KDD 2020: Physics Inspired Models in Artificial Intelligence

KDD 2020: Physics Inspired Models in Artificial Intelligence

Association for Computing Machinery (ACM) via YouTube Direct link

Limitations of the 4th Paradigm

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9 of 31

Limitations of the 4th Paradigm

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Classroom Contents

KDD 2020: Physics Inspired Models in Artificial Intelligence

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  1. 1 Intro
  2. 2 Terminology
  3. 3 Motivation: Why Physics & AI?
  4. 4 Why this Tutorial?
  5. 5 Tutorial Goals
  6. 6 Interplay of Physics and Al
  7. 7 The Four Paradigms
  8. 8 Theory vs. Data?
  9. 9 Limitations of the 4th Paradigm
  10. 10 Cautionary Tale: Problems with Big Data
  11. 11 Parameters Galore!
  12. 12 Physics: Tycho Brahe to Kepler to Newton
  13. 13 A Brief History of Physics & Al
  14. 14 Generalization in Physics & Al
  15. 15 Generalization in Neural Nets
  16. 16 Generalization: Observations
  17. 17 Computational Complexity, Al & Physics
  18. 18 Complexity Classes
  19. 19 3-SAT and Phase Transitions
  20. 20 Problems: Complexity
  21. 21 Interpretability & Explainability in Al/ML
  22. 22 Properties of XAI
  23. 23 Physics Informed Neural Nets (PINN)
  24. 24 Physics-guided Neural Network (PGNN)
  25. 25 Physics & Explainable Al: An Illustration
  26. 26 Results Summary
  27. 27 Open Questions in Neural Networks
  28. 28 Statistical physics theory of Deep Learning?
  29. 29 Information Bottleneck & Neural Nets
  30. 30 Information Bottlenecks & Physics
  31. 31 The Committee Machine

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