This document provides an overview of artificial intelligence (AI) in 3 main sections: 1. It explains what AI is, including how machine learning works using examples, neural networks, and deep learning. AI systems are able to learn on their own from large amounts of data. 2. It discusses some of the challenges with AI, including potential biases in data and systems that can cause issues if not addressed. AI may also replace some jobs but will create new types of work for humans. 3. It argues that humans still have important roles to play in supervising AI to ensure it is developed and used responsibly and for the benefit of humanity. Close collaboration between AI and human experts is needed to manage emerging

1. AI :
what
intelligence?
Some thoughts
on AI
Roald Sieberath
Nov 2018

2. PLAN
0. What this is NOT
1.WHY you should care
2.AI = Amplified Intelligence
3.Under the hood
4.Adult supervision
5.YOU

3. Chapter 0: What this is not

4. NOT a technical explanation nor
coding

5. If you’re looking for AI tech:
Two very good ways to learn :
• DeepLearning.ai
(from Andrew Ng) (Stanford, Google Brain)
• Udacity.com
(from Sebastian Thrun) (Stanford, Google X)

6. Chapter 0.9: WHO am I ?

7. Roald Sieberath
Training
Computer Science Engineer (UCLouvain)
Business : Solvay IACE,
Stanford EPGC + VC Unlocked, Louvain MBA
and business
Entrepreneur
Startup coach Investor

8. Chapter 1: WHY you should
care

9. AI is going to be everywhere
(already is)

10. AI is getting everywhere

11. It *is* a paradigm shift (this time)
Classical computing
• Formal logic
• Results: defined
• Data = tables
• Program though languages
• Science is ± settled
• Coding talent is in demand
AI Computing
• Fuzzy logic
• Results: statistical chances
• Data = big, heterogenous
• Program through learning
• Science is progressing
• AI talent is very rare

12. AI will DECIDE for us
• Our insurance policies
• How our car drives
• Our HR assessment
• What ads we see
• Influence our purchase
• …
• “Social ranking” (China)
• …

13. It can SHAPE your career
• It will become a dominant strand of IT
• How much do you want to be part of it ?

14. What AI is not (until ~2030)
AGI
Artificial GENERAL Intelligence
(autonomous, self-conscious,… )

15. Chapter 2:
AI = Amplified Intelligence

16. A history of inflated expectations

17. AI ”winters”

18. Next “AI winter” may be ahead…

19. Non AI specialists: do ‘miracles’

20. Or “AI become specialists”

21. Chapter 3:
Under the hood

22. 3. How does this work ?
Machine Learning
Validation
Some math
Data pipeline
Reinforcement learning
Deep learning

23. “Old way”: build a model and code
e.g.: spam filter
Rules :
IF …
IF …
From:
Aurélien Géron,
Hands-on Machine Learning

24. Machine learning
Train with (labeled) data
Give (lots of) examples

25. Machine
Learning
revolution:
trains
itself
through
examples

26. Machine learning: updated

27. Data mining: discovering patterns

29. Machine Learning (ML) landscape

30. Supervised

32. Supervised vs unsupervised learning

33. Underfitting & overfitting models

34. Validating & testing the model

35. Warning : maths ahead !

36. Try this: statistics are not “natural”
(only 15% of doctors get it right !)

37. Statistics: the Bayes conspiracy 

38. Beware of statistics ! (vol. 2)

40. Beware the data pipeline
Don’t focus too much
on the algorithm
It’s only a modest part
of the total solution
AI feeds on DATA and PROCESS
GIGO :
Garbage IN
Garbage OUT

41. Reinforcement learning (RL)
Lots of applications in games :
AI playing game,
rewarded by score.
Learns by playing
thousands of games.

42. Reinforcement learning: in gaming

43. Reinforcement learning: in gaming
https://lopespm.github.io/machine_learning/2016/10/06/deep-reinforcement-learning-racing-game.html

44. Deep Learning

45. (aside : on #skills)

46. “New” AI :
Deep Learning Neural Networks (NN)
• NN were already there in the 1980’s / 90’s
• (cf. MNIST, Le Cun )
• ”Neural Networks winter” : 1990-2005
• NN revival :
• Geoff Hinton (UToronto) : Deep Belief Networks
• ...
• Deep Neural Networks
• Availability of GPUs
• (performance increase 50x over a few years)
https://www.rtinsights.com/gpus-the-key-to-cognitive-computing/

47. Neural Networks : a neuron

48. Neural Network

49. Neural Network

50. Neural Network
What we know (training set)
What we are looking for

51. Backpropagation algorithm

52. NN applied to vision

53. NN applied to vision

54. NN applied to vision : CNN

56. Practice
Experiment with Neural Networks in the browser :
https://playground.tensorflow.org
https://cs.stanford.edu/people/karpathy/convnetjs/

57. AI : a (truly) new paradigm

58. Wrap-up application: Alpha Go

59. Alpha Go Zero: no human knowledge
https://www.zdnet.com/article/deepmind-alphago-zero-learns-on-its-own-without-meatbag-intervention/

60. Robot can learn to walk… by itself
https://www.youtube.com/watch?v=gn4nRCC9TwQ

61. Chapter 4:
Adult supervision needed

63. AI gone wrong: racist chatbot

64. AI gone wrong: eating its children!
https://www.youtube.com/watch?v=_m97_kL4ox0&t=1830

65. AI :
just ”optimizing”
according to how
it has been trained!

66. Beware of BIAS !!

67. Beware of BIAS !!
https://www.youtube.com/watch?v=q44XUZdCIMM

68. We have a tremendous responsability
On how we train and treat AI
https://www.youtube.com/watch?v=USqL1V0Sd98People kicking delivery robots

69. Or else…

70. Chapter 5:
the place for HUMANS
(and YOU)

71. Risk of job automation

72. Human vs. AI
Intelligence = Recognizing patterns
Creativity = breaking patterns

73. AI vs. Human
Source : Kai Fu Lee TED talk

74. Another axis: emotions & heart

75. AI vs. Human

76. AI vs. Human

77. AI vs. Human

78. AI vs. Human

79. AI vs. Human

80. AI vs. Human

81. AI vs. Human

82. AI + Human

83. YOU
Have a responsibility in this

84. <end>
Thank you !
Roald Sieberath
roald@roald.com
LinkedIn.com/in/roald