AI+ AI 即將從棋盤，走入我們的真實世界 AI + 社會科學的開始 New Wave of AI Application Example –
Digital Ads AI+ “Misbehaving” Q & A 1.
New Wave of AI 應用例
—
數位廣告 你的點擊， 反映你的選擇決策 Digital Advertising Revenues Hit $19.6 B in Q1 2017, Climbing 23% Year-Over-Year Internet Ads > TV 
全球網路廣告花費已大於電視 Winner Takes All 網路廣告贏者通吃 Google & Facebook Ads Examples 搓合與優化配置 Internet as a mass media 網路廣告優化方程式 七大族群的精準行銷？ 十五大族群的精準行銷？ Common Data Categories 大數據分析找到更多潛在客群 Advertiser Utility: The Value Funnel Range Ads Optimization Formula Data Science The Revolution of Big Data Models Cases Models Cases Optimization Perspective Gradient Descent “New” Wave of Machine Learning “Deep” Learning AI (Big) Data-driven More tolerance for “state-of-the-art” empirical evidence Ensemble with Reinforcement-learning & other methods World, Model & Theory Model?! Artificial Power Artificial Intelligence
體力  腦力 的第四次工業革命 2. AI 與
“不當行為” 以人為中心 
2017 年諾貝爾經濟學獎揭曉，行為經濟學出線 人是自私和理性的 “The Theory of Moral Sentiments” by Adam Smith Every man is, no doubt, by nature first and principally recommended to his own care; and he is fitter to care of himself than of every other person…" (1759, 82) （每個人天生都是為自己活著的，並且他比其他任何人都更有能力為自己精打細算） 市場是有效率的（尤其是金融市場） 正是由於每個人自私自利的天性，Adam Smith 提出的Invisible Hands（看不見的手）才可能最有效的發揮其作用，讓市場在供需影響下達到最有效的狀態。 Loss Aversion & Endowment Effect 「97% 贏得 100 美元」 vs. 「37% 贏得 300 美元」 ？ 如果送你一個賭注，你會願意多少錢轉賣出去？ Loss Aversion & Endowment Effect A: 這項治療法可治 200 人 B: 這項治療法有 1/3 的機會拯救 600 人， 2/3 的機會無人得救 Loss Aversion & Endowment Effect 「97% 贏得 100 美元」 vs. 「37% 贏得 300 美元」 ？ 如果送你一個賭注，你會願意多少錢轉賣出去？ the Coase Theorem Works at Tokens
寇斯定理對於明確價值的交易可行 the Coase Theorem did not Work in Practice
寇斯定理在實務上不可行 「損失的痛苦」是「獲得的快樂」之 2 倍 The Behavioral Economics of BitCoin The Behavioral Economics of Cryptokitties 「損失的痛苦」是「獲得的快樂」之 2 倍 Simon’s Bounded Rationality 人真的是理性/非理性的嗎？ Paul Krugman – Nobel Price(2008) Home DNA Test How Many Kinds of People in the World? 人有幾種？ Know-What, Know-Why, Know-How 
and Decision Making Kinds of Human in the World? 
人有幾種？ Ads Optimization <-> Economic Decision AlphaGo, Master to Zero AlphaZero AlphaZero AlphaGo/Master/Zero, AlphaZero 全宇宙原子數大約為 10^80 以全宇宙可見物質總質量(1.45×10^53) / 氫原子質量(1.67×10^−27) 圍棋的排列組合總數 10^171 AlphaGo 的運算能力 早期的 AlphaGo Fan 使用 176 個 GPU AlphaGo Lee 使用了 48 個 TPU AlphaGo Master 與 AlphaGo Zero 皆只使用 4 個 TPU。 Computation Economics “New” Wave of Machine Learning “Deep” Learning AI (Big) Data-driven More tolerance for “state-of-the-art” empirical

1. AI+
From The New Wave of
AI & Big Data
to See the “Misbehaving”
Big Data Consultant / Data Scientist
Craig Chao ( )
chaocraig@gmail.com
https://www.slideshare.net/chaocraig/

2. AI
AI +

3. Cray
u 現職：大數n顧問、數n科學家
u 數位廣告、物聯網
u 熱f軟體開發與物聯網創t，喜歡t創事業商務模式發展、
p合d資s分析與m索d研究。
u 學歷：Aa資管系h、台大商研博士畢
u 經歷：
u 隆A網絡、vx信e科i 資深數n科學家/大數n顧問
u Vpon 數n科學主管
u 美商安邁（AMI）t創事業 BD 副總
u 國立高雄g用科i大學資管系 專任助理ol
u 龍捲風科i創辦D
u 創立台灣第一個A文 BBS 網站

4. ▣ New Wave of AI Application
Example –
Digital Ads
▣ AI+ “Misbehaving”
▣ Q A
Agenda

5. 1.
New Wave of
AI
—

6. Digital Advertising Revenues Hit $19.6 B
in Q1 2017, Climbing 23% Year-Over-Year
800 /

7. Internet Ads TV

8. Winner Takes All
Src: https://twitter.com/jason_kint/
Src: https://www.iab.com/insights/iab-internet-advertising-revenue-report-conducted-by-pricewaterhousecoopers-pw

9. Google Facebook Ads Examples

13. Internet as a mass media
“Half the money I spend on advertising is
wasted; the trouble is I don‘t know which half.”
-- John Wanamaker, ~ 1875a pioneer in marketing
…

15. Y = F( a, u, c )
ads user context

16. +

18. Common Data Categories
▣ Persona
u Age, Gender, Birth date,
City, …
▣ Attributes
u Phone brand/model, location,
time, App, browser, banner…
▣ Behavior
u Click,
Conversion(Installation, Cart,
Purchase, …), Activation,
Payment…

20. APP
Targeting Capability
1
2
3

21. Advertiser Utility: The Value Funnel
CPM campaign:
Expense = N/1000 ⋅CPM
CPC campaign:
Expense = N ⋅ CTR ⋅ CPC
CPA campaign:
Expense = N ⋅ CTR ⋅ CVR⋅ CPA
CPM = 1000 * impressions
CTR = Click# / impression#
CPC = Cost / Click
CVR = CV# / Click#
CPA = Cost / Click
https://www.clickz.com/static/cpa-calculator

22. Range
- Roger Martin
Rothman School of Management, Toronto
If only attach importance to quantify the business
model, it will not have the ability to find a potential
growth opportunities: The pursuit of quantifying
the biggest problem is that people ignore the
context of the behavior generated, detached from
the context of the event, and have not been
included in the model ignores variables
effectiveness.

23. Ads Optimization Formula
Y = F( a, u, c )
ads user context

24. Range

25. Range
▣ Google trend, Viral install…

26. Range
‘!ll data’ if ,ot exhaustive is also ‘sampli,g’!
即使「大數n」u !ll Data 只要不u窮盡的，仍然u抽樣！

27. Range

28. Data Science
Segments Reports
For Human
(Explanatory)
Models Data-driven
Actions
Intelligence Effectiveness

29. The Revolution of Big Data
DATA
Hypotheses
Statistical Analysis
BIG DATA
Hypotheses
Machine Learning
Data Mining
Machine-generated
Sampling, Multi-variant… All, Hyper space, …
Volume, Velocity, Variety, Veracity
Human-explainable

30. Models ßà Cases
Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical
Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011

31. Models ßà Cases
Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical
Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011
BIG DATA
Hypotheses
Machine Learning
Data Mining
Machine-generated
All, Hyper space, …
Volume, Velocity, Variety, Veracity
deductive inductive
Cases
Models
Models
Cases

32. Optimization Perspective

33. Gradient Descent

34. “New” Wave of Machine Learning
▣ “Deep” Learning AI
▣ (Big) Data-driven
▣ More tolerance for “state-of-the-art” empirical evidence
▣ Ensemble with Reinforcement-learning other methods

35. World, Model Theory
Credit: John F. Sowa
generalized statements,
proven scientifically with evidence
Simplified representation, helpful tool to
understand specific phenomena

36. Model?!

37. Artificial Power Artificial Intelligence
AIR/AIoT
Artificial Power à Artificial Intelligence
à

38. 2.
AI
“ ”

39. 2017

40. ▣ “The Theory of Moral Sentiments” by Adam Smith
u Every man is, no doubt, by nature first and principally
recommended to his own care; and he is fitter to care of
himself than of every other person… (1759, 82)
▣

41. ▣
Adam Smith
Invisible Hands

42. Loss Aversion Endowment Effect
▣ 97% 100 vs. 37% 300
▣

43. Loss Aversion Endowment Effect
▣ A: 200
▣ B: 1/3
600 2/3
▣ A: 400
▣ B: 1/3
2/3
600
▣ A: 72%
▣ B: 28%
▣ A: 22%
▣ B: 78%

44. Loss Aversion Endowment Effect
▣ 97% 100 vs. 37% 300
▣

45. the Coase Theorem Works at Tokens

46. the Coase Theorem did not Work in Practice

47. the Coase Theorem did not Work in Practice
A
.
B
.
C
.
D
.

48. 2

49. The Behavioral Economics of BitCoin

50. The Behavioral Economics of Cryptokitties

51. 2

52. Simon’s Bounded Rationality
Satisfied Systematic Bias

53. /

54. Paul Krugman – Nobel Price(2008)

55. Kinds of Human in the World?

56. Kinds of Human in the World?

57. Kinds of Human in the World?

58. Home DNA Test

59. How Many Kinds of People in the World?

60. Know-What, Know-Why, Know-How
and Decision Making
Know-How
(Feasible?)
Prescriptive
Know-What
(Objective?)
Descriptive
Know-Why
(Scientific?)
Normative
Descriptive D-M:
How decisions are made?
Normative D-M:
How decisions should be made?
Prescriptive D-M:
How decisions could be made better?
RationalityBounded Rationality
H. Simon,
Administrative
Behavior AI
Src: JT Chiang, NTU MBA

61. Kinds of Human in the World?
…

63. Ads Optimization - Economic Decision
Y = F( a, u, c )
ads user context
Y = F( o, u, c )
options user context

64. AlphaGo, Master to Zero

65. AlphaZero

66. AlphaZero

67. AlphaGo/Master/Zero, AlphaZero
▣ 10^80
u (1.45×10^53) /
(1.67×10^−27)
▣ 10^171

68. AlphaGo
▣ AlphaGo Fan 176 GPU
▣ AlphaGo Lee 48 TPU
▣ AlphaGo Master AlphaGo Zero 4 TPU

71. Computation Economics
▣ Wikipedia
■ a research discipline at the interface of computer science,
economics, and management science.
■ encompasses computational modeling of economic systems,
whether agent-based, general-equilibrium, macroeconomic, or
rational-expectations, computational econometrics and statistics,
computational finance
■ Some of these areas are unique to computational economics,
while others extend traditional areas of economics by solving
problems that are difficult to study without the use of computers
and associated numerical methods

72. “New” Wave of Machine Learning
▣ “Deep” Learning AI
▣ (Big) Data-driven
▣ More tolerance for “state-of-the-art” empirical evidence
▣ Ensemble with Reinforcement-learning other methods

73. alchemy
▣ Ali Rahimi
u Google Scientist
u Machine learning Expert
u Test of Time Award, NIPS 2017
▣ NIPS
u The Conference and Workshop on
Neural Information Processing
Systems (NIPS)
▣ Yann LeCun
u Director of AI Research, Facebook
u The famous pioneer of DL expert
u The author of the famous LeeNet
https://keysduplicated.com/~ali/Research.html
LeeCun: people threw the baby with the bath water and focused on
provable convex methods or glorified template matching methods

74. Real-Life Has No Perfect Information
Marcus, NIPS, 2017

75. Interpretable is necessary in machine
learning?!
▣
▣ 10% 1%
▣
▣
Kilian Weinberger, NIPS 2017

76. FDA – Real-world Evidence
FDA expands use of Sapien 3 artificial
heart valve for high-risk patients

77. FDA – Real-world Evidence
(ischaemic heart
disease) (angina pectoris)
(heart attack)

78. Src: Modified from JT Chiang, NTU MBA
AI
AI
AI
AI
AI
AI
AI
AI
AI
AI
AI

79. Artificial Power Artificial Intelligence
AIR/AIoT
Not Evolution but Revolution

80. Models of My Life
▣ The best is the enemy of the good.

81. Thanks!Any questions?
chaocraig@gmail.com
https://www.slideshare.net/chaocraig/