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Introduction

Introduction. Tamara Berg CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Dan Klein, Stuart Russell, Andrew Moore, Svetlana Lazebnik , Percy Liang, Luke Zettlemoyer. Today. Course Info What is AI? History of AI Current state of AI. Course Information.

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Introduction

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  1. Introduction Tamara Berg CS 590-133 Artificial Intelligence Many slides throughout the course adapted from Dan Klein, Stuart Russell, Andrew Moore, Svetlana Lazebnik, Percy Liang, Luke Zettlemoyer

  2. Today • Course Info • What is AI? • History of AI • Current state of AI

  3. Course Information • Instructor: Tamara Berg (tlberg@cs.unc.edu) • Office Hours: FB 236, Tues/Thurs 4:45-5:45pm • Course website: http://tamaraberg.com/teaching/Spring_14/ • Course mailing list: comp590-133@cs.unc.edu • TAs: Shubham Gupta & Rohit GuptaTA office hours: TBD • Announcements, readings, schedule, etc, will all be posted to the course webpage. Schedule may be modified as needed over the semester. Check frequently!

  4. Course Information • Textbook: “Artificial Intelligence A Modern Approach” Russell & Norvig, 3rd edition • Prerequisites: • Programming knowledge and data structures (COMP 401 and 410) are required • Reasonable familiarity with probability, algorithms, calculus also highly desired • There will be a lot of math and programming • Work & Grading • Readings (mostly from textbook) • 5-6 assignments including written questions, programming, or both • 2 midterms (approximate dates are on course website) and final exam • Grading will consist of 60% assignments, 40% exams. For borderline cases participation in class or via the mailing list may also be considered.

  5. Programming • Students are expected to know how to program. • Programming assignments will be in python – useful language to know, used in many current AI courses, not too hard to pick up given previous programming experience. • First week – install python on your laptops, do a python tutorial • TAs will also hold drop in tutorials early next week (probably Mon/Tues, times will be posted to website). Make sure to attend if you’re new to python or want a refresher.

  6. Course Information Late policy: • Assignments must be turned in electronically by 11:59pm on the listed due date. • Students will be allowed 5 free homework late days of their choice over the semester (you don't need to ask ahead of time, just use them and we will keep track). • After those are used late homework will be accepted up to 1 week late, with a 10% reduction in value per day late.

  7. Course Information Honor code: • Students are encouraged to complete the assignments in groups of 2. • You may discuss problems at a high level with other students in the class, but all code and written responses should be original within your pair. • To protect the integrity of the course, we will actively check for code or written plagiarism (both from current classmates and the internet). • Exams will be closed book.

  8. About me 1997-2001 Undergrad at U.W. Madison CS and Math 2001-2007 Grad at U.C. Berkeley Ph.D. in CS 2007-2008 Postdoc at Yahoo! Research 2008-2013 Assistant Prof at SBU 2013- Assistant Prof at UNC

  9. Myresearch interests Object Detection 20 object classes 39% accuracy, Girshick et al leonberg Yellow lady’s slipper Image Classification 1000 classes 62.5% accuracy, Krizhevsky et al Image Parsing 33 labels 55% accuracy, Tighe et al Human-centric Computer Vision Computer Vision

  10. BabyTalk: Generating natural language image descriptions This is a picture of one sky, one road and one sheep. The gray sky is over the gray road. The gray sheep is by the gray road. This is a picture of two dogs. The first dog is near the second furry dog. Here we see one road, one sky and one bicycle. The road is near the blue sky, and near the colorful bicycle. The colorful bicycle is within the blue sky.

  11. Recognizing Clothing

  12. Application: Pose Independent Retrieval

  13. About you? • Undergrad/grad • Year • Major/Minors • Background in: • Programming • Calculus • Probability • Python

  14. Sci-Fi AI

  15. Knowledge representation Planning AI Reasoning Social Intelligence Learning Creativity Natural Language Processing Motion & Manipulation (robotics) Perception (computer vision, speech)

  16. What is AI? • Definitions of AI:

  17. AI definition 1: Thinking humanly • Need to study the brain as an information processing machine: cognitive science and neuroscience

  18. AI definition 1: Thinking humanly Can we build a brain?

  19. AI definition 1: Thinking humanly • Can we build a brain?

  20. AI definition 2: Acting humanly • The Turing Test • What capabilities would a computer need to have to pass the Turing Test? • Natural language processing • Knowledge representation • Automated reasoning • Machine learning A. Turing, Computing machinery and intelligence, Mind 59, pp. 433-460, 1950

  21. The Turing Test • Turing predicted that by the year 2000, machines would be able to fool 30% of human judges for five minutes • Loebner prize • 2008 competition: each of 12 judges was given five minutes to conduct simultaneous, split-screen conversations with two hidden entities (human and chatterbot). The winner, Elbot of Artificial Solutions,managed to fool three of the judges into believing it was human [Wikipedia].

  22. Turing Test: Criticism • Success depends on deception! • Chatbots can do well using “cheap tricks” • First example: ELIZA (1966) • Chinese room argument: one may simulate intelligence without having true intelligence (more of a philosophical objection)

  23. A better Turing test? http://www.newyorker.com/online/blogs/elements/2013/08/why-cant-my-computer-understand-me.html

  24. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • The trophy would not fit in the brown suitcase because it was so small. What was so small? • The trophy • The brown suitcase H. Levesque, On our best behaviour, IJCAI 2013

  25. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • The trophy would not fit in the brown suitcase because it was so large. What was so large? • The trophy • The brown suitcase H. Levesque, On our best behaviour, IJCAI 2013

  26. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • The large ball crashed right through the table because it was made of styrofoam. What was made of styrofoam? • The large ball • The table H. Levesque, On our best behaviour, IJCAI 2013

  27. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • The large ball crashed right through the table because it was made of steel. What was made of steel? • The large ball • The table H. Levesque, On our best behaviour, IJCAI 2013

  28. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • Sam tried to paint a picture of shepherds with sheep, but they ended up looking like golfers. What looked like golfers? • The shepherds • The sheep H. Levesque, On our best behaviour, IJCAI 2013

  29. A better Turing test? • Multiple choice questions that can be easily answered by people but cannot be answered by computers using “cheap tricks”: • Sam tried to paint a picture of shepherds with sheep, but they ended up looking like rabbits. What looked like rabbits? • The shepherds • The sheep H. Levesque, On our best behaviour, IJCAI 2013

  30. A better Turing test? Why are these questions hard for computers?? H. Levesque, On our best behaviour, IJCAI 2013

  31. A better Turing test? • Advantages over standard Turing test • Test can be administered and graded by machine • Does not depend on human subjectivity • Does not require ability to generate English sentences • Questions cannot be evaded using verbal dodges • Questions can be made “Google-proof” H. Levesque, On our best behaviour, IJCAI 2013

  32. AI definition 3&4: Rationality • A rational agent acts to optimally achieve its goals • Goals are application-dependent and are expressed in terms of the utility of outcomes • Being rational means maximizing your (expected) utility • This definition of rationality only concerns the decisions/actions that are made, not the cognitive process behind them • In practice, utility optimization is subject to the agent’s computational constraints (bounded rationality or bounded optimality)

  33. Utility maximization formulation • Advantages • Generality: goes beyond explicit reasoning, and even human cognition altogether • Practicality: can be adapted to many real-world problems • Naturally accommodates uncertainty • Amenable to good scientific and engineering methodology • Avoids philosophy and psychology • Disadvantages?

  34. History of AI Image source

  35. Origins of AI: Early excitement 1940s First model of a neuron (W. S. McCulloch & W. Pitts) Hebbianlearning rule Cybernetics 1950s Turing Test Perceptrons(F. Rosenblatt) Computer chess and checkers (C. Shannon, A. Samuel) Machine translation (Georgetown-IBM experiment) Theorem provers (A. Newell and H. Simon, H. Gelernter and N. Rochester) 1956Dartmouth meeting: “Artificial Intelligence” adopted

  36. Herbert Simon, 1957 • “It is not my aim to surprise or shock you –but … there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until –in a visible future – the range of problems they can handle will be coextensive with the range to which human mind has been applied. More precisely: within 10 years a computer would be chess champion, and an important new mathematical theorem would be proved by a computer.” • Simon’s prediction came true – but forty years later instead of ten

  37. Harder than originally thought • 1966: Elizachatbot (Weizenbaum) • “ … mother …” → “Tell me more about your family” • 1954: Georgetown-IBM experiment • Completely automatic translation of more than sixty Russian sentences into English • Only six grammar rules, 250 vocabulary words, restricted to organic chemistry • Promised that machine translation would be solved in three to five years (press release) • Automatic Language Processing Advisory Committee (ALPAC) report (1966): machine translation has failed • “The spirit is willing but the flesh is weak.” →“The vodka is strong but the meat is rotten.”

  38. Blocks world (1960s – 1970s) ??? Larry Roberts, MIT, 1963

  39. History of AI: Taste of failure 1940s First model of a neuron (W. S. McCulloch & W. Pitts) Hebbianlearning rule Cybernetics 1950sTuring Test Perceptrons(F. Rosenblatt) Computer chess and checkers (C. Shannon, A. Samuel) Machine translation (Georgetown-IBM experiment) Theorem provers (A. Newell and H. Simon, H. Gelernter and N. Rochester) Late 1960s Machine translation deemed a failure Neural nets deprecated (M. Minsky and S. Papert, 1969)* Late 1970sThe first “AI Winter” *A sociological study of the official history of the perceptrons controversy

  40. History of AI to the present day 1980s Expert systems boom Late 1980s- Expert system bust; the second “AI winter” Early 1990s Mid-1980s Neural networks and back-propagation Late 1980s Probabilistic reasoning on the ascent 1990s-Present Machine learning everywhere Big Data Deep Learning History of AI on Wikipedia AAAI Timeline Building Smarter Machines: NY Times Timeline

  41. NY Times article

  42. What accounts for recent successes in AI? • Faster computers • The IBM 704 vacuum tube machine that played chess in 1958 could do about 50,000 calculations per second • Deep Blue could do 50 billion calculations per second– a million times faster! • Dominance of statistical approaches, machine learning • Big data • Crowdsourcing

  43. What canAI do today?

  44. IBM Watson • http://www-03.ibm.com/innovation/us/watson/ • NY Times article • Trivia demo • IBM Watson wins on Jeopardy (February 2011)

  45. Self-driving cars • Google’s self-driving car passes 300,000 miles (Forbes, 8/15/2012) • Nissan pledges affordable self-driving car models by 2020(CNET, 8/27/2013)

  46. Natural Language • Speech technologies • Google voice search • Apple Siri • Machine translation • translate.google.com • Comparison of several translation systems

  47. Vision • OCR, handwriting recognition • Face detection/recognition: many consumer cameras, Apple iPhoto • Visual search: Google Goggles, search by image • Vehicle safety systems: Mobileye

  48. Mathematics • In 1996, a computer program written by researchers at Argonne National Laboratory proved a mathematical conjecture unsolved for decades • NY Times story: “[The proof] would have been called creative if a human had thought of it” • Mathematical software:

  49. Games • IBM’s Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 • 1996: Kasparov Beats Deep Blue“I could feel – I could smell – a new kind of intelligence across the table.” • 1997: Deep Blue Beats Kasparov“Deep Blue hasn't proven anything.” • In 2007, checkers was “solved” (though checkers programs had been beating the best human players for at least a decade before then) • Science article

  50. Logistics, scheduling, planning • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people • NASA’s Remote Agent software operated the Deep Space 1 spacecraft during two experiments in May 1999 • In 2004, NASA introduced the MAPGEN system to plan the daily operations for the Mars Exploration Rovers

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