OECD - AI for productivity in science - limits of AI Session

1. Panelist for “Limits of AI” Session
AI and the Productivity of Science
Jim Spohrer
Board of Directors, ISSIP.org
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations on line at: https://slideshare.net/spohrer
Thanks to Alistair Nolan (OECD) for inviting me to be a panelist
for “Limits of AI” Session on Wednesday November 3, 2021.
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book

2. Moderator and Speakers:
• Tony Hey – Team Science, Open Access, Future is Scientist+AI
• Hector Zenil – Deep Fragility (beyond Big Data method, need “Mental
Models”), Causal Deconvolution, Neural-Symbolic AI
• Ross King – Grand Challenge Science_Team+AI Noble Prize Winner,
What’s missing – a long list, at all stages of the ”science chain”; but
like chess, special purpose or general intelligence?

3. Panelists and others:
• Jim Spohrer – Moore’s Law and Computing Power-Cost Limits,
Leaderboards Tracking Capability Limits
• Jesse Thaler – 5 Sigma challenge
• Odd Erik Gundersen – team member more realistic for Noble Prize,
quantify the transition from tool to team member (legal question)
• Alexander D’Amour – role of AI in scientific discovery, “identification”
– map patterns back to causal structure (stable)
• Mohammed Al Quraishi - systems can be Noble Prize worthy in their
contribution which needs to be significant
• Reinhilde Veugelers – economists perspective

4. Timeline Future of AI: 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
4
11/3/2021 (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
OECD_Alistair Nolan to Everyone (5:11 AM)
It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years

5. Timeline: GDP/Employee
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(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. Timeline: 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 Summarization Values
ImageNet VQA DSTC RALI General-AI
Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation
WMT DeepVideo Alexa Prize ICCMA AT
Learning from Labeled Training Data and Searching (Optimization)
Learning by Watching and Reading (Education)
Learning by Doing and being Responsible (Exploration)
2018 2021 2024 2027 2030 2033 2036 2039
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Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
+3
See: https://paperswithcode.com/sota

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9. The Current Limits of
AI in Science
Tony Hey
Chief Data Scientist
Rutherford Appleton Lab
Harwell Campus
Didcot OX11 0QX

10. “AI won’t replace the scientist, but scientists who
use AI will replace those who don’t.”
Adapted from a Microsoft report, “The Future Computed”

11. Jim Spohrer, Board of Directors, ISSIP.org
Jim Spohrer serves on the Board of Directors of the International Society of
Service Innovation Professionals, and as a contributor to the Linux Foundation
AI and Data Foundation. He is a retired IBM Executive since July 2021, and
previously directed IBM’s open-source Artificial Intelligence developer
ecosystem effort, was CTO IBM Venture Capital Group, co-founded IBM
Almaden Service Research, and led IBM Global University Programs. After his
MIT BS in Physics, he developed speech recognition systems at Verbex (Exxon)
before receiving his Yale PhD in Computer Science/AI. In the 1990’s, he attained
Apple Computers’ Distinguished Engineer Scientist and Technologist role for
next generation learning platforms. With over ninety publications and nine
patents, he received the Christopher Loverlock Career Contributions to the
Service Discipline award, Gummesson Service Research award, Vargo and Lusch
Service-Dominant Logic award, Daniel Berg Service Systems award, and a
PICMET Fellow for advancing service science. Jim was elected and previously
served as LF AI & Data Technical Advisory Board Chairperson and ONNX Steering
Committee Member (2020-2021), UIDP Senior Fellow for contributions to
industry-university collaborations.
11
From 2002 - 2009, Jim co-founded and directed
IBM Almaden Service Research
helping to establish service science,
applying science, technology,
and T-shaped upskilling of people to
business and societal transformation.
Who I am
2021 A big year: (1) 65, (2) award, (3) retired

12. What I study
Service Science and Open Source AI – Trust is key to both
Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation/Collaboration
Responsible Entities Learning to Invest
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities

13. Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
26-30 July 2015 3rd International Conference on The Human Side of Service Engineering
13
Artificial Intelligence
Automation
Generations of machines
Service Science
Transformation
Generations of people
(responsible entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)

16. The Three Stages of Systems Evolution
The Three Ages of Man (Giorgione)
Thanks to Alan Hartman for kind inspiration (slides) (recording) Service, the application of knowledge for mutual benefits
win-win/non-zero-sum games/value co-creation/capability co-elevation

17. IBM’s Service Journey: A Summary Sketch
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Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.

18. IfM, IBM (2010)
Succeeding through
service innovation:
a service perspective
for education, research,
business and government.
University of Cambridge
Institute for Manufacturing,
Cambridge UK
2010

19. 19
How responsible entities (service systems) learn and change over time
History and future of Run-Transform-Innovate investment choices
• Diverse Types
• Persons (Individuals)
• Families
• Regional Entities
• Universities
• Hospitals
• Cities
• States/Provinces
• Nations
• Other Enterprises
• Businesses
• Non-profits
• Learning & Change
• Run = use existing knowledge
or standard practices (use)
• Transform = adopt a new best
practice (copy)
• Innovate = create a new best
practice (invent) Innovate
Invest in each
type of change
Spohrer J, Golinelli GM, Piciocchi P, Bassano C (2010) An integrated SS-VSA analysis of changing job roles. Service Science. 2010 Jun;2(1-2):1-20.
March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87. URL:
exploit
explore

20. Service Science: Conceptual Framework
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Service Science

21. (c) IBM MAP COG .| 21
Service Science: Transdisciplinary Framework to Study Service Systems
Systems that focus on flows of things Systems that govern
Systems that support people’s activities
transportation &
supply chain water &
waste
food &
products
energy
& electricity
building &
construction
healthcare
& family
retail &
hospitality banking
& finance
ICT &
cloud
education
&work
city
secure
state
scale
nation
laws
social sciences
behavioral sciences
management sciences
political sciences
learning sciences
cognitive sciences
system sciences
information sciences
organization sciences
decision sciences
run professions
transform professions
innovate professions
e.g., econ & law
e.g., marketing
e.g., operations
e.g., public policy
e.g., game theory
and strategy
e.g., psychology
e.g., industrial eng.
e.g., computer sci
e.g., knowledge mgmt
e.g., statistics
e.g., knowledge worker
e.g., consultant
e.g., entrepreneur
stakeholders
Customer
Provider
Authority
Competitors
resources
People
Technology
Information
Organizations
change
History
(Data Analytics)
Future
(Roadmap)
value
Run
Transform
(Copy)
Innovate
(Invent)
Stackholders (As-Is)
Resources (As-Is)
Change (Might-Become)
Value (To-Be)

22. “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

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1955 1975 1995 2015 2035 2055
Better Building Blocks

25. Future of Service Science
Smarter and Wiser Service Systems:
Entities transform to better future versions of
themselves by inventing win-win games and competing
for collaborators
Past Present Future
Organizational
Units
Family
Local Clan
Family
Business/Nation
Family
Platform Society
Change Individual
Generalist
(Breadth)
Individual
Specialist
(Depth)
Individual
T-shaped
(L)earners
Constant Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
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26. 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.)
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27. Upskilling…
T-shaped
(l)earners
Gardner P, Maietta HN
(2020) Advancing Talent
Development: Steps
Toward a T-Model
Infused Undergraduate
Education.
Moghaddam Y, Demirkan
H, Spohrer J (2018) T-
Shaped Professionals:
Adaptive Innovators.

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T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration
Advanced Tech: AI to IoT to Quantum, GreenTech, RegTech, etc.
Work Practices: Agile, Service Design, Open Source
Mindset: Growth Mindset, Positive Mindset, Entrepreneurial
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline

29. 10 million minutes of experience
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30. 2 million minutes of experience
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