Cost of Digital Workers - Moore's Law is spooky

1. Preparing for the Future
of Artificial Intelligence (AI)
September 9, 2017
https://www.slideshare.net/spohrer/ai-futures-20170909-v18
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 1

2. Cost of Digital Workers:
Narrow (Petascale) and Broad (Exascale)
• Moore’s Law can be
thought of as lowering
costs by 1000 every 20
years, and a million
every 40 years
• AI Pattern Recognition
• Narrow AI (Fast) I-Shaped
• Petascale
• AI Reasoning
• Broad AI (Slow) T-Shaped
• Exascale
29/10/2017 (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

3. IBM-MIT $240M over 10 year AI mission
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 3
Sept 7, 2017

4. 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 technologies may have a bigger impact than AI?
• What are the implications for stakeholders: individuals, businesses
and other organizations, industries, cities, states, and nations?
• How should we prepare to get the benefits and avoid the risks?
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 4

5. AI Trends
9/10/2017
© IBM Cognitive Opentech Group (COG)
5
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.

6. AI to IA Timeline: Hard unsolved AI problems
• 2012-2019 AI Pattern Recognition and
Learning from Massive Labeled Data
• Speech, image, translation, driverless, games
• Chatbots as digital assistants
• 2020 Video Understanding
• 2021 Episodic Memory
• 2022 Learning from Watching
• 2023 Commonsense Reasoning **
• 2024 Learning from Doing
• 2025 Fluent Conversation
• 2026 Learning from Reading
• 2027-2035 Cognitive Collaborator and
Mediator; Intelligence Augmentation (IA)
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 6

7. AI Leaders
• Who is winning?* (depends on search terms)
• Nations by Publications
• Companies by Publications and Patents
• Leaderboards
• SQuAD – Question Answering
• EFF Measuring AI Progress
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 7

8. History of AI
• The Dartmouth
Conference of
1956[39] was
organized by Marvin
Minsky, John
McCarthy and two
senior scientists:
Claude Shannon and
Nathan Rochester of
IBM
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 8
Today’s Challenge:
Scaling to
Democratize AI

9. Brief History of
Deep Learning
• 1943 Threshold Logic
• 1958 Perceptron
• 1986 Hinton Backprop
• 1989 Lecunn for digits
• 2009 Ng GPUs
• 2012 Hinton ImageNet
• 2014 Facebook DeepFace
• 2016 DeepMind AlphaGo
• 2017 IBM Scaling 95%
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 9

10. Hillery Hunter (IBM)
Distributed Deep Learning
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 10

11. IBM Research
TrueNorth Chip
• Extremely Low Power
• Fast Inference
• High Accuracy
• 6000 frames/sec/watt
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 11

12. 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
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 12

13. AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 13

14. Other Technologies: Bigger impact?
• 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.)
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 14

15. Stakeholders
• Individuals
• Businesses and
other Organizations
• Industries
• Regions:
Cities, States,
Nations
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 15

16. Prepare
• Open AI code + data + models
+ stacks + community
• 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 skills of rapidly
rebuilding from scratch
• Build your open code eminence
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 16
1972 used
Punch cards
2016 used
IBM Watson
Open APIs to win…

17. Cupertino Teens
• IBM Watson on Bluemix
9/10/2017 (c) IBM 2017, Cognitive Opentech Group 17
AI for NLP
entity identification

18. 9/10/2017
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
18

19. 9/10/2017
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
19
Future of Skills

20. Future-Ready T-Shapes
9/10/2017
© IBM UPWard 2016
20

21. In Summary
9/10/2017
© IBM 2015, IBM Upward University Programs Worldwide
accelerating regional development
21
“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