artificial intelligence

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פרופ’ שיזף רפאלי

ביה”ס למנהל עסקים, אונ’ חיפה

Artificial intelligence

יישומי בינה מלאכותית בניהול


ביה”ס למוסמכים במנהל עסקים

אוניברסיטת חיפה

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Overview

הנושאים העקריים בסרט הוידאו:•לחשוב?יכולות האם מכונות –

אם כן, איך? )לוגית או ביולוגית?(–

לחשוב?צריכות האם מכונות –

Turing? מבחן איך נדעאיך מכונות כבר חושבות, –

AIסוגי מערכות של •Expert systemsמערכות מומחה –Neural netרשתות נוירונים –Fuzzy logicלוגיקה מעורפלת –

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שמות ומושגים מתוך הסרט

Weizenbaum

Turing

Feigenbaum

Winograd

Searle

McCarthy

Kurzweil

Lenat

Common Sense

Inference Engine

Forward, Backward chaining

Expert Systems, Neural Networks

Understanding Natural Language, SHRDLU

Top-down, Bottom-up

Genetic Algorithms

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Artificial intelligence

• Goal– To develop computers that can act like

people in every way

• What do humans do?– Inwardly: think, react, emote

– Outwardly: move, see, feelcreate, innovate, invent, crack jokes

– Communicate among ourselves

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AI foundations

Cognitive scienceפסיכולוגיה, קוגניציה •Psychology

Roboticsרובוטיקה •Interfacesממשקים •(Genetics and evolutionגנטיקה ואבולוציה )•

Computer scienceמדעי המחשב •מערכות מידע•

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Developments by AI

• Decision support systems– Support decision-making by humans

• Expert systems– Make decision in place of an expert;

– Acts as an expert assistant to a non-expert user

• Neural nets• Fuzzy logic

• Also: Understanding natural language and robotics

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The AI family

NATURALNATURALLANGUAGELANGUAGE

ROBOTICSROBOTICS PERCEPTIVEPERCEPTIVESYSTEMSSYSTEMS

EXPERTEXPERTSYSTEMSSYSTEMS

INTELLIGENTINTELLIGENTMACHINESMACHINES

ARTIFICIALARTIFICIALINTELLIGENCEINTELLIGENCE

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AI chronology

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The AI paradox / tragedy

• Simon’s prediction

• Promise / fulfillment

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The Turing test

• A computer deserves to be called intelligent if it could deceive

a human into believing that it was human.

• Yes, but… Searle’s Chinese Room...

• See Kurzweil’s “Cybernetic Poet”,at http://www.kurzweiltech.com/

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Eliza as symbol of paradox

• History of Joseph Weizenbaum,

• Computer Power and Human Reason

• Try ELIZA out for yourself, at:– http://www.planetary.net/robots/eliza.html

– or at http://www.parnasse.com/drwww.shtml

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למה זה מעניין מנהלים ? Why would business be interested ?

• PRESERVE EXPERTISE• CREATE KNOWLEDGE

BASE• MECHANISM NOT

SUBJECT TO FEELINGS, FATIGUE, WORRY, CRISIS

• ELIMINATE ROUTINE / UNSATISFYING JOBS

• ENHANCE KNOWLEDGE BASE

שימור הנסיון•

יצירת בסיס ידע•

מכונות אינן “רגישות”, •מתעייפות, דואגות, נלחצות,

יוצאות למילואים או לחופשת לידה

אפשר לוותר על עבודות •משעממות, רוטיניות, לא מספקות

אפשר לשפר את הידע הארגוני •על משימותיו

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מערכות מומחהExpert systems

• Application that acts as an expert

CAPTURES HUMAN EXPERTISE

IN LIMITED DOMAINS

OF KNOWLEDGE• It gives recommendations

– about customer credit– engineering problems

– legal situations– sifting for the exception

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Expert System types (1)

• KNOWLEDGE BASE: Model of Human Knowledge

• RULE - BASED EXPERT SYSTEM : AI System Based on IF - THEN Statements (Bifurcation); Rule Base: Collection of IF - THEN Knowledge

• SEMANTIC NETS: Uses Property of INHERITANCE to Organize, Classify

Interrelated Characteristics

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Expert System types (2)

• AI SHELL: Programming Environment of Expert System

• INFERENCE ENGINE: Search Through Rule Base

– FORWARD CHAINING: Uses Input; Searches Rules for Answer

– BACKWARD CHAINING: Begins with Hypothesis, Seeks Information Until

Hypothesis Accepted or Rejected

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Expert Systems limitations

• Often Reduced to Problems of Classification

• Can be Large, Lengthy, Expensive

• Maintaining Knowledge Base Critical

• Many Managers Unwilling to Trust such Systems

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Top-down in ExSys

•

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Simple example

• If the animal hops and the object is tall, then the animal is a kangaroo.

• If the animal is gray and large, then the animal is an elephant

• The animal is tall. The animal is gray. The animal is large.

• Solve by back chaining: What is the animal?

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Parts of an expert system

• Inference engine– Knows which rules to execute

• Rule base– List of rules

• Fact base– Set of facts we provided

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Example Consultation

Do you generally prefer dry, medium, or sweet wines? DRYDo you generally prefer red or white wines? WHITEDo you generally prefer light, medium or full bodied wines? MEDIUMIs the flavor of the meal delicate, average or strong? AVERAGEDoes the meal have a sauce on it? YESIs the sauce for the meal spicy, sweet, cream, or tomato? SPICYIs the main component of the meal meat, fish, or poultry? MEAT

WINE CERTAINTY==========================Burgundy 90%Zinfandel 64%Chardonnay 64%Cabarnet-Sauvignon 40%Sauvignon-Blanc 40%Gevertzraminer 40%

RECOMMENDATION:

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Other points to be made (1/2)

• Symbolic processing– Forget bits, bytes, and data structures —

think of ‘symbols’

• Applicability– Rules are specific

– Inference engine is general

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Other points to be made (2/2)

• Expert system shell– Provides inference engine

– Helps create rule and fact base

– General tool for a wide variety of problems

– Gives user capability to answer:» How did you determine that answer?

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What is expertise?

• Rules, memory volume, frames?

• Simon’s chess experiment

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ES Examples online

• CLIPS: an ES shell made by NASA. Visit at:– http://www.jsc.nasa.gov/~clips/CLIPS.html

• Try JESS monkey-and-bananas online, at :– http://www.scs.ryerson.ca/~dgrimsha/java/c820a198/

• Try the whale expert system, at:– http://www.vvv.com/ai/demos/whale.html

• Winning AI financial and manufacturing applications:

– http://www.brightware.com/company/awards/index.html

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Neural Nets - back to biology

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רשתות נויורוניםNeural Nets

• Designed to simulate the brain

• Neurons, synapses

• Useful when– Problem is difficult

– An expert can’t say exactly how a decision is arrived at

– Past situations (descriptions and decisions) are available

• Ideal for massively parallel computers

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Neurons, Synapses, Axons, Dendrites

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And again...

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Credit decisions?

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יישומי רשתות נורוניםApplications of neural nets

• Character recognition

• Credit card fraud detection

• Petroleum exploration

• Financial forecasting

• Loan approval

• Missile guidance

• Parking a truck

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רשתות נוירונים לעומת מערכות מומחה

• Neural networks are flexible tools in a dynamic environment.

• Neural networks have the capacity to learn rapidly and change quickly

• Rule based systems are limited to the specific situation for which they were

designed;

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רשתות נוירונים לעומת מערכות מומחה

– Expert systems are rule-based computer programs.

– Expert systems are good for applications with logical separators between decision-influencing

factors.

– Since ES have explicit rules, it is easy to understand how their decisions are made.

– But expert systems are difficult to build because the rules don't always exist and the "expert"

information is difficult to acquire.

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Example: Understanding spoken English

• hominyuwan?

• wuhjusay?

• Ahluv, ahluv an ahluv, cuz ahluv ahluv ‘em lil greentings.

• Time flies like an arrow

• The spirit is willing but the flesh is weak

• He took his leave and my umbrella• Try the START page, at http://sakharov.ai.mit.edu/Start.html

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Fuzzy logic

• Regular logic– Answer is false or true (i.e., 0 or 1)

• Fuzzy logic– Answer ranges from 0 to 1 but can be

anywhere in between (i.e., means maybe yes, maybe no)

• Another “rule-based” development

• Closer to the way people think

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Fuzzy logic: example

• Think of “youngness”

• Not just “young” or “old”; there are degrees

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Applications of fuzzy logic

• Elevators

• Automobile cruise control

• Washing machines

• Camera auto-focus

?Fuzzy Logicבאיזו מידה יש יישום ניהולי ל

”Virtualityמומלץ לקרוא ולחשוב על “

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סיכום

Weizenbaum

Turing

Feigenbaum

Winograd

Searle

McCarthy

Kurzweil

Lenat

Common Sense

Inference Engine

Forward, Backward chaining

Expert Systems, Neural Networks

Understanding Natural Language, SHRDLU

Top-down, Bottom-up

Genetic Algorithms

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References מקורות

• Spielberg’s AI film and web site• Buchanan’s brief history of AI:

http://www.aaai.org/Pathfinder/bbhist.html

• John McCarthy’s “What is AI?”http://www-formal.stanford.edu/jmc/whatisai/whatisai.html

• AI Trends and Controversieshttp://www.computer.org/intelligent/articles/AI_controversies.htm

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