AI 即將從棋盤，走入我們的真實世界 AI 經濟學、AI 管理學的開始 偰子 從機器學習到深度(強化)學習 AI 應用例--數位廣告 （休息） AI 對產業帶來的變革 AI 與社會科學 Q & A 簡介 現職：大數據顧問、數據科學家 數位廣告、物聯網 熱愛軟體開發與物聯網創新，喜歡新創事業商務模式發展、整合性資料分析與探索性研究。 學歷：中山資管系所、台大商研博士畢 經歷： 東森信息科技 大數據顧問 Vpon 數據科學主管 美商安邁（AMI）新創事業 BD 副總 國立高雄應用科技大學資管系 專任助理教授 龍捲風科技創辦人 創立台灣第一個中文 BBS 網站

1. AI
chaocraig@gmail.com

2. AI
AI AI

3. ▣
▣ ( )
▣ AI --
▣
▣ AI
▣ AI
▣ Q & A
Agenda

4. u 現職：k數據顧g、數據科ln
u 數位re、物聯網
u 熱v軟體開發與物聯網創新，h歡新創S業f務模s發p、整dt資
料分析與探索t研究。
u l歷：中山資管系x、ckf研博士畢
u 經歷：
u 東森信u科y k數據顧g
u Vpon 數據科l主管
u 美fm邁GAMI）新創S業 BD 副總
u i立高雄w用科ykl資管系 oV助理教授
u 龍捲風科y創辦人
u 創立c灣第一個中文 BBS 網站

5. 1.
AI

6. 1997-IBM Deep Blue Win Kasporov

7. 2011-IBM Watson Destroys Humans in
Jeopardy
https://www.youtube.com/watch?v=WFR3lOm_xhE

8. 2016-AlphaGo Beat KoreanGo Master
(Lee Se-dol)

9. 2016-CMU AI Libratus

10. 2017-AlphaGo Beat Chinese Go Master
(Ke Jie)

11. AlphaGo, Master to Zero

12. Intelligence to Solve Incomputable
Possibility
▣ 10^80
u (1.45×10^53) /
(1.67×10^−27)
▣ 10^171

13. What
How
Where
When
Who

14. 2.

15. Src: Fortune, 2016/09

17. /

21. LR( )

22. LM( ) vs. LR( )

23. XàY

24. User-based Recommendation

25. Netflix Recommendation Challenge
2006~2009 (Netflix )

26. User-based vs. Item-based Recommendation

27. Clustering/Categorization Recommendation

28. Matrix = Associations
Rose Navy Olive
Alice 0 +4 0
Bob 0 0 +2
Carol -1 0 -2
Dave +3 0 0
Things are associated
Like people to colors
Associations have strengths
Like preferences and
dislikes
Can quantify associations
Alice loves navy = +4,
Carol dislikes olive = -2
We don’t know all
associations
Many implicit zeroes
Source: Sean Owen(2012), Cloudera

29. In Terms of Few Features
▣ Can explain associations by appealing to
underlying features in common (e.g. “blue-ness”)
▣ Relatively few (one “blue-ness”, but many shades)
(Alice)
(Blue)
(Navy)
Source: Sean Owen(2012), Cloudera

30. Losing Information is Helpful
▣ When k (= features) is small, information is lost
▣ Factorization is approximate
(Alice appears to like blue-ish periwinkle too)
(Alice)
(Blue)
(Navy)
(Periwinkle)
Source: Sean Owen(2012), Cloudera

31. Src: AmpCamp, 2015

32. ALS Algorithm
• Optimizing X, Y simultaneously is non-convex, hard
• If X or Y are fixed, system of linear equations: convex, easy
• Initialize Y with random values
• Solve for X
• Fix X, solve for Y
• Repeat (“Alternating”)
X
YT

33. A m
=
n
S
k
k• T’
n
m
•Σ
Singular Value Decomposition
Eigen vector

34. Sample FM Matrix

35. FM with SGD

36. Context-aware Matrix Factorization

37. Richness

38. vs.

39. Natural Brain Activities

40. Deep Neural NetAork

42. http://104.155.212.49:5000/

43. Training with 2 hidden layers
Training with 4 hidden layers
Mnist

44. Demo: N-Dim 2D Projection 2D
http://projector.tensorflow.org/

46. 3.
AI
—

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

48. Internet Ads TV

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

50. Google Facebook Ads Examples

54. 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
…

56. +

58. 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…

60. APP
Targeting Capability
1
2
3

61. 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

63. Valuation of Ads
∑T
i=1CTR * v(ai, ui) = ∑T
i=1e(ai, ui, ci) = eCPM
Advertiser:
Media:

64. Demand-side platform and its bidding engine
in RTB
Landscape?
Targeting Attr.?
Cold start?

65. Demand-side platform and its bidding engine
in RTB

66. Demand-side platform and its bidding engine
in RTB

67. Bid Landscape Forecasting
▣ Only reference market price by base price
▣ Imp, UU, Click, Conv.
▣ DSP, per campaign, per targeting criteria…
▣ the advertisers’ targeting profiles à the winning bid value
▣ Pacing
Source: Ying Cui, Ruofei Zhang, Wei Li, Jianchang Mao(2011), Bid Landscape Forecasting in Online Ad Exchange Marketplace, Yahoo! Labs

68. Traffic forecasting
▣ An impression on Jeremy Lin BBS post of MiuPTT
▣ Two product ads
u A: Linsanity T-Shirt
u B: Baseketball shoes
▣ Not optimized if only bid for highest price
u B bid higher than A
u Inventory A is much fewer than inventory B

69. Bid Landscape Forecasting
Source: Ying Cui, Ruofei Zhang, Wei Li, Jianchang Mao(2011), Bid Landscape Forecasting in Online Ad Exchange Marketplace, Yahoo! Labs

70. Source: Ying Cui, Ruofei Zhang, Wei Li, Jianchang Mao(2011), Bid Landscape Forecasting in Online Ad Exchange Marketplace, Yahoo! Labs
*
*C
1
*
*C
2
*
B1
C1
*
B1
C2
* *
*
* *
*
* *
C1
* *
C2
*
B1
*
• Remove few-imp path for
not too sparse
• Easily to target all
Target Attribute
Bid Landscape Forecasting
-- Bid Star Tree Expansion

71. Bidding Price Calculation
Bidding Price = F (base price, CVR )• In the same campaign, the conversion value is
the same
= base price * φ • φ = CVR / avg CVR
φ = p(c|u, i) / Ej [ p(c|u, j) ]
Ej [ p(c|u, j) ] = Σj p(c|u, j) p(j) = p(c|u)
• I, j : inventory (on Web/App)
φ = p(c|u, i) / p(c|u)
φ = p(c|s, i) / p(c|s)
• All inventories in the same segment are the same,
• i could also be inventory cluster

72. Bidding Price Model Building
• Cold start
• Training feature of segment and inventory
respectively
• Do not train combined feature for preventing over
fitting on few training data
φ = p(c|s, i) / p(c|s)
• Cold start
• Training feature of segment
AUC: the area under the ROC curve(TP/FP)
Lift: target response divided by average response.
bid = BasePrice(s, a) * p(c|s, I, a) / p(c|s, a)
DSP cross-campaigns:

73. Bidding Price Calibration
▣ Forecasting
u Sampling learn
▣ On-line adjustment
u Feedback control Re-learn
▣ Loss reason
u Prior Probability Shift
n Budget, Freq. cap, …
u Competition

74. Model Evaluation

75. ROC Curve

76. ROC Curve

77. 4R: Reach, Richness, Representation, Range
Reach
Richness
High
High
(DAU)
(Behavioral data)
Range
( Affiliate of whole
context)
Representation
(Format Content)

78. Data Economy
Traditional - Internet Economy
HighREACH
RICHNESS
High
Low
Traditional
Economy
Internet Economy
(quality)
(quantity)

79. Reach: The Value Funnel
CPM campaign:
Revenue = N/1000 ⋅CPM
CPC campaign:
Revenue = N ⋅ CTR ⋅ CPC
CPA campaign:
Revenue = N ⋅ CTR ⋅
CVR⋅ CPA
UU Reach (DAU)
ARPU = Life-time Value

80. Richness
Data Quality è Predictive Power

81. Richness: Predictive Power
APP
Conversions Logs
Behavioral Data Attribution Data

82. Richness
▣ Data Quality Richness
u Attr. vs. behavior
▣ Data Utilization Richness
u Call taxi (short vs. long route)
u Download times vs. Activation days
▣ Data Model Richness

83. l
l CTR
3.6
l
l 5
TA
TOTAL
(Impression)

84. Representation

85. Representation

86. Representation
Brand Awareness
View
Rating
Reach
TV campaign
Conversions
Click
Impression
Request
Representation
Mobile Campaign
Actions
Traffic
Buzz
Reach
Offline Campaign
Reach
Richness
Cross-Screen Effect

87. 0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
0
5000
10000
15000
20000
25000
30000
35000
40000
Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun
APP
App Download Rate Optimized Conversion Rate +TV
4R

88. 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.

89. Range

90. Range
▣ Google trend, Viral install…

91. Range
DAll data’ if not eB4austive is also Dsampling’!
a使Mk數據N是 All Data 只要不是窮盡的，U然是抽樣F

92. Range

93. 4R: Reach, Richness, Representation, Range
Reach
Richness
High
High
(DAU)
(Behavioral data)
Range
( Affiliate of whole
context)
Representation
(Format Content)

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

95. Models

96. 4.
AI

98. Robots Backflips

99. Self-driving car in rain

100. 150 Robots in Tesla Factory
Alien Dreadnought

103. Amazon Echo

104. Amazon Echo Dot vs. Google Home Mini
$29 vs. £49
15,000+ skills

105. Google Home Demo

106. Ask Alexa – A Real Demo
▣ Alexa, what time is it? What date is it?
▣ Alexa, sing a happy song. Can you beatbox?
▣ Alexa, Happy Birthday/Xmas? twinkle twinkle little star
▣ Alexa, tell me a joke. Do you have a boy friend?
▣ Alexa, play some music / play Classical Study Music / play some jazz
▣ Alexa, inspire me. / open ocean sounds.
▣ Alexa, what's in the news?
▣ Alexa, what's the weather like?
▣ Alexa, tell me about the movie [title].
▣ Alexa, what's 56 times 33?
▣ Alexa, what's the definition of [word]? how do you spell [word]?
▣ Alexa, Thank you.
https://www.cnet.com/how-to/the-
complete-list-of-alexa-commands/

107. PC Mobile Things

108. Tesla + Uber + X = ?

109. Paradigm Shift(Reverse)
▣ Move
u Data à program
▣ Value
u Things à Product Service à Personal Service
u Value/revenue shift
u What if phone price is near its cost or free?

110. Box Moving
Things Users
For example: Camera

111. IoT Service
Users
CloudThings
IoT Service
For example: Home Surveillance
Low service feeHigh price

112. AIoT Service
Users
Data
Cloud
Training/
Inference
Things
AIoT
Service
For example: Smart Home Surveillance

113. AIoT Service(Reverse)
Users
Data
Cloud
Training/
Inference
Things
AIoT Service
Low price
Main revenue stream

114. Users
Data
Cloud
Training/
Inference
Things
AIoT Service
Low price
Main revenue stream
Assets SpecificitC
From Servant to Master
,GC
Cross!platform connections
AIoT Service(Paradigm Shift)

115. Business Model Canvas

116. Traditional Publishing Industry

117. Long Tail of user-generated niche content

119. Google Multi-sided Business Model

121. Why Vertical Integration?
Microsoft Surface Book

122. Why Vertical Integration?

123. Google RAISR
Rapid and Accurate Image Super-Resolution
Upsampling
aliasing artifacts

124. Google RAISR

125. Google RAISR

126. Google RAISR

127. Why Merge?

131. GoPro

132. GoPro

137. http://www.bbc.com/news/technology-34066941
Automation Risk

139. IoT
Internet of Things
Internet of Transformation

140. AIoT
AI - Internet of Things
AI - Internet of Transformation

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

142. 5.
AI

143. 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

144. 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

145. 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

146. Optimization Perspective

147. Gradient Descent

148. 2017

149. ▣ “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)
▣

150. ▣
Adam Smith
Invisible Hands

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

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

153. 2

154. /

155. Kinds of Human in the World?

156. Kinds of Human in the World?

157. Kinds of Human in the World?

158. How Many Kinds of People in the World?

159. 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

160. Kinds of Human in the World?
…

163. abcnews.com.co
C4allenges of Big Data ! 4-

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

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

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

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

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

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

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

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