IBM's Past and Future in AI: An Overview

AI at IBM: Past, Present, Future
Jim from IBM (Jim Spohrer)
Director, Measuring AI Progress Cognitive Opentech Group (MAP COG)
Center for Opensource Data and AI Technologies (CODAIT)
Northwestern Visit, Evanston, IL, USA, October 4, 2018
https://www.slideshare.net/spohrer/northwestern-20181004-v9
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AI at IBM: Past (Nathan Rochester)
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Center for Open Source
Data and AI Technologies
September 2018 / © 2018 IBM Corporation
Watson West Building
505 Howard St.
San Francisco, California
CODAIT aims to make AI solutions dramatically
easier to create, deploy, and manage in the
enterprise.
Relaunch of the IBM Spark Technology Center
(STC) to reflect expanded mission.
36 open source developers!
Improving Enterprise AI lifecycle in Open Source
Gather
Data
Analyze
Data
Machine
Learning
Deep
Learning
Deploy
Model
Maintain
Model
Python
Data Science
Stack
Fabric for
Deep Learning
(FfDL)
Mleap +
PFA
Scikit-LearnPandas
Apache
Spark
Apache
Spark
Jupyter
Model
Asset
eXchange
Keras +
Tensorflow
CODAIT
codait.org
3
The following slides from
Fred’s ApacheCon keynote

IBMers using
OSS
IBMers
contributing to
OSS
CODAIT
Active IBM Users of Open Source
(Certified to consume and/or contribute open source in 2018)
4September 2018 / © 2018 IBM Corporation
>62,000
>1,000

IBMers using
OSS
IBMers
contributing to
OSS
CODAIT
Active IBM Users of Open Source
(Certified to consume and/or contribute open source in 2018)
>62,000
>1,000

IBM builds open source
software for the enterprise.

https://en.wikipedia.org/wiki/Space_Shuttle_Enterprise

https://commons.wikimedia.org/wiki/File:Enterprise_monument_Vulcan_Alberta_2013.JPG
Author: Canoe1967; Creative Commons Attribution 3.0 Unported license.

en•ter•prise
An organization with an oddly
specific mission for its size.

By tofer618 from Washington, DC (IMG_0494) [CC BY 2.0
(https://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons
The National Aeronautics and Space
Administration
– 17,000-person government agency
– Mission:
• Managing projects…
• …that involve launching large
objects into space…
• …balanced atop a pillar of flame

https://commons.wikimedia.org/wiki/File:STS76_Atlantis_Launch.jpg
Public Domain
Aerojet Rocketdyne
Manufacturer of the Space Shuttle
main engine.
– 5000-person corporation
– Mission:
• Building motors…
• …that burn thousands of pounds of
fuel…
• …per second.

12September 2018 / © 2018 IBM Corporation https://www.ibm.com/case-studies/m255797t29717u03
OmniEarth
– 20-person startup (acquired by
EagleView in 2017)
– Mission:
• Processing, clarifying and fusing
large amounts of satellite and
aerial imagery with other data
sets
– OmniEarth used satellite imagery
to identify precisely which land
parcels needed to reduce water
consumption, and by how much

Watson Visual Recognition is…
IBM Watson / VIsual Recognition / © 2018 IBM Corporation
…an image recognition
service that enables users to
quickly and accurately tag,
classify, and train visual
content using machine
learning.
BASIL
LEAF
HERB
PLANT STEM
GREEN
What is Watson Visual Recognition?
…built on lots of open
source software!

IBM Code Model Asset
eXchange
Free, open-source deep learning models.
Wide variety of domains.
Multiple deep learning frameworks.
Vetted and tested code and IP.
Build and deploy a web service in 30 seconds.
Start training on Fabric for Deep Learning
(FfDL) in minutes.
See our
demo at the
IBM booth!
March 30 2018 / © 2018 IBM Corporation 14

Fast data analysis and transformation are the prerequisite of
ML/DL within the whole enterprise AI life cycle. Apache Spark
answers it.
15
Apache Spark™
A unified analytics engine for large-scale data
processing.
IBM contributions: over 1000 JIRAs, almost 60,000
lines of code, 4 committers.
Many IBM Cloud and Service products depend on or
distribute Apache Spark:
• IBM Analytics Engine
• IBM Apache Spark service
• IBM Spectrum Conductor
• Apache Spark on IBM POWER
• IBM Open Data Analytics for z/OS
• IBM Watson Studio
• IBM SQL Query
• IBM Watson Machine Learning
• IBM Db2 EventStore
• IBM Explorys ….. many more
Apache Spark Github page:
https://github.com/apache/s
park
IBM Related blogs:
https://developer.ibm.com/co
de/category/spark/
July 27 2018 / © 2018 IBM Corporation
Gather
Data
Analyze
Data
Machine
Learning
Deep
Learning
Deploy
Model
Maintain
Model
Python
Data Science
Stack
Fabric for
Deep Learning
(FfDL)
Mleap +
PFA
Scikit-LearnPandas
Apache
Spark
Apache
Spark
Jupyter
Model
Asset
eXchange
Keras +
Tensorflow

Oct 18 – IBM is back on campus
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IBM Global University Programs – access!
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Raise your
hand, if you
are >50%
sure you
know what
type of leaf
this is….
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October 3, 2018: Uploaded…
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Today’s talk
• AI at the peak of the hype cycle
• What’s really going on?
• Your data is becoming your AI… transformation
• Part 1: Solving AI
• Roadmap and implications
• Open technologies, innovation
• Part 2: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
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AI (Artificial Intelligence) is popular again… you see it mentioned on billboards in SF
However, pattern recognition does not equal AI
Deep learning works if you have lots of data and compute power
We finally have lots of data and compute power – hurray!!!
So finally, deep learning for pattern recognition is working pretty well
However, AI is more than deep learning for pattern recognition…
AI requires commonsense reasoning – that will take another 5-10 years of research
How do we know this? Look at the AI leaderboards – we will get to that…

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?

IBM-MIT $240M
over 10 year AI mission

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)

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?

Timeline: Short History
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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.

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

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 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
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->

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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211

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.)

Stakeholders = service system entities
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups and
Professional Associations
• Regional
Governments:
• Cities
• States
• Nations
“there is nothing as practical as a good abstraction” -> service science studies service system entities

“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."

Computing: Then, Now, Projected
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2035
2055

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© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
44

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
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…

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1955 1975 1995 2015 2035 2055
Better Building Blocks

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Cupertino Teens
• IBM Watson on Bluemix
AI for NLP
entity identification

10 million minutes of experience
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2 million minutes of experience

Hardware < Software < Data < Experience < Transformation
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

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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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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Fast Forward 2016:
Consider this…

Microsoft CaptionBot June 19, 2016
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56

Microsoft CaptionBot June 20, 2016
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IBM Image Tagging
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Today: November 10, 2017
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IBM

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60
Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
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61

Watson Discovery Advisor
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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/

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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

Types: Progression of models
Models = instruction set of future
Task & World Model/
Planning & Decisions
Self Model/
Capacity & Limits
User Model/
Episodic Memory
Institutions Model/
Trust & Social Acts
Tool + - - -
Assistant ++ + - -
Collaborator +++ ++ + -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
Cognitive
Tool
Cognitive
Assistant
Cognitive
Collaborator
Cognitive
Coach
Cognitive
Mediator

AI Fairness 360
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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

Prepare for AI Future
• Do you have a GitHub account? Get it.
• Yes: proceed
• No: sign up
• Do you program? Either OK, partnering is best.
• Yes: Learn and do 3 R’s (read, redo, report)
• Github master: Code, Content (Data), Community (IBM Code can help)
• No: Learn to read and execute code with partner (T2T)
• Do you have favorite AI leaderboards?
• Yes: Learn and do 3 R’s (read, redo, report advances)
• Kaggle master: Combine top decorrelated solution, new solution
• No: Find a mentor with favorites, do together
• Are you AI prepared? Do you know/do data, models, solutions?
• Yes: Find favorite leaderboards you can do 3 R’s for today
• Figure-Eight master: Labeled data that matters most
• No: Wait until one model: one model that can do them all
• Then rapidly rebuild in least time, energy (“zorch”), data, code
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1. Where do we get labeled data?
We create it: Figure Eight,
Mechanical Turk, etc.
2. External/internal challenge?
10M minutes from birth to adult
2M minutes from novice to expert
Not just external states, but
internal states are data as well…
The challenge of data for AI models
3. AI models as ”data” instruction set
Computer’s have instruction sets
Arithmetic, Logic, etc.
Models are becoming instructions
Models are data/experience

Trust: Two Communities
Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?

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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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-
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Join us: www.issip.org
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countries
Founders:

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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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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…

By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
10/4/2018 79
Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts

In Summary
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81
“A service science
perspective considers
the evolving ecology of
service system entities,
their value co-creation and
capability co-elevation
interactions, and their
capabilities, constraints,
rights, and responsibilities.”
Cognitive Systems
Entities
Service
Systems
Entities With
Cognitive
Mediators
Add Rights &
Responsibilities

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82
Physics Chemistry Biology
Neuroscience Psychology Artificial
Intelligence
Engineering Management Public
Policy
Education Design Humanities
Natural Systems
Cognitive Systems
Service Systems

What is a biological cognitive system (entity)?

What is a digital cognitive system (entity)?

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 computer 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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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.10/4/2018 (c) IBM MAP COG .| 86

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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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.

10/4/2018 (c) IBM MAP COG .| 90
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.

10/4/2018 (c) IBM MAP COG .| 91
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 Science: Conceptual Framework
10/4/2018 (c) IBM MAP COG .| 92

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.
10/4/2018 IBM #OpenTechAI 93

Disciplines and some of the key entities they study
10/4/2018 (c) IBM MAP COG .| 94
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.

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.
10/4/2018 (c) IBM MAP COG .| 95

Jim from IBM – 20 years today!
10/4/2018 (c) IBM MAP COG .| 96

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