Service in the Humanity-Centered AI Era

2023 PhD_ICT_KES
Service in the
Humanity-Centered AI Era
Jim Spohrer
Retired Industry Executive (Apple, IBM)
UIDP Senior Fellow
Board of Directors (ISSIP, ServCollab)
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations online at: https://slideshare.net/spohrer
Thanks to Arianit Kurti for the invitation
to discuss Service in the Humanity-Centered AI Era
Thursday March 16, 2023, 16:00 CET
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book, see
My summary here.
See also
ServCollab.

PHD ICT KES – https://phdictkes.eu/about/
University academic disciplines
should not compete with each other,
because they are all
on a transdisciplinary journey.
Real world problems do not
respect discipline boundaries.
The “real mission” is to better
empower and motivate
autodidacts (lifelong learners)
to identify and work on
important/meaningful problems.
The core of “service in the
humanity-centered AI era” is about
asking better questions and
working on important problems.

How, What, and Why?
Inspiring AI upskilling (IA)
• How to learn
• AI-powered search can help people - motivated people – to
learn about whatever they put their minds to learning
• What to learn
• AI technological capabilities and limitations – foundational
models
• AI applications that can actually improve processes for how
things get done (case studies - productivity, quality,
compliance, sustainability, decarbonization)
• AI-as-a-service investment cases to motivate stakeholders to
change to better win-win interactions in business and societal
service systems (investment pitch)
• The “startup of you” investment case – learning to invest
systematically and wisely (startup pitch)
• Why learn?
• Challenge and opportunity - nations must upskill with AI and
decarbonize
• Motivation is key – find the very best free online
videos/courses and subscribe
• Universities will play an increasingly important role as industry
research partners and venture testbeds even as learners can do
more and more on their own with online curriculum
National Academies – Service Systems and AI 3

We get the future we invest in:
AI tools to experiment with today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #4 Thing Translator
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #8 This Person Does Not Exist
• #9 Namelix
• #10 Let's Enhance
Thanks to @TessaRDavis
for compiling this list:
“Service providers
will not be replaced by AI,
but trusted service providers
who use AI (well and responsibly)
will replace those who don’t.”
National Academy - Service Systems and AI 4
Try at least two
from the list
as soon as possible
What do you think?
, DALL-E and Stable Diffusion
Every person in a role in an organization is a service provider.
3/16/2023

“The best way to predict the future is to inspire the
next generation of students to build it better.”
Digital Natives Transportation Water Manufacturing
Energy Construction ICT Retail
Finance Healthcare Education Government

Today’s talk
• Introduction
• Service in the humanity-centered AI Era
• From artificial intelligence (AI) to intelligence
augmentation (IA) - AI Upskilling (focus on people)
• Why I am optimistic
• Responsible actors (with digital twins coming soon) are
learning to invest in giving and getting better service
• AI (by 1955 definition) has arrived
• What’s really going on? It took 68 years, but…
• Your data is becoming your AI… IA transformation
• Part 1: Solving AI: Leaderboards
• Roadmap and implications
• Open technologies, innovation
• Part 2: Solving IA: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
3/16/2023 IBM Code #OpenTechAI 6

Icons of AI Progress
• 1955-1956: Dartmouth Conference
organized by:
• Two early career faculty
• John McCarthy (Dartmouth, later Stanford)
• Marvin Minsky (MIT)
• Two senior industry scientists
• Claude Shannon (Bell Labs)
• Nathan Rochester (IBM)
• 1997: Deep Blue (IBM) - Chess
• 2011: Watson Jeopardy! (IBM)
• 2016: AlphaGo (Google DeepMinds)
• 2018: AlphaFold (Google DeepMinds)
• 2022: DALL-E 2 & ChapGPT (OpenAI)
• 2023: GPT-4 (OpenAI) & Microsoft (Bing)
3/16/2023 (c) IBM 2017, Cognitive Opentech Group 7

http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html https://cdn.openai.com/papers/gpt-4.pdf
1955 2023

Narrow AI
Emerging
Broad AI
Disruptive and
Pervasive
General AI
Revolutionary
▼ We are here 2050 and beyond 9
IBM Research AI © 2018 IBM Corporation
The evolution of AI
Borrowed from David Cox, IBM-MIT Lead

Smartphones pass entrance exams? When?
… when will
your smartphone
be able to take and
pass any online
course? And then
be your coach, so
you can pass too? OpenAI Answer:
2023
My Question:
2017

Artificial Intelligence (AI) has come a long way in almost seven decades…
From David Michaelis, I4J Forum

… but we still
have a long
way to go.
https://blog.irvingwb.com/blog/
January 26, 2023
Technical challenges no longer the hardest part, the AI to IA adjustment period is about responsible actors upskilling.

Overlap
Acknowledgement: E. Noei, S. Brisson, Y. Liu
Via Kelly Lyons, NAE Talk Oct 2022
2010
2019
13
Service science has come a long way in two decades…
2004-2011

… but we still
have a long
way to go.
https://blog.irvingwb.com/blog/
December 1, 2022

Today’s Books
Service is the application of resources (e.g., knowledge) for the benefit of another*
* another includes your future self and future generations as well.
The two greatest challenges of the 21st century are simultaneously
upskilling entire nations with AI (knowledge infrastructure, digital transformation)
while decarbonizing entire nations (energy infrastructure, physical transformation).
And accomplishing both with globally sustainable as-a-service models - servitization.

Three views on service and AI
Discipline View on Service View on AI Broader View
Economics Service sector Productivity
Sector productivity &
quality – better economic
systems
Automation
Technology improved
agriculture and
manufacturing, service
sector is next up
Computer Science Web services Capabilities
AI provides human
capabilities on tasks as
technological capability –
better tools
Automation
Robots will further
automate agriculture and
manufacturing, and
eventually service sector
as well
Service science, based on
Service-Dominant Logic
Value cocreation
Service is the application
of resources (e.g.,
knowledge) for the
benefit of another
Augmentation
Responsible actors
(service system entities)
upskilling with AI to give
and get better service
Humanity-Centered
Responsible actors
learning to invest in
improved win-win
interaction and change

Humanity-Centered Harmonization of AI for Service

Humanity-Centered Harmonization of Disciplines - Transdisciplinarity
Why the (holistic) service systems trend is important to future sustainability
Business and societal systems and supply chains are increasingly complex and interconnected.
Real-world problems do not respect discipline boundaries.
Scalable solutions require many schools of practice working together, and current solutions may have unintended
consequences, short-term or longer-term, especially if perspectives are not invited/considered.
Technological progress improved the scalability of agriculture and manufacturing, and next all types of service will be
made more scalable (and currently, energy intensive) by future AI capabilities and progress.
A small sampling of schools and disciplines below – more exist  - apologies for not adding yours to this summary.
School of practice for
Physical Sciences & Engineering
Technology
Behavioral & Social Sciences,
Humanities & Arts
People
Managerial Sciences &
Entrepreneurship
Information & Organizations
Comp. Sci./AI
HCI/Robotics
Electrical &
Mech. Eng.
Systems
Engineering
Economics Public Policy
& Law
Design Information
Systems
Operations
Research
Marketing &
Strategy
Read enough of Kline (1995) to understand conceptual foundation of multidisciplinary thinking
and the techno-extension factor and the accelerating soci—technical system design loop concepts.
3/16/2023 National Academy - Service Systems and AI 21

Why upskilling with AI trend is important to systems thinking
Talent development is moving from I to T to X (eXtended with AI)
6 T-shape Skills
Knowledge Areas
To be eXtended
By AI tools:
1. Disciplines
2. Systems
3. Cultures
4. Technologies
5. Practices
6. Mindsets
3/16/2023

June 12, 2022 – The Economist
Magazine Cover
March of the machines
A SPECIAL ISSUE ON ARTIFICIAL INTELLIGENCE
June 21, 2022 – COSMOPOLITAN
Magazine Cover – The A.I. Issue
Meet the World’s First
Artificially Intelligent Magazine Cover
And it only took 20 seconds to make
Historic Examples of AI’s Foundational Models Becoming Useful

https://service-science.info/archives/6377

DALL-E Prompt:
Create an image that illustrates
a person upskilling with AI,
showing their determination
and resilience in the face of
uncertainty and change.
The image should convey the
idea that upskilling with AI is a
way for individuals to stay
ahead in the job market
and be prepared for the future
of work. The person in the
image should be depicted
as confident and focused,
surrounded by technology and
tools that symbolize their
journey towards upskilling in AI.
The overall feel of the image
should be modern, sleek,
and inspiring.
Upskilling with AI: Staying
Resilient in Uncertain
Times

Upskilling with AI: Staying
Resilient in Uncertain Times
DALL-E Prompt:
Create a magazine cover image
that captures the theme of
"Upskilling with AI: Staying Resilient
in Uncertain Times". The image
should show a person
who is determined and optimistic,
despite the challenges of the
current job market and economic
uncertainty. They should be
depicted as actively engaged in
learning and improving their
AI skills, surrounded by cutting-edge
technology.

Read Wakefield
(2020)
enough to
understand what a
”digital twin” of
you might be like in
the future decades
with very advanced
AI capabilities.
Also see Rouse
(2018; 2022) ”Life
with a Cognitive
Assistant.”
AI Tools
in coming
decades…
3/16/2023

Call to Action: Create SIRs
• Responsible actors need to learn to invest wisely in
getting the future service innovations we want with AI
– guided by “Service Innovation Roadmaps (SIRs).”
Read enough of IfM and IBM (2008)
to understand what a “Service Innovation
Roadmap (SIR)” is – and who should be
creating them.
3/16/2023

Concluding Remarks: Two Books
3/16/2023 Jim Spohrer (ISSIP.org) 29

Better Models (Spohrer, Maglio, Vargo, Warg 2022)
• Increasing complex, interconnected world
• All models are wrong, some are useful
• Better models are needed of
• the world – both physical, social, virtual (science)
• people and win-win interactions (logics)
• organizations and win-win change (architecture)
• technologies (AI)
• Better models for better investing
• “We get the future we invest in, so responsible
actors must learn to invest wisely and
systematically in improved win-win interaction
and change.”

Service in the
AI era
Science science Service
dominant (S-D)
logic
Service Dominant
Architecture
(SDA)
Service in the
AI era
revisited
Core
message?
Better automation
and augmentation
improve service
processes
Better science
improves
understanding
(learning)
processes
Better logics
improve
interaction
processes
Better
architectures
improve change
processes
X+AI requires
learning to
invest
systematically
and wisely to
improve
service
Where are the
better
models?
Technology Disciplines Minds Enterprise Disciplines + AI
Minds + AI
Enterprise + AI
What type of
model?
Digital twins Digital twins Digital twins Digital twins Digital twins
Service in the AI Era: Science, Logic, and Architecture Perspectives
(Spohrer, Maglio, Vargo, Warg – request your digital copy – Spohrer@gmail.com)

From Human-Centered to Humanity-Centered Design (Norman 2023)
• Human-Centered Design
1. Solve the core, root issues, not just the
problem as presented (which is often the
symptom, not the cause).
2. Focus on the people.
3. Take a systems point of view, realizing
that most complications result from the
interdependencies of the multiple parts.
4. Continually test and refine the proposed
designs to ensure they truly meet the
concerns of the people for whom they
are intended.
• Humanity-Centered Design
1. Solve the core, root issues, not just the
problem as presented (which is often the
symptom, not the cause).
2. Focus on the entire ecosystem of people, all
living things, and the physical environment.
3. Take a long-term, systems point of view,
realizing that most complications result from
the interdependencies of the multiple parts
and that many of the most damaging impacts
on society and the ecosystem reveal
themselves only years or even decades later.
4. Continually test and refine the proposed
designs to ensure they truly meet the concerns
of the people and ecosystem for whom they
are intended.
5. Design with the community and as much as
possible support designs by the community.
Professional designers should serve as
enablers, facilitators, and resources, aiding
community members to meet their concerns.

Discussion
• Are you positive or negative about AI?
• If positive, are you using any specific AI tools today?
• See list of AI tools to try on a previous slide
• How are you investing in upskilling with AI?
• If negative, do you have a specific concern (“ditch to avoid”) – for example…?
• AI will take away my job
• AI will be used primarily by “bad actors” for mischief
• Or used by social media platforms to generate more clicks/attention thru angry
reactions
• AI will try to take over people and planet
• AI will deskill and weaken people over time
• … or other concerns about AI?
• Do you believe responsible actors (e.g., people, businesses,
universities, governments, etc.) are learning to to invest
systematically and wisely in getting the future we want? If not, why
not – what is needed?
• Join ISSIP.org (free for individuals) if you would like to continue the
conversation!
Read enough of pages 45-54 of Spohrer, Maglio, Vargo, Warg (2022) to formulate an
opinion on the topic of “investing wisely to get the future service systems we want.”
3/16/2023

Part 1: Solving AI
• Technical challenges and social adjustment period

Questions
• What is the timeline for solving AI and IA?
• TBD: When can a CEO buy AI capability <X> for price <Y>?
• Who are the leaders driving AI progress?
• What will the biggest benefits from AI be?
• What are the biggest risks associated with AI, and are they real?
• What other technologies may have a bigger impact than AI?
• What are the implications for stakeholders?
• How should we prepare to get the benefits and avoid the risks?

Timeline: Short History
3/16/2023
© IBM Cognitive Opentech Group (COG)
36
Dota 2
“Deep Learning” for
“AI Pattern Recognition”
depends on massive
amounts of “labeled data”
and computing power
available since ~2012;
Labeled data is simply
input and output pairs,
such as a sound and word,
or image and word, or
English sentence and French
sentence, or road scene
and car control settings –
labeled data means having
both input and output data
in massive quantities.
For example, 100K images
of skin, half with skin
cancer and half without to
learn to recognize presence
of skin cancer.

Timeline: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
37
3/16/2023 (c) IBM 2017, Cognitive Opentech Group
2080
2040
2000
1960
$1K
$1M
$1B
$1T
2060
2020
1980
+/- 10 years
$1
Person Average
Annual Salary
(Living Income)
Super Computer
Cost
Mainframe Cost
Smartphone Cost
T
P
E
T P E
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

Timeline: GDP/Employee
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

Timeline: Leaderboards Framework
AI Progress on Open Leaderboards - Benchmark Roadmap
Perceive World Develop Cognition Build Relationships Fill Roles
Pattern
recognition
Video
understanding
Memory Reasoning Social
interactions
Fluent
conversation
Assistant &
Collaborator
Coach &
Mediator
Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions
Chime Thumos SQuAD SAT ROC Story ConvAI
Images Context Episodic Induction Plans Intentions Summarization Values
ImageNet VQA DSTC RALI General-AI
Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation
WMT DeepVideo Alexa Prize ICCMA AT
Learning from Labeled Training Data and Searching (Optimization)
Learning by Watching and Reading (Education)
Learning by Doing and being Responsible (Exploration)
2018 2021 2024 2027 2030 2033 2036 2039
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
+3
See: https://paperswithcode.com/sota

Who is winning
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/

Robots by Country
• Industrial robots per 10,000 people by country
3/16/2023 IBM #OpenTechAI 41
34

Sweden
3/16/2023 (c) IBM MAP COG .| 42

Economic Growth Rates 2035: AI Projected Impact
3/16/2023 (c) IBM MAP COG .| 43

AI Benefits
• Access to expertise
• “Insanely great” labor productivity for trusted service providers
• Digital workers for healthcare, education, finance, etc.
• Better choices
• ”Insanely great” collaborations with others on what matters most
• AI for IA = Augmented Intelligence and higher value co-creation interactions

AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors

Other Technologies: Bigger impact? Yes.
• Augmented Reality (AR)/
Virtual Reality (VR)
• Game worlds
grow-up
• Trust Economy/
Security Systems
• Trust and security
immutable
• Advanced Materials/
Energy Systems
• Manufacturing as cheap,
local recycling service
(utility fog, artificial leaf, etc.)

Part 2: Solving IA
• Rapidly advancing technology and social adjustment (regulations)
period

3/16/2023
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
48
Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
3/16/2023
49

Watson Discovery Advisor
3/16/2023
50
Simonite, T. 2014. Software Mines Science Papers to Make New Discoveries. MIT. November 25, 2014.
URL: http://m.technologyreview.com/news/520461/software-mines-science-papers-to-make-new-discoveries/

10 million minutes of experience
3/16/2023 Understanding Cognitive Systems 51

2 million minutes of experience

Hardware < Software < Data < Experience < Transformation
Value migrates to transformation – becoming our future selves; people, businesses, nations = service system entities
Pine & Gilmore (1999)
Transformation
Roy et al (2006)
Data
Osati (2014)
Experience
Life Log

Courses
• 2015
• “How to build a cognitive system for Q&A task.”
• 9 months to 40% question answering accuracy
• 1-2 years for 90% accuracy, which questions to reject
• 2025
• “How to use a cognitive system to be a better
professional X.”
• Tools to build a student level Q&A from textbook in 1
week
• 2035
• “How to use your cognitive mediator to build a
startup.”
• Tools to build faculty level Q&A for textbook in one day
• Cognitive mediator knows a person better than they
know themselves
• 2055
• “How to manage your workforce of digital workers.”
• Most people have 100 digital workers.
3/16/2023 54
Take free online cognitive classes today at cognitiveclass.ai

Types: Progression of models
Models = instruction set of future
Task & World Model/
Planning & Decisions
Self Model/
Capacity & Limits
User Model/
Episodic Memory
Institutions Model/
Trust & Social Acts
Tool + - - -
Assistant ++ + - -
Collaborator +++ ++ + -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
Cognitive
Tool
Cognitive
Assistant
Cognitive
Collaborator
Cognitive
Coach
Cognitive
Mediator

Trust: Two Communities
Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?

3/16/2023
57
I have…
Have you noticed how the building blocks just
keep getting better?

Learning to program:
My first program
3/16/2023
58
Early Computer Science Class:
Watson Center at Columbia 1945
Jim Spohrer’s
First Program 1972

3/16/2023 59
1955 1975 1995 2015 2035 2055
Better Building Blocks

Artificial Leaf
• Daniel Nocera, a professor of energy
science at Harvard who pioneered the
use of artificial photosynthesis, says that
he and his colleague Pamela Silver have
devised a system that completes the
process of making liquid fuel from
sunlight, carbon dioxide, and water. And
they’ve done it at an efficiency of 10
percent, using pure carbon dioxide—in
other words, one-tenth of the energy in
sunlight is captured and turned into fuel.
That is much higher than natural
photosynthesis, which converts about 1
percent of solar energy into the
carbohydrates used by plants, and it
could be a milestone in the shift away
from fossil fuels. The new system is
described in a new paper in Science.

Food from Air
• Although the technology is in its infancy,
researchers hope the "protein reactor"
could become a household item.
• Juha-Pekka Pitkänen, a scientist at VTT,
said: "In practice, all the raw materials
are available from the air. In the future,
the technology can be transported to,
for instance, deserts and other areas
facing famine.
• "One possible alternative is a home
reactor, a type of domestic appliance
that the consumer can use to produce
the needed protein."
• According to the researchers, the
process of creating food from electricity
can be nearly 10 times as energy
efficient as photosynthesis, the process
used by plants.

Exoskeletons for Elderly
• A walker is a “very cost-effective”
solution for people with limited
mobility, but “it completely
disempowers, removes dignity,
removes freedom, and causes a
whole host of other psychological
problems,” SRI Ventures president
Manish Kothari says. “Superflex’s
goal is to remove all of those areas
that cause psychological-type
encumbrances and, ultimately,
redignify the individual."

Be Prepared
• Understand open AI code + data +
models + stacks + communities
• Leaderboards
• Ethical conduct
• Learn 3 R’s of IBM’s Cognitive
Opentech Group (COG)
• Read arXiv
• Redo with Github
• Report with Jupyter notebooks on DSX
and/or leaderboards
• Improve your team’s skills of rapidly
rebuilding from scratch
• Build your open code eminence
• Understand open innovation
• Communities + Leaderboards
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…
2022
OpenAI DALL-E 2
and ChatGPT
2023
GPT-4

Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
What if the ”bad actors” get the upper hand?

Jim Spohrer is a Silicon Valley-based Advisor to industry, academia, governments,
startups and non-profits on topics of AI upskilling, innovation strategy, and win-
win service in the AI era. Most recently with a consulting team working for a top
10 market cap global company, he contributed to a strategic plan for a globally
connected AI Academy for achieving rapid, nation-scale upskilling with AI. With
the US National Academy of Engineering, he co-led a 2022 workshop on “Service
Systems Engineering in the Era of Human-Centered AI” to improve well-being.
Jim is a retired IBM Executive since July 2021, and previously directed IBM’s open-
source Artificial Intelligence developer ecosystem effort, was CTO IBM Venture
Capital Group, co-founded IBM Almaden Service Research, and led IBM Global
University Programs. In the 1990’s at Apple Computer, as a Distinguished Engineer
Scientist and Technologist, he was executive lead on next generation learning
platforms. In the 1970’s, after his MIT BS in Physics, he developed speech
recognition systems at Verbex (Exxon) before receiving his Yale PhD in Computer
Science/AI. In 1989, prior to joining Apple, he was a visiting scholar at the
University of Rome, La Sapienza advising doctoral students working on AI and
Education dissertations. With over ninety publications and nine patents, he
received the Christopher Lovelock Career Contributions to the Service Discipline
award, Gummesson Service Research award, Vargo and Lusch Service-Dominant
Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for
advancing service science. Jim was elected and previously served as Linux
Foundation AI & Data Technical Advisory Board Chairperson and ONNX Steering
Committee Member (2020-2021). Today, he is a UIDP Senior Fellow for
contributions to industry-university collaborations, and a member of the Board of
Directors of the International Society of Service Innovation (ISSIP) and ServCollab.
Jim Spohrer, Advisor
Retired Industry Executive (Apple, IBM)
UIDP Senior Fellow
Board of Directors, ServCollab
Board of Directors, ISSIP.org
Changemaker Priorities
1. Service Innovation
2. Upskilling with AI
3. Future Universities
4. Geothermal Energy
5. Poverty Reduction
6. Regional Development
Competitive Parity
Technologies
1. AI & Robotics
2. Digital Twins
3. Open Source
4. AR/VR/XR
5. Geothermal
6. Learning
Platforms

Who I am: Take 2
The Three Ages of Man (Giorgione)
Thanks to Alan Hartman for kind inspiration (slides) (recording)
Service is an actor applying resources (e.g., knowledge) to benefit another
Service system entities are responsible actors that give and get service
(e.g., people, businesses, universities, nations, etc.)
Service science studies service systems as an evolving ecology
of responsible actors that interact and change.
Service innovations improve win-win interaction and change
in business and society
Service systems are dynamic configurations of four types of resources

What I study
Service Science and Open Source AI – Trust is key to both
Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation/Collaboration
Responsible Entities Learning to Invest
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities

3/16/2023
The International Society of Service Innovation Professionals
(ISSIP.org)
68

IA Progression – Tool, Assistant, Collaborator, Coach, Mediator
3/16/2023 Jim Spohrer (ISSIP) 69
Rouse & Spohrer (2018)
Siddike, Spohrer, Demirkan, Kodha (2018)
Araya (2018)
Spohrer& Siddike (2018)

Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
Dedicated to Douglas E. Engelbart, Inventor
The Mouse (Pointing Device)
The Mother of All Demos
Bootstrapping Practice/Augmentation Theory
Note: Bush (1945) and Licklider (1960) created funding programs that benefitted Engelbart in building working systems.

IA as Socio-Technical Extension Factor on Capabilities & Values
IA (human values) is not AI (technology capability)
Difference 1: IA leads to more capable people even when scaffold removed
Difference 2: IA leads to more responsible people to use wisely the capabilities
Superminds
Malone (2018)
Things that Make
Us Smart
Norman (1994)
Worldboard
Augmented Perception
Spohrer (1999)
Bicycles for the Mind
Kay & Jobs (1984)
Techno-Extension Factor
Measurement
& Accelerating
Socio-Technical Design Loop
Kline (1996)

0 25 50 100 125 150
Automobile
75
Years
50
100
Telephone
Electricity
Radio
Television
VCR
PC
Cellular
%
Adoption
Capability Augmentation and Adoption Rate Increases

Learning to invest
• Run = Routine Activities
• Transform = Copy Activities
• Innovate =
Invent and Apply Activities
Innovate
Invest in each
type of change

74
How responsible entities (service systems) learn and change over time
History and future of Run-Transform-Innovate investment choices
• Diverse Types
• Persons (Individuals)
• Families
• Regional Entities
• Universities
• Hospitals
• Cities
• States/Provinces
• Nations
• Other Enterprises
• Businesses
• Non-profits
• Learning & Change
• Run = use existing knowledge
or standard practices (use)
• Transform = adopt a new best
practice (copy)
• Innovate = create a new best
practice (invent) Innovate
Invest in each
type of change
Spohrer J, Golinelli GM, Piciocchi P, Bassano C (2010) An integrated SS-VSA analysis of changing job roles. Service Science. 2010 Jun;2(1-2):1-20.
March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87. URL:
exploit
explore

“The best way to predict the future is to inspire the
next generation of students to build it better”
Digital Natives Transportation Water Manufacturing
Energy Construction ICT Retail
Finance Healthcare Education Government

3/16/2023 76
1955 1975 1995 2015 2035 2055
Better Building Blocks

Additional Resources
• Arthur WB (2019) Foundations of Complexity Economics. Nature Review Physics.
• Dietrich BL, Plachy EC, Norton MF (2014) Analytics Across the Enterprise.
• Donofrio N, DeMarco M (2022) If Nothing Changes, Nothing Changes: The Nick Donofrio.
• Fleming M (2022) Breakthrough: The Growth Revolution (in an Era of Artificial Intelligence and Worker Engagement).
• IfM and IBM (2008) Succeeding through service innovation: A service perspective for education, research, business and government.
• Larson RC (2022) Model Thinking for Everyday Life Working Wonders with a Blank Sheet of Paper. (Coming Soon).
• Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis. Organization Science.
• Madhavan G, Poste G, Rouse W (2020) Complex Unifiable System. Editors' Note: Systemic Vistas. Winter 2020. The Bridge.
• Maglio PP, Kieliszewki CA, Spohrer JC (2010) Handbook of Service Science
• Maglio PP, Kieliszewki CA, Spohrer JC, Lyons K, Patrício L, Sawatani Y (2019) Handbook of Service Science, Vol II
• McDermid JA (2022) Safe, Ethical & Sustainable: A Mantra for All Seasons?
• Munn L (2022) The uselessness of AI ethics.
• Norman D (2023) Design for a Better World: Meaningful, Sustainable, Humanity Centered
• Rouse WB (2018) Life with a cognitive assistant. (2022) Emily 2.0..
• Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation.
• Schneiderman (2022) Human-Centered AI.
• Spohrer J (2017) Imagination Challenge: Quantify and graph cost of digital workers and GDP per employee USA from 1960-2080.
• Spohrer J, Maglio, PP (2009) Service Science: Toward a Smarter Planet. In Service Engineering.
• Spohrer J, Maglio PP, Vargo SL, Warg M (2022) Service in the AI Era: Science, Logic, and Architecture Perspectives.
• US 110th Congress (2007) SEC. 1005. STUDY OF SERVICE SCIENCE.
• Vargo SL, Lusch RF (2016) Institutions and Axioms: An Extension and Update of Service-Dominant Logic. JAMS.
• Wakefield J (2022) Why you may have a thinking digital twin within a decade. BBC News Online.
• West S, Meierhofer J, Mangla U (2022) Smart Services Summit: Smart Services Supporting the New Normal.
• West S, Stoll O, Muller-Csernetzky P (2022) A Handbook for Smart Service Design - The design of Smart Services in a world of people, process and things.
• Wladalsky-Berger I (2016) The Continuing Evolution of Service Science. (2019) The Increasing Demand for Hybrid, “T-Shaped” Workers . (2021) The Supply Chain Economy - A New Categorization of the US Economy (2022) A New
Measurement Framework for the Digital Economy. (2022) Foundation Models: AI’s Exciting New Frontier.
Service Systems Engineering in the Human-Centered AI Era 77

APPLE
https://podcasts.apple.com/us/podcast/service-science-and-the-impending-ai-revolution/id1612743401?i=1000583800244
SPOTIFY:
https://open.spotify.com/episode/0n3h9rgX6UYDCwxgTzokoK?si=yVF0mtHsRZSmdfy-aMi8DA
GOOGLE
https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xOTQ5NTE3LnJzcw?sa=X&ved=2ahUKEwiPzL-Zxvv6AhXzjo4IHVbTAuUQ9sEGegQIARAC

Service Systems Engineering in the Human-Centered AI Era 79
Value
Science
Engineering
Policy
Investing in Skills
for Diverse Systems to
Sustainably Serve
People and Planet
in the AI Era
Management
Service
Science
Management
Engineering
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline
T-Shaped Skills
Depth and Breadth
People-centered
Data-intensive
+Design-Arts-
Public-Policy

Service Science: Conceptual Framework
3/16/2023 (c) IBM MAP COG .| 80
Service Science

(c) IBM MAP COG .| 81
Service Science: Transdisciplinary Framework to Study Service Systems
Systems that focus on flows of things Systems that govern
Systems that support people’s activities
transportation &
supply chain water &
waste
food &
products
energy
& electricity
building &
construction
healthcare
& family
retail &
hospitality banking
& finance
ICT &
cloud
education
&work
city
secure
state
scale
nation
laws
social sciences
behavioral sciences
management sciences
political sciences
learning sciences
cognitive sciences
system sciences
information sciences
organization sciences
decision sciences
run professions
transform professions
innovate professions
e.g., econ & law
e.g., marketing
e.g., operations
e.g., public policy
e.g., game theory
and strategy
e.g., psychology
e.g., industrial eng.
e.g., computer sci
e.g., knowledge mgmt
e.g., statistics
e.g., knowledge worker
e.g., consultant
e.g., entrepreneur
stakeholders
Customer
Provider
Authority
Competitors
resources
People
Technology
Information
Organizations
change
History
(Data Analytics)
Future
(Roadmap)
value
Run
Transform
(Copy)
Innovate
(Invent)
Stackholders (As-Is)
Resources (As-Is)
Change (Might-Become)
Value (To-Be)

82
Time
ECOLOGY
14B
Big Bang
(Natural
World)
10K
Cities
(Human-Made
World)
Sun
writing
(symbols and scribes)
Earth
written laws
bacteria
(uni-cell life)
sponges
(multi-cell life)
money
(coins)
universities
clams (neurons)
trilobites (brains)
printing press (books)
steam engine
200M
bees (social
division-of-labor)
60
transistor
Where is the “Real Science”? Ecology++
Transdisciplinary sciences that study the natural and human-made worlds…
Unraveling the mystery of evolving hierarchical-complexity in new populations…
To discover the world’s structures and mechanisms for computing non-zero-sum
Value-CoCreation (VCC), Diverse Architectures of Holistic Service Systems (HSS)
Sun
Earth
Bacteria
Sponges
Clams
Universe
Cities
Writing
Laws
Money
Universities

83
University Trend: “Sister Campuses” (People Flows)
University sub-systems
Disciplines in Schools (circles)
Innovation Centers (squares)
E.g., CMU Website (2009)
“Research Centers:
where it all happens –
to solve real-world
problems”
Disciplines in Schools
Award degrees
Single-discipline focus
Research discipline problems
Innovation Centers (ICs)
Industry/government sponsors
Multi-disciplinary teams
Research real-world systems
D
D
D
D
D
D
water & waste transportation
health
energy/grid
e-government
food &
supply chain

84
City Trend: “Sister Cities” (People Flows)
World as System of Systems
World (light blue - largest)
Nations (green - large)
Regions (dark blue - medium)
Cities (yellow - small)
Universities (red - smallest)
Cities as System of Systems
-Transportation & Supply Chain
-Water & Waste Recycling
-Food & Products ((Nano)
-Energy & Electricity
-Information/ICT & Cloud (Info)
-Buildings & Construction
-Retail & Hospitality/Media & Entertainment
-Banking & Finance
-Healthcare & Family (Bio)
-Education & Professions (Cogno)
-Government (City, State, Nation)
Nations: Innovation Opportunities
- GDP/Capita (level and growth rate)
- Energy/Capita (fossil and renewable)
Developed Market
Nations
(> $20K GDP/Capita)
Emerging Market
Nations
(< $20K GDP/Capita)
IBM UP WW: Tandem Awards: Increasing university linkages (knowledge exchange interactions)

Service in the Humanity-Centered AI Era

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