Ntegra 20180523 v10 copy.pptx

Jim Spohrer (IBM)
For Ntegrea, Stanford Park Hotel, Palo Alto, CA, Wed July 23, 2018
http://www.slideshare.net/spohrer/UIDP_20180419_V6
July 23, 2018 1
Future of AI:
Measuring Progress and Preparing

IBM Research, Patents, Data, Cognitive
July 23, 2018
© IBM UPWard 2016
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July 23, 2018 IBM #OpenTechAI 3

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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…

Future of AI
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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?

3/24/2023 Understanding Cognitive Systems 6

IBM-MIT $240M over 10 year AI
mission

GitHub: >50K IBMers have accounts
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Technical Eminence: Explore, Read, Pull Request, Contributor, Committer, Governance

Kaggle: How many know about
leaderboards?
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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?

Future of AI
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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.

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
12
3/24/2023 (c) IBM 2017, Cognitive Opentech Group
2080
2040
2000
1960
$1K
$1M
$1B
$1T
2060
2020
1980
+/- 10 years
$1
Person Average
Annual Salary
(Living Income)
Super Computer
Cost
Mainframe Cost
Smartphone Cost
T
P
E
T P E
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

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

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 Summarizatio
n
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 ->

Leaders and Leaderboards:
Who is winning
• How to Measure Leadership?
– Publications or Patents
– Regions China vs USA vs EU vs ROW
– Companies Microsoft vs Google vs IBM
• How to Measure Progress?
– One capability or all leaderboards?
– SQuAD – Question Answering
– EFF Measuring AI Progress

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
https://maliciousaireport.com/
https://www.wsj.com/articles/automation-makes-us-dumb-1416589342

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
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups
• Regional
Governments:
– Cities
– States
– Nations

“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

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…

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

Prepare for AI Future
• Do you have a GitHub account?
– Yes: proceed
– No: sign up
• Do you program?
– Yes: Learn and do 3 R’s (read, redo, report)
– No: Learn to read and execute code (cheat T2T)
• Do you have favorite AI leaderboards?
– Yes: Learn and do 3 R’s
– No: Find a mentor with favorites, do together
• AI prepared = Favorites you can 3 R together
• … until you find the one favorite that can do them all
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accelerating regional development
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Programs Worldwide accelerating regional
development
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I have…
Have you noticed how the building
blocks just keep getting better?

Learning to program:
My first program
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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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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

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

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

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1955 1975 1995 2015 2035 2055
Better Building Blocks

Build: 10 million minutes of experience

Build: 2 million minutes of experience

Build: Hardware < Software < Data < Experience

Types: Progression of models and
capabilities
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

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Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
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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/

In Sum: Prepare by…
• Participating
– Opentech AI (GitHub)
– Leaderboard Challenges (Kaggle)
• Making
– Smartphone apps become low-cost digital workers
(expertise economy)
– Return of mini-local factories and farms (manufacturing
and agricultural economy)
• Learning
– Service science and knowledge science study the evolution
– As well as influence the evolution of industries and
professions
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In Summary
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“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

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?

Robots by Country
• Industrial
robots per
10,000 people
by country
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Leaderboard - rankings
• Korea leads

Learderboard – rankings 2
• China is below world average

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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Explain external
phenomena

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Explain internal
phenomena

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Explain
value co-creation
phenomena

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

OpenTechAI on GitHub (April 29,
2018)
Code Stars (K) Forks (K)
Google TensorFlow 97 62
Keras-team Keras 28 10
Sci-kit Sci-kit 27 13
BVLC Caffe 23 14
PyTorch PyTorch 14 3
Apache MXNet 13 5
Caffe2 Caffe2 7 1
Baidu PaddlePaddle 6 1
Google Tensor2Tensor 3 0.8
Tencent ANGEL 3 0.8

OpenTechAI on GitHub (April 29,
2018)
Code Stars (K) Forks (K)
Tencent NCNN 3 0.8
Google Kubeflow 3 0.3
Microsoft ONNX 3 0.3
Google TensorBoard 1 0.3
Apache SystemML 0.6 0.2
IBM (watson-intu) INTU (10 Repositories) 0.1 0.07
IBM FfDL 0.1 0.04
IBM ART 0.1 0.03
IBM MAX 0.005 0.017
LFDL AT&T Acumos (35 Repositories) 0.01 0

Headlines
• 2017 Popular
– “AI vs People”
– “X-Y team up to invest big in AI”
• 2025 Commonplace
– “People using AI to become better at their
professions, serving others.”
– “Teenagers using AI to solve challenges,
and improve their communities.”
• 2085 Resilience
– “Teams competing to rapidly rebuild
socio-economic-technical systems (wise
service systems) from scratch”
– “U.N. Pluto-base makes major discovery
about nature of universe. U.F.P.
established.”
IEEE 2017

Majority of IBM offerings
incorporate open technologies
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OPEN CONTAINER
PROJECT
At IBM, employees can win awards for both patents and open source contributions.

News
• CODAIT
• SnapML
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Learning:
Open Innovation Learning (OIL)
By David Ing
• Ing D (2018) Open Innovation Learning:
Theory building on open sourcing while
private sourcing. Foreword by Jim Spohrer.
Publisher: Toronto Canada: Coevolving
Innovations Inc. Publishing.
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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 to IA Timeline: Hard unsolved AI
problems
• 2012-2017 AI Pattern Recognition and
Learning from Massive Labeled Data
– Speech, image, translation, driverless, games
– Chatbots as digital assistants
• 2018 Video Understanding
• 2021 Episodic Memory
• 2022 Learning from Watching
• 2024 Commonsense Reasoning
• 2026 Learning from Reading
• 2028 Learning from Doing
• 2030 Fluent Conversation
• 2031-2039 Cognitive Collaborator and
Mediator; Intelligence Augmentation (IA)

Jim Spohrer (IBM)
For C Mohan
Wednesday February 21, 2018
http://www.slideshare.net/spohrer/nsf-20180124-v18
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The Future of AI:
Measuring Progress and Preparing

Jim Spohrer & Opentech AI group?
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IBM Products and Services to Customers
IBM Research
(Patents Science
Eminence &
Differentiation)
Open Technologies
(Open Source
Technical Eminence &
Industry Standards)
Cognitive Opentech Group (COG) = Open Source AI + Data

Measuring AI Progress
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What exists in 2016?
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360,000 100,000 120,000 60,000 150,000

User Models
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$5M Prize
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“AI will change the world?
Who will change AI?”

“These amazing technologies must be
able to help people like myself…”

Good science = Reproducibility

In Conclusion
• Hundreds of AI Challenge Leaderboards
exist and the number is growing
• Can one system be built to do them all?
See: https://www.youtube.com/watch?v=8FpdEmySsuc

What types of digital cognitive
systems?
• Cognitive Build: Outthink Challenge (250K people)
– Imagine a digital cognitive system to help you do
something important in your personal or professional
lives
– Team to design it and advocate for it, and then
everyone votes
– Winners: reduce waste and human suffering, screen
for health issues and safety threats, learn life skills and
make better choices, find what you are looking for,
move around more effectively, provide emotional
support, provide IT support, learn about important
public policy goals and make better choices
• Types: Tool, Assistant, Collaborator, Coach, Mediator

Types
• Tool
• Assistant
•
Collaborat
or
• Coach
• Mediator

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Machine
Learning
Natural
Language
Processing
High
Performance
Computing
Knowledge
Representation
and Reasoning
Question
Answering
Unstructured
Information

Brief History
of AI
• 1956 – Dartmouth Conference
• 1956 – 1981 Micro-Worlds
• 1981 – Japanese 5th Generation
• 1988 – Expert Systems Peak
• 1990 – AI Winter
• 1997 – Deep Blue
• 1997 – 2011 Real-World
• 2011 – Jeopardy! & SIRI
• 2013 – Cognitive Systems Institute
• 2014 – Watson Business Unit &
• True North Brain Chip
• 2015 – “Cognition as a Service”
on IBM Bluemix
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Definitions: AI vs IA
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AI is Artificial Intelligence, or
intelligence in machines (smart machines)
IA is Intelligence Augmentation, or
people thinking and working together with smart machines.
IA is what IBM calls “Cognitive Computing” and
the smart machines are called “Watson Solutions” or
more generally “Digital Cognitive Systems (Cogs)”
Cognition as a Service (CaaS):
AI building blocks for IA solutions

Augmenting Workers

Startup
Companies

Societal Grand Challenges
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Solving these would mean we are smarter,
but would they mean we are wiser?

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High School Senior: Anish Krishnan
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IBM in Silicon Valley:
From Punch Cards….
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On August 22, 1943, 105 men, women and children,
among them 43 IBM employees, alighted from a special
train that carried them across the continent to establish
new homes and the new IBM Card Manufacturing Plant
Number 5 at 16th and St. John Streets, San Jose, CA.

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Computing: Then, Now, Projected
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2035
2055

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What types of digital cognitive
systems?
• Cognitive Build: Outthink Challenge (250K people)
– Imagine a digital cognitive system to help you do
something important in your personal or professional
lives
– Team to design it and advocate for it, and then
everyone votes
– Winners: reduce waste and human suffering, screen
for health issues and safety threats, learn life skills and
make better choices, find what you are looking for,
move around more effectively, provide emotional
support, provide IT support, learn about important
public policy goals and make better choices
• Types: Tool, Assistant, Collaborator, Coach, Mediator

Partnership for AI formed

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How fast is Reality 2.0 approaching?
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What might it look like?

Reality 2.0 Service Platform:
polite cognitive mediators (CM1,CM2)
do not interrupt people (P1,P2)
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Service Platform
CM2
P2
CM1
P1
100x
100x 100x

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Wise Service System:
All entities’ in network
use cognitive mediators
to enhance
value co-creation interactions
Cognitive Mediators:
Cognitive systems
with deep knowledge of both
customer (user) and provider (expert)
as co-creators of win-win value
Entity augmentation boosts both creativity and productivity of interactions

Two assertions
• Machines seem to be getting smarter fast.
– Agree/disagree?
• People do not seem to be getting wiser fast.
– Agree/disagree?
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Homo Sapiens means Wise Man: True/False?

Yesterday: Servitization
• Rolls Royce: “Power By The Hour”

Tomorrow: Servitization
• Start with any traditional product that is sold to customers
• Make the product part of a smart/wise service system
– Instrument it (sensors) – Internet of Things/Everything
– Set-up an intelligent operation center to monitor all products’
performance across their life-cycles
– Use big data analytics to determine how to improve product
performance, efficiency, maintenance, etc.
– Offer customer the “product-performance-as-a-service” with
financing/Internet of Service
– Customer benefits from cost-savings, predictability
– Provider benefits margin-improvements, predictability
• Every product becomes a platform technology (a vehicle for
service innovation) for innovative university startups

Vision: MMaaRRSS
• Modular Manufacturing as a Regional
Recirculation Service System
– “I am the stuff that will be made into product X for
customer Y.”
– Stuff = Material, Energy, and Information Flows
– Minimize transport costs (for products and waste)
• The Vision: Circular Economy (~4 minutes)

In Summary
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“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

Backup Slides
• Understanding Cognitive Systems

What is a cognitive system (entity)?

What is a digital cognitive system
(entity)?

Backup slides
• Service systems - http://service-
science.info/archives/3368

Assisting individuals and organizations
to close their service innovation skills gap
and co-create wiser service systems
empowering employees, customers, citizens
with cognitive mediators
in the collaborative service economy

What is service science?
• IBM initiated effort to establish a
multidisciplinary field to study
service systems … with a focus on
people-centered, IT-enabled service
innovations for business and society
– based on service-dominant logic
– service = value co-creation
– IT-enabled service architectures
– service systems (socio-technical
systems for win-win value co-creation)
• IBM helped establish
– computer science (1945-present)
– service science (2005-present)
Service systems are dynamic configurations of
resources (people, technology, organizations,
and information) interconnected by
value propositions, internally and externally.
Examples:
- macro: cities, states, nations
- meso: hospitals, universities, businesses
- micro: households, families, individuals
Reference:
Spohrer J, Maglio P, Bailey J, Gruhl D (2007)
Steps toward a science of service systems.
IEEE Computer Society. 40(3):71-77(January).

What is service science?
• Now over 500 universities globally teach a more
multidisciplinary approach to service innovation,
including:
– Service management and marketing
– Service engineering and operations
– Service design and arts
– Service public policy and economics
– Service computing and informatics
• SSME + DAPP =
Service Science Management Engineering +
Design Arts Public Policy
– People, technology, organizations, information
interconnected by value propositions.
Reference:
IfM & IBM (2008). Succeeding through service
innovation: A service perspective for education,
research, business and government.
University of Cambridge Institute for Manufacturing,
Cambridge, UK. 2008.

How to get involved?
• Weekly speaker series
– Service innovation
– Service education & research
– Smart service/cognitive systems
• Discovery summits & book series
• Opportunities
– Institutional memberships
– Leadership & ambassadors
– Volunteer opportunities
– Awards & sponsored
conferences
ISSIP.org is a non-profit society
International Society of Service Innovation Professionals
Membership:
Over 1000 professionals and students from 40+ countries,
50+ companies and 50+ universities.

How to get involved?
• Journals (INFORMS,
etc.)
• Conferences (HICSS,
etc.)
• Courses (MIT, etc.)
• Funding (NSF, etc.)
• Society (ISSIP, etc.)

What are the hot topics?
• Smart Service Systems: Intelligence Augmentation
– AI + AR UX (Artificial Intelligence + Augmented Reality User
Experience)
– Smartphones (mobile, social, secure, etc.)
• Collaborative Economy: Servitization
– From assets to co-creation (e.g., Uber, AirBnB, etc.)
– From product to capability/outcome-as-a-service
– Manufacturing as a local recycling service
• Digital Transformation: Trust and Identity
– Blockchain: Don Tapscott’s TED Talk & book
– Big Data: Service Analytics & HAT (Hub of All Things)

Backups
• T-shaped people

Sciences provide…
• Frameworks for people to ask and answer
questions systematically
• Explanations with instructions on “how to re-do”
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Kline: Conceptual Foundation of Multidisciplinary Thinking -
“To our children and children’s children,
to whom we elders owe an explanation of the world
that is understandable, realistic, forward-looking, and whole.”
Proenneke:
Alone in the Wilderness -
To do a thorough testing,
should each generation
be required to rapidly rebuild
from scratch?
A re-makers movement?

Some paths to becoming 64x smarter:
Improving learning and performance
• 2x from Learning sciences (methods)
– Better models of concepts
– Better models of learners
• 2x from Learning technology (tools)
– Guided learning paths
– Elimination of “thrashing”
• 2x from Quantity effect (overlaps)
– More you know, faster you go
– Advanced organizers
• 2x from Lifelong learning (time)
– Longer lives and longer careers
– Keeps “learning-mode” activated
• 2x from Early learning (time)
– Start earlier: Challenged-based approach
– STEM-2D in K-12 (SSME+DAPP Design of Smart Service Systems)
• 2x from Cognitive systems (performance support)
– Technology & Infrastructure Interactions
– Organizations & Others Interactions

The Maker Movement &
Open Source Ecology
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development
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3/24/2023
development
122
Future of Skills

3/24/2023
development
124
Future of Skills

Next Generation:
Future-Ready T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline

By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
3/24/2023 127
Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts

IBM Cloud Bluemix:
Watson APIs are growing…
3/24/2023
© IBM UPWard 2016
128
So far (June 2016), 100,000 faculty and students globally given access

You say you want a
revolution?
October 8, 2017
https://www.slideshare.net/spohrer/revolution-20171008-v23
3/24/2023 (c) IBM 2017, Cognitive Opentech Group
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