For Prof. David T. Lee (UCSC) - Leadership & Career discussion

1. University of California Santa Cruz, CA USA
Leadership/Tech4Good
Thanks Prof. David T. Lee
To leadership topics and career experiences
March 6, 2024
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
X/Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Slack: https://issip.slack.com
2001 2015 2021 2022 2023 2024

2. Today’s talk
• Leadership & Career Topic
• Collaborating with other
• Team work/collaboration practices
• Storytelling and communication like
how to communicate
• Habits, mindset, practices of leaders
like how to get things done,
hustling, taking ownership and
initiative, communicating in a
responsive and clear way, knowing
yourself (authenticity, motivation,
personality), being
effective/influential in interpersonal
interactions, etc.
2010

3. 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!

4. 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
GDP/Employee
Trend
Estimating Knowledge Worker Productivity
Based on USA
Historical Data
Year Value
1960 $10K
1980 $33K
2000 $78K
2020. $151K
2023 $169K

5. 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 5
AI Tools
in coming
decades…
3/6/2024

6. 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

7. 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
3/6/2024 Jim Spohrer (ISSIP.org) 7
Environmental and ecological sciences
ServCollab

8. 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. We get the future we invest in – so invest wisely.
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)

9. In Memory of Douglas Engelbart (1925-2013)

10. 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
3/6/2024 Jim Spohrer (ISSIP) 10
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)

11. What expertise does
a service scientist require?
What degrees can
a service scientist earn?
Ultimately, what tool will
a service scientist most need?
Ultimately, what purpose should
a service scientist focus on?
How to invest wisely?
Year Delighted when… … and many people to thank when…
2024 IESS in Brno, Cech Republic
2023 Generative AI , Humanoid Robots, nd AI Digital Twins of Service
System Entities
2022 Exploring ServCollab and ISSIP collaborations. NAE Event on SSME in AI
Era and T-shaped Skills mentioned in Nick Donofrio Autobiography
2021 Christopher Lovelock Career Contributions to Service Discipline Award
IBMer Utpal Mangla, Elected to 2022 ISSIP VP/2023 President role
2020 Linux Foundation AI & Data TAC Chair Elected – open-source trusted AI
2019 Handbook of Service Science, Volume 2
2018 IBMer Rama Akkiraju, President of ISSIP
2017 Daniel Berg Award for Technology and Service Systems Award (IAITQM)
2016 NSF invests $13M in smart, human-centered Service Systems
2015 IBMer Jeff Welser, President of ISSIP
2014 IBM hosted Frontiers in Service Conference in San Jose, CA
2013 Vargo & Lusch S-D Logic Award, E. Gummesson Award (Naples Forum)
PICMET Fellow for Advancing Service Science
2012 International Society of Service Innovation Professionals established
2011 IBM Centennial Icon-of-Progress – including SSME and Smarter Planet
2010 Handbook of Service Science, Volume 1
2009 Robin Qiu launches INFORMS Journal of Service Science
2008 Cambridge Report – “Succeeding Through Service Innovation”
HICSS starts a service scince mini-track Paul Maglio/Furen Lin
2007 SSME in USA America COMPETES Act Congressional Legislation
IBM hosted Frontiers in Service Conference in San Francisco, CA
2006 IBM Research Awards for CBM, Data Analytics, Solution, etc. tools
2005 Attended fist Frontiers in Service – ”Big tent” getting bigger
2004 China, Japan, Finland, Germany, etc. Launch knowledge-intensive
service initiatives
2003 ”Big Tent” Service Conference at IBM Almaden, SSME Faculty Awards
2002 IBM established Almaden Service Research (ASR) group

12. Eleven (11) levels – order of magnitude observation
Service system entities that get and give service.
Service is the application of resources (e.g., knowledge) for the benefit of another.
Ref: SDL
Ref: Spohrer,, Kwan, Fi

13. Predict the Timeline: GDP/Employee
National Academy - Service Systems and AI 13
(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

14. Types: Progression of Models : Verified, Trusted, Wise
Models = instruction_set of future: Better building blocks
3/6/2024 Understanding Cognitive Systems 14
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

15. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
3/6/2024 Jim Spoihrer (ISSIP) 15
Part 3: “Solving All Problems”

16. 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

17. T-Shaped Professionals
• Title: Preparing T-Shaped Professionals for Career
Success in the AI Era
• Speaker: Jim Spohrer
• Abstract: As universities transform curriculum in the AI
era, the importance of preparing T-shaped
professionals for career success is an important topic to
explore. T-shaped professionals have deep disciplinary
problem-solving skills and broad communications skills
for improved teamwork and rapid learning of new
areas. T-shaped professionals have depth and breadth
across six areas: Emerging technologies, work practices,
developmental mindsets, academic disciplines, societal
systems, regional cultures. This talk will provide an
overview of the past, present, and future of the T-
shaped skills concept, with special attention to possible
applications in Informatics curriculum development.

18. Innovation
• Incremental
• New Value(s) – Existing Unit(s)
• Example: Vehicles Kilometers/Kilowatt-Hour (Transportation)
• Radical
• New Value(s) – New Combination(s) of Existing Unit(s)
• Example: Smartphones Bits/Joule (Communications)
• Super-Radical
• New Value(s) – New Combination(s) with New Unit(s)
• Example: Goal-level of what percentage of the population, Online Commerce
Trust/Future Prediction Accuracy (Computation); Mtrans. Exploration &
Learning.

19. Next Generation:
Future-Ready T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline

21. Future-Ready T-Shapes
3/6/2024
© IBM UPWard 2016
21

22. Service science transdisciplinary framework
22
SYSTEMS
DISCIPLINES
transportation
& supply chain
water &
waste
food &
products
energy &
electricity
ICT &
cloud
building &
construction
retail &
hospitality
banking &
finance
healthcare
& family
education
& work
city
secure
state
scale nation laws
behavioral sciences
e.g., marketing
management science
e.g., operations
political sciences
e.g., public policy
learning sciences
e.g., game theory & strategy
cognitive sciences
e.g., psychology
system sciences
e.g., industrial engineering
information sciences
e.g., computer science
organization sciences
e.g., knowledge management
social sciences
e.g., econ & law
decision sciences
e.g., stats & design
run professions
e.g., knowledge worker
transform professions
e.g., consultant
innovate professions
e.g., entrepreneurs
change
value
technology
information
organizations
transform
(copy)
systems that govern
stakeholders
resources
customer
provider
authority
competitors
people
Innovate
(invent)
history (data
analytics)
run
future
(roadmap)
systems that focus on flows of things systems that support people's activities
Observing the stakeholders (As-Is)
Change Potential: Thinking (Has-Been & Might-Become)
Observing their Resources & Access (As-Is)
Value Realization: Doing (To-Be)
Entities
Interactions
Change
(Outcomes)
Value
(Identity)

23. Some paths to becoming 64x smarter:
Improving learning and performance
• 2x from Learning sciences (methods)
• Better models of concepts
• Better models of learners
• 2x from Learning technology (tools)
• Guided learning paths
• Elimination (?) of “thrashing”
• 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
• STEM-2D in K-12 (SSME+DAPP Design of Smart Service Systems)
• 2x from Cognitive systems (performance support)
• Technology & Infrastructure Interactions
• Organizations & Others Interactions

25. 3/6/2024 25
1955 1975 1995 2015 2035 2055
Better Building Blocks

27. Domain of Science - The Map of Quantum Computing - Quantum Computing Explained
https://youtu.be/-UlxHPIEVqA

29. “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

30. 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.
3/6/2024 Jim Spohrer (2017) 30

31. 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.
3/6/2024 Jim Spohrer (2017) 31

32. 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."
3/6/2024 Jim Spohrer (2017) 32

33. Part 3: “Solving All Problems”
3/6/2024 (c) IBM MAP COG .| 33

34. 10 million minutes of experience
3/6/2024 Understanding Cognitive Systems 34

35. 2 million minutes of experience
3/6/2024 Understanding Cognitive Systems 35

36. 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.
3/6/2024 National Academy - Service Systems and AI 36

37. 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 37
6 T-shape Skills
Knowledge Areas
To be eXtended
By AI tools:
1. Disciplines
2. Systems
3. Cultures
4. Technologies
5. Practices
6. Mindsets
3/6/2024

38. How, What, and Why?
Inspiring AI upskilling (IA)
• How to learn
• AI-powered search can help people - motivated people – to
learn about whatever they put their minds to learning
• What to learn
• AI technological capabilities and limitations – foundational
models
• AI applications that can actually improve processes for how
things get done (case studies - productivity, quality,
compliance, sustainability, decarbonization)
• AI-as-a-service investment cases to motivate stakeholders to
change to better win-win interactions in business and societal
service systems (investment pitch)
• The “startup of you” investment case – learning to invest
systematically and wisely (startup pitch)
• Why learn?
• Challenge and opportunity - nations must upskill with AI and
decarbonize
• Motivation is key – find the very best free online
videos/courses and subscribe
• Universities will play an increasingly important role as industry
research partners and venture testbeds even as learners can do
more and more on their own with online curriculum
National Academies – Service Systems and AI 38

39. We get the future we invest in:
AI tools to experiment with today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #4 Thing Translator
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #8 This Person Does Not Exist
• #9 Namelix
• #10 Let's Enhance
Thanks to @TessaRDavis
for compiling this list:
“Service providers
will not be replaced by AI,
but trusted service providers
who use AI (well and responsibly)
will replace those who don’t.”
National Academy - Service Systems and AI 39
Try at least two
from the list
as soon as possible
What do you think?
, DALL-E and Stable Diffusion
Every person in a role in an organization is a service provider.
3/6/2024

40. 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 40
Read enough of IfM and IBM (2008)
to understand what a “Service Innovation
Roadmap (SIR)” is – and who should be
creating them.
3/6/2024

41. Learning to invest
• Run = Routine Activities
• Transform = Copy Activities
• Innovate =
Invent and Apply Activities
3/6/2024 Jim Spohrer (ISSIP.org) 41
Innovate
Invest in each
type of change

42. 42
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

43. Concluding Remarks: Two Books
3/6/2024 Jim Spohrer (ISSIP.org) 43

44. 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.”
3/6/2024 Jim Spohrer (ISSIP.org) 44

45. 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)

46. 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.
3/6/2024 Jim Spohrer (ISSIP.org) 46
• 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.

47. Why I am optimistic

48. 3/6/2024 Jim Spohrer (ISSIP.org) 48
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