This file describes an ecosystem approach to artificial intelligence, as developed by Dr. Alex Liu

1. AN ECOSYSTEM SYSTEM APPROACH
TO ARTIFICIAL INTELLIGENCE
Dr. Alex Liu
President of the Global Association for Research Methods and Data Science
Chief Data Scientist at Analytics Services of IBM
alex@ResearchMethods.org
March 8, 2019
Pasadena, California

2. ALEX LIU INTRODUCTION
 President of the Global Association for
Research Methods and Data Science
 Chief Data Scientist – Analytics Services at IBM
 A Data Scientist Thought Leader
 Chief Data Scientist for a few corporations
before joined IBM in 2013
 Taught advanced data analytics for the
University of South California and the
University of California at Irvine
 Consulted for the United Nations, Ingram
Micro …
 M.S. and Ph.D. from Stanford University
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3. AI PROJECTS WITH DATA RETURN VERY
VALUABLE RESULTS, BUT A LOT FAILED
 Netflix, for example, integrates data science
into each part of their business; they estimate
a billion dollars in incremental value from their
personalization and recommendation alone.
 Knight Capital Group, for instance, lost $440
million in 45 minutes after a mistake in
updating a model (New York times).
 In 2018 Gartner predicts 85% AI projects will not
deliver
 Gartner estimated that 60% of big data
projects fail in 2016, and in 2017.
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4. A SOLUTION – AN AI ECOSYSTEM APPROACH
 Ecosystems of data, algorithms, scientists …
 To attack the issues of high data scientists turn over ratios.
 To create a method of sharing raw data and preprocessed data sets.
 To create ways of sharing expertise
 To attack the issues of complexity of predictive modeling
 To take care of tedious tasks
 To optimize cognitive processes
 To produce fast analytics
 To produce instant analytics
▪ Los Angeles Meetup Community
▪ Local face to face community – more than 1100 members
▪ https://www.meetup.com/RMDS_LA/
▪ https://www.linkedin.com/groups/1895501 has 30K participants
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5. WHY ECOSYSTEM AS SOLUTION?
COMPLETE SET OF FEATURES/VARIABLES NEEDED
Theories of Specification Errors
Omission of relevant explanatory variables/features will lead to biases of models.

6. WHY ECOSYSTEM?
VARIOUS OF MODELS/ALGORITHMS NEEDED
No Free Lunch Theorem
The no free lunch (NFL) theorem for supervised machine learning (Wolpert 1996) tells
us that, on average, all algorithms are equivalent.
If the goal is to obtain good generalization performance, there is no context-
independent or usage-independent reasons to favor one algorithm over others
In practice, experience with a broad range of techniques is the best insurance for
solving arbitrary new classification problems

7. AN ECOSYSTEM APPROACH OF ARTIFICIAL
INTELLIGENCE
AN AI ECOSYSTEM HAS THREE BASIC ELEMENTS
1) DATA PORTAL, 2) COMPUTING PLATFORM, 3) DATA SCIENTIST COMMUNITY
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8. THE DEFINING CHARACTERISTICS OF AN ECOSYSTEM - MUTUALITY &
ORCHESTRATION
Markets comprise entities that operate
out of individual self-interest
Ecosystems comprise entities that operate
out of orchestrated, mutual shared-interest
A set of individuals or organizations who
exchange products or services within an
environment governed by the laws of supply
and demand
A set of individuals or organizations who
formally or informally operate together to
produce something of greater value for the
mutual benefit of the ecosystem as a whole
Ecosystems exists because operating in an orchestrated environment, participants
can deliver more value within the ecosystem acting together than acting alone
By IBM Institute of Business ValueCOPYRIGHT@RMDS

9. Ecosystems can yield substantial benefits
New capabilities Improved access Improved Agility
Increase Success Ratio of Data Science Projects
Minimize data scientists turnover risks
Embrace ecosystems’ strategic potential
Ecosystems enable organizations
to access critical capabilities that
they would otherwise have
difficulty obtaining
Ecosystems support greater
access to new or different
resources such as new talents,
new tools, new data sets
Ecosystems support quick
creation of new types of products,
with different combinations of
organizations and assets
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10. 101
010
101
Platform
~ IBM DSX
Events Data Social Media
Analytical
Insights for Smart
Cities
Connecting all
the data
scientists from
a DS
community
Applications
Optimizing Operations Solutions
IoT Data
EX1: City Open Data Serving Business and Citizens
City Open Data + WATSON Studio + RMDS Community
An ecosystem with city open data
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11. EX2: Weather Data Serving Retails
Weather Data + WATSON Studio + RMDS Community
An ecosystem with weather data
101
010
101
Platform
~ IBM DSX
Weather Data Transaction
Analytical
Insights for Smart
Commerce
Connecting all
the data
scientists from
a DS
community
Applications
Optimizing Operations Solutions
IoT Data
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12. RMDS ECOSYSTEM MANAGEMENT SYSTEM
 RM4Es Based Workflow Management
 ResearchMap Based Asset Management
 Data Portal, DS Platforms and DS Communities integration and management
▪ Los Angeles Meetup Community
▪ Local face to face community – more than 1100 members
▪ https://www.meetup.com/RMDS_LA/
▪ https://www.linkedin.com/groups/1895501 has 30K participants
COPYRIGHT@RMDS
RM4Es TM
• Equation
• Estimation
• Evaluation
• Explanantion/Execution

13. ECOSYSTEM SERVICE OFFERINGS
 Developing AI ecosystems for organizations
 Monetizing data with ecosystem approaches
 Building user communities with ecosystem approaches
▪ Los Angeles Meetup Community
▪ Local face to face community – more than 1100 members
▪ https://www.meetup.com/RMDS_LA/
▪ https://www.linkedin.com/groups/1895501 has 30K participants
COPYRIGHT@RMDS