The document discusses the future of AI, including how AI has progressed over time from early systems like Deep Blue and Watson to current advances in deep learning for pattern recognition, but that commonsense reasoning will still take many more years of research. It outlines a timeline for solving different AI problems based on leaderboards and benchmarks, and discusses implications for stakeholders in preparing for both the benefits and risks of advancing AI technologies.
1. The Future of AI
Jim from IBM (Jim Spohrer)
Director, Measuring AI Progress Cognitive Opentech Group (MAP COG)
See Center for Opensource Data and AI Technologies (CODAIT), http://codait.org
IBM Silicon Valley Lab, San Jose, CA, USA, January 31, 2019
https://www.slideshare.net/spohrer/future-20190131-v1
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8. Today’s talk
• Introduction
• AI at IBM: Past, Present, Future (Summary)
• Types of Systems
• AI at the peak of the hype cycle
• What’s really going on?
• Your data is becoming your AI… IA transformation
• Part 1: Solving AI: Leaderboards
• Roadmap and implications
• Open technologies, innovation
• Part 2: Solving IA: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
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9. AI at IBM: Past (Nathan Rochester)
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12. Smartphones pass entrance exams? When?
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… when will
your smartphone
be able to take and
pass any online
course? And then
be your coach, so
you can pass too?
13. IBM-MIT $240M
over 10 year AI mission
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17. 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)
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18. Questions
• What is the timeline for solving AI and IA?
• 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?
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20. 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
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2080204020001960
$1K
$1M
$1B
$1T
206020201980
+/- 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
21. Timeline: GDP/Employee
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(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
22. Timeline: Leaderboards FrameworkAI 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)
2015 2018 2021 2024 2027 2030 2033 2036
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Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
23. Who is winning
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https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/
24. Robots by Country
• Industrial robots per 10,000 people by country
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34
27. 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
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28. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
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29. 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.)
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30. Stakeholders = service system entities
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups and
Professional Associations
• Regional
Governments:
• Cities
• States
• Nations
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“there is nothing as practical as a good abstraction” -> service science studies service system entities
31. “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
32. 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.
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33. 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.
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34. 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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36. Be Prepared
• Understand open AI code + data +
models + stacks + communities
• Leaderboards
• Ethical conduct
• Learn 3 R’s of IBM’s Cognitive
Opentech Group (COG)
• Read arXiv
• Redo with Github
• Report with Jupyter notebooks on DSX
and/or leaderboards
• Improve your team’s skills of rapidly
rebuilding from scratch
• Build your open code eminence
• Understand open innovation
• Communities + Leaderboards
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1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…
39. Cupertino Teens
• IBM Watson on Bluemix
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AI for NLP
entity identification
40. 10 million minutes of experience
1/31/2019 Understanding Cognitive Systems 40
41. 2 million minutes of experience
1/31/2019 Understanding Cognitive Systems 41
42. Hardware < Software < Data < Experience < Transformation
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Value migrates to transformation – becoming our future selves; people, businesses, nations = service system entities
Pine & Gilmore (1999)
Transformation
Roy et al (2006)
Data
Osati (2014)
Experience
Life Log
43. Courses
• 2015
• “How to build a cognitive system for Q&A task.”
• 9 months to 40% question answering accuracy
• 1-2 years for 90% accuracy, which questions to reject
• 2025
• “How to use a cognitive system to be a better
professional X.”
• Tools to build a student level Q&A from textbook in 1
week
• 2035
• “How to use your cognitive mediator to build a
startup.”
• Tools to build faculty level Q&A for textbook in one day
• Cognitive mediator knows a person better than they
know themselves
• 2055
• “How to manage your workforce of digital workers.”
• Most people have 100 digital workers.
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Take free online cognitive classes today at cognitiveclass.ai
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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
59. Step Comment
GitHub Get an account and read the guide
Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook)
Kaggle Compete in a Kaggle competition
Leaderboards Compete to advance AI progress
Figure Eight Generate a set of labeled data (also Mechanical Turk)
Design New Challenges build an AI system that can take and pass any online course, then
switch to tutor-mode and help you pass
Open Source Guide Establish open source culture in your organization
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61. Trust: Two Communities
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Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?
62. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
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International Society of Service Innovation
Professionals.
Membership based non-profit professional association
promoting people-centered smart service systems
Fostering professional thought leadership of members
through joint conferences, workshops, publications,
members mentorship, and awards globally
Catalyzing and elevating industry-academia-
government collaboration in cutting edge research,
best industry practices, innovative educational
models, and policy influencing
Join us: www.issip.org
Members: 1200
+
~200
universities
50
+
companies
42
+
countries
Founders:
68. Our data is AI
• What do companies that profit from AI owe us?
• What do nations that profit from AI owe us?
• What do service systems entities owe service system entities?
• What value propositions and governance mechanisms connect us?
• Henry Ford: “My employees are my future customers, I should
therefore pay employees well today, so my customers pay me well
tomorrow.”
• Irene Ng: ”Your data is your future AI, we should therefore create a
market for your data today (with the help of HATDEX/AI), so your AI
will pay you well tomorrow.”
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69. Ruskin, Unto this last… five great service professions
Gandhi’s transformation into Gandhi
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so that on him falls, in great part, the responsibility for the kind of life they lead;
The lawyer, rather than countenance Injustice…
70. By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
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Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts
80. Computer Science as Empirical Inquiry:
Symbols and Search
• "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…
A physical symbol system is a machine that produces through time an evolving
collection of symbol structures. Such a system exists in a world of objects wider
than just these symbolic expressions themselves. ”
• 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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81. 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.1/31/2019 (c) IBM MAP COG .| 81
82. 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,
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83. Service Science the study of service system entities
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Year Publication Service Science Service System
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
84. Service Science the study of service system entities
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Year Publication Service Science Service System
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.
Service science is an emerging
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.
85. Service Science the study of service system entities
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Year Publication Service Science Service System
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
Service science is an emerging
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.
86. Service Science the study of service system entities
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Year Publication Service Science Service System
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.
88. 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.
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89. Disciplines and some of the key entities they study
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Computer Science: Physical Symbol System Entities
AI: Digital Cognitive System Entities
Chemistry: Auto-Catalytic Molecular System Entities
Biology: Biological Cognitive System Entities
Service science: 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.
90. 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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