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Introduction to Artificial Intelligence - Pengenalan Kecerdasan Buatan

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Kuliah Pengantar Kecerdasan Buatan oleh Dr. Sunu Wibirama. Kuliah ini dibagi menjadi empat bagian, yakni:
Part 1: Revolusi Industri 4.0 dan Kecerdasan Buatan
Part 2: Sejarah Turing Machine dan Teknologi Kecerdasan Buatan
Part 3: Pengantar Machine Learning
Part 4: Pengantar Deep Neural Network

Instruktur:
Dr. Sunu Wibirama (UGM, Indonesia)
http://sunu.staff.ugm.ac.id

Published in: Data & Analytics
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Introduction to Artificial Intelligence - Pengenalan Kecerdasan Buatan

  1. 1. www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada Short profile ▪ Dr. Sunu Wibirama Teknik Elektro, Universitas Gadjah Mada 
 Yogyakarta, Indonesia (S.T.) – 2007
 
 Dept. Electronics, King Mongkut’s Institute
 of Technology Ladkrabang, Bangkok,
 Thailand (M.Eng.) – 2010
 
 Graduate School of Science and Technology,
 Tokai University, Tokyo, Japan
 (Dr.Eng.) – 2014 sunu@ugm.ac.id http://sunu.staff.ugm.ac.id http://bit.ly/gazetracking Affiliation: 
 Intelligent Systems RG
 Dept. of Electrical Engineering & 
 Information Technology 
 Faculty of Engineering 
 Universitas Gadjah Mada
 Area of research • eye-gaze tracking • computer vision • human-computer interaction • user experience • human factors and safety in 3D technology and virtual reality
  2. 2. http://sunu.staff.ugm.ac.id Goal of this introductory session • Understanding basic concept of artificial intelligence (AI) • Differentiating AI with machine learning and deep learning • Identifying various applications of AI that support daily activities • Pointing out some milestones in history of AI • Comparing basic differences of machine learning and deep learning
  3. 3. Overview of the lecture • Part 1: Industrial Revolution 4.0 and Artificial Intelligence • Part 2: History of Turing Machine and Current Status of Artificial Intelligence • Part 3: Introduction to Machine Learning • Part 4: The Rise of Deep Neural Network The Fourth Industrial Revolution
  4. 4. http://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018
  5. 5. What is Artificial Intelligence? Your day-by-day activities
  6. 6. Artificial intelligence (AI) • Intelligence: the ability to acquire and apply knowledge • Artificial intelligence is created to simulates human intelligence processes by machines, especially computer systems. • These processes include learning, decision-making, and self-correction. Particular applications of AI include expert systems (e.g., Google Maps), speech recognition (e.g.,: Apple Siri) and machine vision 
 (e.g., Facebook’s face recognition). Can you find one example of AI technology that you encounter in your life? Short quiz
  7. 7. End of Part 1 www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence 
 (part 2) Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada
  8. 8. History of Turing Machine and Current Status of Artificial Intelligence AI - some years ago Alan Turing - The father 
 of modern computer system The original Enigma 
 machine used by 
 Nazi squad — 
 collection of 
 Museum of Science 
 and Industry, Chicago US (my personal photo
 collection © 2014)
  9. 9. AI - some years ago Alan Turing has developed 
 a Turing Test in 1950 
 while working in 
 University of Manchester, UK. The "standard interpretation" of the Turing test, in which player C (the interrogator) is given the task of trying to determine which player – A or B – is a computer and which is a human. The interrogator is limited to using the responses to written questions to make the determination AI - some years ago IBM's Watson beat two Jeopardy champions in a special edition of game show in 2011
  10. 10. AI - Google I/O2018 What do you think? 
 Has AI passed the Turing Test? Short quiz
  11. 11. End of Part 2 www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence 
 (part 3a) Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada
  12. 12. Introduction to
 Machine Learning (Michael Copeland, 2016)
  13. 13. Machine Learning “A computer program is said to learn from experience (E) with some class of tasks (T) and a performance measure (P) if its performance at tasks in T as measured by P improves with E”
 (Tom Mitchell, 1997) Some important terminologies • Training/Evolution set – Set of data to discover potentially predictive relationships. • Instances – A sample is an item to process (e.g. classify). It can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits. • Features / attributes – The number of features or distinct traits that can be used to describe each item in a quantitative manner. • Feature vector – is an n-dimensional vector of numerical features that represent some object. • Feature extraction – Preparation of feature vector – transforms the data in the high-dimensional space to a space of fewer dimensions.
  14. 14. (class / label) (features) (instance) www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence 
 (part 3b) Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada
  15. 15. Apple What do you mean by Simple example Learning with experience (supervised) Features: 1. Color: Radish/Red 2. Type : Fruit 3. Shape etc… Features: 1. Sky Blue 2. Logo 3. Shape etc… Features: 1. Yellow 2. Fruit 3. Shape etc… Class : red apple Class : apple logo Class : green apple
  16. 16. Now, what is this? Human can make an inference almost effortlessly, but you cannot expect the same thing on computer. We train a computer with training data and we expect the computer to make inference over new (test) data. How machine learning works (simplified)
  17. 17. Machine Learning for 
 Vehicle Plate Number Recognition DIP DIP DIP Researcher: D.Sihombing, H.A. Nugroho, S. Wibirama (2015) A. Prasetyo, S. Wibirama, N.A. Setiawan (2016) Machine learning Machine learning for traffic monitoring Researcher: D.A. Kurniawan, S. Wibirama, N.A. Setiawan (2016)
  18. 18. Machine learning in medical eye tracking San Diego, US Industrial partner: Artificial Intelligence in Autonomous Navigation DARPA Grand Challenge 
 Stanley autonomous car Google intelligent car
  19. 19. Let's see a video about a mind blowing self-driving car....
  20. 20. If AI can drive a car, do you think that in future we do not need human drivers for public transports anymore? Short quiz End of Part 3
  21. 21. www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence 
 (part 4a) Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada Recent advance in Machine Learning: 
 The Rise of Deep Neural Network
  22. 22. (Michael Copeland, 2016) Click to start! Basic concept of neural network algorithm
  23. 23. Neural networks was used to recognize hand writing in a bank check (LeCun,1999) The Hype is Real for Sure... Even the Twilight's girl has published a Deep Learning paper
  24. 24. The Rise of Deep Learning in Silicon Valley • 2012 was the first year that neural nets grew to prominence • Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton used them to win that year’s ImageNet competition (basically, it is the annual olympics of computer vision) • They dropped the classification error record from 26.2% to 15.3%, a remarkable improvement at the time.
 • Ever since then, a host of companies have been using deep learning at the core of their services: • Facebook uses neural nets for their automatic tagging algorithms • Google for their photo search • Amazon for their product recommendations • Pinterest for their home feed personalization • Instagram for their search infrastructure. Mind-blowing citations in 6 years 21645 papers have cited them since 2012
  25. 25. To whom you can refer Dr. Yann LeCun
 Head, Facebook AI Lab Prof. Geoffrey Hinton
 Canadian Institute for Advanced Research / Google Brain Prof. Yoshua Bengio
 Montreal Institute
 for Learning Algorithm Dr. Andrew Ng
 Coursera, Baidu, 
 Stanford University pic of andrew ng https://www.cifar.ca/research/learning-in-machine-and-brains/ Canadian Institute for Advanced Research (CIFAR)
  26. 26. Facebook AI Laboratory Google Brain
  27. 27. https://www.thestar.com/news/gta/2017/03/28/new-toronto-institute-aims-to-be- worldwide-supplier-of-artificial-intelligence-capability.html News on 28 March 2017 Nvidia Deep Learning Institute
  28. 28. www.ugm.ac.idLocally Rooted, Globally Respected Artificial Intelligence – Department of Electrical Engineering and Information Technology Dr. Sunu Wibirama Introduction to Artificial Intelligence 
 (part 4b) Dr. Sunu Wibirama 
 sunu@ugm.ac.id Department of Electrical Engineering 
 and Information Technology
 Faculty of Engineering
 Universitas Gadjah Mada Machine Learning vs. Deep Learning What are the core differences?
  29. 29. Workflow of Traditional Machine Learning Most machine learning research works try to develop novel features for more accurate performance
  30. 30. Deep Learning
  31. 31. Pros and cons Source: Introducing Deep Learning with Matlab (Mathworks, 2018) Summary • Artificial intelligence has been used in so many applications. Practically, we are surrounded by AI technologies, in so many forms. • Machine learning is a sub-field of AI that focuses on giving ability to a computer to learn from data without explicitly being programmed by a human. • Deep-learning is a new form of neural network research. It is now the most popular algorithm of machine learning technology used in various companies.
  32. 32. Q and A

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