Hagel - Beyond Fear - Narrative, Passion, Platform

1. Panelist for Fall 2021 Berkeley Innovation Forum
and John Hagel’s Keynote
Jim Spohrer
Board of Directors, ISSIP.org
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations on line at: https://slideshare.net/spohrer
Thanks to Henry Chesbrough and Solomon Darwin for inviting
me to be a panelist for John Hagel’s presentation (Oct 6, 2021)
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book

2. Journey Beyond Fear – John Hagel
• Narrative – courage to face fear and seize opportunity
• Passion – explorer, lifelong learning – seek understanding to
overcome fear
• Platform – learning to collaborate/co-create/invest together – seeking
to create and share new knowledge

3. My Experience at IBM
• Fear of product to service transformation (for Cloud)
• Fear of proprietary to open source transformation (for AI)
• Face fear – acquire Red Hat $34B
• Face fear – spin-off Kyndryl (~$20B/yr revenue)
Note: I am a retired IBMer these days

4. Jim Spohrer, Board of Directors, ISSIP.org
Jim Spohrer serves on the Board of Directors of the International
Society of Service Innovation Professionals, and as a contributor to the
Linux Foundation AI and Data Foundation. He 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. After his MIT BS in Physics, he developed speech
recognition systems at Verbex (Exxon) before receiving his Yale PhD in
Computer Science/AI. In the 1990’s, he attained Apple Computers’
Distinguished Engineer Scientist and Technologist role for next
generation learning platforms. With over ninety publications and nine
patents, he received the Christopher Loverlock 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 LF AI & Data Technical Advisory
Board Chairperson and ONNX Steering Committee Member (2020-
2021).
4
From 2002 - 2009, Jim co-founded and directed
IBM Almaden Service Research
helping to establish service science,
applying science, technology,
and T-shaped upskilling of people to
business and societal transformation.
Who I am
2021 A big year: (1) 65, (2) award, (3) retired

5. 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

6. 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.
26-30 July 2015 3rd International Conference on The Human Side of Service Engineering
6
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)

7. Service & Global Value Chain
10/6/2021 (c) IBM MAP COG .| 7
Source:
Fed Reserve
St. Louis

10. Timeline Future of AI: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
10
10/6/2021 (c) IBM 2017, Cognitive Opentech Group
2080
2040
2000
1960
$1K
$1M
$1B
$1T
2060
2020
1980
+/- 10 years
$1
Person Average
Annual Salary
(Living Income)
Super Computer
Cost
Mainframe Cost
Smartphone Cost
T
P
E
T P E
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

11. Timeline: GDP/Employee
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 11
(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

12. Accelerating shift: digital transformation and AI/robotics…
How will COVID-19 effect the need for and use of robots in a service world
with less physical contact?
Will robots improve or harm livelihoods/jobs?
Robots Rule Retail?
Taking away jobs
Telepresence Robot World?
Adding more jobs
Robots at Home?
Reducing need to have a job
T-shaped (L)earners
You will be assigned to a small team to discuss. Please have a team member to take notes of
the most important insights and/or questions that emerge from your discussion. Your notes
will be crucial for us to create a conference report, send to contact@creatingvalueconf.com
What is most probable to happen? What is desirable?
Spohrer

13. Accelerating shift: From employees to earners in
platform society – upskilling (l)earners is key
Farrrel D, Grieg F (2014)
Online Platform
Economy.

14. The Three Stages of Systems Evolution
The Three Ages of Man (Giorgione)
Thanks to Alan Hartman for kind inspiration (slides) (recording) Service, the application of knowledge for mutual benefits
win-win/non-zero-sum games/value co-creation/capability co-elevation

15. IBM’s Service Journey: A Summary Sketch
10/6/2021 (c) IBM MAP COG .| 15
Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.

18. IfM, IBM (2010)
Succeeding through
service innovation:
a service perspective
for education, research,
business and government.
University of Cambridge
Institute for Manufacturing,
Cambridge UK
2010

27. 27
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

28. Service Science: Conceptual Framework
10/6/2021 (c) IBM MAP COG .| 28
Service Science

29. (c) IBM MAP COG .| 29
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)

30. “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

31. 10/6/2021 31
1955 1975 1995 2015 2035 2055
Better Building Blocks

32. Trust: Two Communities
10/6/2021 32
Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation
Responsible Entity Collaborators
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities

34. Future of Service Science
Smarter and Wiser Service Systems:
Entities transform to better future versions of
themselves by inventing win-win games and competing
for collaborators
Past Present Future
Organizational
Units
Family
Local Clan
Family
Business/Nation
Family
Platform Society
Change Individual
Generalist
(Breadth)
Individual
Specialist
(Depth)
Individual
T-shaped
(L)earners
Constant Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
10/6/2021 (c) IBM MAP COG .| 34

35. 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
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 35
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

36. Who is winning
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 36
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/

37. Robots by Country
• Industrial robots per 10,000 people by country
10/6/2021 IBM #OpenTechAI 37
34

38. 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
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 38

39. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 39

40. Other Technologies: Bigger impact? Yes.
• Augmented Reality (AR)/
Virtual Reality (VR)
• Game worlds
grow-up
• Blockchain/
Security Systems
• Trust and security
immutable
• Advanced Materials/
Energy Systems
• Manufacturing as cheap,
local recycling service
(utility fog, artificial leaf, etc.)
10/6/2021 (c) IBM 2017, Cognitive Opentech Group 40

41. Upskilling…
T-shaped
(l)earners
Gardner P, Maietta HN
(2020) Advancing Talent
Development: Steps
Toward a T-Model
Infused Undergraduate
Education.
Moghaddam Y, Demirkan
H, Spohrer J (2018) T-
Shaped Professionals:
Adaptive Innovators.

42. 10/6/2021 (c) IBM MAP COG .| 42
T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration
Advanced Tech: AI to IoT to Quantum, GreenTech, RegTech, etc.
Work Practices: Agile, Service Design, Open Source
Mindset: Growth Mindset, Positive Mindset, Entrepreneurial
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline

43. 10 million minutes of experience
10/6/2021 Understanding Cognitive Systems 43

44. 2 million minutes of experience
10/6/2021 Understanding Cognitive Systems 44