Spohrer EMAC 20230509 v14.pptx

Service Marketing Seminar (EMAC, IRSSM, AMA ServSIG)
Service research, innovation,
and practice 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 Jenny van Doorn, Bart Lariviere, and Jen Hendrik Schumann
for the invitation to discuss
Service innovation in the humanity-centered AI era.
Tuesday May 9, 2023, 3pm CET/6am PT
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.

Some of my
influencers / you may
already know…
• Ray Fisk and others in ServCollab community (AI and under-served populations, UN SDG)
• Roland Rust and Ming-Hui Huang (Feeling Economy)
• Jochen Wirtz (AI and Service)
• Irene Ng (your data, digital identity, new AI-enabled marketplaces)
• Christina Mele (Social Robots)
• Jenny van Doorn, Gaby Odekerkan-Schroder (Social Robots – JSR Call)
https://www.servsig.org/wordpress/2022/11/cfp-jsr-human-robot-interactions-in-service/
• Co-authors on AI writings, including Paul Maglio, Steve Vargo, and Markus Warg (Service in the AI
Era)
• Many other authors from skimming Werner Kunz’s AMA ServSIG newsletters on recent
publications in service-related journals
• Many outside service community that I follow on social media (separate slide later)
• Invitation: If you want to have the opportunity to be added to my list of influencer on the topic of
service in the AI era, please send me the PDF of one of your relevant articles to
Spohrer@gmail.com – many thanks in advance for sharing!
• Happy to send you a copy of “Serivce in the AI Era” – ping me on LinkedIn or email.
5/9/2023 Jim Spohrer (ISSIP.org) 2

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.

Good news: Scaling benefits for responsible actors is getting faster
Research -> Innovation -> (Safe) Practice
From David Michaelis, I4J Forum
Note:
Separate talk
Would be on
How AI can
Transform
Methods of
Service Research

Bad News: Scaling harms by bad actors is happening faster too…

Solution 1: Informed Actors = Better Education
Responsible actors >> Bad actors
Ethan Mollick (UPenn Wharton) Scott Pelley (CBS, 60 Minutes)

Solution 2:
Kaldor-Hicks

Solution 3: Historical Perspective (too slow?)
Technologies emerge, new business models scale benefits, regulatory bodies mitigate harms
Technology Example Companies Safety Regulatory
Bodies
(Founded)
Stakeholder
Harms
Stakeholder
Benefits
Firearms Smith & Wesson ATF (1886) Armed criminals Defense
Boilers Babcock & Wilcox NBBPVI (1911) Boiler explosions Railroads, steam-powered
factories, building
heating, etc.
Radio & TV RCA, NBC FCC (1934) Misinformation News, entertainment
Drugs Bayer FDA (1938) Addiction Save lives, reduce pain
Airplanes Boeing, PanAm FAA (1958) Pandemic, Weapons Faster Transportation
Automobiles Ford NHTSA (1966) Accidents, Pollution Faster Transportation
Nuclear Energy Westinghouse NRC (1975) Accidents, Weapons Sustainable energy
Social Media Facebook/Meta ?TBD – “Social Dilemma”
GDPR beginnings
Misinformation, Addiction Communications reach,
staying in touch
AI OpenAI, Microsoft,
Google
?TBD – “A.I. Dilemma”
Ban, Policy beginnings
Misinformation, Wealth
Concentration
Boost for creativity,
productivity

Break-out rooms: Discussion
 20 min
 1 spokesperson per group, to report in 2 to 3 min a summary of what you have been discussing
Discussion: Guiding Questions
1. What are potential benefits (select 2) of emerging technologies like AI, Digital Twins, VR/AR (metaverse), Quantum Computing, etc.
2. What are potential harms (select 2) of emerging technologies like AI, Digital Twins, VR/AR (metaverse), Quantum Computing, etc.
3. Do you have any ideas/solutions to optimize them (= these 2 benefits + 2 harms): We are especially interested in new business models and
institutional arrangements that can both scale the benefits, while mitigating the harms of these emerging technologies
Benefits Harms
Identify + select two
potential
benefits/harms of
ermerging
technologies
1. 2.
Any ideas/solutions to
• Scale the benefits
• Mitigate the harms
1. 2.

Today’s talk
• Getting started is very easy
• Upskilling with AI
• Service research, innovation, and practice challenges and opportunities
• My learning journey – searching for scaling laws of service science
• Part A: Scale Benefits (Responsible Actors)
• Learning about tools and prompts by trying what other have done
• Learning to verify output and go beyond uniquely
• Part B: Scale Harms (Bad Actors)
• Learning about what has gone wrong
• Learning about even worse things that might go wrong
• Part C: Scale Understanding (Informed Actors)
• Learning about how systems actually work
• Learning to scale benefits and mitigate harms is being responsible
• Concluding Remarks
• Future Vision (X+AI): Service system design and innovation
• “All responsible actors can learn to invest more systematically and wisely to
improve win-win interaction and change in business and society. Remember, we
get the future we invest in… so let’s learn to invest more wisely.” – JCS
• “The important thing is not to stop questioning.” – Albert Einstein
• Happy to take questions on a range of topics
• Please share your stories and journeys
Deploying advanced
AI
Modified from XKCD 1232

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
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
Environmental and ecological sciences
ServCollab

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

Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in
computer science that studies automation and builds more capable
technological systems. AI tries to understand the intelligent things that
people can do and then does those things with technology.
(https://deepmind.com/about “... we aim to build advanced AI - sometimes known as
Artificial General Intelligence (AGI) - to expand our knowledge and find new answers. By
solving this, we believe we could help people solve thousands of problems.”)
Service science is an emerging transdiscipline some twenty-years-
old that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
Artificial Intelligence
Focus: Automation
Generations of machines
(designed, untiring, unfeeling doing)
Service Science
Focus: Transformation
Generations of people
(informed, responsible service system entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)
Service is the glue that connects us, and service is
defined as the application of resources (e.g., knowledge)
for the benefit of another – including future self and
future generations in an evolving ecology of entities.

“Anything that you cannot do forever is not sustainable.” – Sir David Attenborough
Why? Witness to a lot of technology-driven change since 1956
For people and organizations, we have scaled up access to amazing technological tools to increase productivity and wealth,
But all too often we scale benefits and harms alike… without good designs to scale primarily benefits.
The way we scale innovations creates too much waste; it also leaves out many - too many are under-served.
I recommend spending 82 minutes watching David Attenborough tell his story about his journey since 1926!

5/9/2023 19
1955 1975 1995 2015 2035 2055
Better Building Blocks
Jim Spohrer (ISSIP.org)

Overview Effect

Service System Design and Safety Engineering: A Service Science Perspective
Designs that improve human capabilities while improving the safety and sustainability of Service Systems – Tech4Good
Service innovations leverage emerging technologies, new business models, and institutional arrangement and other means
Service is the application of resources (e.g., knowledge) for the benefit of another
Technology Example Companies Safety Regulatory
Bodies
(Founded)
Stakeholder
Harms
Stakeholder
Benefits
Firearms Smith & Wesson ATF (1886) Armed criminals Defense
Boilers Babcock & Wilcox NBBPVI (1911) Boiler explosions Railroads, steam-powered
factories, building
heating, etc.
Radio & TV RCA, NBC FCC (1934) Misinformation News, entertainment
Drugs Bayer FDA (1938) Addiction Save lives, reduce pain
Airplanes Boeing, PanAm FAA (1958) Pandemic, Weapons Faster Transportation
Automobiles Ford NHTSA (1966) Accidents, Pollution Faster Transportation
Nuclear Energy Westinghouse NRC (1975) Accidents, Weapons Sustainable energy
Social Media Facebook/Meta ?TBD – “Social Dilemma”
GDPR beginnings
Misinformation, Addiction Communications reach,
staying in touch
AI OpenAI, Microsoft,
Google
?TBD – “A.I. Dilemma”
Ban, Policy beginnings
Misinformation, Wealth
Concentration
Boost for creativity,
productivity

Some current issues and challenges

Revisit Books
Service is the application of resources (e.g., knowledge) for the benefit of another*
* another includes your future self and future generations/ecosystem 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

systems
Automation
Technology improved
agriculture and
sector is next up
AI provides human
better tools
Automation
Robots will further
manufacturing, and
as well
Scaling benefits & harms
of resources (e.g.,
knowledge) for the
benefit of another
Augmentation
Responsible actors
Humanity-Centered
Responsible actors
improved win-win

systems
Automation
Technology improved
agriculture and
sector is next up
AI provides human
better tools
Automation
Robots will further
manufacturing, and
as well
of resources (e.g.,
knowledge) for the
benefit of another
Augmentation (X+AI)
Responsible actors
Humanity-Centered
Responsible actors
improved win-win

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 socio-technical system design loop concepts.

Benefits (Responsible Actors)
• Emerging technology help responsible actors solve important
problems – and can boost the creativity and productivity of people
willing to learn to use the tools well
• New business models can rapidly scale the benefits of emerging
technologies
Part A: Benefits (Responsible Actors)

We get the future we invest in:
AI tools to experiment with today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr GPT-3, ChatGPT, GPT-4, Bing, Bard
• #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.”
Jim Spohrer (ISSIP.org) 31
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.
5/9/2023

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

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.

Ethan Mollick
IA Productivity

One Sentence – Ethan Mollick
Yesterday Monday April 17, 2023: https://www.oneusefulthing.org/p/one-sentence
• Business use case: “I want to create a new mascot for the Wharton School's program for
elementary school students, so please research the latest trend in mascot design, give
five suggested designs, pick the best and describe it in detail and pass the full description
to DALL-E to make a prototype image.”
• Collaboration Example: “I am Ethan Mollick and I am going to speak at the ASU-GSV
summit on the Brave Stage on April 19, so I need a dossier chart listing the other
speakers talking around my time, their topics, and what we might be able to talk about
that could be interesting.”
• Code/Programing: “Write code (and tell me how to run it) that will take whatever is in my
Windows clipboard, send it to GPT, ask it to summarize the results using text-davinci-003,
and display the results on the screen.”
• Learning/Leisure: “Invent and explain a fun game using emoji that we can play against
each other, based on chess, but more exciting, and with a theme from Tennyson”
• Morale: “As impressive as these results are, this is actually the wrong way to use AI. The
common misconception about utilizing ChatGPT is that users should aim for the perfect
prompt. To use AI more effectively, consider the following strategies”: (1) Focus on tasks
where you are an expert and use the tool, (2) Provide context for the tool, (3) Offer step
by step directions, (4) Iterate and verify (find where the tool gets it wrong – and let the tool
maker know).

Philippe Deridder
AI Tools for Innovation
• URL -
https://www.linkedin.com/p
osts/philippederidder_innov
ation-design-ux-activity-
7044345913770700800-
6_V7

Harms (Bad Actors)
• It is hard to prevent bad actors from using tools to harm others
• Especially as the tools become more powerful (e.g., nuclear
weapons), and can do greater and greater harm
• Bad actors should be brought to justice - identified, caught in the
act/before they act, and successfully prosecuted and made an
example of publicly to help deter other would-be bad actors
• We do not know how to design to scale benefits, without scaling
harms
Part B: Harms (Bad Actors)

Bad Actor:
Tricks

Design for Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.

Understanding (Informed Actors)
• Many people remained uninformed actors – using a technology
without understanding how it works
• Informed actors can decide to be responsible actors (e.g., ethical
entrepreneur) or bad actors (e.g., criminals)
Part C: Understanding (Informed Actors)

Stephen Wolfram

Who I follow (learn from about many tools)…
• Higher Bar
• Substack: Gary Markus (NYU)
• Facebook: Ernest Davis (NYU)
• LinkedIn & Twitter: Stephen Wolfram
• Blog: Irving Wladawsky-Berger (MIT, retired IBM)
• LinkedIn: Jochen Wirtz (Service Scholar on AI)
• Book “Human Compatible”: Stuart Russell (Berkeley) Alignment/Assistance_Game
• Community: Ben Shneiderman (HCAI)
• Practical AI Upskilling Advice
• Substack: Ethan Mollick (U Penn Wharton)
• LinkedIn & Website: Terri Griffith (Simon Frasier)
• Tracking AI Capabilities
• Youtube: AI Explained, Matthew Berman, Digital Engines, and others
• ArXiv publications from Google, Deepmind, Microsoft, OpenAI, Facebook/Meta, IBM, etc.
• Website: PapersWithCode/SOTA
• Fun - Overly (?) Optimistic on AGI & AI Upskilling & Tracking
• Youtube: Alan D. Thomas
• Many others - and constantly sampling, and evaluating others to follow regularly…

Adjustment Period:
“Drinking from a firehose”
• Everyday, new AI announcements (globally)
• Hype at all time high
• Progress at all time high
• Adjustment period will last for a few decades
• Stay focused on a problem you are trying to solve
• What problem would you be working to solve…
• if you had 100 digital workers working for you every minute of the day (24x7)?
• UN Sustainable Development Goals (UN-SDGs)
• … How much decarbonized/carbonized energy are AI Digital Workers consuming?
• … How much harm are bad actors causing using AI Digital Worker tools, etc.?
• … How well are the populations of whole nations doing on AI upskilling?
• Problem #1: All responsible actors/service system entities (people, businesses, nations, etc.)
focus on becoming better future versions of themselves to get and give better service
• … How to help responsible actors learning to invest better in win-win interactions and change, to get the
future that they want?
DALL-E 2 Prompt:
A crowd of people struggling to drink
from a steampunk robot firehouse
gushing knowledge
in the style of Norman Rockwell

Concluding Remarks
• Future Vision (X+AI) – you and your digital twin

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…
5/9/2023

PersonalAI.com

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
5/9/2023

What’s Next:
Generative Agents

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
5/9/2023

Predict the 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
Alistair Nolan (OECD AI for Science Productivity): “It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years.”
Rouse WB, Spohrer JC. (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Apr 3;8(1-2):1-21.
Read Rouse & Spohrer (2018)
enough to understand this slide
including what ”exascale” means
5/9/2023
Part 1: Solving AI – Artificial Intelligence (Smarter Tools)

Types: Progression of Models : Verified, Trusted, Wise
Models = instruction_set of future: Better building blocks
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
Part 2: Solving IA – Intelligence Augmentation (Smarter People)
Solving IA also requires
All of this and done well
As a “bicycle for the mind”
To make us stronger,
Not weaker
When tech is all removed

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 the X+AI vision (digital twins) and “investing wisely to get the
future service systems we want.”
5/9/2023

Concluding Remarks:
What’s your story? Any questions?

But first, let’s level set – how many know about…
Ethan Mollick (UPenn Wharton)
Don Norman (UC San Diego)
Scott Pelley (CBS, 60 Minutes)
Tristan Harris and Aza Raskin
(Center for Humane Technology)
Generative AI Tools
To Output:
Text/Writing
Images
Code/Programming
Videos
Audio
Music
Game Worlds
Digital Twin
Other?

Three scenarios to consider
• Scenario 1: Humanity exists because God created the universe
• If you believe in God, then you already believe in the ultimate super-intelligence
• Everything will be OK
• If you don’t believe in God, do you believe in the possibility of super-intelligences?
• Entities both benevolent and hostile towards humanity?
• Scenario 2: Alien created super-intelligences exists elsewhere in the universe
• Benevolent super-intelligence will befriend humanity
• Everything will be OK. (See Bostrom ”Superintelligence”, Chapter 13)
• Hostile (or uncaring) super-intelligence will destroy humanity
• Should, can, may will we try to create a benevolent super-intelligence to protect humanity?
• Scenario 3: Humanity creates one or more super-intelligences
• If you don’t believe in God and do believe hostile super-intelligence may already exist somewhere in our universe
• 50% chance we can create our own benevolent super-intelligence to help protect us
• Fighting chance everything will be OK
• 50% chance we create our own hostile super-intelligence who ultimately destroys us
• Will our humanity-created super-intelligence be the only super-intelligence, or will it be one of many in the universe?
What would a society of (some hostile super-intelligences ) be like?

Sayings & Expectations
• Sayings
• “Garbage in, garbage out.”
• “Mixed-bag in, mixed-bag out.”
• Expectations
• Had expected AI to be more accurate, but it needs a lot of verification.
• Mathematical correctness expectation
• Reality “Stochastic Parrot” with “Emergent Properties” from unsupervised
learning to predict next thing trained on massive amount of data, and
compressed into artificial neural nets

Today’s talk
• Intro: AI (by 1955 definition) has arrived
• Just took 68 years, but…
• What’s really going on?
• Your data is becoming your AI… IA transformation
• AI Digital Twin = IA (Intelligence Augmentation)
• Adjustment period underway…
• Part 1: Solving AI: Smarter Tools (Technical)
• Artificial Intelligence (AI) roadmap – speed, data, energy/cost
• From leader boards scores to professional exams scores
• Open technologies, innovation
• Part 2: Solving IA: Smarter People (Social)
• Intelligence Augmentation (IA) better building block
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
• Part 3: ”Solving All Problems”: Wiser Humanity (Service)
• UN Sustainable Development Goals (UN-SDG) – our problems
• What could go wrong? Be prepared.
• 37-year long adjustment period is now underway…

Icons of AI Progress
• 1955-1956: Dartmouth Workshop 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)
• 2017: All you need is attention (Google) - Transformers
• Attention heads (working memory) to predict what comes next
• 2018: AlphaFold (Google DeepMinds)
• 2020: Language models are few-shot learners (OpenAI)
• 2022: DALL-E 2 & ChapGPT (OpenAI)
• 2022: Constitutional AI (Anthropic) – “Behave yourself!”
• 2023: New Bing+ (Microsoft) & GPT-4 (OpenAI)

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 (2023)
2050 and beyond 70
IBM Research AI © 2018 IBM Corporation
The evolution of AI
Borrowed from David Cox, IBM-MIT Lead
▼ We were here (2018)
▼ Alan D Thompson
AGI prediction (2025)
70

(Source)
5/9/2023
Part 1: Solving AI – Artificial Intelligence (Smarter Tools)

Task & World
Model/
Planning &
Decisions
Self Model/
Capacity &
Limits
User Model/
Episodic
Memory
Institutions
Model/
Trust & Social
Acts
Tool + - - -
Assistant ++ + - -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
Cognitive
Tool
Cognitive
Assistant
Cognitive
Collaborator
Cognitive
Coach
Cognitive
Mediator
Not weaker

Resilience:
Books.
Part 3: “Solving All Problems” (Wiser Humanity)

Adjustment Period:
“Drinking from a firehose”
• Everyday, new AI announcements (globally)
• Hype at all time high
• Progress at all time high
• Adjustment period will last for a few decades
• Stay focused on a problem you are trying to solve
• What problem would you be working to solve…
• if you had 100 digital workers working for you every minute of the day (24x7)?
• UN Sustainable Development Goals (UN-SDGs)
• … How much decarbonized/carbonized energy are AI Digital Workers consuming?
• … How much harm are bad actors causing using AI Digital Worker tools, etc.?
• … How well are the populations of whole nations doing on AI upskilling?
• Problem #1: All responsible actors/service system entities (people, businesses, nations, etc.)
focus on becoming better future versions of themselves to get and give better service
• … How to help responsible actors learning to invest better in win-win interactions and change, to get the
future that they want?
DALL-E 2 Prompt:
A crowd of people struggling to drink
from a steampunk robot firehouse
gushing knowledge
in the style of Norman Rockwell

• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #9 Namelix
Try at least two
from the list
as soon as possible
What do you think?
5/9/2023

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.
Resilient in Uncertain
Times

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.

Who I follow (learn from)…
• Higher Bar
• Blog: Irving Wladawsky-Berger (MIT, retired IBM)
• LinkedIn: Jochen Wirtz (Service Scholar on AI)
• Book “Human Compatible”: Stuart Russell (Berkeley) Alignment/Assistance_Game
• Community: Ben Shneiderman (HCAI)
• Youtube: AI Explained
• Fun - Overly (?) Optimistic on AGI & AI Upskilling & Tracking
• Many others - and constantly sampling, and evaluating others to follow regularly…

Ethan Mollick
IA Productivity

Philippe Deridder
AI Tools for Innovation
• URL -
https://www.linkedin.com/p
osts/philippederidder_innov
ation-design-ux-activity-
7044345913770700800-
6_V7

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
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.

Rapid Progress
• History and Future

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
87
5/9/2023 Jim Spohrer (ISSIP.org)
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

(Source)
5/9/2023
Part 1: Solving AI

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

From leaderboards
to profession exams

Leader Boards:
Professional
Benchmarking

Professional
Benchmarking

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

GPT-4:
Needs more
planning
capability

OpenAI GPT:
Enterprise
Customers
Alan D. Thompson
Lifearchitect.ai

Open Source AI:
Not Wise?
Or Super Wise?
Some say: When you have nothing,
And you want attention – open source.
However, as soon as you have something,
And you are competitive, you put up
A “CLOSED” sign – as quick as you can.
Nonzero sum trailer, becomes zero-sum leader.

LLaMa &
Alpaca
• Open Source
• Training Cost
Declining

Who I track to learn about new AI tools to use
• Alan D. Thompson
• https://lifearchitect.ai/how-do-
i-talk-to-gpt/
• https://youtu.be/D3tTsoX02d8

Jim Spohrer (2022):
3-4x the time seems
more realistic to me,
so perhaps by 2050-2060.
AI advances and adoption
are both very hard.

Robots by Country
• Industrial robots per 10,000 people by country
34

Sweden

Economic Growth Rates 2035: AI Projected Impact

Science gathers
knowledge
>>>
Society gathers
wisdom

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.)

Emergence
• Capabilities in larger
models, not in
smaller models

Theory of Mind
• Theory of Mind: When an
entity has an accurate
idea of what’s going on in
other entities’ minds,
including when the other
entity is right and wrong.
• Sutskever (OpenAI Chief
Scientist): ”We are
starting to reach a point,
where the language of
psychology is starting to
be appropriate to
understand the behavir of
these neural networks.”

AGI:
Embodied
• Stages
• OpenAI >
Anthropic
• Getting harder to
keep up with
improvements
• Outlier tests get
harder and
harder

AGI
Thinking
Acting
• 34 months away?
• 2026?
• Human-level across
all disciplines

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

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

Task & World Model/
Planning & Decisions
Self Model/
Capacity & Limits
User Model/
Episodic Memory
Institutions Model/
Trust & Social Acts
Tool + - - -
Assistant ++ + - -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
Cognitive
Tool
Cognitive
Assistant
Cognitive
Collaborator
Cognitive
Coach
Cognitive
Mediator
Not weaker

Cognitive Mediators
for all people in all roles

Occupations = Many Tasks

Watson Discovery Advisor
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

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

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

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.
5/9/2023 125
Take free online cognitive classes today at cognitiveclass.ai

Read Wakefield
(2020)
enough to
understand what a
the future decades
with very advanced
AI capabilities.
Also see Rouse
(2018; 2022) ”Life
with a Cognitive
Assistant.”
AI Tools
in coming
decades…
5/9/2023

PersonalAI.com

TacitPath.Com: Entity Coordination Via Shared
Information – John Wolpert

Part 3: “Solving All Problems”

I have…
Have you noticed how the building blocks just
keep getting better?

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

5/9/2023 138
1955 1975 1995 2015 2035 2055

“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

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."

What I study
Service
Science
Artificial
Intelligence
Trust:
Trust:

Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
Artificial Intelligence
Automation
Generations of machines
Service Science
Transformation
Generations of people
(responsible entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)

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
147
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

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?

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.

systems
Automation
Technology improved
agriculture and
sector is next up
AI provides human
better tools
Automation
Robots will further
manufacturing, and
as well
Value cocreation
of resources (e.g.,
knowledge) for the
benefit of another
Augmentation
Responsible actors
Humanity-Centered
Responsible actors
improved win-win

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.
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 socio-technical system design loop concepts.

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
5/9/2023

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
5/9/2023

• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #9 Namelix
Try at least two
from the list
as soon as possible
What do you think?
5/9/2023

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.
5/9/2023

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

162
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

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)

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.”
5/9/2023

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:
Books.
What if the ”bad actors” get the upper hand?

The best tool: Our brains and models of the
resources in the world & responsible actors
https://www.pdfdrive.com/the-discipline-of-organizing-e34689259.html

Why I am optimistic

Who I follow (learn from) regarding AI …
• Higher Bar
• Massive reader and synthesizer
• LinkedIn: Gary A. Bolles
• Youtube: AI Explained
• Overly (?) Optimistic on AGI & AI Upskilling & Tracking
• Win-win, clear, and concise
• Substack: Lee Nackman

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
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
Artificial
Intelligence
Trust:
Trust:

IA Progression – Tool, Assistant, Collaborator, Coach, Mediator
Rouse & Spohrer (2018)
Siddike, Spohrer, Demirkan, Kodha (2018)
Araya (2018)
Spohrer& Siddike (2018)

“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

5/9/2023 178
1955 1975 1995 2015 2035 2055

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.
5/9/2023

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

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
5/9/2023

Service Science: Conceptual Framework
Service Science

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)
5/9/2023

184
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

185
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

186
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)

Spohrer EMAC 20230509 v14.pptx

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