Ai titech-virach-20191026
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This document discusses the development of artificial intelligence and perspectives on its impact. It provides quotes from experts expressing both optimism and pessimism about AI's development. It also summarizes the advancement of AI technologies like deep learning and self-driving cars. Additionally, it profiles three major AI research institutes and their focus on areas such as machine learning, robotics, and developing open platforms for industry and academic collaboration. In summary, the document outlines the progress and challenges of AI, as well as perspectives on both its risks and opportunities.
![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
“It’s a big thing to integrate [causality] into AI,” Bengio says. “Current
approaches to machine learning assume that the trained AI system will be
applied on the same kind of data as the training data. In real life it is often
not the case.” [Deep learning is blind to cause and effect]](https://image.slidesharecdn.com/ai-titech-virach-20191026-191026063718/85/Ai-titech-virach-20191026-4-320.jpg)
Ai titech-virach-20191026
- 1. When AI becomes a Data-driven Machine!! Virach Sornlertlamvanich Professor, AAII, Musashino University Chair of Digital Cluster, RUN SIIT, Thammasat University National Distinguished Researcher Award 2003, NRCT [email protected] The 12th Thai Kuramae Kai General Meeting, Disruptive Technology Seminar, TNI, October 26, 2019
- 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 Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”— Stephen Hawking “Artificial intelligence will reach human levels by around 2029.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 28 Best Quotes About Artificial Intelligence, 2018 Pessimists
- 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, 2018 “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 Optimists
- 4. 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 “It’s a big thing to integrate [causality] into AI,” Bengio says. “Current approaches to machine learning assume that the trained AI system will be applied on the same kind of data as the training data. In real life it is often not the case.” [Deep learning is blind to cause and effect]
- 5. Differences within AI Artificial Intelligence • General AI • Vertical AI (Expert Systems) • Natural Language Processing • Computer Vision • Machine Learning • ...
- 6. Thinking, Fast and Slow by Nobel laureate Daniel Kahneman (2011) --The two systems-- http://upfrontanalytics.com/market-research-system-1-vs-system-2-decision-making/
- 7. AI advancement that brings about the 3rd AI Boom • Thinking Machines • DeepBlue Chess Machine (1997) • IBM Watson Quiz Show (2011) • DeepMind AlphaGo (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 • Self-Driving Cars • RHINO Museum Tour Guide (1997) • DARPA Grand Challenge (2005) • Google Self-driving Car (2011) Pocket-lint• Smart Assistants • Apple Siri Personal Assistant (2011) • Amazon Echo & Alexa (2014) • Google Home & Assistant (2016)
- 9. 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) Recommendation (Accenture’s Future Workforce, 2018): - Needs of new skilling to work with intelligent machines. - Map new skills to new roles.
- 10. Three Big AI Research Institutes
- 11. 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
- 12. 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
- 13. DFKI, Germany German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz) • 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
- 14. 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
- 15. 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
- 16. Source:
- 17. 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. • Deep learning is good at finding patterns in reams of data, but can't explain how they're connected. (Turing Award winner Yoshua Bengio) • AI will result in net job gain. Reskill for new job role to work with AI