AI Orange Belt training for business managers and top executives

1. AI Orange Belt
Week 1 - Harness the power of AI abilities
1

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. 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. 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. 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. 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. 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. 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
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9. 0. Roundtable
In one sentence, what is your work and what do you think this program
will allow you to accomplish?
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10. 1. Quick Background
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11. Theory of computation
We need machines that can solve complex operations
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12. 12

13. Turing (1936)
Algorithme
“General Computer”
“Universal Machine”
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14. Turing (1936)
Algorithme
“General Computer”
“Universal Machine”
Von Neumann (1945)
Processeur
RAM
14

15. Shannon (1948)
Information theory
Turing (1936)
Algorithme
“General Computer”
“Universal Machine”
Von Neumann (1945)
Processeur
RAM
15

16. 16

17. Then, 3 convergences
algorithms, data, computing power
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18. 18

19. 19

20. 20

21. 21

22. Today’s consequences
Why all this hype now?
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23. A wave of innovation
1
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24. 2
An economical race
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25. 3 A job transformation
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26. 2. What is artificial intelligence?
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27. What is artificial intelligence for you?
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28. Turing test (1950)
How can we determine if AI exists
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29. Turing Test Demo
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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“
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31. Can you recognise the real one?
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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. Strong AI(AGI)
reasoning, knowledge representation, planning,
learning, communication, …
!33

34. Weak AI (ANI)
Able to solve a very specific task
Strong AI(AGI)
reasoning, knowledge representation, planning,
learning, communication, …
!34

35. AGI vs ANI
GENERALITY
HUMAN
(PERCEIVED) COMPLEXITY
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36. AGI vs ANI
GENERALITY
SOFTWARE
HUMAN
(PERCEIVED) COMPLEXITY
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37. AGI vs ANI
GENERALITY
SOFTWARE
NARROW AI
HUMAN
(PERCEIVED) COMPLEXITY
37

38. AGI vs ANI
(PERCEIVED) COMPLEXITY
GENERALITY
SOFTWARE
NARROW AI
HUMAN
STRONG AI
38

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

41. Why not use hard-coded rules?
41

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. Learning, as a technique to “solve” AI
Humans learn, let’s try to teach machines
43

44. 44

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. TASK
!46

47. EXPERIENCETASK
!47

48. MEASUR
E
EXPERIENCETASK
!48

49. Find the task (A > B), experience, performance
Exercise
49

50. Exercice
50

51. Exercice
51

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
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
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
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
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
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
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
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
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. 3. How does it work? (+terminology)
61

62. INPUT OUTPUT
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63. 3.4
OUTPUT
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64. 3.4 12
64

65. 3.4, 0.1, 2.8 12
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66. 3.4, 0.1, 2.8 4.2, 1.2, 7.1
Lots of encodings of input/output
(text, image, sound, etc)
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67. Simple example
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68. MEASUR
E
EXPERIENCETASK
INPUT > OUTPUT
Surface > Loyer
!68

69. MEASUR
E
EXPERIENCETASK
INPUT > OUTPUT
Surface > Loyer
!69

70. MEASUR
E
EXPERIENCETASK
INPUT > OUTPUT
Surface > Loyer
Différence entre
Loyer prédit et Loyer réel
!70

71. Simple example
€
m²
71

72. θ
Simple example
72

73. 100 m² 3200€
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74. 100 m² 3200€
100 x θ
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75. 100 m² 3200€
100 x θ
“Learning” = approximate this
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76. !76

77. Data
Observations
!77

78. Model
Parameters (Constraint)
Reduce complexity
Data
Observations
!78

79. !79

80. !80

81. Data
Observations
Model
Parameters
Reduce complexity
!81

82. Data
Error
Performance measure
Error function of parameters
Observations
Information loss
Model
Parameters
Reduce complexity
!82

83. ❌
!83

84. ❌✅
!84

85. Minimise an error function
By iteratively adjusting the parameters
85

86. A complete example
86

87. 4. What tasks can we accomplish?
87

88. Task categories
Typology of realistic AI tasks
!88

89. • Classification
• Estimation continue (Régression)
• Clustering
• Détection d’anomalie
• Recommandations
• Génération de données
!89

90. !90

91. !91

92. !92

93. !93

94. And so much more!
94

95. And so much more!
95

96. • Classification
• Regression
• Clustering
• Détection d’anomalie
• Recommandations
• Génération de données
!96

97. !97

98. !98

99. !99

100. • Classification
• Regression
• Clustering
• Détection d’anomalie
• Recommandations
• Génération de données
!100

101. Exemple
!101

102. • Classification
• Estimation continue
• Clustering
• Anomaly (outlier) detection
• Recommandations
• Génération de données
!102

103. !103

104. • Classification
• Regression
• Clustering
• Anomaly detection
• Recommandations
• Génération de données
!104

105. • Classification
• Regression
• Clustering
• Anomaly detection
• Recommandations
• Data generation
!105

106. !106

107. !107

108. !108
https://speech2face.github.io/

109. My job - categorise tasks
109

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. 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. 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. 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. 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. 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. 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. 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. Domain typology
Find your way through the jungle of algorithms
118

119. Supervised Learning
119

120. Unsupervised Learning
120

121. Reinforcement Learning
121

122. 122

123. 123

124. 124

125. 125

126. 126

127. 127

128. 128

129. 129

130. 130

131. 5. Latest advances
131

132. 132

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. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html 134

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

137. 137

138. 138

139. https://www.technologyreview.com/s/
611424/this-is-how-the-robot-
uprising-finally-begins/
139

140. 201
5
140

141. 2018
201
5
141

142. 2013
142

143. 2013
2016
143

144. 2013
2016
2017
144

145. 2013
2016
2017
2018
145