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Introduction to Artificial Intelligence – Unit 1 What is AI? Course 240530 Dr. Avi Rosenfeld Based on slides from The Hebrew University of Jerusalem School.

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Presentation on theme: "Introduction to Artificial Intelligence – Unit 1 What is AI? Course 240530 Dr. Avi Rosenfeld Based on slides from The Hebrew University of Jerusalem School."— Presentation transcript:

1 Introduction to Artificial Intelligence – Unit 1 What is AI? Course 240530 Dr. Avi Rosenfeld Based on slides from The Hebrew University of Jerusalem School of Engineering and Computer Science Instructor: Jeff Rosenschein

2 Week Breakdown 1.Introduction to A.I., Course Organization, Introduction to Search 2.A*, Minimax, BFS, DFS, Heuristic search 3.Local Search Constraint Satisfaction Problems, DSP and DCOP algorithms 4.STRIPS and planning algorithms, Probability Theory and Bayesian Networks 5.Web based A.I., information retrieval and recommender systems 6.Neural Nets, Perceptrons and Machine Learning 7.Knowledge Representation, Game Theory, Bounded Rationality and Fuzzy Logic 8.NLP 9.Agents and Multi-agent systems 10.Robotics and Vision 11.Multidisciplinary Topics And Applications 12.Business Intelligence Applications 13.Project #3 (B.I.) 14.Review 2 Topics

3 What is A.I.?

4 Views of AI fall into four categories: 4 What is AI? Thinking humanly Thinking rationally Acting humanly Acting rationally The AMAI textbook advocates acting rationally

5 The Brain vs. a Computer Computer Human Brain Computational Units 1 CPU, 10 9 gates 10 11 neurons Storage Units 10 10 bits RAM 10 11 neurons Cycle time 10 -9 seconds 10 -3 seconds Bandwidth 10 10 bits/sec 10 14 bits/sec Memory updates/second 10 9 10 14 5

6 Why is it difficult to program computers to do what humans easily do? Recognize faces Understand human language (Ironically, we can more successfully program computers to do what humans cannot easily do Play chess at world champion levels Carry out massive optimization problems) Processing power? – doesnt seem to be the real issue Software? Scruffy vs. Neat debate 6 Artificial Intelligence

7 The Scruffy approach says, Build systems that work, and principles will emerge. E.g., the Wright Brothers building a heavier-than-air flying machine The Neat approach says, Explore principles first, and having understood them, embody them in systems. E.g., radar 7 Artificial Intelligence: Scruffy vs. Neat

8 Turing (1950) Computing machinery and intelligence: Can machines think? Can machines behave intelligently? Operational test for intelligent behavior: the Imitation Game 8 Acting humanly: Turing Test

9 Rational behavior: doing the right thing The right thing: that which is expected to maximize goal achievement, given the available information Doesnt necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action 9 Acting rationally: rational agent

10 Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 Proved a mathematical conjecture (Robbins conjecture) unsolved for decades No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego); DARPA Grand Challenges (and Google) show that cars can drive themselves inside and outside of cities 10 State of the art

11 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 NASAs on-board autonomous planning program controlled the scheduling of operations for a spacecraft, and for the Mars Rover Proverb solves crossword puzzles better than most humans 11 State of the Art

12 Introduction and Background: ½ week Search: 2 ½ weeks Knowledge Representation: 2 weeks Planning: 2 weeks Learning: 3 weeks Game Theory: 3 weeks Summation: 1 week 12 Topics Well Cover

13 IJCAI07 Papers 1,365 papers submitted (authors from 45 dierent countries) Accepted 471 papers (unusually high percentage that year, 34.4% accepted) 13 Number of accepted papers, by topic:

14 Self-driving cars Watson Siri Data-intensive applications that use information analysis and learning to do things previously beyond machine capabilities AIs Recent High-Visibility Successes 14

15 DARPAs Grand Challenge First Challenge: Driver-less vehicle go 130 miles across desert This was not a simple task: it involved unclear roads, tunnels, roads along cliffs, and the path was given to teams only hours before the race 2004: $1 million prize, utter failure 2005: $2 million prize 15

16 Google Cars, New York Times, IEEE Spectrum 16 How Googles cars work: driving-car-works

17 Google Cars, New York Times 17

18 Jeopardy is a quiz show, where answers are given, and 3 contestants compete to be the first to provide the question: –Freud published this landmark study in 1899. What is The Interpretation of Dreams? In 2011, Watson competed against Ken Jennings (who had the longest championship streak, 75 days), and Brad Rutter, the all-time biggest money winner on the show Final Score: Watson, $77,147; Jennings, $24,000, Rutter, $21,600 IBMs Watson 18

19 The first person mentioned by name in The Man in the Iron Mask is this hero of a previous book by the same author. Hemophilia is a hereditary condition in which this coagulates extremely slowly. This director, better known as an actor, directed his wife Audrey A long, tiresome speech delivered by a dessert topping. IBMs Watson, Jeopardy Winner 19

20 20 Googles News Page

21 Microsoft, Yahoo, and Google all take this very, very seriously Peter Norvig, Google Director of Research Ron Brachman, built AT&Ts AI Research group, now Vice President of Worldwide Research Operations at Yahoo Eric Horvitz, head of Adaptive Systems & Interaction Group, Microsoft Research AI Theory and AI Practice are looked to for solutions 21 AI Researchers Head Major Industry Research Labs

22 Ad Auctions Google reported revenues of $5.19 billion for the quarter ended March 31, 2008 The vast majority of this is from those little ads on the right of the page 22

23 23 Recommendation Systems Collaborative Filtering Pioneered by, among others, Konstan and Riedl, GroupLens Commercial sites that use collaborative filtering include: Amazon Barnes and Noble iTunes Musicmatch Netflix (the Netflix Prize, grand prize of $1,000,000 for algorithm that beats Netflix's own by 10%) TiVo …

24 Go through large amounts of data Extract meaningful insight Local Example: Ronen Feldman, Business School professor at Hebrew University (formerly Bar Ilan University), founded ClearForest (bought by Reuters) 24 Data Mining

25 Collaborative Filtering plus Data Mining The search for a better recommendation continues with numerous companies selling algorithms that promise a retailer more of an edge. For instance, Barneys New York, the upscale clothing store chain, says it got at least a 10 percent increase in online revenue by using data mining software that finds links between certain online behavior and a greater propensity to buy. Using a system developed by Proclivity Systems, Barneys used data about where and when a customer visited its site and other demographic information to determine on whom it should focus its e-mail messages. – New York Times, 19.5.08 25

26 26 Spam Filters When all those emails from Barneys New York become oppressive…

27 27 Comparative Shoppers Pioneered by, among others, Bruce Krulwich (BargainFinder), Oren Etzioni (MetaCrawler, NetBot [bought by Excite in 1997])

28 Comparison Shopping Plus Learning FareCast (formerly Hamlet) tracks airline prices, advises whether to buy now or wait until later Founded by Oren Etzioni, bought by Microsoft in April 2008 28

29 Integrate information in a more sophisticated way What were the combined earnings from ad auctions across Google, Yahoo, and Microsoft in 2007? Plan How can I drive from San Francisco to Los Angeles, in a way that reasonably maximizes the number of Starbucks stores I pass? 29 What Cant They Do (Yet)?

30 30 Translation

31 Speech Understanding Nuances Dragon NaturallySpeaking and IBMs ViaVoice 31

32 "FREE VOICE MAIL TRANSCRIPTIONS: From now on, you dont have to listen to your messages in order; you dont have to listen to them at all. In seconds, these recordings are converted into typed text. They show up as e- mail messages or text messages on your cellphone." 32 Google Voice, 11.3.09

33 iPhone Voice Control (pre-Siri) 33

34 Computational Biology Techniques from Computer Science in general, and Artificial Intelligence in particular, are being used in the exploration of biological questions AI researchers have played an important role in this (e.g., Daphne Koller, Nir Friedman) 34 Biology

35 Computer Games Realistic single-agent and multi-agent activity in cooperative and competitive environments What they call AI often isnt But they are getting more serious about it: Companies have started up exploring Game AI Training programs (often military training) for reacting to realistic situations 35

36 Other Games: Poker Active research and competitions (machine vs. machine, machine vs. person) in Texas Hold-Em [University of Alberta, Carnegie-Mellon University] Different domain than chess – imperfect information CMU team is making use of game theoretic equilibrium concepts in their software 36

37 More Game Theory… Milind Tambes group at USC studied optimal strategies for intrusion detection, Playing Games for Security: An Efcient Exact Algorithm for Solving Bayesian Stackelberg Games, AAMAS08 Interesting theoretical work, focused on efficient algorithms Deployed for last 18 months at LAX airport in Los Angeles to tell guards how to patrol 37

38 Operating Systems Programming Languages SmallTalk Lisp User Interface Design Advances in use of (not invention of) windows, pointing devices, bitmapped graphics Web Services XML 38 Contributions to Other Computer Science Fields

39 Who is responsible if a self-driving car is at fault in a crash? – The software developer? – The company that installed the software? – The driver that trusted the software? – Society? A Moment on AI Ethics 39

40 The agent function maps from percept histories to actions: [f: P* A ] The agent program runs on the physical architecture to produce f agent = architecture + program 40 Agents and environments

41 An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators Robotic agent: cameras and infrared range finders for sensors; various motors for actuators 41 Agents

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