The document summarizes a tutorial on Opentech AI given by Jim Spohrer and Daniel Pakkala, discussing trends in lowering the cost of AI technologies, benchmarks for measuring AI progress, and types of cognitive systems ranging from tools to mediators. It also provides an outline for Daniel Pakkala's presentation on the Opentech AI architecture, ecosystem, and roadmap, discussing frameworks for understanding intelligence evolution and the need for an architecture framework for AI systems.
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Tutorial helsinki 20180313 v1
1. Jim Spohrer (IBM)
IBM Finland HQ, Tuesday March 13 2018
http://www.slideshare.net/spohrer/tutorial-Helsinki-20180313-v1
3/13/2018 1
Tutorial: Opentech AI Helsinki
4. 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
43/13/2018 (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
5. GDP/Employee
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 5
(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
6. Measuring AI Progress (MAP)
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
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 6
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
12. Opentech AI -
Architecture, Ecosystem
and Roadmap:
Drafting the big picture and future directions of open artificial
intelligence technology13.3.2018 Helsinki, Opentech AI Workshop Tutorials
Daniel Pakkala,
IBM Distinguished Visiting Researcher - IBM Research Almaden.
Principal Scientist - VTT Technical Research Centre of Finland Ltd.
E-mail: Daniel.Pakkala@vtt.fi
LinkedIn: www.linkedin.com/in/dpakkala
13. Outline0. Introduction
1. Walkthrough on Opentech AI
– Applications & impact
– Process & tools
– Ecosystem & learning
– Platforms & technologies
2. Drafting the big picture of AI
– Is AI more than ML/DL?
– AI Beyond ML/DL
– AI amongst sciences
– Takeaways from the AI related sciences jungle
– AI systems today and in the future
3. A big picture of AI
Intelligence evolution
“AI Cube” framework
No architecture framework and reference
architecture for AI systems – do we need one?
Towards architecture framework for AI
systems
4. Wrapping it up…
Catalysing and measuring progress of AI
AI Architecture, Roadmap and Benchmarks
Balancing theory and practice:
a deep learning experiment
14. Introduction (1/2)
• Research exchange co-operation between VTT Technical Research Centre of
Finland and IBM Research – Almaden.
• Co-created blog:
– http://opentechai.blog/
15. Introduction (2/2)
• Open Source
– Tools
– Platforms
– Code
• Open Datasets
• Open Models
• Open Challenges & Leaderboards
Science & Education:
– Reproducibility, Progress, Training
Industries & Society:
– Applications, Impact, Skills
Why?
16. Walkthrough on Opentech AI
- Applications & impact
- Process & tools
- Ecosystem & learning
- Platforms & technologies
17. Applications and impact
• Artificial Intelligence ~ intelligence exhibited by
machines
– Wide application potential on different fields of
society and industry
– One technology (in addition to many other
technologies) to solve real problems in real world
• Opentech AI is highly relevant both for companies
and academia
– Rapidly evolving open source platforms, tools,
data sets and community development
– Hard to match the speed of Opentech AI
development with a pure “in-house” AI
technology stack and R&D practices
• Industry specific open AI challenges are emerging
– Tool to measure progress of AI in industry specific
tasks
https://opentechai.blog/applications/
18. Process and tools
• R&D of Opentech AI:
– no single right way to do it
– plenty of tools available to utilize
• General 3 step learning and discovery process, and
supporting tools, can be identified for Opentech AI R&D
1. READ the state of the art – know and learn from what
others have done in the area (e.g. from arXiv). Check the
performance of the existing solutions to benchmark your
results on leaderboards.
2. REDO an experiment to learn or to improve it – retrieve the
open source code and data sets (e.g. from GitHub) and by
running/reusing/improving those.
3. REPORT the outcome and results of your experiment -
(using e.g. Jupyter notebook). Share modified code as open
source and publish your results also in form of a research
paper (e.g. in arXiv). If you have made an advancement that
can be reported on an AI leaderboard, that is especially
significant to advance AI progress.
https://opentechai.blog/process-tools/
19. Ecosystem and learning• R&D ecosystem around Opentech AI systems
requires
– open governance by multiple organizations working
to unlock the benefits of AI while mitigating any risks,
or downsides.
– variety of skills, making also learning resources and
their availability an important part of Opentech AI.
– vibrant open technology provider, developer,
contributor and user community
– commercialization friendly licenses
• Ecosystem has emerged and is evolving rapidly
• AI technology development pace is challenging the
education systems
• On-line courses and mini-degrees gaining popularity
in response to rapid skill development needs
– Evolution pace of Opentech AI technologies exceeding
capabilities of the education systems
https://opentechai.blog/ecosystem/
20. Platforms and technologies
• Growing variety of available open source technologies for
planning and implementing experiments in Opentech AI
R&D, e.g.
– TensorFlow – machine learning framework
– Keras – higher level API for machine learning
– PyTorch – deep learning framework for Python
– Anaconda – Python and R distribution for data science
– Microsoft Cognitive toolkit (CNTK) – ML/DL libraries
• Also commercial MLaaS/PaaS offerings with free trial
possibility
– IBM Watson offering
– Amazon AI Stack offering
– Microsoft Azure ML offering
– Google Cloud ML offering
https://opentechai.blog/platforms-technologies/
21. Drafting the big picture of AI
- Is AI more than ML/DL?
- AI Beyond ML/DL
- AI amongst sciences
- Takeaways from the AI related sciences jungle
- AI systems today and in the future
22. Is AI more than Machine/Deep
Learning?• Mainstream of AI R&D today: ML/DL
– Narrow AI applications are reaching or
outperforming human capabilities:
• Jeopardy! (IBM Watson, 2011)
• Go (Google AlphaGo, 2016)
• ”Reading comprehension”/Searching
Wikipedia for answers (SQuAD, 2018)
• What more is needed?:
– Address the real (open) world problems
and tasks!
• common sense & world model
• learning concepts (in addition to
categorizing & mapping),
• multiple/adaptive learning strategies and
• context-awareness, adaptation and
natural interaction
blog: https://opentechai.blog/2017/09/18/ai-is-more-than-machinedeep-learning/
23. AI beyond machine and deep
learning• The current state and progress of AI
– AI is matching human (task) performance in conceptually
narrow tasks and the quest is towards a wider AI, perhaps
one day matching the general intellectual capability of
humans.
• Beyond ML/DL and ICAT the literature related to AI
“explodes”, and comes from many different disciplines
with different approaches and methodologies
– Fuzzy/chaotic landscape from AI system engineering
viewpoint
• No architecture framework or reference architecture
24. AI amongst other sciences
Core:
Systems science, Computer science
and Mathematics
Interfaces:
1. AI implementing cognitive architectures within intelligent
agents, multi-agent systems and other intelligent systems for
various applications.
1.1 Artificial neurons and artificial neural networks
1.2 Natural language processing, understanding and
generation. Word vectors.
2. Intelligence is a natural (biological) phenomena, which AI aims
to mimic. Potential new findings related to evolution, cells,
neurons and brain.
3. Availability and applicability of physical materials and energy
for building and operating any construct. Physical environment
and physical laws highly relevant for AI applications in the
physical word.
4. Acceptance, impact, need and use of AI applications by
individuals and organizations. Value of the AI applications is
defined by humans and human organizations in social context.
blog: https://opentechai.blog/2017/11/10/navigating-in-the-ai-sciences-jungle/
25. Takeaways from the AI
related sciences jungle
1. Progress of AI exploitation is dependent on the progress of R&D on
the related fields
– still active fields of research with potential for new discoveries relevant
for AI R&D.
2. Multiple parallel tracks of AI research and development are
proceeding in parallel
– not necessarily mutually inter-operable from the viewpoint of building
new AI systems and applications.
• ML/DL - Artificial Neural Networks
– “data classification/segmentation engines”
• Cognitive Science - Cognitive Architectures
– “data based processes”
• Symbolic, sub-symbolic, hybrid approaches
84 / 49 active…
FF, RNNs, Spiking,…~25
~1042…complexity
26. AI systems today and in the future
• AI systems today: Mainly evolution of
smart features of existing systems
– enabled by machine learning, increasing
computing power and availability of data.
• AI systems in future?: Systems
autonomously maintaining themselves,
operating, learning and interacting over
extended periods as part of society and
culture
– Forbus, K. D. (2016). Software social
organisms: implications for measuring AI
progress. AI Magazine, 37(1), 85-90.
~10∞
27. A big picture of AI
- Intelligence evolution
- AI Cube
- No architecture framework for AI systems – do we need
one?
- Towards architecture framework for AI systems
28. Intelligence evolution (1/2)• Co-evolution of Natural and Artificial Intelligence
– AI is a product of natural intelligence
– We share time, environment and context
• Time, where to start and how to approach different time scales?
– BB -13.7 Ga, Earth -4.5 Ga, LUA -3.7 Ga or Dartmouth 1956?
– It took about 3.7 billion (109) years for natural intelligence to evolve
and come up with artificial intelligence in 1956!
– Borrowed time concept from Biology:
• Evolutional and generational time dimensions
• Environment
– all life, materials, entities and artifacts
– all biological, physical, social and artificial processes and systems
– in constant change as a result of all the processes and systems
interacting within it
29. Intelligence evolution (2/2)
• Context
– subset of the environment at given time
– addressing an AI system (or family of AI systems) co-existence and interaction
with the relevant processes, systems and entities in the environment
– enables value, requirements and impact analysis of AI systems in different
phases of the system life-cycle
Aspects of interest regarding AI
systems
AI Research & Development,
AI Craft/Production and
AI System Implementations
AI Genome
AI Genome: codes for co-evolution of
human and artificial intelligence over
generations of both
30. A big picture of AI – “AI Cube”
~10∞
~1042
~109
see: https://opentechai.blog/2017/12/08/framing-the-big-picture-and-evolution-of-ai/
31. No architecture framework for AI
systems – do we need one? (1/2)
• Without architecture framework there will be very little knowledge
accumulation on structure and behavior of AI systems
– Encoded only in individual system implementations and their descriptions
Architecture framework:
- Enables architecture descriptions
- Modeliing conventions & practices
- Stakeholders & concerns
- Viewpoints
32. No architecture framework for AI
systems – do we need one? (2/2)• Agile manifesto and it’s naïve interpretation have hindered progress on
system and software architectures
• Time to rethink prevalent assumptions on architecture as we move towards
autonomous, intelligent cyber-physical systems!!!
– If not: “Who let the dogs out?”
• ISO/IEC/IEEE 42010:2011 Systems and software engineering - Architecture
description
– Sets requirements for defining an architecture framework and architecture
descriptions for systems and software engineering
– Challenge: Agile processes leading to flexible architecture
See, https://philippe.kruchten.com/2013/12/11/agile-architecture/
See, http://wasp-sweden.org/custom/uploads/2017/03/01_Introduction-to-Software-Architecture-for-Autonomous-Systems-
Pellicione.pdf (Picture source)
33. Towards architecture framework for
AI systems (1/2)
• The transition from current AI systems towards
future AI systems deserves special attention from
the viewpoint of systems architecture:
– Complex autonomic behavior of a system
• safety, security, trust, governance and institutional
control
– Incremental learning and adaptive behavior
• learning from data and past interaction outcomes,
capabilities/knowledge
– System life-cycle and maintainability
• applicable methodologies and processes, quality
– Social, cultural, ethical and environmental
compliance to norms
• acceptance and value
– Interaction and value
• collaborating vs. using
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A
Standard Model of the Mind: Toward a Common
Computational Framework Across Artificial Intelligence,
Cognitive Science, Neuroscience, and Robotics. AI
Magazine, 38(4).
Kotseruba, I., Tsotsos, J. K. (2016). A Review of 40 Years
of Cognitive Architecture Research: Core Cognitive
Abilities and Practical Applications, eprint
arXiv:1610.08602.
blog: https://opentechai.blog/2018/01/17/towards-an-architecture-framework-for-opentech-ai-systems/
34. AI systems
(2/2)
Stakeholders:
• R&D Community, System stakeholders and System Collaborators
(~users)
• Environment as a stakeholder – env.&soc. responsibility
Subsystems:
• Embodiment – physical material structure enabling
implementation of other subsystems and bounding interfaces with
the natural environment.
• Perception and Actuation – all mechanisms related to sensing and
actuation of the system with the environment
• Memory – all different memory mechanisms
• Presence – all spatiotemporal and cyber-temporal presence and
system behavior related mechanisms
• Cognition –all higher cognition related mechanisms accumulating
the internal world model of the system, and possibly adapting the
behavior of the AI System over time.
• Internal world model – The natural environment including human
culture as the AI system comprehends it internally. All knowledge
contained by the AI System as a result of training and learning
while in operation.
• Internal data processing –The internal continuous data processing
and analysis pipeline of the AI System defining the mechanism
converting data flow from perception subsystem into information,
knowledge and wisdom as modifications of the internal world
model and behavior.
Hybrid model driven approach combining agent-orientation and holistic two-
dimensional system orientation, where system thinking is applied both to the
external environment and to the internal organization of the AI system.
https://opentechai.blog/2018/01/17/towards-an-architecture-framework-for-opentech-ai-systems/
35. Wrapping it up…
- Catalysing and measuring progress of AI
- AI architecture, roadmap and benchmarks
- Balancing theory and practice:
a deep learning experiment
36. Catalysing and measuring progress
of AI
• Architecture framework might catalyze AI systems development
– Breaking down the complexity involved in AI systems engineering
– Analysis and design framework for new AI systems
– Enabling hybrid/mash-up approaches in applications, when identifying synergies
between different AI systems becomes easier
• Measuring progress of AI
– AI Challenges and Leaderboards
• see: https://opentechai.blog/progress/
– More holistic view to AI progress, performance also in terms of:
• Energy
– Matching human energy consumption and environmental footprint in a task (in addition to just task
outcome performance)
» Brain simulation with current supercomputers: ~20 GW
» Human brain: ~20 W
– New metrics to be considered
» E.g. grams of CO2/task – flops/task – Wh/task - …
38. Balancing Theory and Practice (1/3):
a deep learning experiment
• Predicting power consumption of a property/building as a
function of weather forecast
• Data
– 2012-2015 hourly power consumption and outside
temperature time series (32784 data points)
• Approach
– Multi-layered dynamic RNN-LSTM network
• # of layers
• # of LSMT units
• feature engineering (3 - 7 input
variables, sliding history windows, …)
• Implementation
– Python 3, TensorFlow 1.2.1, numpy, scikit, matplotlib
Figures a & b: arXiv:1610.09460
a)
b)
39. Balancing Theory and Practice (2/3):
a deep learning experiment
• Process – 3Rs
– READ
• Read a few publications on the topic
comparing different ML/DL approaches to
building energy prediction.
• Selected approach for the experiment (LSTM-
RNN).
– REDO
• Did dome LSTM sine wave prediction
examples from GitHub
• Code to be made available to GitHub after
“cleaning and polishing”
– REPORT
• Kind of doing it now
• Let’s see if there are results worth publishing
as a research paper
• Maybe a Jupyter notebook at some point
40. Balancing Theory and Practice (3/3):
a deep learning experiment
• Preliminary results:
– cumulative prediction error (%):
• 24h ahead: ~30% -> 70% accuracy achievable?
• 48h ahead: ~20% -> 80% accuracy achievable?
• What did I learn?
– Basic process and building a deep learning pipeline
– Python 3, TensorFlow, Spyder, numpy, scikit, matplotlib,
setting up Linux virtual environments, …
– To appreciate (remotely accessible) computing power
(CPU+GPU)
• Thanks IBM & VTT!
– The border between science and alchemy is fuzzy in field
of deep learning
• Hypothesis first and then experiment, or
• Experiment and try to explain results and figure out
why something works or doesn’t
50. TED Arai Todai Robot
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 50
… when will
your smartphone
be smart enough to
pass a university
entrance exam?
51. 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.)
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 51
52. 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”
55. Jim Spohrer (IBM)
For C Mohan
Wednesday February 21, 2018
http://www.slideshare.net/spohrer/nsf-20180124-v18
3/13/2018 55
The Future of AI:
Measuring Progress and Preparing
60. TED Arai Todai Robot
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 60
… when will
your smartphone
be smart enough to
pass a university
entrance exam?
61. 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?
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 61
62. 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
623/13/2018 (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
63. GDP/Employee
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 63
(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
64. 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
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 64
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
65. 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)
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 65
67. 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
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 67
68. 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)
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 68
69. 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
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 69
70. AI Risks
• Job Loss
– Shorter term
bigger risk
= de-skilling
• Super-intelligence
– Shorter term
bigger risk
= bad actors
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 70
71. 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.)
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 71
72. Stakeholders
• Individuals
• Families
• Businesses and
other Organizations
• Industry Groups
• Regional
Governments:
– Cities
– States
– Nations
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 72
73. 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
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 73
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…
74. Cupertino Teens
• IBM Watson on Bluemix
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 74
AI for NLP
entity identification
75. 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.
3/13/2018 75
Take free online cognitive classes today at cognitiveclass.ai
76. 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.”
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 76
IEEE 2017
77. IBM-MIT $240M over 10 year AI
mission
3/13/2018 (c) IBM 2017, Cognitive Opentech Group 77
90. 3/13/2018 90
1955 1975 1995 2015 2035 2055
Can better service help us be wiser?
Cognitive Mediator (2035): Tool, Assistant, Collaborator, Coach
91. 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.
3/13/2018 91
98. “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
110. 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
111. 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
3/13/2018 Understanding Cognitive Systems 111
121. You say you want a
revolution?
October 8, 2017
https://www.slideshare.net/spohrer/revolution-20171008-v23
3/13/2018 (c) IBM 2017, Cognitive Opentech Group
121
122. By 2035, T-Shaped Makers with great
Building Blocks and Cognitive Mediators
3/13/2018 122
Empathy & Teamwork
sector
region/culture
discipline
Depth
Breadth
STEM
Liberal Arts
139. 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
3/13/2018 Understanding Cognitive Systems 139
164. 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
165. 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)
171. 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
172. Next Generation:
Future-Ready T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deepinonesector
Deepinoneregion/culture
Deepinonediscipline
180. 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
184. “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
187. Today’s Talk: Cyber-Social-Learning-
Systems• What is the impact of Artificial Intelligence (AI) on CSLS?
– Augmented Intelligence (IA) via digital cognitive systems
– White House OSTP Response(s)
• Other topics to think about:
– What are more of the implications of digital cognitive systems?
• Tool > Assistant > Collaborator > Coach > Mediator
• Transformation > Experience > Data > Software > Hardware
– What does social intelligence require? Episodic Memory?
– What is the impact of augmented reality on CSLS?
– What are possible connections to service systems science
(SSME+DAPP)?
– What type of adaptive innovator with growth mindset needed
(T-shapes)?
3/13/2018 Understanding Cognitive Systems 187
189. White House OSTP Response(s)
• AI for public good
• Social & economic implications
• Education to harness AI
• Research questions and gaps
• Data sets and model sets
• Multidisciplinary research
• Role of incentives and prizes
• Safety and control protocols
• Legal and governance issues
3/13/2018 Understanding Cognitive Systems 189
192. Understanding
Cognitive Systems
Jim Spohrer (IBM), August 25, 2016
CSIG (Cognitive Systems Institute Group) Speaker
Series
http://www.slideshare.net/spohrer/understanding_20
160825_v3
3/13/2018 Understanding Cognitive Systems
192
193. Today’s Talk: Understanding Cognitive
Systems• What is a cognitive system (entity)?
– biological
– technological
– types of digital cognitive systems
• How to…
– build them?
– understand them?
– work with them?
• Steps toward a next generation cognitive
curriculum…
3/13/2018 Understanding Cognitive Systems 193
194. But first…. Cognitive Science, a young
field
• Society
– cognitivesciencesociety.org
• People
– Founders: Roger Schank, Donald Norman,
Allan Collins
– Others: David Rumelhart, Herbert Simon,
Allen Newell
– Today: Patrick Langley, Wayne Gray,
Kenneth Forbus, Ashok Goel, Paul Maglio,
etc.
• Systems Conference
– cogsys.org
– (JCS wishes this was part of HICSS)
3/13/2018 Understanding Cognitive Systems 194
195. Advances in Cognitive Systems -
cogsys.org
3/13/2018 Understanding Cognitive Systems 195
197. Today’s Talk: Understanding Cognitive
Systems• What is a cognitive system (entity)?
– biological
– technological
– types of digital cognitive systems
• How to…
– build them?
– understand them?
– work with them?
• Steps toward a next generation cognitive
curriculum…
3/13/2018 Understanding Cognitive Systems 197
198. What is a cognitive system (entity)?
3/13/2018 Understanding Cognitive Systems 198
199. What is a digital cognitive system
(entity)?
3/13/2018 Understanding Cognitive Systems 199
203. Backup slides
• Service systems - http://service-
science.info/archives/3368
3/13/2018 Understanding Cognitive Systems 203
204. 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)
3/13/2018 Understanding Cognitive Systems 204
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).
205. 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.
3/13/2018 Understanding Cognitive Systems 205
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.
206. 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
3/13/2018 Understanding Cognitive Systems 206
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.
207. How to get involved?
• Journals (INFORMS,
etc.)
• Conferences (HICSS,
etc.)
• Courses (MIT, etc.)
• Funding (NSF, etc.)
• Society (ISSIP, etc.)
3/13/2018 Understanding Cognitive Systems 207
208. 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)
3/13/2018 Understanding Cognitive Systems 208
210. 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
211. Next Generation:
Future-Ready T-Shaped Adaptive Innovators
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deepinonesector
Deepinoneregion/culture
Deepinonediscipline