Jaist 20180228 v4

Jim Spohrer (IBM)
JAIST World Conference Wednesday February 28, 2018
http://www.slideshare.net/spohrer/jaist-20180228-v4
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Preparing for Our Future
with Open Artificial Intelligence (AI):
A Service Science Perspective

Thank-you for invitation
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© IBM MAP COG2018
2
Thank-you
Prof. Youji Kodha
Md. Abul Kalam Siddike
… and green beetle car driver

Quantum Computer
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Four Books
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Past and Future
• Scientific progress increases our understanding of the past,
but what does it tell us about preparing for the future?
• What is our responsibility to future generations?
– Kline: “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.”
• References:
– Kline (1995) Conceptual Foundation of Multidisciplinary Thinking.
– Wright (2001) Non-Zero: The Logic of Human Destiny.
– Deacon (2011) Incomplete Nature: How Mind Emerged from Matter.
– Domingos (2015) The Master Algorithm: How the Quest for the
Ultimate Learning Machine will Remake Our World.
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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

SYSK Evolution
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AEIOU = Abstract Entity Interaction Outcome Universals

IBM’s Service Journey
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12
Henry Chesbrough at Berkeley sent some of us a paper…
Henry suggested IBM
“help to do it again;
you need it, others do too”
academics and government
to establish Service Science
The biggest costs were in changing the organization.
One way to think about these changes is to treat the
Organizational costs as an investment in a new asset.
Firms make investments over time in developing anew
process, rebuilding their staff or designing a new
organizational structure, and the benefits from these
Investments are realized over a long period of time.”
Eric Brynjolfsson, “Beyond the Productivity Paradox”

13
What disciplines to hire to be the best in service innovation?
The center balances three key factors
Technology & Information
Business
& Value
People
& Organizations
5
1
9 2527
14
28
10
26
24
8
4
1. Service Engineering
2. Service Operations
3. Service Management
4. Service Marketing
5. Social Complexity
6. Agent-based comput-
ational economics
7. Computational
Organization Theory
14. Computer &
Information Sciences
15. Management of
Innovation
16. Organization Theory
17. Operations Research
18. Systems Engineering
19. Management Science
20. Game Theory
21. Industrial Engineering
22. Marketing
23. Managerial
Psychology
236
7
11
12
13
15
16
17
18
19
20
21
22
23
1990-2004
1960-1990
1900-1960
Before 1900
8. Management of
Technology
9. Experimental
Economics
10. AI & Games
11. Management of
Information Systems
12. Computer Supported
Collab. Work (CSCW)
13. Human Capital
Management
24. Business
Administration (MBA)
25. Economics
26. Law
27. Sociology
28. Education

Service Science is just….
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Service Science
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Service Science
• IBM helped to establish Computer Science from 1945-1975
• IBM SSME (2002-present); Twelve concepts/ISPAR model
• References
– Spohrer, J., & Maglio, P. P. (2008). The emergence of service science:
Toward systematic service innovations to accelerate co‐creation of
value. Production and operations management, 17(3), 238-246.
– Spohrer, J. C. (2011). On looking into Vargo and Lusch's concept of
generic actors in markets, or “It's all B2B… and beyond!”. Industrial
Marketing Management, 40(2), 199-201.
– Spohrer, J., Kwan, S. K., & Fisk, R. P. (2014). Marketing: a service
science and arts perspective. Handbook of service marketing research,
489-526.
– Spohrer, J. (2017). IBM's service journey: A summary sketch. Industrial
Marketing Management, 60, 167-172.
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Service-Dominant Logic
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SYSK DIKUW
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(K)Nowing 1
(K)Nowing 2
(K)Nowing 3
(K)Nowing 4
(K)Nowing 5

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© IBM UPWard 2016
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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…

TED Arai Todai Robot
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… when will
your smartphone
be smart enough to
pass a university
entrance exam?

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?

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
222/28/2018 (c) IBM 2017, Cognitive Opentech Group
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

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 ->

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 Trends
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© IBM Cognitive Opentech Group (COG)
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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.

AI Leaders
• Who is winning?
– Regions China vs USA vs EU vs ROW
– Companies Microsoft vs Google vs IBM
• Leaderboards
– SQuAD – Question Answering
– EFF Measuring AI Progress

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)

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

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© IBM UPWard 2016
32

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

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

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1955 1975 1995 2015 2035 2055
Can better service help us be wiser?
Cognitive Mediator (2035): Tool, Assistant, Collaborator, Coach

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…

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

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.
2/28/2018 42
Take free online cognitive classes today at cognitiveclass.ai

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

IBM-MIT $240M over 10 year AI
mission

2/28/2018
© IBM 2015, IBM Upward University
Programs Worldwide accelerating regional
development
45
I have…
Have you noticed how the building
blocks just keep getting better?

Learning to program:
My first program
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development
46
Early Computer Science Class:
Watson Center at Columbia 1945
Jim Spohrer’s
First Program 1972

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© IBM UPWard 2016
47
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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© IBM UPWard 2016
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Today: November 10, 2017
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IBM

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development
52
Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
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development
53

Watson Discovery Advisor
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development
54
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/

Types
• Tool
• Assistant
• Collaborator
• Coach
• Mediator
2/28/2018 Understanding Cognitive Systems 55

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

Build: 10 million minutes of experience

Build: Hardware < Software < Data < Experience

Measuring AI Progress
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Future-Ready T-Shapes
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In Summary
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development
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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

In Sum
• Service science studies the evolving ecology of service system
entities
• Future: Miniaturization and Lower Costs
– Smartphones + AI = Low-Cost Digital Workers = Miniaturization of
Expertise Economy
– Material + Energy Sciences = Low-Cost Local Factories and Farms =
Miniaturization of Things Economy
– Blockchain = Local-Cost Trust = Miniaturization of Justice Economy
(Government global cost of 25T ready for disruption)
– AR/VR = Getting used to the the responsibilities of super-powers =
Putting the world in a box = Miniaturization of Super-Powers Economy
• Prepare: Opentech AI and Leaderboard Challenges
– Best way to prepare is master GitHub and Kaggle - open challenge
leaderboards; and focus on resilience and better building blocks
(rapidly rebuilding from scratch)
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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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Computing: Then, Now, Projected
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2035
2055

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development
68

Build:
Hardware < Software < Data < Experience < Transformation

What exists in 2016?
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72
360,000 100,000 120,000 60,000 150,000

User Models
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development
73

“AI will change the world?
Who will change AI?”

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

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development
77
Future of Skills

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

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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© IBM Cognitive Opentech Group 2018
84

You say you want a
revolution?
October 8, 2017
https://www.slideshare.net/spohrer/revolution-20171008-v23
2/28/2018 (c) IBM 2017, Cognitive Opentech Group
85

By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
2/28/2018 86
Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts

In Summary
2/28/2018
development
87
“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

2/28/2018
development
88

The Maker Movement &
Open Source Ecology
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development
89

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

Dedication: Doug Engelbart
Father of the mouse and
augmentation theory
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development
91

Jaist 20180228 v4

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