Artificial Intelligence: Success, Limits, Myths and Threats - Lecture 1

Artificial Intelligence: Success, Limits, Myths and Threats - Lecture 1

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Artificial Intelligence: Success, Limits, Myths and Threats - Lecture 1

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  1. 1 DATE: 06 January 2020, 16:00 to
  2. 2 Lecture 1 Public Lecture: 6 January 2020, PM
  3. 3 Lecture 2: Tuesday 7th January 2020, PM
  4. 4 Lecture 3: Wednesday 8th January 2020, PM
  5. 5 Artificial intelligence: success, limits, myths and threats Lecture 1
  6. 6 ICTS
  7. 7 ICTS Campus in Bangalore
  8. 8 What is the Goal of the ICTS?
  9. 9 Enabled by 3 interactive missions:
  10. 10 During the past decade ICTS has achieved some measure of success in all its three missions!
  11. 11 Programs:
  12. 12 Sample Programs...
  13. 13 Programs in Machine Learning
  14. 14 ICTS-Infosys Foundation Lecture series:
  15. 15 Research
  16. 16 ICTS People: Faculty
  17. 17 Students and Postdocs
  18. 18 ICTS as a platform for new initiatives:
  19. 19 Science Outreach
  20. 20 Public Lectures
  21. 21 Kaapi with Kuriosity
  22. 22 Abdus Salam Memorial Lectures
  23. 23 Einstein Lectures
  24. 24 Vishveshwara Lectures
  25. 25 D.D. Kosambi Lectures
  26. 26 Mathematics of Planet Earth MPE 2013, Bengaluru
  27. 27 Bangalore Area Science Habba
  28. 28 Mathematics Circles
  29. 29 ICTS Organization
  30. 30 ICTS Resources
  31. 31 Thank You!
  32. 32 Artificial Intelligence: Success, Limits, Myths and Threats
  33. 33 Chapter One - Myths and Reality
  34. 34 The new era of AI
  35. 35 ImageNet Database and Challenge
  36. 36 Convoy of self-driving trucks completes first European cross-border trip
  37. 37 The new era of AI
  38. 38 2 - Language understanding
  39. 39 AlphaGo
  40. 40 July 2019 : Pluribus
  41. 41 Chapter Two - Machine learning
  42. 42 Machine Learning
  43. 43 Test phase=present new picture, that the machine has not yet seen
  44. 44 Chapter Three - The Machines: Artificial neural networks
  45. 45 Everyone recognizes
  46. 46 Artificial neural networks
  47. 47 Frank Rosenblatt's perception
  48. 48 What is new since Rosenblatt's perceptron?
  49. 49 Neural network reading digits
  50. 50 Performance on handwritten digits
  51. 51 Deep neural networks
  52. 52 Bigger networks, more parameters. Larger database!
  53. 53 New computing paradigm. Collective representation of information, going to larger scales. Robust.
  54. 54 Chapter Four - Why deep networks are not yet? a panacea
  55. 55 Three main problems:
  56. 56 1- Huge amount of labelled data is necessary for learning in deep networks
  57. 57 Oh, look at ko bamoule! Do you see ko bamoule?
  58. 58 Chapter Five - About scientific Intelligence
  59. 59 Quote from Chris Anderson -The end of Theory: The data deluge makes the scientific method obsolete
  60. 60 Thought experiment :
  61. 61 We are still very very far from General Artificial Intelligence
  62. 62 Conclusion - So, what is going to happen?
  63. 63 Predicting the future
  64. 64 Predicting the future ?
  65. 65 A major concern for the present:
  66. 66 In 2018:
  67. 67 Take-home message
  68. 68 The End
  69. 69 Q&A

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