Future 20171110 v14

Jim Spohrer (IBM)
Friday November 10, 2017
http://www.slideshare.net/spohrer/future-20171110-v14
11/10/2017 1
A look towards the future

AI to IA (Cognitive) 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 Responsible Doing
• 2030 Fluent Conversation
• 2031-2039 Cognitive Collaborator and
Mediator; Intelligence Augmentation (IA)
11/10/2017 (c) IBM 2017, Cognitive Opentech Group 2
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?

April 2017: TED Arai Todai Robot
… when will
your smartphone
be capable of
passing a university
entrance exam?

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© IBM UPWard 2016
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First consider this…

Microsoft CaptionBot June 19, 2016
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Microsoft CaptionBot June 20, 2016
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IBM Image Tagging – June 19, 2016
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Today: November 10, 2017
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IBM

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.

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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1955 1975 1995 2015 2035 2055
A look towards the future…
Cognitive Mediator (2035): Progression from Tool, to Assistant, to Collaborator, to Coach

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
1311/10/2017 (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

GPD/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 ->

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

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…

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.
11/10/2017 23
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

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© IBM 2015, IBM Upward University
Programs Worldwide accelerating regional
development
27
Cognitive Mediators
for all people in all roles

Occupations = Many Tasks
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development
28

Watson Discovery Advisor
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development
29
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/

User Models
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development
30

Computing: Then, Now, Projected
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2035
2055

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32

Build: 10 million minutes of experience
11/10/2017 Understanding Cognitive Systems 33

Build: 2 million minutes of experience

Build:
Hardware < Software < Data < Experience < Transformation

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

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

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© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
38

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development
39
Future of Skills

Future-Ready T-Shapes
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In Summary
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development
41
“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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© IBM UPWard 2016
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IBM in Silicon Valley:
From Punch Cards….
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development
43
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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44

Types
• Tool
• Assistant
• Collaborator
• Coach
• Mediator

Types: Progression of models and capabilities
Task & World Model/
Planning & Decisions
Self Model/
Self Capacity & Limits
User Model/
User’s Episodic Memory and Identity
Institutions Model/
Trust & Social Acts
Tool + - - -
Assistant ++ + - -
Collaborator +++ ++ + -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
tool assistant collaborator coach mediator

Partnership for AI formed

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

IBM Cloud Bluemix:
Watson APIs are growing…
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So far (June 2016), 100,000 faculty and students globally given access

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Explain external
phenomena

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Explain internal
phenomena

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development
52
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

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

Next Generation:
Future-Ready T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deepinonesector
Deepinoneregion/culture
Deepinonediscipline

The Maker Movement &
Open Source Ecology
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57

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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What is a cognitive system (entity)?

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)

Future 20171110 v14

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