Service at the Dawn of the AI Era: Service Innovation Roadmaps and Responsible Entities Learning

1. Service at the Dawn of the AI era:
Service Innovation Roadmaps and
Responsible Entities Learning
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 Evert, Francesco, and Cristina for the invitation for
Sept 8th, 2021, and for founding the Naples Forum on Service!
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book

2. 9/8/2021 2
Where do we go now?

3. 9/8/2021 3
Our maps change the landscape

4. ”… economist W. Brian Arthur, a founder of the field
of “Complexity Economics”. Brian Arthur received a
doctorate in Operations Research from UC Berkley
and has served as both resident and external faculty
at the Santa Fe Institute. He is the recipient of the
Schumpeter Prize in Economics and the Lagrange
Prize in Complexity Sciences, and is the author of
several books, including
The Nature of Technology (Free Press, 2009) and
Complexity and the Economy (Oxford, 2014).”
A Conversation with
W. Brian Arthur
(Sante Fe Institute)
Wednesday April 28, 2021

5. Motivation
• Service at the Dawn of the AI Era
• Service at its core is about “responsible entities” applying knowledge for mutual
benefits (e.g., people, businesses, nations = service system entities)
• Human-like AI is very, very, very hard, and decades away from being solved
• Even more hard issues arise to get an AI to become a “responsible entity”
• Service Innovation Roadmaps (SIRs)
• SIRs help responsible entities (and “would be” “responsible entities”) learn to be
better future versions of themselves (AKA ”upskilling”)
• SIRs should be commonplace by now… but they are not.
• Responsible Entities Learning
• SIRs are investments plans for three activities: Run-Transform-Innovate
• An investment plan can be made explicit using a Business-Model-Canvas (BMC)
• AI is enabling “digital twins” of many things, including “responsible entities”
9/8/2021 (c) IBM MAP COG .| 5

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. Accelerating digital transformation and shift to 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

8. Timeline: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
8
9/8/2021 (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

9. Importance of a plan – or roadmap

10. 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

11. 11
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

12. 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. 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. He was CTO
IBM Venture Capital Group, co-founded IBM Almaden Service Research,
and led IBM Global University Programs. 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).
9/8/2021 12
In 2008, 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.
2021 A big year: (1) 65, (2) retired, (3) Lovelock

13. 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

14. Trust: Two Communities
9/8/2021 14
Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation
Responsible Entity Collaborators
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities

15. Accelerating digital transformation and shift to 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

16. Accelerating shift - from employees to earners in
platform society
Farrrel D, Grieg F (2014)
Online Platform
Economy.

17. Upskilling…
T-shapes (l)earners…
on multiple platforms
Rodgers S (2016) Jeremiah
Owyang on the Collaborative
Economy.
Kenny M, Zysman J (2016) The
Rise of the Platform Economy.

18. 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.

19. 9/8/2021 (c) IBM MAP COG .| 19
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

20. References – Post-pandemic world
• Autor D, Mindell D, Reynolds E (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. MIT Work of the Future Task Force. URL:
https://workofthefuture.mit.edu/wp-content/uploads/2020/11/2020-Final-Report.pdf
• Farrel D, Grieg F (2014) Online Platform Economy. JP Morgan Chase. URL: https://www.jpmorganchase.com/institute/research/labor-markets/jpmc-institute-
online-platform-econ-brief
• Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Business Expert Press. URL:
https://www.amazon.com/Advancing-Talent-Development-Undergraduate-Education/dp/1951527062
• Hunt V, Simpson B, Yamada Y (2020) The case for stakeholder capitalism. McKinsey Report. URL: https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/the-case-for-stakeholder-capitalism
• ILO (2017) Helping the gig economy work better for gig workers. URL: https://www.ilo.org/washington/WCMS_642303/lang--en/index.htm
• Kenny M, Zysman J (2016) The Rise of the Platform Economy. Issues in Science and Technology. Vol. XXXII, No. 3, Spring 2016. URL: https://issues.org/the-rise-of-
the-platform-economy
• Moghaddam Y, Demirkan H, Spohrer J (2018) T-Shaped Professionals: Adaptive Innovators. Business Expert Press. URL: https://www.amazon.com/T-Shaped-
Professionals-Innovators-Yassi-Moghaddam/dp/194784315X
• Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Dassault Systemes – Navigate the Future. URL: https://blogs.3ds.com/northamerica/jeremiah-
owyang-on-the-collaborative-economy/
• Sapjic DJ (2019) The Future of Employment –30 Telling Gig Economy Statistics. Small Business by the Numbers. URL: https://www.smallbizgenius.net/by-the-
numbers/gig-economy-statistics/#gref
• Spohrer JC (2011) On looking into Vargo and Lusch's concept of generic actors in markets, or “It's all B2B… and beyond!”. Industrial Marketing Management.
2011;2(40):199-201.
• Spohrer J (2017) IBM's service journey: A summary sketch. Industrial Marketing Management. 2017 Jan 1;60:167-72.
• Spohrer J, Kwan SK, Fisk RP. (2014) ”Marketing: A Service Science and Arts Perspective”. In Roland T. Rust and Ming-Hui Huang Handbook of Service Marketing
Research (489-526). [Competing for collaborators is the constant across time]
• Torpey E, Hogan A (2016) Working in a gig economy. USA Bureau of Labor Statistics. URL: https://www.bls.gov/careeroutlook/2016/article/mobile/what-is-the-
gig-economy.htm
• Van Dijck J, Poell T, De Waal M (2018) The platform society: Public values in a connective world. Oxford University Press. [book review]
• WEF (2017) Towards a reskilling revolution - a future of jobs for all. URL: http://www3.weforum.org/docs/WEF_FOW_Reskilling_Revolution.pdf

21. 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
21
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)

22. 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
9/8/2021 (c) IBM MAP COG .| 22

23. Service Science: Conceptual Framework
9/8/2021 (c) IBM MAP COG .| 23
Service Science

24. (c) IBM MAP COG .| 24
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)

25. 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

26. Importance of a plan – or roadmap

27. What is a SIR?
• Service Innovation Roadmap (SIR) is a kind of Business Model Canvas
(BMC) that responsible entities create for themselves to describe
three types of investments in learning/upskilling activities:
• Run: BMC for optimize activities (e.g., agile improvement method)
• Transform: BMC for copy activities (e.g., find role models)
• Innovate: BMC for invent activities (e.g., research, pilot, prove, monetize)
• Based on March (1991)
• March JG (1991) Exploration and exploitation in organizational learning.
Organization science. 1991 Feb;2(1):71-87.

28. Business
Model
Canvas

30. 30
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

31. 9/8/2021 (c) IBM MAP COG .| 31
Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3

33. AI Considerations: Do
• Do dashboard the opportunity investment
• Perform an audit of existing projects (machine learning, robotics) and opportunities
• Evolve evaluation criteria (e.g., performance, Trusted AI) and investment approach
• Celebrate victories (internally/externally)
• Do reward talent development
• Diversity: T-Shaped Upskilling (e.g., business, technical, systems, customer empathy)
• Open-Source Software: LF AI & Data, Github Rankings
• Open Data: Kaggle Rankings
• Do monitor technology developments
• AI is very hard and will take decades to solve
• LF AI & Data Landscape
• PapersWithCode.Com
9/8/2021 (c) IBM MAP COG .| 33

34. AI Considerations: Don’t
• Don’t under-estimate the on-going cost of data cleansing and architecture
• The “crude oil” refinery process metaphor is apt (many stages)
• Mitigating bias and ensuring security
• Synthetic data generation can be explored as well (e.g., autonomous vehicles)
• Don’t under-estimate the on-going cost of the full system
• The full system includes technology, people, governance, and other costs
• The full system includes AI ethics boards and monthly technical steering committee
• This is where being a good ecosystem player/partner can help stabilize costs
• Don’t over-estimate short-term impact/under-estimate long-term impact
• Rigorous business and technical evaluation criteria needed
• Evolve monthly (cross-functional steering committees to review cases)
• Open-source office coordinates business, development, research, ecosystem players
9/8/2021 (c) IBM MAP COG .| 34

35. Future of AI
• What is the timeline for solving AI and IA?
• TBD: When can a CEO/anyone 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?
9/8/2021 (c) IBM 2020, Cognitive Opentech Group 35

36. Timeline: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
36
9/8/2021 (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

37. Timeline: GDP/Employee
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 37
(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

38. 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
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 38
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

39. Who is winning
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 39
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/

40. Robots by Country
• Industrial robots per 10,000 people by country
9/8/2021 IBM #OpenTechAI 40
34

41. 9/8/2021 (c) IBM MAP COG .| 41
The company says its first product, LettuceBot,
already has a hand in roughly 10 percent of US lettuce production.

42. 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
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 42

43. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 43

44. 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.)
9/8/2021 (c) IBM 2017, Cognitive Opentech Group 44

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. 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.

47. 9/8/2021 (c) IBM MAP COG .| 47
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

48. 9/8/2021 48
1955 1975 1995 2015 2035 2055
Better Building Blocks

49. 10 million minutes of experience
9/8/2021 Understanding Cognitive Systems 49

50. 2 million minutes of experience
9/8/2021 Understanding Cognitive Systems 50

51. ”… economist W. Brian Arthur, a founder of the field
of “Complexity Economics”. Brian Arthur received a
doctorate in Operations Research from UC Berkley
and has served as both resident and external faculty
at the Santa Fe Institute. He is the recipient of the
Schumpeter Prize in Economics and the Lagrange
Prize in Complexity Sciences, and is the author of
several books, including
The Nature of Technology (Free Press, 2009) and
Complexity and the Economy (Oxford, 2014).”
A Conversation with
W. Brian Arthur
(Sante Fe Institute)
Wednesday April 28, 2021

52. The Naples Forum on Service
Evert
Gummesson
Francesco
Polese
Cristina
Mele
Stephen
Vargo
Jim
Spohrer
W. Brian
Arthur
Sergio
Barile

53. The Nature of Technology
• The

54. • The term “technological unemployment” is from John Maynard Keynes’s 1930 lecture, “Economic
possibilities for our grandchildren,” where he predicted that in the future, around 2030, the production
problem would be solved and there would be enough for everyone, but machines (robots, he thought)
would cause “technological unemployment.” There would be plenty to go around, but the means of getting
a share in it, jobs, might be scarce. We are not quite at 2030, but I believe we have reached the “Keynes
point,” where indeed enough is produced by the economy, both physical and virtual, for all of us. (If total US
household income of $8.495 trillion were shared by America’s 116 million households, each would earn
$73,000, enough for a decent middle-class life.) And we have reached a point where technological
unemployment is becoming a reality. The problem in this new phase we’ve entered is not quite jobs, it is
access to what’s produced. Jobs have been the main means of access for only 200 or 300 years. Before that,
farm labor, small craft workshops, voluntary piecework, or inherited wealth provided access. Now access
needs to change again. However this happens, we have entered a different phase for the economy, a new
era where production matters less and what matters more is access to that production: distribution, in other
words—who gets what and how they get it. We have entered the distributive era.
From Arthur WB (2017) Where is technology taking the economy. McKinsey Quarterly. October.

55. Complexity Economics
• The

56. • Some models in complexity economics use mathematics (such as nonlinear stochastic processes), but, often,
the sheer complication of keeping track of the decision processes of multiple agents requires the use of
computers. We then build models around agents’ individual behaviour, and, so, agent-based modelling
arises naturally. Agent-based models are now used all across economics. Some have a few hundred agents; a
recent one has 120 million. Some take account of legal and regulatory institutions. Some are designed to
simulate reality — the 2008 subprime mortgage meltdown or the economics of the 2020 COVID-19
pandemic. Some investigate theoretical issues — financial asset pricing. But whatever the design of these
studies, the idea, as in all of economics, is to explore how outcomes follow from assumed behaviour.
• An ecology of behaviours
• In the El Farol problem, agents’ forecasting methods vie to be valid in a situation that is dependent on other
agents’ forecasts — they compete in an ‘ecology’ of forecasts. Indeed, a general feature in complexity
economics is that agents’ beliefs, strategies or actions are tested for survival within a situation or ecology
that these beliefs, strategies or actions together create. They act in a way like species, continually competing
or mutually adapting and co-evolving. As a result, a distinct biological evolutionary theme emerges.

58. 9/8/2021
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
58
Cognitive Mediators
for all people in all roles

59. Occupations = Many Tasks
9/8/2021
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
59

60. 9/8/2021 (c) IBM MAP COG .| 60
Microsoft acquiring GitHub $7.5B
2018 John Marks on Open Source
Models will run the world
Why SW is eating the world

61. Step Comment
GitHub Get an account and read the guide
MAX CODAIT’s Model Asset Exchange
Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook)
PapersWithCode Stay on top of recent advances; Do 3 R’s.
Kaggle Compete in a Kaggle competition
Leaderboards Compete to advance AI progress
Linux Foundation AI Help end-to-end open source industry AI & Data infrastructure
Mozilla Common Voice Donate your speech; Label and verify data; Recruit others.
Figure Eight Generate a set of labeled data (also Mechanical Turk)
Design New Challenges Build for Call for Code/Code and Response; Build your AI Helper;
Build test-taker, that can switch to tutor-mode; Etc.
Open Source Guide Establish open source culture in your organization
9/8/2021 IBM Code #OpenTechAI 61

62. Is it fair?
Is it easy to
understand?
Is it accountable?
So what does it take to trust a decision made by
a machine?
(Other than that it is 99% accurate)?
Did anyone
tamper with it?
#21, #32, #93
#21, #32, #93

63. 63
9/8/2021

64. In conclusion…
Situation
Competence
3 R’s
On Ramps
1. Platform & ecosystem competition for data and AI workloads
2. However, AI is hard; many capabilities 2-4 decades away
3. Industry in open source collaboration-competition mode
1. Read: Learn state-of-art
2. Redo: Apply and infuse in use cases/workloads
3. Report: Share back, others may improve
1. LF AI Landscape: Community projects
2. IBM CODAIT: Cloud Pak for Data (CPD), etc. – Enterprise workloads with Trusted AI
3. Red Hat ODH: OpenShift – Hybrid cloud platform and ecosystem