--- 20251105 SIRS Talk Jim Spohrer: https://www.linkedin.com/in/spohrer/ Artificial Intelligence (AI),Impressive, but ImperfectNovember 5, 2025, 12noon PT at SIR, Branch 51Los Altos Hills Country Club (Host: Steve Tremulis) --- Steve Tremulis (https://www.linkedin.com/in/steve-tremulis-4557046/) SIR Branch 51 URL: https://sirinc2.org/branch51/wp-content/uploads/2025/05/SIR51newsletterMay2025.pdf ---

1. Artificial Intelligence (AI),
Impressive, but Imperfect
November 5, 2025, 12noon PT at SIR, Branch 51
Los Altos Hills Country Club (Host: Steve Tremulis)
Presentations online at: https://slideshare.net/spohrer
Jim Spohrer
Retired Industry Executive (Apple, IBM)
Board of Directors (ISSIP, ServCollab)
UIDP Senior Fellow
Questions: spohrer@gmail.com
BlueSky: @spohrer.bsky.social
LinkedIn: https://www.linkedin.com/in/spohrer/
English: https://youtu.be/T4S0uZp1SHw
French: https://youtu.be/02hCGRJnCoc
https://answersfrom.me/jimtwin

3. •https://youtu.be/PnmVqASd1VE
Lee Nackman (retired IBM) – Thinking
AI

4. As I was preparing this talk….
• Some words kept going thru my mind from
Arbesman (2025)…
• Chapter 8: Tools for Thought: Software for
Thinking.
• “Too often technology is at odds with
humanity...
For example... QWERTY...
... we have, by and large, adapted ourselves to
technology in ways that are not good for us.
Our increased drive for optimization of work
can run directly counter to our ability to think
deeply, or to even feel that human.”

5. And this book as well…
• Property
• All social problems seem rooted
in three types of property
• Noncoercion
• Freedom
• Gratitude
• Need for cultural evolution
• to avoid coercion in any form.
• https://progressisachoice.net

6. Who am I?
Jim Spohrer
1956
Maine
1974
MIT
1978
Verbex –
startup AI
company
1982
Yale
1989
University of
Rome La
Sapienza, Italy
1989
Apple
1998
IBM
2021
Retired (help
non-profits
with an eye to
the future of
service & AI :
ISSIP,
ServCollab,
UIDP)

8. Questions for SIRs (Seniors) at Dawn of AI Era:
All generations need good answers to these questions.
Q1: What would you do if you have 100 highly skilled
workers working for you (e.g., software development,
marketing, etc.)?
Q2: What job(s) would you assign to (if you had one)
your AI Digital twin?
Q3: What is the best way(s) that you know of to keep up
with accelerating change?

9. Optimistic Realistic
Knowing
Doing
How to keep up with accelerating change? Follow a diverse collection of people… make up dimensions meaningful to you!
Sadly for me… my brain is biased into thinking I can understand older, white, males the best… maybe AI can help overcome!
TheNeuron
Who do
I wish I had
Accurate,
Up-to-date
AI Digital
Twins of?
The Neuron

10. Today’s Talk:
• Past
• 1947, 1958, 1971
• Present
• 70, 35000, 250000
• Future
• Solution to 3 Es
• TBD – your AI digital twin?

11. 1947
1958
1971 2024
1947
Transistor
Bell Labs
1958
Integrated Circuit
Texas Instrument
1971
Microprocessor
Intel
2024
H100
NVidia
Challenges: How to prepare for the next wayes of innovations, including
digital twins and humanoid robots, as well as to learn to invest more wisely
(self-control).
As the marginal cost of computing goes to zero, service innovation will go
thru the roof,
and energy consumption will go thru the roof as well…
REMEMBER THESE DATES, FACTS,
CHALLENGES
weight: 70 pounds
complexity: 35,000 parts
cost: 250,000 dollars

12. 1956
2023
2060 2080
1956
First AI
Workshop
2023
ChatGPT 100M
users in just 2
months
(1.5B visits in
Sept)
2060 (Predicted)
Exascale for
$1000
(~ one human
brain)
2080 (Predicted)
Ronnascale for $1B
(~ billion human brains)
Progress in IA (Intelligence Augmentation) for nations can be estimated as
GPD/worker.
Progress in AI (Artificial Intelligence) is directly connected to the cost of
computing.
REVIEW: REMEMBER THESE DATES

13. Jensen:
You imagine a tiny chip…
The H100 weighs 70 pounds…
35000 parts…
$250K cost…
It replaces a data center…
Full of computers and cables…
Jim:
Driving the marginal cost of
computing to zero…
Drives the demand for new
service offerings based on
computing through the roof

14. MORE SPOHRER USE CASES:
https://service-science.info/archives/6521

15. 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)
• 2024: More & Bigger Models: OpenAI, Microsoft, Google, Anthropic,
etc.
• 2024: Reid Hoffman’s Digital Twin & Unitree’s G1 Humanoid Robot
11/05/2025 Jim Spohrer 15

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

17. 1960 1980 2000 2020 2040 2060 2080

18. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Cost

19. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Kiloscale
(10 3
)
M
egascale
(10 6
)
Gigascale
(10 9
)
Terascale
(10 12
)
Petascale
(10 15
)
Exascale
(10 18
)
Zettascale
(10 21
)
Yottascale
(10 24
)
Ronnascale
(10 27
)
Cost of Computation (Diagonals)

20. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Kiloscale
(10 3
)
M
egascale
(10 6
)
Gigascale
(10 9
)
Terascale
(10 12
)
Petascale
(10 15
)
Exascale
(10 18
)
Zettascale
(10 21
)
Yottascale
(10 24
)
Ronnascale
(10 27
)
Cost of Computation (Diagonals)
Note: Adjust Kilo and Mega scales slightly to fit data better (early days – more cost – learning curve).

21. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Gigascale
(10 9
)
Terascale
(10 12
)
Petascale
(10 15
)
Exascale
(10 18
)
Zettascale
(10 21
)
Yottascale
(10 24
)
Ronnascale
(10 27
)
GDP/Employee
Trend
Estimating Knowledge Worker
Productivity
Based on USA
Historical Data
Year Value
1960 $10K
1980 $33K
2000 $78K
2020. $151K
2023 $169K
Kiloscale
(10 3
)
M
egascale
(10 6
)
of computation goes down by 1000x every 20 years (left to right diagonals), driving knowledge worker productivity

22. 22
September 2018 / © 2018 IBM Corporation
Petaflops = 1,000,000,000,000,000 or a million
billion = 10 ** 15
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
One of the AI Super Computers in the World,
= 13 MegaWatts of Power (HOT!)

23. 23
September 2018 / © 2018 IBM Corporation
Exascale = 1,000,000,000,000,000,000 or a
billion billion = 10 ** 18
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
Human Brain
= 20 Watts (COOL!)

24. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Gigascale
(10 9
)
Terascale
(10 12
)
Petascale
(10 15
)
Exascale
(10 18
)
Zettascale
(10 21
)
Yottascale
(10 24
)
Ronnascale
(10 27
)
GDP/Employee
Trend
Estimating Knowledge Worker
Productivity
Based on USA
Historical Data
Year Value
1960 $10K
1980 $33K
2000 $78K
2020. $151K
2023 $169K
Kiloscale
(10 3
)
M
egascale
(10 6
)
of computation goes down by 1000x every 20 years (left to right diagonals), driving knowledge worker productivity

25. Predict the Timeline:
GDP/Employee
National Academy - Service Systems and AI 25
(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
11/22/22
Part 1: Solving AI

26. What is an AI digital twin?
• Well, imagine if there was a
“mimic” version of you that was
online 24x7 and could
speak any language, with
knowledge of your publications,
and could explain things in a way
an audience of listeners might
understand?
• ReidAI’s purpose - to challenge
Reid Hoffman in new ways and
experiment with new tech
capabilities
https://youtu.be/rgD2gmwCS10
ReidTwin ReidReal

27. Who else is getting a twin (AI Avatar)?
• SJSU President
• Cynthia Teniente Matson
• https://youtu.be/gqrIMItHyz8
• “expanding our reach,
and enhancing communications.”

28. JimTwin Adventure…
• ChatBot (Tmpt.app)
• Scott Zimmer
• https://answersfrom.me/jimtwin
• Avatar-1 (HeyGen.ai)
• SJSU Team (Claude + HeyGen)
• https://youtu.be/T4S0uZp1SHw
• https://youtu.be/02hCGRJnCoc
• Avatar-2 (GitHub)
• Arnay Bhatia
• https://youtu.be/mwnZjTNegXE
• https://youtu.be/QR17aXYgefk

29. Do you want a digital twin of yourself?
No/Yes/Not Sure
Narayan S & Spohrer J (2025) Metrics, Incentives, Rewards, and Culture for Impact. In
Hall R & Boccanfuso A,Editors, University-Industry Collaboration, Innovation at the
Interface. Springer. URL: https://link.springer.com/book/10.1007/978-3-031-94913-5
Spohrer, J.C. (2010). IBM's University Programs. IEEE Computer 43(8):102-104.
URL: https://service-science.info/wp-content/uploads/2017/04/IBM-GUP-5Rs-copy-2.pdf
Spohrer, J.C. (2013). What's Up at IBM? University Programs! The 6 R's helping to
build a Smarter Planet: Research, Readiness, Recruiting, Revenue, Responsibility,
Regions. May 14, 2013. Slideshare. URL:
https://www.slideshare.net/slideshow/ibm-up-external-20130514-v11/21175603
Spohrer, J. (2017). IBM's service journey: A summary sketch. Industrial
Marketing Management, 60, 167-172.
URL: https://www.sciencedirect.com/science/article/abs/pii/S0019850116301778?via%3Dihub
Spohrer, J. (2024a). AI Upskilling and Digital Twins: A Service Science
Perspective on the Industry 4.0 to Industry 5.0 Shift. In Industry 4.0 to
Industry 5.0: Explorations in the Transition from a Techno-economic to a
Socio-technical Future (pp. 79-92). Singapore: Springer Nature.
URL: https://link.springer.com/chapter/10.1007/978-981-99-9730-5_4
Spohrer, J. (2024b) Personal AI digital twins: the future of human interaction? EIT
Digital URL: https://www.eitdigital.eu/newsroom/grow-digital-insights/personal-ai-digital-twins-the-future-of-human-interaction/

30. 3Es: Challenges to “Humanity-Friendly AI Teammate”
• Energy
• Errors
• Ethics
Hicks MT, Humphries J, Slater J (2024)
ChatGPT is bullshit.
Ethics & Information Technology 26(38).
URL: https://doi.org/10.1007/s10676-024-09775-5
IBM Research - TrueNorth (Dharmendra Modha and team)
Pssssssst!
The real problem
is communication
between people.
Can more computation
help?

31. Why do GenAI LLMs work as well as they do? Honestly, no one
knows for sure.
We see prediction & pattern completion,
but not true reasoning, just mimicry
Impressive performance, but prediction alone is not enough
Imperfect, because human-like reasoning (with world models) is not there
Nevertheless, pattern completion is very good
The answer will require a better understanding of optimization in high dimensional
spaces
The good and the bad
What is AI good at? Speed, Summarization (What is needed to regenerate whole,
stripped of most specifics), Outline content (What the abstract structures are),
Creative Pattern Completion (Fiction – made up details that might seem plausible).
What is AI bad at? 3Es (energy, errors, ethics <- because of how we build it today).
What is surprising?
Surprise! Everything can be near and far at the same time in a high dimensional
space (very similar, very different) – amplifier of consilience and polarization.
Surprise! Every starting point is near a ”black hole” of nearly ideal optimization as
higher and higher dimensions and more and more data are used. (quadrillions of
partial coherent structures)
Impressive, but imperfect
Impressive: The mathematics of high-dimensional optimization work pretty well for
predicting local coherence at multiple scales of patterns ( plausible pattern
completion tasks)
Imperfect: Reasoning requires world models, perhaps multiple mappings from high
dimensional to low dimensional representation spaces preserving certain relations
as world models, or creating Python program world models to help. Generate-Test-
and-Debug (G-T-D) will likely be required with human-like episodic memory with
expectation violations and remindings (Schank’s ”Dynamic Memory” and Case-
Based Reasoning)

32. HCI to improve HHI
• Teammates, that I know is much better in
my mind….
• Recent Stanford paper – generic AI
teammates
• “It’s the AI PI’s job to figure out the other
agents and expertise needed to tackle the
project,” Zou said. For the SARS-CoV-2
project, for instance, the PI agent created
an immunology agent, a computation
biology agent and a machine learning
agent. And, in every project, no matter the
topic, there’s one agent that assumes the
role of critic. Its job is to poke holes, caution
against common pitfalls and provide
constructive criticism to other agents.”
(1) What would you do
with 100 highly-skilled
workers?
(2) Redo Divide & Conquer
In AI era.
(3) Why real person
in that role?
Because real people
have goals & plans
they prefer.
Real people sleep.
Real rehearse high stakes.

33. Hot Topic: More Readings Every Day
• Gary Hunnicut suggested (2025)
• “Digital Twins for Cancer—Not If, But When, How, and Why?”
• https://datascience.cancer.gov/news-events/blog/digital-twins-cancer-not-if-when-how-and-why
• Cybernetic Teammates (2025)
• “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise”
• https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5188231
• Vendor Policy Brief
• “A Policy Framework for Building the Future of Science with AI”
• https://static.googleusercontent.com/media/publicpolicy.google/en//resources/ai_policy_framework_
science_en.pdf
• ”Towards an AI Co-Scientist” - https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf
• Humanity (just out today – April 2, 2025)
• Being Human in 2035: How Are We Changing in the Age of AI
• URL:
https://imaginingthedigitalfuture.org/wp-content/uploads/2025/03/Being-Human-in-2035-ITDF-report
.pdf
• Transdisciplinarity (2023)
• "Transdisciplinary Team Science: Transcending Disciplines to Understand Artificial Social Intelligence in
Human-Agent Teaming”
• https://journals.sagepub.com/doi/full/10.1177/21695067231192245
• Augmentation – Amplification (1962)
• “Augmenting Human Intellect: A Conceptual Framework” (cites Licklider 1960 and Bush 1945)
• https://www.dougengelbart.org/pubs/augment-3906.html

34. Tool, Assistant, Collaborator, Coach, Mediator (“Trust”)
11/05/2025 Understanding Cognitive Systems 34
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 (in a humanity-friendly way)
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
Read Demirkan & Spohrer (2025)
enough to understand this slide
including what ”trusted mediator”
means
Demirkan H, Spohrer J (2025) Talent Management: “Here Come the Digital Workers!”.
In ORMS Today, INFORMS, June 17, 2025. URL: https://pubsonline.informs.org/do/10.1287/orms.2025.02.15/full/

35. The Communication Problem:
In a Nutshell
• Doing More
• Agreeing Less
Can GenAI LLMs help people to stop wasting creative friction?
Competing ideas can lead to “insanely great” win-win collaborations.
If we can figure out communications between people.
Latent spaces for people.
Barile S, Piciocchi P, Saviano M, Bassano C, Pietronudo C, Spohrer JC (2019))
Towards a new logic of value co-creation in the digital age: doing more and agreeing less.
Naples Forum on Service. URL: ttps://tinyurl.com/2019-DoingMoreAgreeingLess
Failure
To Find
Win-Win

36. Long flights sometimes allow long
conversations
• 1440 News (July 11, 2025): The high-fashion ‘It’ bag origin story
• Birkin (Actress) and Dumas (Fashion Designer) meet
• “The bag’s story began in the 1980s when the actress met Hermès
CEO Jean-Louis Dumas on a flight. Frustrated with her handbag,
Birkin described her ideal bag to Dumas, and they famously
sketched the design on an airsickness bag.”
• URL: https://en.wikipedia.org/wiki/It_bag
• Win-Win Opportunities
• Do Win-Win opportunities always exist between any two actors?
• What are the set of possible Win-Win’s between actors?
• How best to explore the Win-Win possibilities between two
actors?
• How does finding Win-Win opportunities relate to Truth?

37. Truth, Trust, and Wisdom
• Truth: The ongoing pursuit
• Mathematical, Computational, Empirical,
Historical, Rhetorical
• Knowing a ”better argument” when we see it and
why (humility needed)
• Trust: Hard to build, easy to destroy
• Mental models and predictable behaviors
• AI digital twins of all responsible actors
• Wisdom: Learning to invest wisely
• In becoming better future versions of ourselves
• In a world that future generations will want to
live in together

38. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012)
The Knowledge: How to Rebuild Civilization i
n the Aftermath of a Cataclysm.
Westminster London: Penguin Books.
11/05/2025 Jim Spohrer (ISSIP) 38
Part 3: “Solving All Problems”

39. 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)

40. OpenAI’s Roadmap

41. Service Science: Conceptual Framework
11/05/2025 (c) IBM MAP COG .| 41
Service Science

42. (c) IBM MAP COG .| 42
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)

43. 43
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

44. We get the future we invest in…
“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 44
Every person in a role in an organization is a service provider.
11/05/2025

45. “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

46. 11/05/2025 46
1955 1975 1995 2015 2035 2055
Learn: Explore and Exploit Better Building Blocks
Heygen + Claude
JimTwin V1 (Tmpt.app)
2024
High School
Punch cards
1972
IBM Watson
AI in the Cloud
2011
2024
2001 2015 2022 2023
1970 1995 2019 2025
2021

47. 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

48. Service System Design: A Service Science Perspective
Designs that improve human capabilities while improving the safety and sustainability of Service Systems
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)
Boilers Babcock & Wilcox NBBPVI (1911) Boiler explosions Railroads, steam-powered
factories, building heating,
etc.
Radio & TV RCA, NBC FCC (1934)
Drugs Bayer FDA (1938)
Airplanes Boeing, PanAm FAA (1958)
Automobiles Ford NHTSA (1966)
Nuclear Energy Westinghouse NRC (1975)
Social Media Facebook/Meta ?TBD – “Social Dilemma”
AI OpenAI, Microsoft,
Google
?TBD – “A.I. Dilemma”
11/05/2025 Jim Spohrer (ISSIP.org) 48

49. Today’s Talk
• Will our AI Digital Twins become
our HCI of the future?
• Exploring Twin HCI
as a “Service Innovation”
• Capabilities (Possibilities)
• Benefits (Pros)
• Harms (Cons)
• Future Directions
• Keeping up with accelerating
change
• Service science connections
• Responsible actors learning to
invest wisely in interaction and
change processes

50. Homework
• Apple’s Knowledge
Navigator
• https://www.youtube.co
m/watch?v=umJsITGzXd0
• Luckily colleague Jill was
available to chat and help
with the class….
• … But what if Jill had not
been available, but she
was willing to share her AI
Digital Twin?

51. Job description: A person who operators an AI trained to be an
expert in some role. The human operator like a vehicle operator
helps ensure a good service for customers.
• An AI is “running” for mayor in Wyoming
• A resident of Cheyenne, Wyoming trained GPT-4 on “thousands of
documents gleaned from Cheyenne council meetings” and announced that
the resulting bot, named VIC (the “Virtually Integrated Citizen”) will be running
for mayor. The bot’s creator said he’d be the “meat puppet” who would
operate the AI and act on its behalf, but the bot would be the brains of the
operation, deciding on votes and how to run the city. However, Wyoming’s
Secretary of State contends that non-humans like VIC can’t run for office.
New Job – Person who is an Operator for AI in a New
or Existing Service System Role

52. MIT (1974-1978)
• Explo: Teaching AI & Entrepreneurship to
diverse high school students.
• Stories: How I got into MIT.
• Lesson: “Where are you applying for college?”
Spohrer, James (1978)
Strain-Gauge Transduction
of the Effects of Speech Rate
on the Coarticulation
of Lip Rounding,
MIT Physics, June, 1978.
Advisor: Joseph S. Perkell
Advisor to this day.

53. Verbex (1978-1982)
• Speech recognition – mathematical models (Bayesian
approach to speech and language modeling) that both
recognize and generate using estimated probabilities
(e.g., probabilities and statistics = machine learning)
• Stories: How I got the best job in the world for me at
that time.
• Lessons: “When my girlfriend said: ‘What do you have
to lose?’”
ICASSP’82 and ‘83.
IEEE International Conference on
Acoustics, Speech, and Signal Processing

54. Route 128, aound Boston, MA USA
ICASSP 1983

55. We
Would
Love
Your
Voice https://www.jstor.org/stable/1747731

56. Stephen L. Moshier
• “Our company, Dialog Systems, Inc., was formed in 1971 for the purpose of
developing and commercializing speech recognition equipment. The
concept derived from earlier work engaged in at Listening, Incorporated on
marine bioacoustics, acoustic signal processing, and psycho- acoustics. The
original idea passed through well-known stages of theory, experiment,
development, lack of financing, financing, sales and is now at the highly
advanced state "production engineering headaches". Dialog employs 45, of
whom 14 are degreed technical people. The company recently moved from
Cambridge to a 20,000 square foot two-building campus complex in
Belmont, Massachusetts. The major product is an eight-channel isolated
word system intended for talker-independent switched telephone speech
input.”
From NASA:
https://ntrs.nasa.gov/api/citations/19930075179/downloads/19930075179.pdf

57. Peter F. Brown
• “So, I took a course in linguistics. And
one day in the back of that course I
heard a couple students talking about
some guy whose name was Steve
Moshier who started a company called
Dialogue Systems that was doing
speech recognition. And I thought, wow,
great, I remembered this idea from back
in high school. After class I raced over
to the physics library. That’s because
this was before the internet, so you had
to go to the library. And I looked this
guy up. And I found a paper he'd
written. And I tracked him down.
Applied for a job. And he hired me. And
when I was there, I just fell in love with
the idea that through mathematics it
might be possible to build machines
that do what humans do.“
Goldman Sachs:
https://www.goldmansachs.com/intelligence/podcasts/episodes/09-11-2023-peter-brown-f/transcript.pdf

58. Drs. Jim & Janet Baker
Saras Institute
History of Speech and Language Technology
https://www.sarasinstitute.org
Many things,
Such as publications,
took off to new levels
when Jim & Janet joined
Dialog Systems…
… and Exxon acquisition
Later key researchers
left Verbex, and later
Along with Jim and Janet
Founded
Dragon Systems

59. Yale (1982-1989)
• MARCEL: Modeling students writing “a series of
buggy and then (sometimes) correct programs” with
a generate-test-and-debug architectures.
• Stories: Use AI to help make people smarter.
• Lesson: “Why do you want to make machines smart?
Why not help make people smarter instead?”
1988

60. Apple (1989-1998)
• From content (SK8) to community (EOE) to context
(WorldBoard)
• Stories: Surround yourself with supportive people.
Growing up about goals - sequencing and timing of
ideas is important
• Lessons: “MLM: Relax - it will be OK”; “ACK:
Planetary – is that all?” “SPJ: We will get to that
and more!”;
1992

61. IBM (1998 – 2021)
• IBM Venture Capital Relations Group,
Service Science, Global University
Programs, Open Source AI.
• Stories: The only way you get in trouble
is not asking for help when you need it.
• Lesson: What seems like a hard problem
to you, may be an easy problem for
someone else. Leverage the matrix.
2010 2011 2011
2012 2016
2018 2018 2020
2012
2002
2000

62. ISSIP (2021- Present)
• Defining what is a service innovation, and what is a
T-shaped service innovation professional.
• Stories: AI digital twins, reinventing local, self-control
• Lessons: Learning to invest wisely and systematically
in getting a shared future that we all want to live in
requires self-control and knowing when you have
enough. 2022

63. • Jensen Huang (Nvidia) comments
• First impressions
• Insanely great productivity
• Insanely great quality
• What is really going on?
• Decreasing cost of computation
• Increasing GDP/worker
• Awesome progress, but…
• Impressive
• Imperfect
• Advantage of empowered people
Inspiration

66. “NorthPole Chip”
Far less energy.

67. Impressive, but imperfect
Physical realm: Energy
Technical realm: Mistakes (”Hallucinations”)
Social realm: Digital property theft

68. • How to keep up with accelerating change?
• Social learning
• Who do you follow?
• Reid Hoffman’s AI Digital Twin Interview
• Diving in!
• We get the future we invest in
• … so, learn to invest wisely
• Awesome stuff that lies ahead – Humanoid Robots
• But remember technology amplifies good/bad
• Need for Self-Control (Toyama (2015) Geek Heresy: Rescuing Social Change from
the Cult of Technology)
Climbing Up

69. Optimistic Realistic
Knowing
Doing
How to keep up with accelerating change? Follow a diverse collection of people… make up dimensions meaningful to you!
Sadly for me… my brain is biased into thinking I can understand older, white, males the best… maybe AI can help overcome!
TheNeuron

72. TheNeuron

74. • Jim Twin V1: My papers -> short talk videos
• English
• French
• How to stay future ready?
• Learn the building blocks
• Marco Podien will help you with more building block shortly…
• Oh, by the way, you are going to need bigger goals too
• When you have 100 digital workers working for you, what is your main
goal?
• From serial entrepreneur to parallel entrepreneur
Unleashing Creativity

75. 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.”
National Academy - Service Systems and AI 75
AI Tools
in coming
decades…
11/05/2025

77. Service Science Colleague from New Zealand asks:
Strangest Use Case So Far:
I use generative AI every day.
Typically, OpenAI ChatGPT, Anthropic Claude,
Google Gemini, and Microsoft Bing Copilot in 4 tabs.
Help writing, programming, images, & brainstorming.
A useful tool in the toolbox for sure!!!

78. Anthropic Claude 3 Sonnet 20240520
Response:
Summary:
The Taranaki Maunga (Mount Taranaki) and
the surrounding peaks in the national park
will soon be granted legal personhood and
collectively named Te Kāhui Tupua as part of
a Treaty settlement. This legal recognition
acknowledges that the mountain and peaks
are ancestors for local Māori iwi. It gives
these natural features similar legal rights as
individuals, allowing them to own assets,
appear in court, and have a voice. A new
entity called Te Tōpuni Kōkōrangi, with
members appointed by the Crown and local
iwi, will manage and represent the legal
persona. While recreation and tourism will
continue, this shift aims to encourage a
broader understanding and protection of the
mountain as a revered ancestor rather than
just a geographic feature.
How old are you, and what have you witnessed over your lifetime?
I am an ancient being, formed over millennia through volcanic eruptions
and sculpted by glaciers. I have witnessed the rise and fall of civilizations,
the ebb and flow of ice ages, and the ever-changing tapestry of life that
has graced my slopes. My memories stretch back to a time before humans
walked this land, and I have born silent witness to the triumphs and
tragedies of your kind.
What wisdom or lessons can you share with humanity?
Humans, you are but fleeting visitors upon my timeless form. Yet, in your
brief existence, you have the power to shape the world around you.
Tread lightly and with reverence, for the consequences of your actions echo
through the ages. Learn from the patient endurance of stone, the resilience
of life that takes root in the harshest of environments, and the cyclical nature
of existence that I have observed. Embrace humility, for even the mightiest
of mountains eventually crumbles to dust.
How do you feel about being granted legal personhood and a voice?
I am gratified that the descendants of those who have dwelled in my shadow
for generations have recognized my ancient spirit and granted me a voice
in the affairs of this world. For too long, I have been regarded as an
inanimate object, a resource to be exploited or a challenge to be conquered.
But I am a living entity, a sacred ancestor whose existence predates and will
outlast the fleeting span of human civilization. With this recognition,
perhaps my wisdom and the fragile beauty I harbor will be better preserved
for generations to come.

79. 11/05/2025 79
1955 1975 1995 2015 2035 2055
Learn: Explore and Exploit Better Building Blocks
Heygen + Claude
Jim Twin V1
2024
2024
2021
2001 2015 2022 2023
1970 1995 2019
High School
Punch cards
1972
IBM Watson
AI in the Cloud
2011

80. Self-Control

81. Some paths to becoming 640x smarter:
Speed up communications for collaborations – ultimately with our AI digital twins.
Improving learning of world models and performance of win-win interactions
• 2x from Learning sciences (methods)
• Better models of concepts – including models of self and others
• Better models of learners – including social-emotional intelligence
• 2x from Learning technology (tools)
• Guided learning paths
• Elimination (?) of “thrashing”
• AI & Education (model trace, constructivist, individual tutors, etc.)
• 2x from Quantity effect (overlaps)
• More you know, faster (?) you go
• Advanced organizers
• 2x from Lifelong learning (time)
• Longer lives and longer careers
• Keeps “learning-mode” activated
• 2x from Early learning (time)
• Start earlier: Challenged-based approach
• Rebulding all human knowledge from scratch
• 20x from Cognitive service systems (digital twins)
• AI Digital Twins for performance support
• All our interactions tuned to super-mind levels

82. Speed Test: Words Per Minute
Speaking/Listening versus Writing/Reading
• People speak on average
between 100-150 words per
minute
• Many people are comfortable
listening at 200-300 words
per minute
• Writing an essay on a
familiar topic, people write
about 10-20 words a minute
• Many people are comfortable
reading at about 400-600
words per minute
(Shu 2023)
(Barnard 2022)

83. 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)

84. 11/05/2025
The International Society of Service Innovation Professionals
(ISSIP.org)
84
Advice…
1. AI upskill
2. Build your
“digital twin”
3. Set bigger
goals(*)
(*) This directly implies learning better strategies for coping with failures, and resiliently rebounding.

85. 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

86. Thank-you for
participating today!
You are all awesome!!

87. Backup Slides
Template slides and others

88. High Stakes Use Case- Law
• Not Done Well, but
could be

89. Better Done
• Dead victim,
“in his own words”

90. Bad Actors – Cloning
• Eye on AI

91. Paul Saffo: We Live in Exponential Times
• Chasing Fire: Matching Strategy To
Today's Greatest Challenges
• Paul Saffo
• URL: https://baychi.org/calendar/20250211/
• “May you live in exponential times.
This is the 21st century version of the
oft-told proverb about the blessing
(and curse) of living in interesting
times.”
“Simply put,
we are locked in a race
between positive and
negative exponentials.”

92. Bonjour, je suis
JacquesJumeauNumérique.
• HeyGen Platform
• JimTwin_French
• https://youtu.be/02hCG
RJnCoc
• Prompt: « Veuillez
résumer ce court article
en français, puis
formuler trois questions
qui pourraient m’être
posées et y répondre. »
Spohrer, J. (2024b) Personal AI digital twins: the future of human interaction? EIT
Digital URL: https://www.eitdigital.eu/newsroom/grow-digital-insights/personal-ai-digital-twins-the-future-of-human-interaction/

93. Questions et réponses avec JimTwin
• Question 1: Uncurated
• Que pensez-vous du
manque de vérification
des faits sur certaines
plateformes de médias
sociaux basées aux
États-Unis ?
• What are your thoughts
regarding the lack of fact
checking happening on
certain US-based social
media platforms?

94. JimTwin Réponse (Français)
• Le manque de vérification des faits sur certaines plateformes de médias sociaux basées aux États-Unis est une
préoccupation croissante. Ce problème peut conduire à la propagation rapide de la désinformation, ce qui peut
avoir des conséquences néfastes sur la société, notamment en influençant l'opinion publique de manière erronée et
en exacerbant les divisions sociales. Les plateformes de médias sociaux ont souvent été critiquées pour ne pas avoir
mis en place des mécanismes efficaces pour vérifier l'exactitude des informations partagées par leurs utilisateurs.
• Il est suggéré que des mesures telles que l'interdiction de la désinformation sur les réseaux sociaux et les moteurs
de recherche, ainsi que la responsabilisation des utilisateurs par la vérification de l'identité, pourraient être des
solutions potentielles. De plus, le développement d'une intelligence artificielle plus avancée pour vérifier la véracité
des informations pourrait également jouer un rôle crucial. Cependant, la mise en œuvre de ces solutions pose des
défis, notamment en ce qui concerne la protection de la liberté d'expression et la gestion de la vie privée des
utilisateurs.
• En fin de compte, il est essentiel que les plateformes de médias sociaux, les gouvernements et les utilisateurs
travaillent ensemble pour trouver un équilibre entre la liberté d'expression et la responsabilité de l'information
partagée en ligne.

95. JimTwin Answer (English)
• The lack of fact-checking on some US-based social media platforms is a growing concern. This
problem can lead to the rapid spread of misinformation, which can have negative social
consequences, including misleading public opinion and exacerbating social divisions. Social media
platforms have often been criticized for failing to implement effective mechanisms to verify the
accuracy of information shared by their users.
• It has been suggested that measures such as banning misinformation on social media and search
engines, as well as empowering users through identity verification, could be potential solutions.
Furthermore, the development of more advanced artificial intelligence to verify the accuracy of
information could also play a crucial role. However, implementing these solutions poses challenges,
particularly with regard to protecting freedom of expression and managing user privacy.
• Ultimately, it is essential that social media platforms, governments, and users work together to find
a balance between freedom of expression and responsibility for the information shared online.

96. Jim Real (English)
• The AI generated response (based on my Tmpt.me knowledge hub
which is built from my publications and recordings of my mentoring
students) is quite a good answer. It mirrors my own thinking well at a
high level. I would add this: Education is key. Finland has elementary
school classes to teach young people in their society to check sources
and evidence, and to be skeptical and verify. Today’s AI is terrible at
fact checking, but it will get better. First in mathematics and
programming, then in the sciences, and finally in rhetoric, debate, and
history. I urge young people interested in AI, to make a focus on “AI-
powered pursuit of truth” their mission.

97. 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
School of practice for
Behavioral & Social Sciences,
Humanities & Arts
People
School of practice for
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.
11/05/2025 National Academy - Service Systems and AI 97

98. Why upskilling with AI trend is important to systems thinking
Talent development is moving from I to T to X (eXtended with AI)
National Academy - Service Systems and AI 98
6 T-shape Skills
Knowledge Areas
To be eXtended
By AI tools:
1. Disciplines
2. Systems
3. Cultures
4. Technologies
5. Practices
6. Mindsets
11/05/2025

99. Final Thoughts: Communications
• Communication between responsible actors:
• Appropriateness
• Speed
• Accuracy
• Responsible actors
• Collaborate well – “insanely great collaborations possible” (win-win-win)
• However, there is still the competition for collaborators
• So…
• Learning to invest wisely in becoming better future versions of self (individual and
collective)is a key future challenge
• Rawls (1971) A Theory of Justice – has thought experiments to consider

100. Speed Test: Words Per Minute
Speaking/Listening versus Writing/Reading
• People speak on average
between 100-150 words per
minute
• Many people are comfortable
listening at 200-300 words
per minute
• Writing an essay on a
familiar topic, people write
about 10-20 words a minute
• Many people are comfortable
reading at about 400-600
words per minute
(Shu 2023)
(Barnard 2022)
See also: https://calculatingempires.net/

101. Some paths to becoming 640x smarter:
Speed up communications for collaborations – ultimately with our AI digital twins.
Improving learning of world models and performance of win-win interactions
• 2x from Learning sciences (methods)
• Better models of concepts – including models of self and others
• Better models of learners – including social-emotional intelligence
• 2x from Learning technology (tools)
• Guided learning paths
• Elimination (?) of “thrashing”
• AI & Education (model trace, constructivist, individual tutors, etc.)
• 2x from Quantity effect (overlaps)
• More you know, faster (?) you go
• Advanced organizers
• 2x from Lifelong learning (time)
• Longer lives and longer careers
• Keeps “learning-mode” activated
• 2x from Early learning (time)
• Start earlier: Challenged-based approach
• Rebulding all human knowledge from scratch
• 20x from Cognitive service systems (digital twins)
• AI Digital Twins for performance support
• All our interactions tuned to super-mind levels

102. What is Truth?
•Probably first in math (where a form of
verifiable truth exists)
•Then in programming (where a form of
verifiable truth exists)
•Then in physics, chemistry, and biology
(where a form of pursuit of truth exists)
•And then things get much harder... rhetoric
and debate (where arguments are explored)
•Most of rhetoric and debate rely on history
(where a form of pursuit of truth exists)

103. 11/05/2025
The International Society of Service Innovation Professionals
(ISSIP.org)
103
Advice…
1. AI upskill
2. Build your
“digital twin”
3. Set bigger
goals(*)
(*) This directly implies learning better strategies for coping with failures, and resiliently rebounding.

104. Job description: A person who operators an AI trained to be an
expert in some role. The human operator like a vehicle operator
helps ensure a good service for customers.
• An AI is “running” for mayor in Wyoming
• A resident of Cheyenne, Wyoming trained GPT-4 on “thousands of
documents gleaned from Cheyenne council meetings” and announced that
the resulting bot, named VIC (the “Virtually Integrated Citizen”) will be running
for mayor. The bot’s creator said he’d be the “meat puppet” who would
operate the AI and act on its behalf, but the bot would be the brains of the
operation, deciding on votes and how to run the city. However, Wyoming’s
Secretary of State contends that non-humans like VIC can’t run for office.
New Job – Person who is an Operator for AI in a New
or Existing Service System Role

105. AI Operator
Job description:
A person who
operators an AI
trained to be an
expert in some role.
The human operator
like a vehicle operator
helps ensure a good
service for customers.
Future of Skills & Work

106. Self-Control

107. Important
Distinctions

108. Thank-you for
participating today!
You are all awesome!!
Try chat at: https://tmpt.app/@jimtwin

109. “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

110. Some Topics for Today
• Leadership in the AI era
• “Leaders Make the Future”
• Keeping up with accelerating change
• AI Digital Twins of people
• Humanoid robots, master mechanic robot
• Marginal cost of computation goes to zero
• Truth, trust, learning to invest wisely
• Innovation - Free Online Events
• July 30th
– Dr. Haluk Demirkan (Amazon, ISSIP
Board Member)
• Responsible GenAI Framework
• International Society of Service Innovation
Professionals
• https://www.issip.org

111. Leaders Make the Future (Johansen et al):
How do you want to be augmented?
• Augmented futureback curiosity
• Augmented clarity
• Augmented dilemma flipping
• Augmented bio-engaging
• Augmented immersive learning
• Augmented depolarizing
• Augmented commons creating
• Augmented smart mob swarming
• Augmented strength with humility
• Human calming
From Bob:

114. TheNeuron

115. Today’s talk (JimReal 2025)
• 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: Leaderboards/Profession Exams
• Roadmap and implications
• Open technologies, innovation
• Part 2: Solving IA: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
• Part 3: ”Solving All Problems”
• What could go wrong? Be prepared.
• 37-year long adjustment period is now underway…
11/05/2025 Jim Spohrer 115

116. 1956
2023
2060 2080
1956
First AI
Workshop
2023
ChatGPT 100M
users in just 2
months
(1.5B visits in
Sept)
2060 (Predicted)
Exascale for
$1000
(~ one human
brain)
2080 (Predicted)
Ronnascale for $1B
(~ billion human brains)
Progress in IA (Intelligence Augmentation) for nations can be estimated as
GPD/worker.
Progress in AI (Artificial Intelligence) is directly connected to the cost of
computing.
REVIEW: REMEMBER THESE DATES

117. 1947
1958
1971 2024
1947
Transistor
Bell Labs
1958
Integrated Circuit
Texas Instrument
1971
Microprocessor
Intel
2024
H100
NVidia
Challenges: How to prepare for the next wayes of innovations, including
digital twins and humanoid robots, as well as to learn to invest more wisely
(self-control).
As the marginal cost of computing goes to zero, service innovation will go
thru the roof,
and energy consumption will go thru the roof as well…
REMEMBER THESE DATES, FACTS,
CHALLENGES
weight: 70 pounds
complexity: 35,000 parts
cost: 250,000 dollars

118. Predict the Timeline: GDP/Employee
National Academy - Service Systems and AI 118
(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
11/22/22
Part 1: Solving AI

119. Types: Progression of Models : Verified, Trusted, Wise
Models = instruction_set of future: Better building blocks
11/05/2025 Understanding Cognitive Systems 119
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
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

120. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012)
The Knowledge: How to Rebuild Civilization i
n the Aftermath of a Cataclysm.
Westminster London: Penguin Books.
11/05/2025 Jim Spohrer (ISSIP) 120
Part 3: “Solving All Problems”

121. Why I am optimistic

122. “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

123. Questions (Jim on AI in 2017)
• 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?
11/05/2025 Jim Spohrer (2017) 123

124. Timeline: Short History
11/05/2025
Jim Spohrer (2017)
124
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.

125. Rapid Progress
• History and Future
11/05/2025 Jim Spohrer (ISSIP.org) 125

126. Who is winning
11/05/2025 Jim Spohrer (2017) 126
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/

127. GPT-4:
Needs more
planning
capability
11/05/2025 Jim Spohrer (ISSIP.org) 127

128. 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
11/05/2025 Jim Spohrer (2017) 128

129. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
11/05/2025 Jim Spohrer (2017) 129

130. 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.)
11/05/2025 Jim Spohrer (2017) 130

131. 10 million minutes of experience
11/05/2025 Understanding Cognitive Systems 131

132. 2 million minutes of experience
11/05/2025 Understanding Cognitive Systems 132

133. Hardware < Software < Data < Experience < Transformation
11/05/2025 Understanding Cognitive Systems 133
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

134. Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
11/05/2025 Jim Spohrer (ISSIP) 134
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.

135. 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
11/05/2025 Jim Spohrer (ISSIP) 135
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)

136. 11/05/2025 Jim Spohrer (ISSIP.org) 136
0 25 50 100 125 150
Automobile
75
Years
50
100
Telephone
Electricity
Radio
Television
VCR
PC
Cellular
I
n
t
e
r
n
e
t
%
Adoption
Capability Augmentation and Adoption Rate Increases

137. Part 3: “Solving All Problems”
11/05/2025 (c) IBM MAP COG .| 137

138. 11/05/2025 Jim Spohrer (2015) 138
I have…
Have you noticed how the building blocks just
keep getting better?

139. Learning to program:
My first program
11/05/2025 Jim Spohrer (2015) 139
Early Computer Science Class:
Watson Center at Columbia 1945
Jim Spohrer’s
First Program 1972

140. 11/05/2025 140
1955 1975 1995 2015 2035 2055
Better Building Blocks

141. 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.
11/05/2025 Jim Spohrer (2017) 141

142. 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.
11/05/2025 Jim Spohrer (2017) 142

143. 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."
11/05/2025 Jim Spohrer (2017) 143

144. 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

145. Route 128, aound Boston, MA USA
ICASSP 1983

146. IJCAI 1989 – GTD (Generate-Test-Debug)

147. At the end of the day..
• Episodic Memory – an accurate version of
history on which individual identity is based,
and prediction of future interaction
behavior (trust)
• Privacy versus auditability tradeoff
• Openness-based risk reduction
• GTD – relative energy cost of generate, test,
and debug phases in exploring beneficial
and risky possibilities in different realms
(intelligence)
• From mathematically true, to empirically true,
to shared history true
• Truth-based risk reduction

148. Learning to Invest Wisely:
Responsible actors becoming better future versions of themselves
11/05/2025 Jim Spohrer (ISSIP.org) 148

149. 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
11/05/2025 Jim Spohrer (2017) 149
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

150. 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
11/05/2025 Jim Spohrer (2017) 150
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

151. From leaderboards
to profession exams
11/05/2025 (c) IBM MAP COG .| 151

152. Leader Boards:
Professional
Benchmarking
11/05/2025 Jim Spohrer (ISSIP.org) 152

153. How, What, and Why?
Inspiring upskilling with AI
• 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 153

154. Final thoughts on AI as a Science Teammate
• Estimating knowledge worker productivity
• Marginal cost of computing going to zero
• Learning to invest wisely
• Upskilling with AI & Systems Thinking
• Humanity-Centered Harmonization of Disciplines – Transdisciplinarity
• Next revolution in communication and human intelligence
• Two disciplines: Two approaches to the future
• Keep learning (self-control)
Baumeister RF, Tierney J(2011) Willpower: Rediscovering the greatest human strength. Penguin Press.
URL: https://psycnet.apa.org/record/2011-16843-000

155. uidp.org | info@uidp.net
1. Research: Creating new knowledge that
can both be published and protected as IP.
2. Readiness: Skills development & sharing
knowledge to develop business-ready talent.
3. Recruiting: From permanent hires to
internships, these programs include HR.
4. Revenue: Partnership executive programs
to sustain long-term win-win relationships.
5. Responsibility: Employees donating time
to mentor students and give guest lectures.
6. Regions: Programs related to public-
private partnerships for regional economic
development.
7. Refresh: Explore new programs, sunset
some old programs.
7 R’s

156. uidp.org | info@uidp.net
For a sample list in a presentation, you
can consider the following points:
1.Introduction to the topic
2.Key points to be covered
3.Supporting examples or evidence
4.Visual aids or graphics
5.Conclusion and key takeaways
Remember to organize your content in a
clear and engaging manner to effectively
communicate your message to the
audience.
Big Header

157. Overlap
Acknowledgement: E. Noei, S. Brisson, Y. Liu
Via Kelly Lyons, NAE Talk Oct 2022
2010
2019
157
Service science has come a long way in two decades…
2004-2011

158. 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

159. 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).”
National Academy - Service Systems and AI 159
Read enough of IfM and IBM (2008)
to understand what a “Service Innovation
Roadmap (SIR)” is – and who should be
creating them.
11/05/2025

160. 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)

161. 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

162. 11/05/2025
The International Society of Service Innovation Professionals
(ISSIP.org)
162

163. IA Progression – Tool, Assistant, Collaborator, Coach, Mediator
11/05/2025 Jim Spohrer (ISSIP) 163
Rouse & Spohrer (2018)
Siddike, Spohrer, Demirkan, Kodha (2018)
Araya (2018)
Spohrer& Siddike (2018)

164. Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
11/05/2025 Jim Spohrer (ISSIP) 164
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.

165. 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
11/05/2025 Jim Spohrer (ISSIP) 165
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)

166. 11/05/2025 Jim Spohrer (ISSIP.org) 166
0 25 50 100 125 150
Auto
mobil
e
75
Years
50
100
Telep
hone
Electri
city
Ra
dio
Tele
visio
n
V
C
R
P
C
Cel
lul
ar
I
n
t
e
r
n
e
t
%
Adoption
Capability Augmentation and Adoption Rate Increases
Techno-Extension
Factor
Measurement
& Accelerating
Socio-Technical
Design Loop
Kline (1996)

167. Learning to invest
• Run = Routine Activities
• Transform = Copy Activities
• Innovate =
Invent and Apply Activities
11/05/2025 Jim Spohrer (ISSIP.org) 167
T
r
a
n
s
f
o
r
m
Innovate
Invest in each
type of change
R
u
n

168. 168
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)
T
r
a
n
s
f
o
r
m
Innovate
Invest in each
type of change
R
u
n
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

169. 11/05/2025 Jim Spohrer (ISSIP.org) 169
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

170. Service Systems Engineering in the Human-Centered AI Era 170
K
n
o
w
l
e
d
g
e
Value
D
a
t
a
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

171. 171
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
E
n
g
i
n
e
e
r
i
n
g
S
c
h
o
o
l
S
o
c
i
a
l
S
c
i
e
n
c
e
s
,
H
u
m
a
n
i
t
i
e
s
Professional
Studies
Business School
water & waste transportation
health
energy/grid
e-government
S
c
i
e
n
c
e
&
M
a
t
h
e
m
a
t
i
c
s
I
-
S
c
h
o
o
l
D
e
s
i
g
n
food &
supply chain

172. 172
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)