20210309 jim spohrer future ai v8

Future of AI and Business Value:
A Service Science Perspective
Jim Spohrer
Director, IBM Cognitive OpenTech
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 Prof. Peder Inge Furseth for invitation to present!

Jim Spohrer, IBM Director, Cognitive OpenTech
Jim Spohrer directs 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 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 as LF AI Technical
Advisory Board Chairperson and ONNX Steering Committee
Member (2020-2021).
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© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
3
Physics Chemistry Biology
Neuroscience Psychology Artificial
Intelligence
Engineering Management Public
Policy
Education Design Humanities
Natural Systems (MIT)
Cognitive Systems (Yale)
Service Systems (IBM)

IBM’s Service Journey: A Summary Sketch
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Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.

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Trust: Two Communities
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Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation
Responsible Entity Collaborators
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?

Today’s Talk:
• Title: Future of AI and IA: A Service Science
Perspective
Abstract: The 2020 pandemic is accelerating the
digital (information technologies) transformation of
society, including online working, learning, playing
and belonging. The future of Artificial Intelligence
(AI) will bring even greater acceleration and
transformations, including Intelligence Augmentation
(IA). Service science predicts that in this
transformation of business and society that
competing for collaborators will increasingly shape
value co-creation interactions and capability co-
elevation outcomes between entities in the coming
decades. A decade-based (2020-2080) view of IT, AI,
IA< society, and service science is provided.
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Consumers and society at large are expecting
more from business. Embracing those
responsibilities can be good for shareholders, too.

Outline
1. Pandemic perspective - accelerating digital transformation – platform society and T-shaped (l)earners.
2. Service science perspective – transformation (collaborate with people/responsible entities)
3. Artificial intelligence perspective – automation (collaborate with machines)
4. Intelligence Augmentation perspective - transformation and automation (collaboration with people and machines,
and the organizations, AKA responsible entities, that produce the machines)
5. Measurement question: How do we measure socio-technical extension factors? Augmented physical, perceptual,
cognitive, and social capabilities.
6. Philosophical question: What do we do ourselves and what can we safely delegate in win-win games? Build vs buy.

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

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

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.

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.

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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

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

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
15
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)

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
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Service Science: Conceptual Framework
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Service Science

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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)

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?

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
20
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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

Timeline: GDP/Employee
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

Timeline: Leaderboards Framework
AI Progress on Open Leaderboards - Benchmark Roadmap
Perceive World Develop Cognition Build Relationships Fill Roles
Pattern
recognition
Video
understanding
Memory Reasoning Social
interactions
Fluent
conversation
Assistant &
Collaborator
Coach &
Mediator
Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions
Chime Thumos SQuAD SAT ROC Story ConvAI
Images Context Episodic Induction Plans Intentions Summarization Values
ImageNet VQA DSTC RALI General-AI
Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation
WMT DeepVideo Alexa Prize ICCMA AT
Learning from Labeled Training Data and Searching (Optimization)
Learning by Watching and Reading (Education)
Learning by Doing and being Responsible (Exploration)
2018 2021 2024 2027 2030 2033 2036 2039
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
+3
See: https://paperswithcode.com/sota

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

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

Sweden
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Economic Growth Rates 2035: AI Projected Impact
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AI Benefits
• Access to expertise
• “Insanely great” labor productivity for trusted service providers
• Digital workers for healthcare, education, finance, etc.
• Better choices
• ”Insanely great” collaborations with others on what matters most
• AI for IA = Augmented Intelligence and higher value co-creation interactions

AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors

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

“The best way to predict the future is to inspire the
next generation of students to build it better”
Digital Natives Transportation Water Manufacturing
Energy Construction ICT Retail
Finance Healthcare Education Government

Artificial Leaf
• Daniel Nocera, a professor of energy
science at Harvard who pioneered the
use of artificial photosynthesis, says that
he and his colleague Pamela Silver have
devised a system that completes the
process of making liquid fuel from
sunlight, carbon dioxide, and water. And
they’ve done it at an efficiency of 10
percent, using pure carbon dioxide—in
other words, one-tenth of the energy in
sunlight is captured and turned into fuel.
That is much higher than natural
photosynthesis, which converts about 1
percent of solar energy into the
carbohydrates used by plants, and it
could be a milestone in the shift away
from fossil fuels. The new system is
described in a new paper in Science.

Food from Air
• Although the technology is in its infancy,
researchers hope the "protein reactor"
could become a household item.
• Juha-Pekka Pitkänen, a scientist at VTT,
said: "In practice, all the raw materials
are available from the air. In the future,
the technology can be transported to,
for instance, deserts and other areas
facing famine.
• "One possible alternative is a home
reactor, a type of domestic appliance
that the consumer can use to produce
the needed protein."
• According to the researchers, the
process of creating food from electricity
can be nearly 10 times as energy
efficient as photosynthesis, the process
used by plants.

Exoskeletons for Elderly
• A walker is a “very cost-effective”
solution for people with limited
mobility, but “it completely
disempowers, removes dignity,
removes freedom, and causes a
whole host of other psychological
problems,” SRI Ventures president
Manish Kothari says. “Superflex’s
goal is to remove all of those areas
that cause psychological-type
encumbrances and, ultimately,
redignify the individual."

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1955 1975 1995 2015 2035 2055
Better Building Blocks

Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
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Dedicated to Douglas E. Engelbart, Inventor
The Mouse (Pointing Device)
The Mother of All Demos
Bootstrapping Practice/Augmentation Theory
Note: Bush (1945) and Licklider (1960) created funding programs that benefitted Engelbart in building working systems.

IA as Socio-Technical Extension Factor on Capabilities & Values
IA (human values) is not AI (technology capability)
Difference 1: IA leads to more capable people even when scaffold removed
Difference 2: IA leads to more responsible people to use wisely the capabilities
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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)

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

Bigger IA Trend in Human Time Usage & Skills
As smartphone apps grow up and people have 100 digital workers “earning” for them (owners) on platforms
• Hunter Gathers – local sourcing,
generalist
• Agriculture – local sourcing,
generalist – cities specialists
• Manufacturing – outsourcing to
production business, specialists
• Clothing to Shopping
• Service (pre-AI) – outsourcing to
service businesses, specialist
• Cooking to Restaurants
• Service (post-AI &
miniaturization) – insourcing, T-
shapes
• T-shaped (l)earners in platform
society, home again
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Spohrer & Maglio (2006) SSME, Slide #42
Spohrer (2020) Platform Economy
and Shift in Work

References
• Araya D (2018) Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28.
• Bush V (1945) As we may think. The Atlantic Monthly. 1945 Jul 1;176(1):101-8.
• Engelbart D (1962) Augmenting human intellect. Summary report AFOSR-3223 under Contract AF. 1962 Oct;49(638):1024.
• 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
• Kay A, Jobs S (1984) Wheels for the Mind. Apple Computer.
• Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press; 1995.
• Licklider JC (1960) . Man-computer symbiosis. IRE transactions on human factors in electronics. 1960 Mar(1):4-11.
• Malone TW (2018) Superminds: The surprising power of people and computers thinking together. Little, Brown Spark; 2018 May 15.
• Norman D (1994) Things that make us smart: Defending human attributes in the age of the machine. Diversion Books; 2014 Dec 2.
• Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Feb 7:1-21.
• Siddike MA, Spohrer J, Demirkan H, Kohda Y (2018) A Framework of Enhanced Performance: People's Interactions With Cognitive Assistants. International Journal
of Systems and Service-Oriented Engineering (IJSSOE). 2018 Jul 1;8(3):1-7.
• Spohrer JC (1998) Information in places. IBM Systems Journal. 1999;38(4):602-28.
• Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy
of Sciences. 2004 May;1013(1):50-82.
• Spohrer J, Siddike (2018) The Future of Digital Cognitive Systems: Tool, Assistant, Collaborator, Coach, Mediator. In Ed. Araya D. Augmented Intelligence: Smart
Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28.
• Spohrer J (2020) Online Platform Economy and Gig Workers: A USA Perspective. Presentation.
• Spohrer J & Maglio PP (2006) Service Science Management and Engineering (SSME): An Emerging Discipline. IBM Presentation.
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Smartphones pass entrance exams? When?
… when will
your smartphone
be able to take and
pass any online
course? And then
be your coach, so
you can pass too?

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

Artificial Intelligence/
Computer Science
• "Computer science is the study of the phenomena surrounding computers. ... We
build computers and programs for many reasons. We build them to serve society
.... One of the fundamental contributions to knowledge of computer science has
been to explain, at a rather basic level, what symbols are. ... Symbols lie at the
root of intelligent action, which is, of course, the primary topic of artificial
intelligence. For that matter, it is a primary question for all of computer science.
For all information is processed by computers in the service of ends, and we
measure the intelligence of a system by its ability to achieve stated ends in the
face of variations, difficulties and complexities posed by the task environment.”
• Tenth Turing Awards Lecture: Allen Newell and Herbert A. Simon, “Computer
Science as Empirical Inquiry: Symbols and Search,”Communications of the ACM.
vol. 19, No. 3, pp. 113-126, March,1976. Available online at:
• https://www.cs.utexas.edu/~kuipers/readings/Newell+Simon-cacm-76.pdf
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IBM-MIT $240M
over 10 year AI mission

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45

IBM Quantum
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“Teddy Bear”
Meret Oppenheim, Le Déjeuner en fourrure

Narrow AI
Emerging
Broad AI
Disruptive and
Pervasive
General AI
Revolutionary
▼ We are here 2050 and beyond 49
IBM Research AI © 2018 IBM Corporation
The evolution of AI
Borrowed from David Cox, IBM-MIT Lead

Timeline: Short History
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© IBM Cognitive Opentech Group (COG)
50
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.

51
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
Larges Super Computer in the World,
= 13 MegaWatts of Power (HOT!)

52
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!)

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53
I have…
Have you noticed how the building blocks just
keep getting better?

Learning to program:
My first program
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54
Early Computer Science Class:
Watson Center at Columbia 1945
Jim Spohrer’s
First Program 1972

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© IBM UPWard 2016
55
Fast Forward 2016:
Consider this…

Microsoft CaptionBot June 19, 2016
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© IBM UPWard 2016
56

Microsoft CaptionBot June 20, 2016
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© IBM UPWard 2016
57

IBM Image Tagging
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© IBM UPWard 2016
58

Today: November 10, 2017
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© IBM DBG COG 2017
59
IBM

10 million minutes of experience
3/10/2021 Understanding Cognitive Systems 60

2 million minutes of experience
3/10/2021 Understanding Cognitive Systems 61

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62
Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
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Watson Discovery Advisor
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64
Simonite, T. 2014. Software Mines Science Papers to Make New Discoveries. MIT. November 25, 2014.
URL: http://m.technologyreview.com/news/520461/software-mines-science-papers-to-make-new-discoveries/

Outline
• Context
• Disciplines (and the entities they study)
• Computer Science, AI, SD logic, Service Science
• Part 1: AI
• Seven Questions
• Better Building Blocks
• Your data is becoming your AI… transformation
• Part 2: Service Science
• Covid accelerating AI, Robotics adoption
• Open Technologies: From Smarter to Wiser
• Access Rights: Trust and Responsibility
“there is nothing as practical as a good abstraction.”

Icons of AI Progress
• 1956: Dartmouth Conference
organized by:
• John McCarthy (Dartmouth, later
Stanford)
• Marvin Minsky (MIT)
• and two senior scientists:
• Claude Shannon (Bell Labs)
• Nathan Rochester (IBM)
• 1997: Deep Blue (IBM) - Chess
• 2011: Watson Jeopardy! (IBM)
• 2016: AlphaGo (Google DeepMinds)

AI at IBM: Past (Nathan Rochester)
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Disciplines and some of the key entities they study
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Computer Science: Hardware, Software, Algorithms
Physical Symbol System Entities
AI: Intelligence, “NN Models”
Digital Cognitive System Entities
Chemistry: Atoms, Molecules, States of Matter,
Auto-Catalytic Molecular System Entities
Biology: Cells, DNA,
Biological Cognitive System Entities
Service science: Service, Value Co-Creation, Service system entities
Service science studies the evolving ecology
of service system entities,
their capabilities, constraints, rights, and responsibilities
their value co-creation and
capability co-elevation interactions, as well as
their outcome identities and reputations.

Brian Arthur - Economist
• 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.
3/10/2021 IBM #OpenTechAI 69
Arthur WB (2017) Where is technology taking the economy. McKinsey Quarterly. October.

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https://www.youtube.com/watch?v=WGK8MY8iZHA

Service-Dominant logic worldview and mindset
Year Publication Service Resource Integrators
2004 Vargo SL, Lusch RF (2004)
Evolving to a new dominant
logic for marketing. Journal of
marketing. 68(1):1-7.
The application of specialized skills
and knowledge is the fundamental
unit of exchange.
Operant resources are resources that
produce effects
2011 Vargo SL, Lusch RF (2011) It's
all B2B… and beyond: Toward
a systems perspective of the
market. Industrial marketing
management. 40(2):181-7.
The central concept in S-D logic is
that service — the application of
resources for the benefit of another
party — is exchanged for service
That is, all parties (e.g. businesses,
individual customers, households, etc.)
engaged in economic exchange are
similarly, resource-integrating, service-
providing enterprises that have the
common purpose of value (co)creation —
what we mean by “it is all B2B.”
2016 Vargo SL, Lusch RF.
Institutions and axioms: an
extension and update of
service-dominant logic.
Journal of the Academy of
Marketing Science. 2016 Jan
1;44(1):5-23.
value creation can only be fully
understood in terms of integrated
resources applied for another
actor’s benefit (service) within a
context, including the institutions
and institutional arrangements that
enable and constrain value creation.
To alleviate this limitation and facilitate a
better understanding of cooperation (and
coordination), an eleventh foundational
premise (fifth axiom) is introduced, focusing
on the role of institutions and institutional
arrangements in systems of value
cocreation: service ecosystems.
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Service Science the study of service systems entities
Year Publication Service Science Service System
2007 Spohrer J, Maglio, PP, Bailey J,
Gruhl, D (2007) Steps toward
a science of service
systems, IEEE Computer,
(40)1:71-77.
Services science is an emerging field
that seeks to tap into these and
other relevant bodies of knowledge,
integrate them, and advance three
goals—aiming ultimately to
understand service systems, how
they improve, and how they scale.
The components of a service system are
people, technology, internal and external
service systems connected by value
propositions, and shared information (such
as language, laws, and measures.
2008 Spohrer, J, Vargo S, Caswell N,
Maglio PP (2008) The service
system is the basic abstraction
of service science, HICSS-41,
NY: IEEE Press, Pp. 1-10.
Service science is the study of the
application of the resources of one
or more systems for the benefit of
another system in economic
exchange.
Informally, service systems are
collections of resources that can
create value with other service systems
through shared information.
2008 Maglio PP, Spohrer J (2008)
Fundamentals of service
science. Journal of the
academy of marketing
science. 36(1):18-20.
Service science is the study of
service systems, aiming to create a
basis for systematic service
innovation.
Service systems are value-co-creation
configurations of people, technology, value
propositions connecting internal and
external service systems, and shared
information (e.g., language, laws, measures,
and methods).
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Service Science the study of service system entities
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2009 Spohrer J, Maglio PP (2009)
Service science: Toward a
smarter planet. In
Introduction to service
engineering, Eds. Karwowski
and Salvendy. Pp. 3-10
Service science is a specialization of
systems science. So service science
seeks to create a body of knowledge
that accounts for value-cocreation
between entities as they interact…
Service system entities are dynamic
configurations of resources. As described
below, resources include people,
organizations, shared information, and
technology.
2012 Spohrer J, Piciocchi P, Bassano
C (2012) Three frameworks
for service research: exploring
multilevel governance in
nested, networked systems.
Service Science. 4(2):147-160.
SSME+D is built on top of the
Service-Dominant logic (SD Logic)
worldview
A service system entity is a dynamic
configuration of resources (at least one of
which, the focal resource, is a person with
rights).
2013 Spohrer J, Giuiusa A,
Demirkan H, Ing D (2013)
Service science: reframing
progress with universities.
Systems Research and
Behavioral Science. 30(5):561-
569
Service science is an emerging
branch of systems sciences with a
focus on service systems (entities)
and value cocreation (complex non-
zero-sum interactions).
… complex adaptive entities - service
systems - within an ecology of nested,
networked entities… From a service science
perspective, progress can be thought of in
terms of the rights and responsibilities of
entities

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2014 Spohrer J, Kwan SK, Fisk RP
(2014)Marketing: a service sci
ence and arts perspective,
Handbook of Service Market
ing Research, Eds. Rust RT,
Huang MH, NY:Edward Elgar,
pp. 489-526.
Service science (short for Service
Science, Management, Engineering,
Design, Arts, and Public Policy) is an
emerging transdiscipline for the (1)
study of evolving service system
entities and value co-creation
phenomena, as well as (2) pedagogy
for the education of 21st century T-
shaped service innovators from all
disciplines, sectors, and cultures.
So like all early stage scientific
communities, the language for talking
about service systems and value co-creation
phenomena continues to evolve. … Service
system entities are economic and social
actors, which configure (or integrate)
resources. … A formal service system entity
(SS-FSC3) is a legal, economic entity with
rights and responsibilities codified in
written laws.
2015 Spohrer J, Demirkan H,
Lyons (2015) Social Value: A
Service Science Perspective.
In: Kijima K. (eds) Service
Systems Science. Translational
Systems Sciences, vol 2.
Tokyo: Springer. Pp. 3-35.
transdiscipline for the (1) study of
evolving service system entities and
value co-creation phenomena and
(2) pedagogy for the education of
twenty-first-century T-shaped
service innovators from all
disciplines, sectors, and cultures
Formal service system entities (as opposed
to informal service system entities) can be
ranked by the degree to which they are
governed by written (symbolic) laws and
evolve to increase the percentage of their
processes that are explicit and symbolic.

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2016 Spohrer J (2016) Services
Science and Societal
Convergence. In W.S.
Bainbridge, M.C. Roco
(eds.),Handbook of Science
and Technology Convergence,
pp. 323-335
transdiscipline for the (1) study of
evolving ecology of nested,
networked service system entities
and value co-creation phenomena,
as well as (2) pedagogy for the
education of the twenty-first-
century T-shaped (depth and
breadth) service innovators from all
disciplines, sectors, and cultures.
As service science emerges, we can begin
by “seeing” and counting service system
entities in an evolving ecology, working to
“understand” and make explicit their
implicit processes of valuing …
2016 Spohrer J (2016) Innovation
for jobs with cognitive
assistants: A service science
perspective, In Disrupting
Unemployment ,
Eds. Nordfors, Cerf,
Seng, Missouri: Ewing Marion
Kauffman Foundation, Pp.
157-174.
Service science is the emerging
transdiscipline that studies the
evolving ecology of nested,
networked service system entities,
their capabilities, constraints, rights,
and responsibilities.
There are perhaps twenty billion formal
service system entities in the world today,
each governed in part by formal written
laws. Every person, household, university,
business, and government is a formal
service system entity, but my dog, my
smartphone, and my ideas are not.

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2017 Spohrer J, Siddike MAK,
Kohda Y (2017) Rebuilding
evolution: a service science
perspective. HICSS 50.
Service science is the study of the
evolving ecology of service system
entities, complex socio-technical
systems with rights and
responsibilities – such as people,
businesses, and nations.
Service systems are dynamic configurations
of people, technology, organization and
information that interact through value
proposition and co- create mutual value.
2019 Pakalla D, Spohrer J (2019,
forthcoming) Digital Service:
Technological Agency in
Service Systems. HICSS 52.
For the purposes of this paper,
service science can be summarized
as the study of the evolving ecology
of service system entities, their
capabilities, constraints, rights, and
responsibilities, including their
value co-creation and capability co-
elevation mechanisms .
Service systems are a type of socio-
technical system, such as people,
businesses, and nations, all with unique
identities, histories, and reputations based
on the outcomes of their interactions with
other entities.

Service Research
• Artificial Intelligence in Service
• "The theory specifies four intelligences required for service tasks—mechanical,
analytical, intuitive, and empathetic—and lays out the way firms should decide
between humans and machines for accomplishing those tasks.”
• Huang MH and Rust RT (2018) Artificial Intelligence in Service. Journal of
Service Research. 21(2):155–172.
• Customer Acceptance of AI in Service Encounters: Understanding
Antecedents and Consequences
• "expand the relevant set of antecedents beyond the established constructs and
theories to include variables that are particularly relevant for AI applications
such as privacy concerns, trust, and perceptions of “creepiness.”
• Ostrom AL, Foheringham D, Bitner MJ (2018, forthcoming) Customer
Acceptance of AI in Service Encounters: Understanding Antecedents and
Consequences. In Handbook of Service Science, Volume 2, Eds, Maglio,
Kieliszewski,Spohrer,Lyons,Patricio,Sawatani. New York: Springer. Pp. x-y.
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Microsoft acquiring GitHub $7.5B
2018 John Marks on Open Source
Models will run the world
Why SW is eating the world

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

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

IBM AI
Explain a transaction
Deployment: Claim Approval Model name: Claim Model
AI Fairness
360 toolkit
Trust and transparency
integral to AI on the IBM Cloud
Explainability, fairness, lineage
are critical principles of trusted AI
Open source toolkit
to check for unwanted bias in datasets
and machine learning models
© 2019 IBM Corporation 81
DENIED APPROVED
CONFIDENCE
90% 10%
POLICY HOLDER AGE: 18 RESPONSIBLE PARTY: Self
CAR BRAND: Oldsmobile Cutlass POLICE REPORT: Yes
CAR VALUE: $20,000 POLICY AGE: 5 Years
65% 17%
23% 13%
13% 5%
Factors contributing to a DENIED confidence level Factors contributing to an APPROVED confidence level

Is it fair?
Is it easy to
understand?
Is it accountable?
Did anyone
tamper with it?
FAIRNESS EXPLAINABILITY
ROBUSTNESS
LINEAGE
Our vision for Trusted AI
Pillars of trust, woven into the lifecycle of an AI application

Join: https://callforcode.org/
86
This multi-year global initiative rallies developers to create practical, effective, and high-quality applications based on cloud, data, and artificial
intelligence that can have an immediate and lasting impact on humanitarian issues. Call for Code brings startup, academic, and enterprise developers
together and inspires them to solve the most pressing societal issues of our time - for example, faster and more resilient recovery from natural disasters.

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

3/10/2021 (c) IBM MAP COG .| 88

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