Introduction to Artificial Intelligence –Unit 1

What is AI?Course 240530

Dr. Avi RosenfeldBased on slides from

The Hebrew University of JerusalemSchool of Engineering and Computer Science

Instructor: Jeff Rosenschein

Week Breakdown

1. Introduction to A.I., Course Organization, Introduction to Search2. A*, Minimax, BFS, DFS, Heuristic search3. Local Search Constraint Satisfaction Problems, DSP and DCOP algorithms4. STRIPS and planning algorithms, Probability Theory and Bayesian Networks5. Web based A.I., information retrieval and recommender systems6. Neural Nets, Perceptrons and Machine Learning7. Knowledge Representation, Game Theory, Bounded Rationality and Fuzzy Logic8. NLP9. Agents and Multi-agent systems10. Robotics and Vision11. Multidisciplinary Topics And Applications12. Business Intelligence Applications13. Project #3 (B.I.)14. Review

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Topics

What is A.I.?

Views of AI fall into four categories:

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What is AI?

Thinking humanlyThinking humanly Thinking rationallyThinking rationally

Acting humanlyActing humanly Acting rationallyActing rationally

The AMAI textbook advocates “acting rationally”

The Brain vs. a ComputerComputerComputer Human BrainHuman Brain

Computational Computational UnitsUnits

1 CPU, 101 CPU, 1099 gatesgates

10101111 neurons neurons

Storage UnitsStorage Units 10101010 bits RAM bits RAM 10101111 neurons neurons

Cycle timeCycle time 1010-9-9 seconds seconds 1010-3-3 seconds seconds

BandwidthBandwidth 10101010 bits/sec bits/sec 10101414 bits/sec bits/sec

Memory Memory updates/secondupdates/second

101099 10101414

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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

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Artificial Intelligence

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

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Artificial Intelligence:Scruffy vs. Neat

Turing (1950) “Computing machinery and intelligence”:

“Can machines think?” “Can machines behave intelligently?”

Operational test for intelligent behavior: the Imitation Game

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Acting humanly: Turing Test

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

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Acting rationally: rational agent

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

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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

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State of the Art

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

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Topics We’ll Cover

IJCAI’07 Papers

1,365 papers submitted (authors from 45 different countries)

Accepted 471 papers (unusually high percentage that year, 34.4% accepted)

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Number of accepted papers, by topic:

• Self-driving cars• Watson• Siri• Data-intensive applications that use

information analysis and learning to do things previously beyond machine capabilities

AI’s Recent High-Visibility Successes

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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

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Google Cars, New York Times,IEEE Spectrum

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

Google Cars, New York Times

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• 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

IBM’s Watson

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• “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.”

IBM’s Watson, Jeopardy Winner

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Google’s News Page

Microsoft, Yahoo, and Google all take this very, very seriously

Peter Norvig, Google Director of Research Ron Brachman, built AT&T’s 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

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AI Researchers Head Major Industry Research Labs

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

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Recommendation SystemsCollaborative

FilteringPioneered by, among others, Konstan and Riedl, GroupLens

Commercial sites that use collaborative filtering include:AmazonBarnes and NobleDigg.comhalf.ebay.comiTunesMusicmatchNetflix (the Netflix Prize, grand prize of $1,000,000 for algorithm that beats Netflix's own by 10%)TiVo…

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)

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Data Mining

Collaborative Filtering plusData 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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Spam Filters

When all those emails from Barneys New York become oppressive…

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Comparative Shoppers

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

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

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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?”

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What Can’t They Do (Yet)?

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Translation

Speech Understanding

Nuance’s Dragon NaturallySpeaking and IBM’s ViaVoice

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"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."

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Google Voice, 11.3.09

iPhone Voice Control (pre-Siri)

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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)

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Biology

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

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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

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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

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Operating Systems Programming Languages

SmallTalk Lisp

User Interface Design Advances in use of (not invention of) windows,

pointing devices, bitmapped graphics Web Services XML

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Contributions to Other Computer Science Fields

• 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

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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

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Agents and environments

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

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Agents