This document discusses artificial intelligence (AI) and provides several quotes about AI from experts such as Stephen Hawking, Ray Kurzweil, Elon Musk, and others. It then summarizes the history of AI and key developments that led to the current "third AI boom". These include advances in machine learning, deep learning, self-driving cars, smart assistants, and more. The document also discusses challenges for AI such as the need for AI systems to interact and react, as well as the impact of AI on jobs and the need for reskilling workers.

1. When AI becomes a data-driven
machine, and digital is everywhere!
Making of Thailand 4.0!!
Virach Sornlertlamvanich
SIIT, Thammasat University
Chair of Digital Cluster, RUN
virach@siit.tu.ac.th

2. “The development of full artificial intelligence could
spell the end of the human race….It would take off
on its own, and re-design itself at an ever increasing
rate. Humans, who are limited by slow biological
evolution, couldn't compete, and would be
superseded.”— Stephen Hawking
28 Best Quotes About Artificial Intelligence
“Artificial intelligence will reach human levels by
around 2029. Follow that out further to, say, 2045,
we will have multiplied the intelligence, the human
biological machine intelligence of our civilization a
billion-fold.” —Ray Kurzweil, author
“The pace of progress in artificial intelligence is
incredibly fast. ... The risk of something seriously
dangerous happening is in the five-year timeframe.
10 years at most.” —Elon Musk, CEO of Tesla

3. “Using up data and AI is only a means to survive.”
—Kenichiro Yoshida, Sony President
“Some people call this artificial intelligence, but the
reality is this technology will enhance us. So instead
of artificial intelligence, I think we'll augment our
intelligence.” —Ginni Rometty, CEO of IBM
28 Best Quotes About Artificial Intelligence
“Artificial intelligence would be the ultimate version
of Google. The ultimate search engine that would
understand everything on the web. ...” —Larry
Page, CEO of Alphabet

4. AI Boom
Chances and Risks

5. Begin of AI
• The Dartmouth Conference of 1956 was organized by
Marvin Minsky, John McCarthy and two senior scientists:
Claude Shannon and Nathan Rochester of IBM. The proposal
for the conference included this assertion: "every aspect of
learning or any other feature of intelligence can be so
precisely described that a machine can be made to
simulate it".
• At the conference Newell and Simon debuted the "Logic
Theorist" and McCarthy persuaded the attendees to accept
"Artificial Intelligence" as the name of the field.
• The 1956 Dartmouth conference was the moment that AI
gained its name.

6. What is AI?
--classic and modern aspects--
• From a behavioral point of view, is an artificial
agent that shows certain characteristics of
intelligence like:
• Perception
• Knowledge acquisition
• Knowledge representation
• Reasoning
• Planning
ó Regression
ó Deep learning
ó Modeling
ó Prediction
ó Recognition

7. Differences within AI
Artificial Intelligence
• General AI
• Vertical AI (Expert Systems)
• Natural Language Processing
• Computer Vision
• Machine Learning
• ...

8. Thinking, Fast and Slow by Daniel Kahneman (2011)
--The two systems--
http://upfrontanalytics.com/market-research-system-1-vs-system-2-decision-making/

9. Multilayered neural
networks to vast amounts
of data
Enable machines to
improve at tasks with
experience
Mimic human intelligence
using logic, if-then rules,
decision trees, machine
learning and deep learning
Deep Learning (Neural learning from data with high quality, but imperfect results)
Watson (Associative learning from data with high quality, but imperfect results)
Semantic Web (Knowledge graph links formation from extraction, clustering and learning)
Modern AI is making some huge strides

10. A Brief History of AI
NLP & Robot
Expert System
Chatbot
Games
1960s 1980s 2000s
1st AI Boom
(Inference/Search)
1970s
2nd AI Boom
(Knowledge)
1990s 3rd AI Boom
(Machine Learning/
Feature Representation Learning)
2010s
Data
Explosion

11. AI advancement that brings about the 3rd AI Boom
• Thinking Machines
• DeepBlue Chess Machine (1997)
• IBM Watson Quiz Show (2011)
• DeepMind AlphaGo (2016)
• Self-Driving Cars
• RHINO Museum Tour Guide (1997)
• DARPA Grand Challenge (2005)
• Google Self-driving Car (2011)
• Smart Assistants
• Apple Siri Personal Assistant (2011)
• Amazon Echo & Alexa (2014)
• Google Home & Assistant (2016)
Byoung-Tak Zhang, “Human-Level AI and Video Turing Test”
Google’s AlphaGo AI narrowly beats the
world’s top human Go player 2017
SIliconangle
Geospatialworld
Pocket-lint

12. Big Data Challenge
• Internet, Big Data, AI, Machine
Learning, Deep Learning have
brought along the possibilities.
Germin8, Social Intelligence
In 2015
Facebook:-
Adds 0.5 petabyte (1015) of data every 24 hours
Twitter:-
Adds 340 million tweets per day
Youtube:-
Adds 100 hours of new videos every minute
Less number of character per message, much more number of messages => data sparsity

13. Gartner Hype Cycle for Emerging Technologies AI
Platform
Experience
2014 2015
2016 2017

15. Uncertainty about AI on Job
20%
40%
60%
80%
100%
10% 15% 20% 25% 100%
India Brazil
China
Germany
Spain
Italy
Aus.
Global average
(15, 62)
France
UK
USA
Japan
(25, 22)
Workers are impatient
to work with AI
Less aware of AI on job
Accenture (Future Workforce and Reworking the Revolution)
Needs of new skilling to work with
intelligent machines.
Map new skills to new roles.

16. 1,000,000 Programmers needs in 2037
http://www.thansettakij.com/2017/03/08/133452
Thailand 4.0
Data
Science
Big Data
Text
Analytics
Fintech
Machine
Learning
Deep
LearningKnowledge
Science
Language
Engineering
IoT
Data
Mining
Data
Surveillance
Artificial
Intelligence
Robotics
Autonomous
Vehicle
NLP
Machine
Translation
• 50,000 programmers (2017)
• 100,000 programmers (needed in 2017)
• 6,000 programmers/year (graduated)
• 2,000 programmers (qualified)

17. R&D Trap
1980
1990
2000
2010

18. Three Big AI Research
Institutes

19. Three Big AI Research Institutes
• AIRC by AIST, Japan
• 2015
• Focusing on translational research
• Researchers: 77 (2015) -> 400++ (2017)
• 20-50 members in each research team
• DFKI, Germany
• Since 1988
• 900 researchers (510 employees), 80 spin-offs
• World largest AI research center
• USC, US
• Data Science Platform

20. AI
Ontology /
Knowledge
Simulation
/ Multi-
agent
Machine
Learning
AIRC/AIST, Japan
Sensing Recognition Modeling Planning Action
Inference
HPC for AI
AI x RobotAI x IoT
Data acquisition
Recognition
Action, Planning
and Execution
Prof. Dr. Jun-ichi Tsujii
Target areas
1. Mobility
2. Productivity
3. Healthcare, Welfare
4. Safety, Security
Development in 3-layers for collaboration
L3: Shared Tasks and Benchmark Data
Geo, Life, Robot, Science
L2: AI Framework and Advanced Modules
Data acquisition, Recognition, Planning, NLP
L1: Large-scale fundamental Research
Machine Learning, Probabilistic, Brain-inspired
AI, Data, Knowledge

21. DFKI, Germany
German Research Center for Artificial Intelligence
(Deutsches Forschungszentrum für Künstliche Intelligenz)
• Since 1988
• 900 researchers (510 employees), 80 spin-offs
• World largest AI research center
• PPP/JV on AI
• Develop Open Platform for
• Setting up network for industry and research
• Academia
• Industry
• Collaboration framework
• Digital reality to scale AI
• CERN of AI
Prof. Dr. Philipp Slusallek

22. Data Science Institute (DSI), USC, US
• Data science platforms
DSI
Data Platform
Societal impact
Research publication
Technology transfer
Real world
problem
Prof. Dr. Cyrus Shahabi

23. Action
Execution
Control
Input
Acquisition
Recognition
Comparable AI Frameworks
Data Processing Storage Analysis
AI x RobotAI x IoT
Visualization
Sensing Recognition Modeling Planning Action
Accumulating Knowledging Understanding Solving
Data Visualization

24. RUN Collaboration
Digital Cluster Framework

25. RUN Digital Cluster
Co-Research and Resource Sharing for
Capacity Building and New Business Development

26. Life
•Wellness
•Elderly care
•Indoor positioning
City
•Smart Mobility
•City Surveillance
•Environmental
friendly
•Mobility Optimization
• Industry 4.0
• Cyber-Physical
System
•Crop Health
Monitoring
•Crop Growth
Monitoring
Digital
AI, Big Data, IoT, NLP
Manufac
turing
Agriculture
Unified Platform

27. Challenges
• Current AI is nothing more than a machine that has a
capability to learn.
• AI should not only be able to learn and reason, it should also be able
to interact and react.
• AI platforms should do more than answer simple questions.
They should be able to learn at scale, reason with purpose,
and naturally interact with humans. They should gain
knowledge over time as they continue to learn from their
interactions, creating new opportunities for business and
positively impacting society.
• AI will result in net job gain. Reskill for new job role to work
with AI