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How to drive AI from Invention, Technology to Innovation. Challenge in driving technology to Innovation and Usecase from global AI startup.

Technology

STelligence Company Limited
Santisook Limpeeticharoenchot
Managing Director
23 September 2018,Impact,Thailand
Special Topic
AI & Innovation Roadmap

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
About myself..

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3

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4
Sundar Pichai, Google I/O 2017

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5

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Samrat Kar, (2017), AI Mind Map [ONLINE]. Available at: http://www.inatrix.com/ai-ml-deep-learning[Accessed
12 February 2018]
Artificial Intelligence Landscape

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Gartner Tech Trend 2017 to 2018

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Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Rothwell ' s five generations of innovation approaches Own figure adapted from
Perunovic and Christiansen (2005)
Starting with Innovation Approach

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11
New Innovation Model.
http://www.innovativedisruption.com/disruptive-innovation-model/

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12
http://dfkoz.com/big-data-landscape/
Innovation is growing form Startup

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15https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-0
https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence
HBR 2016, Competitive Landscape

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Top 100 AI.2018

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How a specific technology evolves: From Birth to Death
Utterback and Anderson-Tushman
Innovation Adoption : Roger

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Taxonomy of technology to Industry
Pavitt ́s industry taxonomy based on technological trajectories.

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20
AI-driven companies will take $1.2
trillion from competitors by 2020.
-Forrester Predictions 2017
PwC estimated, AI will drive global GDP
gains of $15 trillion by 2030

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21
BARRIERS OF MACHINE LEARNING IN ENTERPRISE
Top 3 Adoption Barriers
57%
Lack of data and skill sets
49%
Challenges with stakeholder buy-in
25%
High cost of solutions
“ 73% of organizations do not have data
scientists or AI specialists in-house ”
Challenges
• Shortage of data scientists or AI specialists, with some
organizations not having any
• Overwhelming effort needed in data preparation and
exploration, largely due to data quality problems
• Generating meaningful insights requires convergence
of both data science and domain expertise
https://www.cio.com/article/3235811/artificial-intelligence/3-barriers-to-ai-adoption-across-the-enterprise.html
68%
of Leaders investment
outside CIOs.
67%
See value of AI.
Pick the right problems to solve
20%
of staff has right Skills

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22
Filling the Gap
Time
Accelerate the process of researching, testing,
and deploying predictive algorithms.
Enable more people to help research, test, and
deploy predictive algorithms.
Increase Data
Scientist
productivity
Turn Analysts &
Engineers into Data
Scientists
Demand for predictive models
Supply of data scientists
Copyright ,DataRobot all right reserved

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Why DataRobot are Different
Jeremy Achin
CEO & Co-Founder
Highest: 20th
Xavier Conort
Chief Data Scientist
Highest: 1st
Tom de Godoy
CTO & Co-Founder
Highest: 20th
Owen Zhang
Product Advisor
Highest: #1
Sergey Yurgenson
Data Scientist
Highest: 1st
Amanda Schierz
Data Scientist
Current: #1 Female and #1 in UK
The top ranked
Data Scientists in
the world
The best
technologies in
the world
Copyright ,DataRobot all right reserved

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Model Building
Data
Categorical
Variables
Numeric
Variables
TextVariables
One-hot
Encoding
Univariate
Credibility
Estimates with
Elastic Net
Category Count
Missing Value
Imputation
Converter for
TextMining
AutoTuned
Worn N-Gram
Text modeler
using token
occurrences
Search for
Ratios
Search for
Differences
Gradient
Boosted
Greedy Trees
Classifier with
EarlyStopping
Prediction
Copyright ,DataRobot all right reserved

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
Gartner show trajectory from BI to AI-2017

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Visual Analytic

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Visual based to Augmented
https://www.gartner.com/doc/reprints?id=1-53T7OAP&ct=180618&st=sb

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28
Conclusion
1. Algorithm is invention, AI is technology and but we need
Innovation.
2. Positioning your AI to the right market.
3. Disruptive Innovation and Open Innovation are key for building AI.
4. Focus, speed and networks for competitiveness and sustainable
growth.

Copyright STelligence Co.,Ltd. 2018 All rights reserved.
www.stelligence.com
www.facebook.com/stelligence.com
@stelligence.com
Tel : 0 20246661
Contact Us

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Santisook stelligence ai-innovation-digital big bang-thailand2018-share

1. STelligence Company Limited Santisook Limpeeticharoenchot Managing Director 23 September 2018,Impact,Thailand Special Topic AI & Innovation Roadmap

6. Copyright STelligence Co.,Ltd. 2018 All rights reserved. Samrat Kar, (2017), AI Mind Map [ONLINE]. Available at: http://www.inatrix.com/ai-ml-deep-learning[Accessed 12 February 2018] Artificial Intelligence Landscape

10. Copyright STelligence Co.,Ltd. 2018 All rights reserved. Rothwell ' s five generations of innovation approaches Own figure adapted from Perunovic and Christiansen (2005) Starting with Innovation Approach

15. Copyright STelligence Co.,Ltd. 2018 All rights reserved. 15https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-0 https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence HBR 2016, Competitive Landscape

20. Copyright STelligence Co.,Ltd. 2018 All rights reserved. 20 AI-driven companies will take $1.2 trillion from competitors by 2020. -Forrester Predictions 2017 PwC estimated, AI will drive global GDP gains of $15 trillion by 2030

21. Copyright STelligence Co.,Ltd. 2018 All rights reserved. 21 BARRIERS OF MACHINE LEARNING IN ENTERPRISE Top 3 Adoption Barriers 57% Lack of data and skill sets 49% Challenges with stakeholder buy-in 25% High cost of solutions “ 73% of organizations do not have data scientists or AI specialists in-house ” Challenges • Shortage of data scientists or AI specialists, with some organizations not having any • Overwhelming effort needed in data preparation and exploration, largely due to data quality problems • Generating meaningful insights requires convergence of both data science and domain expertise https://www.cio.com/article/3235811/artificial-intelligence/3-barriers-to-ai-adoption-across-the-enterprise.html 68% of Leaders investment outside CIOs. 67% See value of AI. Pick the right problems to solve 20% of staff has right Skills

22. Copyright STelligence Co.,Ltd. 2018 All rights reserved. 22 Filling the Gap Time Accelerate the process of researching, testing, and deploying predictive algorithms. Enable more people to help research, test, and deploy predictive algorithms. Increase Data Scientist productivity Turn Analysts & Engineers into Data Scientists Demand for predictive models Supply of data scientists Copyright ,DataRobot all right reserved

23. Copyright STelligence Co.,Ltd. 2018 All rights reserved. Why DataRobot are Different Jeremy Achin CEO & Co-Founder Highest: 20th Xavier Conort Chief Data Scientist Highest: 1st Tom de Godoy CTO & Co-Founder Highest: 20th Owen Zhang Product Advisor Highest: #1 Sergey Yurgenson Data Scientist Highest: 1st Amanda Schierz Data Scientist Current: #1 Female and #1 in UK The top ranked Data Scientists in the world The best technologies in the world Copyright ,DataRobot all right reserved

24. Copyright STelligence Co.,Ltd. 2018 All rights reserved. Model Building Data Categorical Variables Numeric Variables TextVariables One-hot Encoding Univariate Credibility Estimates with Elastic Net Category Count Missing Value Imputation Converter for TextMining AutoTuned Worn N-Gram Text modeler using token occurrences Search for Ratios Search for Differences Gradient Boosted Greedy Trees Classifier with EarlyStopping Prediction Copyright ,DataRobot all right reserved

28. Copyright STelligence Co.,Ltd. 2018 All rights reserved. 28 Conclusion 1. Algorithm is invention, AI is technology and but we need Innovation. 2. Positioning your AI to the right market. 3. Disruptive Innovation and Open Innovation are key for building AI. 4. Focus, speed and networks for competitiveness and sustainable growth.

30. Thank You : Q&A

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