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When AI becomes a data-driven machine, and digital is everywhere!

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Thammasat University, Musashino University
Thammasat University, Musashino University

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.

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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
“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 Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”— Stephen Hawking 28 Best Quotes About Artificial Intelligence “Artificial intelligence will reach human levels by around 2029.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
“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
AI Boom Chances and Risks
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.
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
Differences within AI Artificial Intelligence • General AI • Vertical AI (Expert Systems) • Natural Language Processing • Computer Vision • Machine Learning • ...
Thinking, Fast and Slow by Daniel Kahneman (2011) --The two systems-- http://upfrontanalytics.com/market-research-system-1-vs-system-2-decision-making/
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
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
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
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
Gartner Hype Cycle for Emerging Technologies AI Platform Experience 2014 2015 2016 2017
When AI becomes a data-driven machine, and digital is everywhere!
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.
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)
R&D Trap 1980 1990 2000 2010
Three Big AI Research Institutes
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
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
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
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
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
RUN Collaboration Digital Cluster Framework
RUN Digital Cluster Co-Research and Resource Sharing for Capacity Building and New Business Development
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
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

More Related Content

When AI becomes a data-driven machine, and digital is everywhere!

  • 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
  • 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)
  • 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
  • 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