The most conservative part in a company is mediocre experts who love status quo. Top tier experts tend to climb up mountains from one peak to another peak, so as to explore new ideas and products. You must move also from one to another one. It is said, “You can raise the bar or you can wait for others to raise it, but it’s getting raised regardless.” Raise your bar higher enough no to succeed now but in the future eventually. Your life is counted hoe many oh-shit moment you experienced. Gotta run.

1. 1
•This presentation does not represent the view of
Osaka University, Mirai Translate, Cotoba Design, and
NTT DOCOMO.
•Slides are from my sole view for which I will take
full ethical responsibility.

2. Move out from your
comfort zone!
Mick Etoh
6/25/2018
2

3. –Head of CEO Office at Panasonic H.Q. (1985)
3
Who are most conservative people in this company?
Who love status quo here?

4. –Head of CEO Office at Panasonic H.Q. (1985)
3
Who are most conservative people in this company?
Who love status quo here?
Mediocre Engineers

5. 4
If you are top-notch,
Once you get the peak, you’ll move to another peak.

6. 5
My Mantra

7. 6
Raise the bar higher
enough not to succeed at
this moment,
but to do eventually.
My Motto

8. ⼦⽈、
吾⼗有五⽽志乎學、
三⼗⽽⽴、
四⼗⽽不惑、
五⼗⽽知天命、
六⼗⽽⽿順、
七⼗⽽従⼼所欲、不踰矩
The Master said,
1. "At fifteen, I had my mind bent on learning.
2. "At thirty, I stood firm.
3. "At forty, I had no doubts.
4. "At fifty, I knew the decrees of Heaven.
5. "At sixty, my ear was an obedient organ for the
reception of truth.
6. "At seventy, I could follow what my heart
desired, without transgressing what was right."
7
論語 為政第二 Mencius (372-289 BCE),
the second master of
Confucianism

9. 8
Acceptance Ratio 4%
ICCV Aural Presentation
8
I had my mind bent on learning.

10. 9
MPEG raised me up.

11. 9
MPEG raised me up.
三十而立 "At thirty, I stood firm.”

12. i-motion (2001), the fist MP-4 product
10

13. i-motion (2001), the fist MP-4 product
10

14. 11
Second MP-4 Product

15. 11
Second MP-4 Product
四十而不惑 "At forty, I had no doubts.

16. Alan Kayʼs quote at Stanford
12

17. Invent the future,
Prepare us for it.
13

18. 14conservative
Disruptive
time
Spectrum sharing
in time/space
Fate of
Keitai walled garden
2008 2010 2015
Keitai I/O devices
(e.g., UWB)
Context (i.e., location)-Aware Services
Telephony
integration
to Web Application
Content Delivery over
heterogeneous network
Vertically Integrated Service System
with VoIP by Search Engines L2 Mobility
Optical Switching Network
Beyond ENUM
(universal ID)
Web 2.0
RoF RAN
Integration
Customer Data
Mining and Yield Management
Another
Thin Client
Unified Optical
Mobile Network
People and Data
Consumer
Network
Keitai
as Ubiquitous Hub
Architectural Change
of Mobile Networks
Convergence
and Divergence
Lightweight
computing
XML
chips
Fuel Cell
Energy Starvation
Continuous Wireless Capacity Expansion
End of
the Software
Release Cycle
Collaborative
Innovation
Context Management and sharing
Superior User
Interfaces
AAA for Ubiquitous world
MIMO
Coverage Expansion
Community as wisdom of
crowds
Cellular Integration of
Heterogeneous
Networks
Foreseen Technical Events in the decade from
2006

19. 15
http://oreilly.com/web2/archive/what-is-web-20.html
TL;TR

20. 15
Quoted from “What Is Web 2.0” by Tim O’Reilly, 09/30/2005
“The race is on to own certain classes of core data:
location, identity, calendaring of public events, product
identifiers and namespaces.the winner will be the company
that first reaches critical mass via user aggregation, and
turns that aggregated data into a system service”
http://oreilly.com/web2/archive/what-is-web-20.html
TL;TR

21. 15
NOTE: Knowledge from the aggregation is the
next “Next Intel Inside.”
Quoted from “What Is Web 2.0” by Tim O’Reilly, 09/30/2005
“The race is on to own certain classes of core data:
location, identity, calendaring of public events, product
identifiers and namespaces.the winner will be the company
that first reaches critical mass via user aggregation, and
turns that aggregated data into a system service”
http://oreilly.com/web2/archive/what-is-web-20.html
TL;TR

22. People and Data:
Dipping to Subtransactional Data (2006)
16
Business
Customer
Transactional
Subtransactional
Products
People
Financials
Contact
info
Accounts
Business
events
Sales
Production
M
onitoring
Usage
Netw
ork
W
eb
traffic
2005+
2000
1990s
1980s
Quoted from Subtransactional Data: Pushing the Limits of Business Performance,
Technology, and Information Value
Mr. Doug Laney, Evalubase Research
Tera-Byte data per day is becoming our vital asset.
Barriers to subtransactional data capture and use include the sheer volume of data, its
rate of production (data velocity), and the variety of data types and schemas in real-

23. Data Mining(2007-) by Magic Five
1717
17

24. Data Mining(2007-) by Magic Five
Massive AWS Usage
Media Understanding
Operation Optimization
Search Engine Development
1717
17
Natural Language Processing
Cloud Natives

25. Internet
Promotion of service
utilization and
usability
improvement by
analyzing behavioral
patterns from big
data
Improve accuracy
with cross-log
recommendation /
tuning based on
various logs and
improve coverage of
target user content
Improving the
accuracy of character
recognition and
image recognition by
building a dictionary
using big data, and
grasping user trends
by using social media
information
Discovery of
behavioral patterns
leading to
optimization /
cancellation of
network routing
using big data
High accuracy and
coverage of hazard
maps and crime
maps using big data
Minimizing waiting
time by optimizing
transportation and
public facilities
E-CommerceDigital Marketing
media understanding
Operational Optimization Security
Optimization of
social
infrastructure
business data
a variety of
log file
CRM system
Customer Data
Web site,
Blog
Social
media
still images, moving pictures
sensor data
Business Intelligence Data-Driven Innovation !18
Use of big data at DOCOMO (a decade later)
BD analysis and ML on distributed parallel computing

26. 300 +
kW
500 +
Servers Servers
5000 +
HDDs
2000 +
NW cables
80 + Racks
19
Unforgettable Green Plum (2010)

27. Everything in the cloud
System Expansion Completed (2017)
20
Data Source ET
Temporary
Storage
Client
Forwarder
LoaderState DB
Data
Collector
DWH (Redshift)
Data Encryption
Key
Long Term
Storage (S3)
Operation
ML
(Spark)
External Data Source
External Data
Storage
Data Hubs

28. @ AntiBayesian blog
A Funny Article in Nikkei Computer:
As a result of a big data analysis, a shoemaker
who analyzed a large amount of POS data found
that they sold boots in winter and sold sandals in
summer.
(Japanese original)
https://twitter.com/yawachi/status/326460494154194944
21

29. Business Intelligence Evolution
Long tail analysis
Marketing emerging products of small quantities in niche segments.
Detection of extremely sparse but salient signals
fraud detection
Pandemic Analysis
Finding a new word
Privacy Protection
Joint Analysis and Sequence Processing
Product of Observation List by Log List
Time series context
Each Individual processing for personalization
!22

30. Catching smart signals
23

31. 24
Twitter realtime data stream DOCOMO
Speech Recognition
Marketing
Failure Detection
Recommendation
Growth Hacking
Firehose
10,000 tweet/sec or more
Twitter x DOCOMO

32. 25https://www.nttdocomo.co.jp/support/area/speed_test/
Collaborating with our customers to
improve the operation

33. 26
Twitter realtime data stream DOCOMO
Speech Recognition
Marketing
Failure Detection
Recommendation
Growth Hacking
Firehose
10,000 tweet/sec or more
Twitter x DOCOMO

34. 940000
people
770000
people
910000
people
580000
people
150000
people
050,000100,000150,000
20 years old
25 years old
30 years old
35 years old
40 years old
45 years old
50 years old
55 years old
60 years old
65 years old
66 YEARS
OLD
0 50,000 100,000 150,000men female
s
mobile spatial statistics
Demographic Estimation Population DecompositionPopulation Estimation
operational data
27
Optimization of Our Land from Cellular
Operational Data

35. Working with Department of Culture, Tourism and Sports, Okinawa Prefecture (2013)
Distribution by Gender and Age Group in
Okinawa Prefecture (October)
Number of visitors at Nago
Municipal Baseball Stadium
Nago Municipal Baseball Stadium
by Prefecture
* Fiscal 24 Strategic repeater Creation Business Report
http://www.pref.okinawa.jp/site/bunka-sports/kankoseisaku/kikaku/report/houkokusixyo/repeater.html 28
From Hokkaido. Why?
Okinawa Local
Chiba
Tokyo
Osaka
Others

36. nanos gigantum
humeris insidentes
29
Predicting the future of an ongoing service based on its the number of Daily Active Users(DAU)

37. advertising frame
1
advertising frame
2
advertising frame
3
store ad frame
*
*
*
30
BANDIT's Menu Optimization
on DOCOMO Portal Site
Where do you get ads?
Automatically learn this online.
-> bundit algorithm

38. Slot A Slot B Slot C
Total Compensation
Example of a slot machine
player Asset The player is based on limited
assets,
But players don't know how easy
each slot is.
You actually have to bet on each
*
*
*
*
*
*
Replace "Slot" with
"advertising".
The bundit algorithm was
bet
compensa
tion
31
What is the (reference) Banddit algorithm?
TL;TR

39. Data-Driven Innovation
toward AI
32

40. 33
Paradigm Shift in Search

41. Microsoft Bing
Google Voice
Search
33
Paradigm Shift in Search

42. 34
Siri v.s. DOCOMOʼs Voice Agent (from a YouTube content, ２０１１）
34

43. 34
Siri v.s. DOCOMOʼs Voice Agent (from a YouTube content, ２０１１）
34

44. 34
Siri v.s. DOCOMOʼs Voice Agent (from a YouTube content, ２０１１）
34

45. AZ1
Same as
AZ1
AZ2speech recognition task determination
logging
35
Loosely Coupled System of Micro Services
Voice Agenet (2012 ~)

46. 36
"At fifty, I knew the decrees of Heaven.”
五⼗⽽知天命

47. Digital Transformation and Liberation of Engineers
37
Business
R&D
Lectica: R&D as a Resource

48. 38
–Prof. Henry Chesbrough
Innovation = Invention + Business Model
+

49. 38
–Prof. Henry Chesbrough
Innovation = Invention + Business Model
+
An Independent Team which combines Engineering, Marketing, and Sales.

50. 38
–Prof. Henry Chesbrough
Innovation = Invention + Business Model
+
An Independent Team which combines Engineering, Marketing, and Sales.
Like Marins as the force that fights in air, on ground and at sea

51. Now launched two start-ups.
39
Mirai Translate, Inc. (Late 2014)
Cotoba Design (Mid 2017)

52. 40

53. Time
Performance
Research = Business
speech
recognition
2012 2014 2016
DNN
41

54. Time
Performance
Research = Business
speech
recognition
2012 2014 2016
DNN
single image
recognition
multilayer CNN
41

55. Time
Performance
Research = Business
speech
recognition
2012 2014 2016
DNN
single image
recognition
multilayer CNN
Neural
machine translation
LSTM
Google Neural Machine Translation
Comercial Debut
41

56. Time
Performance
Research = Business
speech
recognition
2012 2014 2016
DNN
single image
recognition
multilayer CNN
Neural
machine translation
LSTM
Google Neural Machine Translation
Comercial Debut
???
Transformer
41

57. Trends
42

58. IoT or artificial intelligence,
machine learning
Big Data
Business Design
sensor network technology
Cloud and database technology
system engineering
Corporate culture and
organizational reform
ICT Human Resource Development a
digital transformation
AI has entered the era of integration from technology modules
43

59. =
democratization of AI
power of software
44

60. Data ScientistsDatabase Engineers
45
Clouds are filling the gap to beat the right side.

61. Data ScientistsDatabase Engineers
45
Clouds are filling the gap to beat the right side.

62. 46
analysisSave
Amazon Glacier
Amazon
S3
Amazon DynamoDB
Amazon RDS / Aurora
AWS Glue
Amazon CloudSearch
Amazon EMR Amazon EC2
Amazon Redshift Amazon Machine
Learning
AWS IoT
AWS Direct Connect
collection
Amazon Kinesis
Amazon Kinesis Firehose
Amazon Elasticsearch
Amazon
Kinesis Analytics
Amazon
QuickSight
AWS DMS
Snowball
Visualization
Amazon EC2
Amazon
Athena
AWS Managed Services: Big Data Area
‘Cloud’ is eating
the world.

63. • Mathematical modeling (IsingModel, Graphical Model, State Space Model, GMM,... )
• Optimization (nonlinear optimization (Newton, GaussNewton, etc.) LevelSet,
GraphCut,....)
• L0/1 optimization (ADMM, etc.), shortest path search (DP, Dijkstra, A *, etc.)
• Machine learning (SVM, RVM, Boosting, RandomForests, DeepXXX, XXX-learning,
Clustering,...)
• probability, statistics (MRF, CRF,...)
• Signal processing (FIT, IIT, Kalman, Particle,... ), DCT, FFT, Wavelet,... )
• Sensing principle (Time of Flight,... )
47
However, you must learn the followings:

64. 48
From legacy methods to new black boxes.

65. Domain KnowledgeDigital +
IoT = ICT + OT (Operational Technology)
49

66. ※Pitchbook調査に
AI Ecosystem in US

67. Talents which we need
51

68. Problem Finding
（Enthusiasm/Passion to
Real Business)
Data Science/Engineering Domain Knowledge
for Curation
52

69. Take Aways
53

70. — Seth Godin
““You can raise the bar or you can wait for others
to raise it, but it’s getting raised regardless.”
54

71. You have two options:
55
Surf on the wave
or
Drown under the wave.

72. Personal Goals
56

73. Vision
Mission
The Reason Why?
Personal Goals
56

74. Move out from
your comfort zone.
Raise your bar!
57

75. Your Small Step Forward as A Cultural Big Step Forward
58

76. 59

77. 60
What is the biggest "oh shit" moment in your life?
https://www.youtube.com/watch?reload=9&v=cQwH7SPypLA