Complexity Solutions forHigh Complexity Problems • Convexity • Stability (Metastability) • Sampling • Ergodicity • Convergence • Regularization • Software and Hardware

  6. Top-Down Bottom-Up InteractionsThe Framework to Study a System

  7. How much can we represent and model a complex and evolving network ?

  8. Review of Lecture One • Overview of AI • Knowledge-based rules in logics (expert system, automata, …) : Symbolism in logics • Kernel-based heuristics (neural network, SVM, …) : Connection for nonlinearity • Learning and inference (Bayesian, Markovian, …) : To sparsely sample for convergence • Interactive and stochastic computing (Uncertainty, heterogeneity) : To overcome the limit of Turin Machine • Course Content • Focus mainly on learning and inference • Discuss current problems and research efforts • Perception and behavior (vision, robotic, NLP, bionics …) not included • Exam • Papers (Nature, Science, Nature Review, Modern Review of Physics, PNAS, TICS) • Course materials

  9. Outline • Knowledge Representation • Searching and Logics • Perceiving and Acting • Learning • Uncertainty and Inference

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