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

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

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About myself..

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Sundar Pichai, Google I/O 2017

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

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Gartner Tech Trend 2017 to 2018

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Rothwell ' s five generations of innovation approaches Own figure adapted from
Perunovic and Christiansen (2005)
Starting with Innovation Approach

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

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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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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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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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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

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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
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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
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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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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.

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30. Thank You : Q&A