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AI Orange Belt - Session 1

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AI Orange Belt training for business managers and top executives

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AI Orange Belt - Session 1

  1. 1. AI Orange Belt Week 1 - Harness the power of AI abilities 1
  2. 2. Technical Prerequisites The foundations necessary to understand this relatively new fast growth domain 3 Tactics & Methods Implementation of AI at the product level. How to find new use cases with interesting impact and roadmap the implementation 2 Strategy & Governance How to think AI as a leader, manager and citizen 1 2
  3. 3. AI ORANGE BELT SKILLS © PROPERTY OF AI BLACK BELT ORANGE BELT The prerequisites : WHAT is AI, how does it work in real life DEFINITION PROJECT 3
  4. 4. AI ORANGE BELT SKILLS © PROPERTY OF AI BLACK BELT ORANGE BELT The prerequisites : WHAT is AI, how does it work in real life HOW to manage and implement an artificial intelligence project DEFINITION PROJECT 4
  5. 5. AI ORANGE BELT SKILLS © PROPERTY OF AI BLACK BELT ORANGE BELT The strategy to put in place when innovating with AI The prerequisites : WHAT is AI, how does it work in real life HOW to manage and implement an artificial intelligence project DEFINITION PROJECT STRATEGY 5
  6. 6. AI ORANGE BELT SKILLS © PROPERTY OF AI BLACK BELT ORANGE BELT The strategy to put in place when innovating with AI The implications of this new technology for various verticals The prerequisites : WHAT is AI, how does it work in real life HOW to manage and implement an artificial intelligence project DEFINITION PROJECT STRATEGY IMPLICATIONS 6
  7. 7. AI ORANGE BELT SKILLS © PROPERTY OF AI BLACK BELT ORANGE BELT The prerequisites : what is AI, how does it work in real life DEFINITION PROJECT 7
  8. 8. Plan for today 1. Quick Background 2. What is Artificial Intelligence? What’s the difference with “classic” software 3. How does it work? How come a machine can learn things? 4. What can we do with it? What are the type of tasks a machine can solve with A.I. 5. Latest advances, new directions. What is the trend for future applications 8
  9. 9. 0. Roundtable In one sentence, what is your work and what do you think this program will allow you to accomplish? 9
  10. 10. 1. Quick Background 10
  11. 11. Theory of computation We need machines that can solve complex operations 11
  12. 12. 12
  13. 13. Turing (1936) Algorithme “General Computer” “Universal Machine” 13
  14. 14. Turing (1936) Algorithme “General Computer” “Universal Machine” Von Neumann (1945) Processeur RAM 14
  15. 15. Shannon (1948) Information theory Turing (1936) Algorithme “General Computer” “Universal Machine” Von Neumann (1945) Processeur RAM 15
  16. 16. 16
  17. 17. Then, 3 convergences algorithms, data, computing power 17
  18. 18. 18
  19. 19. 19
  20. 20. 20
  21. 21. 21
  22. 22. Today’s consequences Why all this hype now? 22
  23. 23. A wave of innovation 1 23
  24. 24. 2 An economical race 24
  25. 25. 3 A job transformation 25
  26. 26. 2. What is artificial intelligence? 26
  27. 27. What is artificial intelligence for you? 27
  28. 28. Turing test (1950) How can we determine if AI exists 28
  29. 29. Turing Test Demo 29
  30. 30. “It seems unfair to ask if a squirrel can count to 10 if counting is not really what a squirrel’s life is about“ 30
  31. 31. Can you recognise the real one? 31
  32. 32. Intelligence measures an agent’s ability to achieve goals in a wide range of environments. https://www.researchgate.net/publication/1904177_Universal_Intelligence_A_Definition_of_Machine_Intelligence Intelligence as a “measure” 32
  33. 33. Strong AI(AGI) reasoning, knowledge representation, planning, learning, communication, … !33
  34. 34. Weak AI (ANI) Able to solve a very specific task Strong AI(AGI) reasoning, knowledge representation, planning, learning, communication, … !34
  35. 35. AGI vs ANI GENERALITY HUMAN (PERCEIVED) COMPLEXITY 35
  36. 36. AGI vs ANI GENERALITY SOFTWARE HUMAN (PERCEIVED) COMPLEXITY 36
  37. 37. AGI vs ANI GENERALITY SOFTWARE NARROW AI HUMAN (PERCEIVED) COMPLEXITY 37
  38. 38. AGI vs ANI (PERCEIVED) COMPLEXITY GENERALITY SOFTWARE NARROW AI HUMAN STRONG AI 38
  39. 39. “Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some response (B)” Andrew NG (2016) !39
  40. 40. !40
  41. 41. Why not use hard-coded rules? 41
  42. 42. • Interactions and environment too complex to be directly in the model • A classic system cannot adapt to change, too many possible environments • Generalise to different scenarios, too many possibilities • Can only do what it’s been encoded to do !42
  43. 43. Learning, as a technique to “solve” AI Humans learn, let’s try to teach machines 43
  44. 44. 44
  45. 45. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Tom Mitchell (1997) !45
  46. 46. TASK !46
  47. 47. EXPERIENCETASK !47
  48. 48. MEASUR E EXPERIENCETASK !48
  49. 49. Find the task (A > B), experience, performance Exercise 49
  50. 50. Exercice 50
  51. 51. Exercice 51
  52. 52. Teacher 1. Research about the topic (gather information) 2. Outline desired student learnings around that topic 3. Structure a progressive curriculum 4. Create the content of each chapter / parts 5. Illustrate with examples and exercises 6.Teach the course with the content 52
  53. 53. 53 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into big clusters
  54. 54. 54 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles
 
 Topic -> set of related queries
 Set of queries -> pieces of content from website (classic)
 Videos -> text transcript 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into big clusters
  55. 55. 55 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic
 
 Piece of content -> relevant or not (0 or 1) 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into big clusters
  56. 56. 56 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content
 
 Content -> Informations 4.Categorise those informations into big clusters
  57. 57. 57 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations of content into big clusters
 
 Piece of information -> category
  58. 58. 58 6. Teach the course 1. Layout the different concepts according to the structure
 
 Text -> speech 2. Answer questions 3. Give and correct exercises
  59. 59. 59 6. Teach the course 1. Layout the different concepts according to the structure 2. Answer questions 
 
 Speech -> text
 
 Question -> answer 3. Give and correct exercises
  60. 60. 60 6. Teach the course 1. Layout the different concepts according to the structure 2. Answer questions 3. Give and correct exercises
 
 Text -> speech
 
 Text -> grade
  61. 61. 3. How does it work? (+terminology) 61
  62. 62. INPUT OUTPUT 62
  63. 63. 3.4 OUTPUT 63
  64. 64. 3.4 12 64
  65. 65. 3.4, 0.1, 2.8 12 65
  66. 66. 3.4, 0.1, 2.8 4.2, 1.2, 7.1 Lots of encodings of input/output (text, image, sound, etc) 66
  67. 67. Simple example 67
  68. 68. MEASUR E EXPERIENCETASK INPUT > OUTPUT Surface > Loyer !68
  69. 69. MEASUR E EXPERIENCETASK INPUT > OUTPUT Surface > Loyer !69
  70. 70. MEASUR E EXPERIENCETASK INPUT > OUTPUT Surface > Loyer Différence entre Loyer prédit et Loyer réel !70
  71. 71. Simple example € m² 71
  72. 72. θ Simple example 72
  73. 73. 100 m² 3200€ 73
  74. 74. 100 m² 3200€ 100 x θ 74
  75. 75. 100 m² 3200€ 100 x θ “Learning” = approximate this 75
  76. 76. !76
  77. 77. Data Observations !77
  78. 78. Model Parameters (Constraint) Reduce complexity Data Observations !78
  79. 79. !79
  80. 80. !80
  81. 81. Data Observations Model Parameters Reduce complexity !81
  82. 82. Data Error Performance measure Error function of parameters Observations Information loss Model Parameters Reduce complexity !82
  83. 83. ❌ !83
  84. 84. ❌✅ !84
  85. 85. Minimise an error function By iteratively adjusting the parameters 85
  86. 86. A complete example 86
  87. 87. 4. What tasks can we accomplish? 87
  88. 88. Task categories Typology of realistic AI tasks !88
  89. 89. • Classification • Estimation continue (Régression) • Clustering • Détection d’anomalie • Recommandations • Génération de données !89
  90. 90. !90
  91. 91. !91
  92. 92. !92
  93. 93. !93
  94. 94. And so much more! 94
  95. 95. And so much more! 95
  96. 96. • Classification • Regression • Clustering • Détection d’anomalie • Recommandations • Génération de données !96
  97. 97. !97
  98. 98. !98
  99. 99. !99
  100. 100. • Classification • Regression • Clustering • Détection d’anomalie • Recommandations • Génération de données !100
  101. 101. Exemple !101
  102. 102. • Classification • Estimation continue • Clustering • Anomaly (outlier) detection • Recommandations • Génération de données !102
  103. 103. !103
  104. 104. • Classification • Regression • Clustering • Anomaly detection • Recommandations • Génération de données !104
  105. 105. • Classification • Regression • Clustering • Anomaly detection • Recommandations • Data generation !105
  106. 106. !106
  107. 107. !107
  108. 108. !108 https://speech2face.github.io/
  109. 109. My job - categorise tasks 109
  110. 110. Back to Teacher 1. Research about the topic (gather information) 2. Outline desired student learnings around that topic 3. Structure a progressive curriculum 4. Create the content of each chapter / parts 5. Illustrate with examples and exercises 6.Teach the course with the content 110
  111. 111. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups 111
  112. 112. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles
 
 Topic -> set of related queries 
 Set of queries -> pieces of content from website (classic)
 Videos -> text transcript 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups 112
  113. 113. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles
 
 Topic -> set of related queries [GENERATION]
 Set of queries -> pieces of content from website (classic) 
 Videos -> text transcript [CLASSIFICATION] 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups 113
  114. 114. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic
 
 Piece of content -> relevant or not (0 or 1) 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups 114
  115. 115. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic
 
 Piece of content -> relevant or not (0 or 1) [PREDICTION] 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups 115
  116. 116. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content
 
 Content -> Informations [CATEGORY] 4.Categorise those informations into main groups 116
  117. 117. 1. Research about the topic 1.Crawl the web with different related queries and get the content of the websites and articles 2.Determine relevance regarding the topic 3.Extract useful informations from most relevant pieces of content 4.Categorise those informations into main groups
 Piece of information -> category [CLUSTERING] 117
  118. 118. Domain typology Find your way through the jungle of algorithms 118
  119. 119. Supervised Learning 119
  120. 120. Unsupervised Learning 120
  121. 121. Reinforcement Learning 121
  122. 122. 122
  123. 123. 123
  124. 124. 124
  125. 125. 125
  126. 126. 126
  127. 127. 127
  128. 128. 128
  129. 129. 129
  130. 130. 130
  131. 131. 5. Latest advances 131
  132. 132. 132
  133. 133. “Google’s AI beats doctors at spotting eye disease in scans” “AI beats doctors at predicting heart disease deaths” https://www.thehindu.com/sci-tech/ai-beats-doctors-at-predicting-heart-disease-deaths/article24872914.ece https://www.ft.com/content/3de44984-9ef0-11e8-85da-eeb7a9ce36e4 “Chinese AI beats 15 doctors in tumor diagnosis competition” “AI Beats Humans At Emotional Recognition Test In Landmark Study” http://www.pnas.org/content/early/2018/03/16/1716084115 “Machine-learning algorithm beats 20 lawyers in NDA legal analysis” https://www.techspot.com/news/77189-machine-learning-algorithm-beats-20-lawyers-nda-legal.html https://thenextweb.com/science/2018/07/02/chinese-ai-beats-15-doctors-in-tumor-diagnosis-competition/ Diagnostics (2018) 133
  134. 134. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html 134
  135. 135. "In the last 10 years, the number of global industrial robots has grown 72%, while the number of US manufacturing jobs has fallen 16%," Bank of America, 2016 135
  136. 136. 136
  137. 137. 137
  138. 138. 138
  139. 139. https://www.technologyreview.com/s/ 611424/this-is-how-the-robot- uprising-finally-begins/ 139
  140. 140. 201 5 140
  141. 141. 2018 201 5 141
  142. 142. 2013 142
  143. 143. 2013 2016 143
  144. 144. 2013 2016 2017 144
  145. 145. 2013 2016 2017 2018 145

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