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Ai history to-m-learning


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This is used for brief talk about AI and its recent application in Machine Learning and Deep Learning field.
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Ai history to-m-learning

  1. 1. Problem Solving with Knowledge From Artificial Intelligence To Machine Learning Kyung Eun Park, D.Sc. Augusta Ada King, Countess of Lovelace
  2. 2. Contents 1. AI Overview 2. How AI is implemented? 3. From AI to Machine Learning 4. Machine Learning 5. Examples of AI and Machine Learning 6. Behavior Training with BCI and Motion Recognition 7. Conclusion
  3. 3. What is Artificial Intelligence? D E F I N I T I O N “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable,” by John McCarthy, 1956. … “Broadly, AI is the computer-based exploration of methods for solving challenging tasks that have traditionally depended on people for solution. Such tasks include complex logical inference, diagnosis, visual recognition, comprehension of natural language, game playing, explanation, and planning” by Eric Horvitz, 1990.
  4. 4. AI Timeline Ada (1842) Alan Turing (1950) The first conference on AI by John McCarthy, Marvin Minsky (1956) Demonstrated by Newell (1957) Unimations working on GE (1961) Joseph Weizenbau m (1965), E. Geigenbau m (1965) Chess- playing program by Greenblatt at MIT (1968) Jack Myers Harry Pople (1979) 1980s Ian Horswill (1993) TiVo Suggestions (2005) Apple, Google, Micorsoft (2011) Machine Learning, Deep Learning (2013 ~)
  5. 5. Knowledge in AI • Human knowledge • Converted into a format suitable for use by an AI system • AI generated/learned knowledge • Generated by an AI system • By gathering data and information, and • By analyzing data, information, and knowledge at its disposal Knowledge acquisition process is pretty similar to the normal learning procedure. In brief, AI stores and uses the knowledge to solve problems.
  6. 6. Predicate Logic Object Properties: Is-a relationship Instance-of relationship ex) isSymptomOf: … maybeSymptomOf: … mayHaveSymptom: … shouldHaveSymptom: … Knowledge Representation by Healthcare Example Classes: SuperclassOf SubclassOf ex) Disease Class Symptom Class Object: Sym Tachycar dia Subject: Hypo perfusion shouldHave Symptom predicate
  7. 7. Semantic Network • Building relationship between Diseases and Symptoms • Constructing semantic graph with Nodes (instance objects) and Edges (object properties) Sym Tachycardia Congestive HeartFailure HeatStroke Hypo perfusion Overdose Acute Myocardial Infarction shouldHave Symptom maybe SymptomOf maybe SymptomOf maybe SymptomOf maybe SymptomOf
  8. 8. Minsky’s Insights into Human and Machine Intelligence • Computer’s role in this context: • It will help us to understand our own brains, to learn what is the nature of knowledge. • It will teach us how we learn to think and feel. • This knowledge will change our views of Humanity and enable us to change ourselves. … in an interview in 1998, Sabbatini
  9. 9. From Artificial Intelligence To Machine Learning IBM’s Watson (2011)
  10. 10. Machine learns by itself. SELF STUDY
  11. 11. Machine Learning D E F I N I T I O N “Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data”
  12. 12. In 1956, he wanted this computer to beat him at checkers. He made the computer play against itself thousands of times and learn how to play checkers, and indeed IT WORK! By 1962 this computer had beaten the Connecticut state champion. Arthur Samuel
  13. 13. Machine Learning NOW Machine learning is actively being used today. • The search engine • The spam filter • The recommender system • The face/handwriting /fingerprint recognition • The location/context-based security system • The disease diagnosis & prediction • The weather forecast, etc.
  14. 14. Self-Driving Car
  15. 15. Diagnosis & Prediction
  16. 16. • Turning data via information into knowledge • A tool that can be applied to many problems. • Uses statistics for solving the problem of not being able to model the problem fully. • ex) Maximize human’s happiness • For these problems, we need to use some tools from statistics. Machine Learning Process
  17. 17. • Human-created data from the World Wide Web • More non-human generated data coming online • Challenge & Opportunity: How to connect the data to the WWW and use them? Sensors and Data Deluge
  18. 18. Key Terminologies by Example Bird Classification System • Expert system: ornithologist • Features (or attributes): Weight, Wingspan, Webbed feet, Back color • Target variable: Species (predicted) • Instance: each row made up of features • The first two features: numeric • The 3rd feature: binary (0 or 1) • The 4th feature: enumeration (integer)
  19. 19. • How do we decide if a bird is an Ivory-Billed Woodpecker or something else? Classification task is needed! • Many machine learning algorithms good at classification Choose a machine learning algorithm (Classifier) to use • Train the classifier Feed it quality data known as a training set Classification as a Machine Learning Algorithm Bird Classification
  20. 20. • Training set of data and a separate dataset, called a test set • Multi-step Machine Learning TRAINING/LEARNING ▬ TESTING ▬ USING Testing a Machine Learning Algorithm Raw Data (Training Set) ClassifierFeature Raw Data (Test Set) ClassifierFeature Feature Extraction Feed Data Acquisition Data Acquisition Feature Extraction Training Feed Classification Result Training Phase: Testing Phase: Knowledge Representation
  21. 21. Key Tasks of Machine Learning • In the previous classification task, The job is to predict what class an instance should fall into. • Another task, regression, The prediction of a numeric number • Both classification and regression are examples of Supervised Learning We are telling the algorithm what to predict. • Unsupervised Learning There’s no label or target value given for the data • Clustering Group of similar items in unsupervised learning • Density estimation Statistical values that describe data in unsupervised learning
  22. 22. Supervised learning tasks Classification Regression k-Nearest Neighbors Linear Naïve Bayes Locally weighted linear Support vector machines Ridge Decision trees Lasso Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window Machine Learning Algorithms
  23. 23. Behavior Training Platform NeuroSky Interface Narrative Contents Manager Interactive Intervention Controller Sensor & Intervention Data CenterScene Manager Kinect Interface Brainwaves & Motion Recognition Interface Sensor & Intervention Data Repository Scene 3 Brainwaves Scene 2 Scene 1 . . . Motion Character, Space, Action, Item, Quest, Contexts, etc. Behavior Training Platform
  24. 24. Motion Recognition Learning Skeletal tracking with Kinect: recognizing 22 different motions Head tracking with Kinect: recognizing 6 different motions
  25. 25. Brainwave Recognition Learning Data Center Brainwaves MindWave Interface Collect and Save brainwaves Collect event logs Send to database
  26. 26. Interactive Game Scenario Scene Purpose Graphic Character Space Item Action Text Interactive Intervention 2 Go to the sea with fishing bag on the shoulder Via Kinect: Monitors the player’s motion and has the character pause when the player moves. Via MindWave: Increase the character’s moving speed when the attention level increases. Let’s go to the sea for fishing. Can you help me with the fishing bag? You sit still and see the way to go. Walking or running Fishing bag Mountai n path to the sea Fisherman (Example: a set of fairy tale contents within a scene)
  27. 27. Motion Recognition
  28. 28. Behavior Training with Intervention 34 Normal playing with the player sitting in place Paused upon recognizing the player’s motion
  29. 29. Interactive Intervention
  30. 30. Summary • Problem solving with knowledge from through AI to through Machine Learning • Knowledge learned by machine itself using Big data of IoT/IoE • AI  Machine Learning  Deep Learning Internet of Everything
  31. 31. R E F E R E N C E S 1. John McCarthy, “What is Artificial Intelligence?” http:// www. formal. Stanford. EDU/ jmc/ whatisai/ 2. Wiki, “Timeline of Artificial Intelligence,” 3. Eric Horvitz, “Computation and action under bounded resources,” 1990 4. David Moursund, “Brief Introduction to Educational Implications of Artificial Intelligence,” 2005, 2006 5. Peter Harrington, “Machine Learning in Action,” Manning Publications, 2012 6. Henrik Brink, Joseph W. Richards, "Real-World Machine Learning,”Manning Publications, 2015 7. IBM Watson, 8. Google’s IoT operating system, Brillo, google-iot-operating-system-codenamed-brillo-may-arrive.html