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

Topics

Week Breakdown 1.

2.

3.

4.

5.

6.

Introduction to A.I., Course Organization, Introduction to Search A*, Minimax, BFS, DFS, Heuristic search Local Search Constraint Satisfaction Problems, DSP and DCOP algorithms STRIPS and planning algorithms, Probability Theory and Bayesian Networks Web based A.I., information retrieval and recommender systems Neural Nets, Perceptrons and Machine Learning 7.

8.

Knowledge Representation, Game Theory, Bounded Rationality and Fuzzy Logic 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

What is A.I.?

What is AI?

Views of AI fall into four categories: Thinking humanly Thinking rationally Acting humanly Acting rationally The AMAI textbook advocates “ acting rationally ” 4

The Brain vs. a Computer

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

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Artificial Intelligence

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? – doesn’t seem to be the real issue Software?

 Scruffy vs. Neat debate 6

Artificial Intelligence: Scruffy vs. Neat  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

Acting humanly: Turing Test

  Turing (1950) “ Computing machinery and intelligence ” : “ Can machines think?

” behave intelligently?

”  “ Can machines  Operational test for intelligent behavior: the Imitation Game  8

Acting rationally: rational agent

   Rational behavior: doing the right thing The right thing: that which is expected to maximize goal achievement, given the available information Doesn’t necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action  9

State of the art

 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

   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 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

Topics We’ll Cover

       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

IJCAI’07 Papers

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

AI’s Recent High-Visibility Successes

• • • • Self-driving cars Watson Siri Data-intensive applications that use information analysis and learning to do things previously beyond machine capabilities 14

DARPA’s 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

Google Cars, New York Times, IEEE Spectrum

How Google’s cars work: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self driving-car-works 16

Google Cars, New York Times

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IBM’s Watson

• • • 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 18

IBM’s Watson, Jeopardy Winner

• • • • “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.” 19

Google’s News Page

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AI Researchers Head Major Industry Research Labs      Microsoft, Yahoo, and Google all take this very, very seriously Peter Norvig, Google Director of Research Ron Brachman, built AT&T Operations at Yahoo ’ s AI Research group, now Vice President of Worldwide Research Eric Horvitz, head of Adaptive Systems & Interaction Group, Microsoft Research AI Theory and AI Practice are looked to for solutions 21

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

Recommendation Systems

Collaborative Filtering Pioneered by, among others, Konstan and Riedl, GroupLens Commercial sites that use collaborative filtering include: Amazon Barnes and Noble Digg.com

half.ebay.com

iTunes Musicmatch Netflix (the Netflix Prize, grand prize of $1,000,000 for algorithm that beats Netflix's own by 10%) TiVo … 23

Data Mining

   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

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

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When all those emails from Barneys New York become oppressive…

Spam Filters

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Comparative Shoppers

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

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

What Can’t They Do (Yet)?

 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

Translation

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Speech Understanding

 Nuance’s Dragon NaturallySpeaking and IBM’s ViaVoice 31

Google Voice, 11.3.09

 "FREE VOICE MAIL TRANSCRIPTIONS: From now on, you don’t have to listen to your messages in order; you don’t 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

iPhone Voice Control (pre-Siri)

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Biology

  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

Computer Games

   Realistic single-agent and multi-agent activity in cooperative and competitive environments What they call “ AI ” often isn’t 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

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

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More Game Theory…

Milind Tambe’s group at USC studied optimal strategies for intrusion detection, “ Playing Games for Security: An Efficient Exact Algorithm for Solving Bayesian Stackelberg Games ” , AAMAS ’ 08 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

Contributions to Other Computer Science Fields

    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

A Moment on AI Ethics

• 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?

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Agents and environments

   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

 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