Frontiers 20140628 v3

AEIOU Framework:
Abstract-Entity-Interaction-Outcome-Universals
Towards “Laws of Service” Across Time-Space-Scale
Jim Spohrer, IBM
Haluk Demirkan, University of Washington
Frontiers in Service, Miami, FL
June 28, 2014
6/28/2014 (c) 2014 IBM UP (University Programs) 1
This presentation with speaker notes is available for download at:
http://www.slideshare.net/spohrer/frontiers-20140628-v3

S-D Logic

Entities (Actors) as Resource Integrators

Entities (Actors) as Institutions
Ostrom
Framework Ontology Logic Language
Theory Epistemology “Lawful” Learning
Model Axiology Likeness Levels
• A set of designed constraints imposed on
human interactions for a purpose
(desired outcomes)
• “Lawful”
– Entities
– Interaction
– Outcomes

Evolving Ecology of
Entities-Interactions-Outcomes

Types of Structure

Entities (Actors) as Service Systems
7
Economics
Marketing
Computer
Science
Operations
Supply Chain
Management
Management
Science
Design
“service science is
the transdisciplinary study of
service systems &
value co-creation”
“a service system is
a human-made system to improve
provider-customer interactions
and value co-creation outcomes,
studied by many disciplines,
one piece at a time.”
Systems
Engineering
Systems
Sciences
Psychology
Political
Sciences
Management
Finance
Law
Information
Systems
Information
Systems
Cognitive Science
and many others…

Types of Service System Entities
6/28/2014
© IBM 2013 IBM University Programs
worldwide accelerating regional
development (IBM UPward)
8
http://www.service-science.info/archives/1056
Nation
State/Province
City/Region
University
College
K-12
Cultural &
Conference
Hotels
Hospital
Medical
Research
Worker
(professional)
Family
(household)
For-profits:
Business Entrepreneurship
Non-profits
Social Entrepreneurship
U-BEE
Job Creator/Sustainer
U-BEEs = University-Based Entrepreneurial Ecosystems
“The future is already
here (at universities),
it is just not evenly
distributed.”
“The best way to
predict the future
is to (inspire the next
generation of students
to) build it better.”
“Multilevel nested,
networked
holistic service
systems (HSS)
that provision
whole service (WS) to
the people inside them.
WS includes
flows (transportation,
water, food, energy, com
development (buildings,
retail ,finance, health,
education),
and governance (city,
state, nation). ”
University Four Missions
1. Learning
2. Discovery
3. Engagement
4. Convergence

Scale: Nested, Networked Structure

ISPAR: Interactions & Outcomes
Maglio, P. P., Vargo, S. L., Caswell, N., & Spohrer, J. (2009).
The service system is the basic abstraction of service science.
Information Systems and e-business Management, 7(4), 395-406.

Why now?

Drivers shaping service phenomena
• Global economic change
• ICT-enablement or technology change
• Outsourcing
• Business model change (value migration)
• Where people live (demographic change)
• How long people live
• The nature of family life
• A rising education level
• A rising dependence on universities
• A rising dependence on non-profit organizations
12

New Era of Computing:
Cognitive Technologies & Componentry
13
 Natural Language
– Reasoning, Logic & Planning
– Symbolic Processing
– Natural Language Processing
– Ranking of Hypotheses
– Knowledge Representations
– Domain-Specific Ontologies
– Information Storage/Retrieval
– Machine Learning, Reasoning
– Von Neumann Componentry
– OpenPOWER Systems
 Pattern Recognition
– Recognition, Sensing & Acting
– Pattern Processing
– Image & Speech Processing
– Ranking of Hypotheses
– Pattern Representations
– Domain-Specific Neural Nets
– Information Storage/Retrieval
– Machine Learning, Perception
– Neuromorphic Componentry
– SyNAPSE Systems
AI for IA:
Intelligence
Augmentation
Cognitive Systems
that boost learning,
discovery,
engagement,
transformation, and
long-range planning.
Cognition as a Service

Questions
This view leads to a new set of questions for service scientists to answer,
about the nature of entities, interactions, outcomes, and their dynamics over
time.
• What types of entities are capable of service interactions?
• What types of interactions do service system entities engage in?
• What types of outcomes can result when service system entities interact?
• How do the types of entities and interactions change over time?
• How do the spatial distributions of types of entities change over time?
• How do the hierarchical structure and network relationships of entities
change over time?
• How do the knowledge, competencies, resources owned and accessed by
the entities change over time?
14

A tableau of primitive economic activities
15
{P,D,C,R} P|{D,C,R} {P,D}|{C,R} {P,D,C}|R {P,D}|C|R {P,C}|{D,R} {P,C}|D|R {P,R}|{D,C} {P,R}|D|C D|{P,C,R} C|{P,D,R} R|{P,D,C} P|D|C|R
{P,D,C,R}
P|{D,C,R}
{P,D}|{C,R}
{P,D,C}|R
{P,D}|C|R
{P,C}|{D,R}
{P,C}|D|R
{P,R}|{D,C}
{P,R}|D|C
D|{P,C,R}
C|{P,D,R}
R|{P,D,C}
P|D|C|R
P Production D Distribution C Consumption R Recycling
Jointness {}
Separation |
TIME
S
P
A
C
E
169 possible patterns of service system interactions, time, space, and scale. For
example, energy generation at home, city, state, national levels. For example, local to
global to local again (e.g., circular economy).
Based on: Betancourt, R., & Gautschi, D. (2001). Product innovation in services: A
framework for analysis. Advances in Applied Microeconomics, 10, 155-183.

Value Co-Creation Process
service
systems
(market and
business
strategy)
resources
(people,
technology,
information,
organizations)
dynamically
configure
access rights
(own,
leased,
shared,
privileged)
stakeholders
(customers,
providers,
authorities,
competitors)
have
value
propositions
have
Interactions
(person-to-person,
system-to-system,
person-to-system,
system-to-person)
outcomes
(lose-win,
win-win,
lose-lose,
win-lose)
with
develop
coordinate/
motivate
measures
(quality,
productivity,
compliance,
sustainable
innovation,
others)
establish
impacted
impact
risks
impact
generate
enable
win-win value-
cocreation
services
(business
processes,
architecture and
infrastructure
services)
execute
have
Demirkan, H & Spohrer, J (2014) “Understanding Service Systems & Innovations in Time-Space Complexity:
The Abstract-Entity-Interaction-Outcome-Universals Theory,” Working Paper.

Intuitive Examples:
Entities-Interaction-Outcomes
Domain Entities Pattern
Physics Atoms fission, fusion, reactions
Physics Celestial Bodies orbit, collide, sling shot
Chemistry Molecules equilibrium, reactions
Biology Organisms mutualism, predator-prey
Business Firms exchange, divest, merge
Government Nations trade, dissolution, annex

Six questions
18
Question Description
Does the entity still exist after the
interaction?
Some interactions do or do not preserve
(conserve) entities.
Does the interaction giver rise to new
entities?
Some interactions do or do not give rise
to new entities.
Does the interaction change the state of
the entities?
Some interactions do or do not change
an entity’s state.
Does the state change include a record
of the interaction?
Some entities can and some cannot
record interaction histories.
Does the state change include a
process-of-valuing the outcome?
estimate value of outcomes.
Does the state change include the result
of simulating other entities?
simulate other entities valuing.

What is meant by “lawful”
• Physical interaction laws do not change*
– However, innovations change the costs
– Intel, IBM, OpenPOWER (computing costs)
– AT&T, Corning, Cisco (communications costs)
• Social interaction laws do change
– And innovations change the costs
– Google (Internet search) and copyright
– Uber (ride sharing) and taxi regulations
– Airbnb (home sharing) and rental regulations
* = of course, our understanding of physical laws does change, other caveats apply.

Do any social interaction laws not
change? Yes, mathematical truths!
• Ricardo – Law of Comparative Advantage
– Do a little more of what you do best (low cost)
– Do a little less of what you do least well (high cost)
– Learning curve effects in people, businesses,
countries (interaction can be mutually beneficial)
• Assumptions (On when to specialize…)
– Entities can do multiple things at variable costs
– Learning interaction is not zero cost transfer

Dynamics: Changing Rules

Dynamics: Changing Roles

Dynamics: Changing Values
Service science offers fresh perspective to reorient the debate on
what is ‘progress’ and whether or not it is slowing down, and if so,
what might be done to reframe progress ‘at the speed limit of what is possible’
with universities.

In Sum, The Quest
• Service science to better understand the laws
of service that can inform systematic service
innovation
– Get the conceptual foundations right
– Not unlike “Factory Physics” quest
• Articulate a “Moore’s Law” of service system
scaling
– Investments that lead to sustainable and resilient
value co-creation and capability co-elevation

Abstract
Entity-Interaction-Outcomes
Universals
• When entities interact, what are the logically
possible outcomes?
• For example:
– Game Theory: Win-Win, Lose-Lose, Win-Lose,
Lose-Win
– Pi-Calculus: Set of rules for agents-processes-
channels to model and reason about complex
systems (cell to city)
– Service Science: ISPAR, Ecology, etc.

From Biological to Organizational Ecology:
Populations of Entities-Interactions-Outcomes

Populations of Entities Evolving

Come visit!
IBM Research – Almaden, San Jose, CA
spohrer@us.ibm.com

Test

Service science transdisciplinary framework
30
SYSTEMS
DISCIPLINES
transportation
& supply chain
water &
waste
food &
products
energy &
electricity
ICT &
cloud
building &
construction
retail &
hospitality
banking &
finance
healthcare
& family
education
& work
city
secure
state
scale nation laws
behavioral sciences
e.g., marketing
management science
e.g., operations
political sciences
e.g., public policy
learning sciences
e.g., game theory & strategy
cognitive sciences
e.g., psychology
system sciences
e.g., industrial engineering
information sciences
e.g., computer science
organization sciences
e.g., knowledge management
social sciences
e.g., econ & law
decision sciences
e.g., stats & design
run professions
e.g., knowledge worker
transform professions
e.g., consultant
innovate professions
e.g., entrepreneurs
changevalue
technology
information
organizations
transform
(copy)
systems that govern
stakeholdersresources
customer
provider
authority
competitors
people
Innovate
(invent)
history (data
analytics)
run
future
(roadmap)
systems that focus on flows of things systems that support people's activities
Observing the stakeholders (As-Is)
Change Potential: Thinking (Has-Been & Might-Become)
Observing their Resources & Access (As-Is)
Value Realization: Doing (To-Be)

A New Era of Smart Systems
• Cognitive systems allow us to do more and dream
bigger, boosting both productivity and creativity

How many cognitive systems?
• Are you using cognitive systems yet?

Non-Zero is a deep principle
• Service Definition – Win-win (Non-Zero Sum)
– Informal: Knowledge application for mutual benefits
– Formal: Value co-creation and capability co-elevation
– Context: Abstract-Entity-Interaction-Outcome-
Universals (AEIOU) [Evolving Ecology of Nested-
Networked Service System Entities]
• Service Science in Brief (How to integrate…)
– An emerging transdiscipline that borrows from
all disciplines without replacing any of them
– Short for Service Science, Management, Engineering,
plus Design, Arts, Public Policy (SSME+DAPP)

Early Motivations (Aspiration 1)

Ten Reasons
• Universities are complex service systems of fundamental importance.
• Disciplines are infusing service innovation concepts into curriculum.
• Service science can help universities overcome discipline silos.
• University-based startups are often new types of online service.
• Professional associations are adding service science SIGs.
• Cities, home to most universities, are complex service systems.
• Service failures can be costly and can derail the careers of students.
• Service science can help universities move up in rankings.
• Service science can contribute to good industry-university relations and
interactions.
• Service science can help all universities improve their service excellence
"game.”

Higher Education: Five Trends
• Revenue from key sources is continuing to fall,
putting many institutions at severe financial risk.
• Demands are rising for a greater return on
investment in higher education.
• Greater transparency about student outcomes is
becoming the norm.
• New business and delivery models are gaining
traction.
• The globalization of education is accelerating.

Higher Education Business Model
• Who do we serve, and what are they trying to do?
• How do we help those we intend to serve do what they are trying to do?
• How do we deliver our services to those we are trying to serve?
• What is the nature of the relationship we have with those we serve?
• How do these prior components translate into revenue for our institution?
• What are the key activities that create the services we provide?
• What are the key resources we need to create the services we provide?
• Who are the key partners that help us create the services we provide to
those we serve?
• How do the key partners, resources, and activities translate into our
institution's cost model?
Denna, E. (2014) The Business Model of Higher Education. ViewPoint.
EDUCAUSE Review. March 24, 2014.
URL: http://www.educause.edu/ero/article/business-model-higher-education

IT Solutions
• Administrative solutions for education
• Asset management for education
• Campus solutions for higher education
• Classroom solutions for education
• Data and analytics for Smarter Education
• Enterprise risk management for higher education
• Framework for smarter education
• Academic performance and insights
• Business analytics software for education
• VCL solutions for cloud
• Innovation in research
• School solutions

What is Service Science?
• Early motivations &
aspirations
• Six principles, concepts,
scope
• Growing literature
• Service-Dominant Logic
• In sum, service science

Early Motivations (Growth 1)

Early Motivations (Convergence)

The Elephant in the Room

Growing literature

Scope

HAT: Hub-of-All-Things
• The HAT project’s impact on policy lies in informing current
policies on personal data privacy and legal issues. By creating
a platform for ‘digital labour’, we aim to demonstrate how
markets could be created from incentivising more digital
visibility in return for offerings to serve lived lives.

Innovation (Rick Miller, Olin)

Gartner Maverick Report
• Control
– Institutions
– Individuals
• Autonomy
– Low
– High
From:
Surviving the Rise of 'Smart Machines,'
the Loss of 'Dream Jobs' and
'90% Unemployment.

AI Will Disrupt Higher Education
• Our next move: My [Dr. Dyens deputy provost McGill University]
proposal is to think of chess as an analogy for education.
• Gary Kasparov, in the New York Review of Books… wrote:
• The surprise came at the conclusion of the event. The winner was
revealed to be not a grandmaster with a state-of-the-art PC but a
pair of amateur American chess players using three computers at
the same time. Their skill at manipulating and “coaching” their
computers to look very deeply into positions effectively
counteracted the superior chess understanding of their
grandmaster opponents and the greater computational power of
other participants. Weak human + machine + better process was
superior to a strong computer alone and, more remarkably,
superior to a strong human + machine + inferior process.

Universities Matter #1
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55
Japan
China
Germany
France
United KingdomItaly
Russia SpainBrazil
Canada
India
Mexico AustraliaSouth Korea
NetherlandsTurkey
Sweden
y = 0,7489x+ 0,3534
R² = 0,719
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6 7 8 9
%globalGDP
% top 500 universities
Nation’s % WW GDP and % Top 500 Universities (2009 Data)

6/28/2014
56
…But it can be costly, American student loan debt is over $900M

6/28/2014
57
“When we combined the impact of Harvard’s direct spending on payroll, purchasing and construction – the
indirect impact of University spending – and the direct and indirect impact of off-campus spending by Harvard
students – we can estimate that Harvard directly and indirectly accounted for nearly $4.8 billion in economic
activity in the Boston area in fiscal year 2008, and more than 44,000 jobs.”

6/28/2014
58
Nation
State/Province
City/Region
University
College
K-12
Cultural &
Conference
Hotels
Hospital
Medical
Research
Worker
(professional)
Family
(household)
For-profits:
Business Entrepreneurship
Non-profits
Social Entrepreneurship
U-BEE
Job Creator/Sustainer
U-BEEs = University-Based Entrepreneurial Ecosystems
“The future is already
here (at universities),
it is just not evenly
distributed.”
“The best way to
predict the future
is to (inspire the next
generation of students
to) build it better.”
“Multilevel nested,
networked
holistic service
systems (HSS)
that provision
whole service (WS) to
the people inside them.
WS includes
flows (transportation,
water, food, energy, com
development (buildings,
retail ,finance, health,
education),
and governance (city,
state, nation). ”
University Four Missions
1. Learning
2. Discovery
3. Engagement
4. Convergence

Reimagining Higher Education
• “Universities weren’t designed to change curricula and introduce
new classes at the pace required by changing industry
requirements.”
– Dennis Yang, president and chief operating officer of Udemy

Example

Trading Zone:
Economist, Policymakers & Service Scientists

Future of higher education
(one possible path & assumptions)
Years Change – Possible Progress Path Service Science Aspect
0-5 Revenue Continuous Improvements Data Science & Cloud
5-10 Learning Continuous Improvement Organization Science
10-15 Engagement Continuous Improvement Economic Science
15-20 Discovery Continuous Improvements Cognitive Science
Four Missions Four Types of Costs Service Science Aspect
Learning/Teaching & Lectures Knowledge Transfer Specialization
Discovery/Research Knowledge Creation Specialization
Engagement/Entrepreneurship
& Employment
Knowledge Application Integration
Convergence/Consilience Knowledge Integration Integration

Backup: Readings (some details)
• Spohrer, J., Fodell, D., & Murphy, W. (2012). Ten Reasons Service Science Matters to Universities. Educause Review, 47(6),
52-64.
• Lusch, R., & Wu, C. (2012). A service science perspective on higher education—Linking service productivity theory and
higher education reform. Center for American Progress, August.
• Denna, E. (2014) The Business Model of Higher Education. Educause ViewPoint. March 24, 2014.
• Henry, T, Pagano, E, Puckett, J, Wilson, J (2014) Five Trends to Watch in Higher Education. BCG Perspectives.
• Meeker, M (2014) Internet Trends 2014 – Code Conference
• Sledge, L & Dovey-Fishman, T (2014) Reimagining higher education: How colleges, universities, businesses, and
governments can prepare for a new age of lifelong learning. Deloitte University Press.
• IBM (2014) Education for a Smarter Planet
• Goldbloom, A (2011) Making data science a sport. O’Reilly Media Strata Conference.
• Johnson, RC (2013) IBM Unveils Cognitive Systems Institute. EETimes. October 3, 2013.
• MSU & IBM (2014) T-Summit 2014: Cultivating Tomorrow’s Talent Today. Website & Conferences.
• Spohrer J (2014) 21C Talent and 21C Citizens. Service Science Community Website Blog Post Entry.
• Dyens, O (2014) How artificial intelligence is about to disrupt higher education. UA/AU University Affairs Affaires
universitaires. April 30, 2014.
• Kenneth F. Brant , KF, Gupta, A, Sommer, D (2013) Maverick* Research: Surviving the Rise of 'Smart Machines,' the Loss
of 'Dream Jobs' and '90% Unemployment.’
• Spohrer, J., Giuiusa, A., & Demirkan, H. (2013). Service science: reframing progress with universities. Systems Research
and Behavioral Science, 30(5), 561-569.
• Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin.
• Moore, GA (2012) Escape Velocity: Free Your Company’s Future From The Pull Of The Past. Harper Business.
• Florida, R (2009) Who’s Your City? Basic Books.
• Ng, Irene (2013) Hat: Hub-of-All-Things website. Research Councils UK (RCUK) Digital Economy.
• Carmichael, A (2011) Announcing: The Complete QS Guide to Self Tracking. Quantified Self website. January 12, 2011.
• Board of Life Science (2014) Convergence: Facilitating Transdisciplinary Integration of Life Science, Physical Sciences,
Engineering, and Beyond.

Internet Trends 2014

Industry Transformation
Donald Clark, TEDxGlasgow

Kaggle: Making Data Science a Sport
(146 competitions)

Cognitive Systems

T-Shaped Talent
• Academia Optimizes
– I for individual work
– Individual IQ
– Disciplines
• Business Optimizes
– T for team work
– Team IQ
– Systems
• Both Important
– Depth & Breadth
– Disciplines & Systems

University & Industry Score Card
• do your annual performance evaluations for
your employees include coaching student
teams?
• do the coached team projects have
multidisciplinary participants?
• do the coached team projects include
industry participants from diverse sectors
• do the coached team projects have
multicultural participants?
• do the coached team projects focus on real
world challenges to improve local systems?
• what percentage of your customer offerings
change every year?
• do new offerings highlight new research
finding from journals that highlight new
knowledge?
• do new offerings highlight new
entrepreneurial ecosystem partners,
applying new knowledge to create value?
• do new offerings and team projects build
the social networks of your employees?
• do your courses include team projects for
your students?
• do the team projects have multidisciplinary
teams?
• do the team projects include industry
participants from diverse sectors?
• do the team projects have multicultural
teams?
• do the team projects focus on real-world
challenges to improve local systems?
• what percentage of your course lectures
change every year?
• do new lectures highlight new research
finding from journals that highlight new
knowledge?
• do new lectures highlight new
entrepreneurs, applying new knowledge to
create value?
• do new lectures and team projects build the
social networks of your students?

Escape Velocity
• What if there is some
hidden force that is
working against your
best efforts? That force, I
submit, is the pull of the
past...
• The larger and more
successful the enterprise,
the greater the inertial
mass, the harder it is to
alter course and speed.

What are Cognitive Systems?
• 3 L’s = Language, Learning, Levels
• How many cognitive systems?
• How much investment?
• Technology underlying new era…
• In sum, a picture…

Questions & Framing
• In 5-10-15-20 years, what will be different?
– How will higher education have changed?
– How will skills & jobs have changed?
– How will business & society have changed?
• Service science, a lens for looking at change
– Capabilities & constraints – technology systems
– Rights & responsibilities – rule systems
– What is “lawful” (physical, social) change?

Watson Business Unit
• $1B Investment: Far beyond Jeopardy!
Watson Foundations
Big Data and Analytics
Cognitive Systems82
Ecosystem Program
Business Partners
Developers
Researchers
Solutions
Customer Engagement
Healthcare
Finance
Accelerated Research
Services
Watson Discovery Advisor
Watson Explorer
Watson Analytics

83
Land-population-energy-carbon
Carlo Ratti:
Senseable Cities

IBM Platforms for Entrepreneurs
• Smarter Cities Intelligent Operations Center Platform
• IBM Watson & Cognitive Computing Platform
• IBM UP helping university startups to scale-up (growth)
6/28/2014
84

Universities Matter

Concepts

In sum, service science
• Service System Entities
– Types: Businesses, Universities, Governments, etc.
– Nested & Networked Globally
– S-D Logic (A2A; Resource Integrators)
• Value Co-Creation Interactions
– Types: Value-Proposition & Governance Mech-based
– Collaboration & Competition Blended
– SD Logic (Operant & Operant Resources)
• Builds On…
– Decades of Service Research (Marketing, Operations, etc.)
– SSME+DAPP; From I to T to Pi-shapes… and beyond!
– T Summit 2014 & 2015..
• Measures
– Productivity, Quality, Compliance, Sustainable Innovation
– Holistic Service Systems
• Quality of Life, Balance Challenge & Routine
• Innovativeness, Equity, Sustainability, Resilience

Today’s talk
• Preamble
• What is service science? Service systems?
• What are cognitive systems?
• What are the trends?
– Why makes universities/cities such special service
systems/cognitive systems?
• Backup: Readings (some details)

Preamble
• Abstract & Readings Summary
• Future of higher education
– One possible path & assumptions
– Best way to predict future is to design it
• Questions & framing
• What is meant by “lawful”
• Do any social interaction laws not change?
• Service science preliminaries
• Who I am & my biases
• Universities and our future/history

Abstract
• Patterns of Change: Transformation of Higher Education From Service Science
and Cognitive Systems Perspectives
This talk will discuss the forces reshaping higher education from service science
and cognitive systems perspectives, and presents an optimistic view of the likely
outcome. These same forces are reshaping business and society globally. Higher
education is just one of many interconnected service systems that make up our
world. However, higher education is special in many ways. For most, higher
education is the bridge to cross from youthful family life to meaningful service to
society. Also, all great cities have a major university that includes the broad
spectrum of human knowledge, concentrated in experts and an army of energetic
students within typically a square mile region. Universities are increasingly
startup engines for regional economic development and growth. Within two
decades most people on the planet will have a smart phone (disrupted and
reconfigured), including a personal cognitive system, which is both an expert
professional coach and an executive assistant. Cloud, Big Data Analytics, Mobile,
Social, Cognitive, Internet of Things and Humans provide the integrated platform
for reframing the meaning of progress with universities, leading to an era of T-
shaped professionals engaged in meaningful, creative cognitive sport.

Readings Summary
• Spohrer, J., Fodell, D., & Murphy, W. (2012). Ten Reasons Service Science Matters to Universities. Educause Review, 47(6),
52-64.
• Lusch, R., & Wu, C. (2012). A service science perspective on higher education—Linking service productivity theory and
higher education reform. Center for American Progress, August.
• Denna, E. (2014) The Business Model of Higher Education. Educause ViewPoint. March 24, 2014.
• Henry, T, Pagano, E, Puckett, J, Wilson, J (2014) Five Trends to Watch in Higher Education. BCG Perspectives.
• Meeker, M (2014) Internet Trends 2014 – Code Conference
• Sledge, L & Dovey-Fishman, T (2014) Reimagining higher education: How colleges, universities, businesses, and
governments can prepare for a new age of lifelong learning. Deloitte University Press.
• IBM (2014) Education for a Smarter Planet
• Goldbloom, A (2011) Making data science a sport. O’Reilly Media Strata Conference.
• Johnson, RC (2013) IBM Unveils Cognitive Systems Institute. EETimes. October 3, 2013.
• MSU & IBM (2014) T-Summit 2014: Cultivating Tomorrow’s Talent Today. Website & Conferences.
• Spohrer J (2014) 21C Talent and 21C Citizens. Service Science Community Website Blog Post Entry.
• Dyens, O (2014) How artificial intelligence is about to disrupt higher education. UA/AU University Affairs Affaires
universitaires. April 30, 2014.
• Kenneth F. Brant , KF, Gupta, A, Sommer, D (2013) Maverick* Research: Surviving the Rise of 'Smart Machines,' the Loss
of 'Dream Jobs' and '90% Unemployment.’
• Spohrer, J., Giuiusa, A., & Demirkan, H. (2013). Service science: reframing progress with universities. Systems Research
and Behavioral Science, 30(5), 561-569.
• Pentland, A. (2014). Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin.
• Moore, GA (2012) Escape Velocity: Free Your Company’s Future From The Pull Of The Past. Harper Business.
• Florida, R (2009) Who’s Your City? Basic Books.
• Ng, Irene (2013) Hat: Hub-of-All-Things website. Research Councils UK (RCUK) Digital Economy.
• Carmichael, A (2011) Announcing: The Complete QS Guide to Self Tracking. Quantified Self website. January 12, 2011.
• Board of Life Science (2014) Convergence: Facilitating Transdisciplinary Integration of Life Science, Physical Sciences,
Engineering, and Beyond.

What is meant by “lawful”
• Physical interaction laws do not change*
– However, innovations change the costs
– Intel, IBM, OpenPOWER (computing costs)
– AT&T, Corning, Cisco (communications costs)
• Social interaction laws do change
– And innovations change the costs
– Google (Internet search) and copyright
– Uber (ride sharing) and taxi regulations
– Airbnb (home sharing) and rental regulations
* = of course, our understanding of physical laws does change, other caveats apply.

Do any social interaction laws not
change? Yes, mathematical truths!
• Ricardo – Law of Comparative Advantage
– Do a little more of what you do best (low cost)
– Do a little less of what you do least well (high cost)
– Learning curve effects in people, businesses,
countries (interaction can be mutually beneficial)
• Assumptions (On when to specialize…)
– Entities can do multiple things at variable costs
– Learning interaction is not zero cost transfer

Who I am & my biases
• Change is hard to make happen (“predict”)
– My professional experiences
• No shortage of useful things to do
– I am very optimistic about the future
• Better mechanisms needed
– “Cognitive sport” & “improve weakest link”

Universities and our future
• The future is already here at universities, it is
just not yet well distributed.
– With apologies to Gibson/King
• The best way to predict the future is to inspire
the next generation of students to build it
better.
– With apologies to Kay/Engelbart

Early Motivations (Growth 2)
Gerstner decides
to grow service

Early Motivations (Aspiration 2)

Principle 1: Resources

Principle 2: Value Propositions

Principle 3: Access Rights

Principle 4: Outcomes

Principle 5: Dynamics

Principle 5: Dynamics (Revisited)

Principle 6: Entities

In sum, a picture…
Courtesy Jean Paul Jacob, IBM Research Emeritus
Professional
Cognitive
Assistants
- Language
- Learning
- Levels

106
What are the (system) trends?
Digital Immigrant
Born: 1988
Graduated College with PhD: 2014
Digital Native
Born: 2014
Enters College: 2032

107
Transportation: Self-driving cars

109
Manufacturing: Circular EconomyBaxter: Building the Future
Maker-Bot: Replicator 2

111
Technology: Cognitive Computing

112
112
Example: Leading Through Connections with…
Universities Collaborate with IBM Research to Design Watson
for the Grand Challenge of Jeopardy !
Assisted in the development of the Open
Advancement of Question-Answering Initiative
(OAQA) architecture and methodology
Pioneered an online natural language question
answering system called START, which provided the
ability to answer questions with high precision using
information from semi-structured and structured
information repositories
Worked to extend the
capabilities of Watson, with a
focus on extensive common sense
knowledge
Focused on large-scale
information extraction,
parsing, and knowledge
inference technologies
Worked on a visualization component to visually
explain to external audiences the massively parallel
analytics skills it takes for the Watson computing
system to break down a question and formulate a
rapid and accurate response to rival a human brain
 Provided technological advancement enabling a
computing system to remember the full interaction,
rather than treating every question like the first one -
simulating a real dialogue
Explored advanced machine learning
techniques along with rich text
representations based on syntactic and
semantic structures for the Watson’s
optimization
Worked on information retrieval
and text search technologies
http://w3.ibm.com/news/w3news/top_stories/2011/02/chq_watson_wrapup.html

113
Buildings: Circular Economy
China Broad Group:
30 Stories in 15 Days

114
Retail & Hospitality: Social Media

116
Health: Robotics & 3D Printing

117
Education: Challenge-Based Sport

118
Government: Parameterized Meta-Rules
• Innovativeness
• Equity
– Improve
weakest
link
• Sustainability
• Resiliency

119
Competitive Parity – Achieved.
• The NFL touts parity—the idea
that any team can win on any
given Sunday. But this year,
parity has truly run wild.
• Through six weeks, 11 of the
NFL's 32 teams are 3-3.
• The Journal asked the statistical
gurus of Massey-Peabody
Analytics to run a coin-flip
simulation…

120
2030 and Beyond…. Government, Health, Education, Finance, etc.

Next Generation:
T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deepinonesector
Deepinoneregion/culture
Deepinonediscipline

Welcome to the new age of
platform technologies and
smarter service systems
for every sector of
business and society
nested, networks systems

Frontiers 20140628 v3

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