This document provides a summary of key topics covered during a multi-day AI training session. Day 1 covered introductions to AI and what it can and cannot do. Day 2 focused on selecting AI projects and the steps for a successful machine learning project. Day 3 discussed AI strategy, governance, management, ethics and leadership. The remainder of the document recaps machine learning models and neural networks, discusses building vs buying solutions, reviews cloud architectures and services, and covers ethics, privacy and risk considerations for human interfaces.

1. Session 4: Wrap-up
AI Orange Belt

2. 3
2
1
Day 1 : Technical prerequisites
• What is AI
• What can AI do and what it
can’t do
Day 2 : Tactics & Methods
• How to select a project
• What are the steps
necessary for a first
successful ML project
Day 3 : Strategy & Governance
• AI Transformation Playbook
• Steps to AI Maturity
• AI Management/Ethics
• How to think like a leader
What we have seen so far
2

3. Plan for today
1. Recap on ML model learning + Neural Network learning
2. Build vs Buy
3. Cloud eco-system + Cloud architecture
4. Ethics / Privacy / Risk
5. Human Interface
6. AI Business Game
3

4. Recap on learning
mechanisms
4

5. Simple example (House pricing)
Input : size Output :
price
𝒑𝒓𝒊𝒄𝒆 = 𝟓𝟎 + 𝟎, 𝟏 𝒔𝒊𝒛𝒆
ba
𝒑𝒓𝒊𝒄𝒆 = 𝒂 + 𝒃 𝒔𝒊𝒛𝒆
“Learning” = finding this
5

6. Simple example (Breast Cancer)
Input : tumor size, age Output : malignant or benign
6

7. 7

8. ❌
❌
8

9. ✅
✅
9

10. Minimise an error function
• By iteratively adjusting the parameters
10

11. A complete example
11

12. ?
source: 3Blue1Brown12

13. source: 3Blue1Brown13

14. source: 3Blue1Brown14

15. source: 3Blue1Brown15

16. source: 3Blue1Brown16

17. source: 3Blue1Brown17

18. source: 3Blue1Brown18

19. GoogleNet (InceptionV1)
(Szegedy et al, 2014)
19

20. Convolution
Convolution examples on images
What can you do with it ?
Blurs the image
Kernel
(Filters)
Detects edges 20

21. source: 3Blue1Brown21

22. ResNet
(He et al, 2015)
22

23. Inception v3
23

24. 24

25. What does a Convolution sees ?
VGG16, convolutional layer 1-1, a few of the
64 filters
25

26. What does a Convolution sees ?
Variation of kernel
size
Source : 26

27. Adversarial Examples
The left image is predicted with 99.9% confidence as a
magpie.
27

28. Adversarial Examples
Machine Learning classifiers today are easily
fooled !
28

29. Adversarial Examples
Machine Learning classifiers today are easily
fooled !
29

30. Adversarial Examples
30

31. The problem with explainability
https://distill.pub/2019/activation-atlas/
31

32. Convolutional Neural Networks on Text
From 2 dimensions to 1 dimension convolutions…
32

33. Object Detection : Yolo
33

34. Face Detection: Siamese Networks
Cosine similarity ?
34

35. Auto Encoders
Encoder
Encoding
Decoder
35

36. Denoising AutoEncoders
Add noise
Reconstruction Loss
36

37. Denoising AutoEncoders
Original Images Noisy Images Reconstructed Images
37

38. Build vs Buy
The eternal dilemma
38
Source : https://landing.ai/ai-transformation-playbook/

39. 39

40. With a consultant you don’t know, always look to start with a small
proof of concept deliverable to prove to yourself that this consultant
knows their stuff. Work with the consultant to come up with a project that
is a low hanging fruit. Something that they can deliver on quickly without
much development effort (e.g. based on existing code they already have,
and data you have already collected). If this first step goes well, then you
can confidently move to a bigger project scope.

42. Infrastructure
database, computing,
storage, monitoring
https://mattturck.com/bigdata2018/
42

43. Industry specific
use case
43

44. Specific use case : research!
Examples in HR
44

45. 45

46. 46

47. 47

48. 48

49. 49

50. 50

51. 51

52. 52

53. 53

54. 54

55. 55

56. 56

57. 57

58. Should you build vs buy ?
1. Is the task core-business ?
2. Is the task generic or should it be customized to your company ?
3. Is the cost of building it yourselves (total cost of ownership) < an off the shelve
solution sold by a vendor ?
4. To which extent are you data strictly confidential ?
If the answer is generally YES, then you should BUILD, otherwise consider BUYING.
The cost will highly depend on the building strategy!
58

59. Cloud Architecture
to support Machine Learning
Source : https://landing.ai/ai-transformation-playbook/

60. 60

61. Major Players in Cloud Platforms
61

62. 62
Major Players in Cloud Platforms

63. 63

64. 64

65. 65

66. Cloud machine learning engine
66

67. 67

68. Cloud Vision API
Recommendations
AMAZON PERSONALIZE
Image & Video
AMAZON REKOGNITION Text Analytics
AMAZON COMPREHEND
Document Analysis
AMAZON TEXTRACT
Forecasting
AMAZON FORECAST
Conversational Agents
AMAZON LEX
Transcription
AMAZON TRANSCRIBE
Voice
AMAZON POLLY
Translation
AMAZON TRANSLATE
68

69. Decision
Speech
Language
Search
Vision
Content Moderator
Anomaly Detector
Personaliser
Speech-to-text
Speaker recognition
Entity
Video
Image
Auto-suggest
Entity
Video
Image
Auto-suggest
Face detection / emotion / etc
Indexer
Form recogniser
Image classification
Azure ML
69

70. Mock example
build a face detection app
https://azure.microsoft.com/en-us/pricing/details/cognitive-services/
https://cloud.google.com/vision/pricing
https://aws.amazon.com/rekognition/pricing/
70

71. Mock example
build a face detection app
Number of detections per month?
Training price and refresh of the model?
Accuracy comparison?
71

72. 3
2
1
Day 1 : Technical prerequisties
• What is AI
• What can AI do and what it
can’t do
Day 2 : Tactics & Methods
• How to select a project
• What are the steps
necessary for a first
successful ML project
Day 3 : Strategy & Gouvernance
• AI Transformation Playbook
• Steps to AI Maturity
• AI Management/Ethics
• How to think like a leader
What we have seen so far
73

73. 01
03
02
06
04
05
Monitoring & Updates
Have the right talents & solutions
Maintenance
Select the right question
Choose the performance metric
Decide the level of explainability
Identify
Use the right architecture
Have the talents in place
Deploy
Find the right data
Structure annotate data
Clean Data
Data
Decide on an acceptable error
Test on the right scope
Evaluate
Select the right algorithm
Tune the model
Model
Applied AI Lifecycle © PROPERTY OF AI BLACK BELT
74

75. Where will you get it?
Then prioritise by availability, accessibility & cost
- existing data sources
- data enrichment (feature engineering)
- data augmentation
- data generation
- manual data labeling
- create new data sources (e.g. sensors)
- Public data, scraping, etc
76

76. Bias in a typical ML paradigm
77

77. Classification
Regression
Clustering
Anomaly detection
Recommendations
Data generation
7
8

78. 79

79. 80

80. 5) Tuning hyperparameters (with cross-validation)
81

81. Precision & Recall metrics
Let us speak in terms of seeing your doctor:
● Recall: Over all the times you should go see your doctor,
how many times you really went?
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
● Precision: Over all the times you did go see your doctor,
how many of times you really needed to see him?
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑃
TP
TNFP
FN
YES NO
YES
NO
Predicted
Actual
82

82. AI Transformation Playbook
1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications
83

83. but we can still assess feasibility
84

84. Foster trust
By Element AI
85