2 Lectures 2 4 AI Introduction

1Dr. Tarek Helmy, ICS-KFUPM

ICS-381Principles of Artificial Intelligence

Lectures 2- 4

Introducing Artificial Intelligence

Dr.Tarek Helmy El-Basuny

Dr. Tarek Helmy, ICS-KFUPM 2

Introduction to Artificial Intelligence

Brain StormingWhy do we study AI?What is Artificial Intelligence?

Characteristics of IntelligenceAI is a Multi-Disciplinary FieldCommonly Accepted Definitions of Artificial IntelligenceWhy Does Industry and the Government Care about AI?What might be involved in building a “smart” computer?Typical AI ProgramsFeatures of Using Artificial IntelligenceHow to Achieve AIAI Technologies and ApplicationsAI Brief HistoryCan a machine be truly “intelligent”?: Turing Test


Why do we study AI?

Search enginesScience

Medicine/Diagnosis

LaborWhat else?Appliances


Honda Humanoid Robot

Walk Turn

Stairshttp://world.honda.com/ASIMO/


Sony AIBO

http://www.aibo.com Smart/liveMarket

http://www.aibo.com/


Brain Storming: What is Artificial Intelligence?

Good Question, but exactly, what is intelligence? Can we say, he is intelligent means:He knows a lotHe thinks fastHe talks muchHe learns quicklyHe memorizes well

Is it learned?Are you born with it?Can we use tests to measure it?

IQ Test!Intelligence = Knowledge + ability to perceive, feel, understand, process, communicate, judge, and learn.What is Artificial Intelligence?There is no official agreed upon definition of Artificial Intelligence.Why?

In practice, it is an “umbrella term”It is multidisciplinary subjectTechnologies enter and exit the AI “umbrella” regularly.


Characteristics of Intelligence

Ability to Communicate

Creativity

Internal Knowledge

Ability to Learn

Perceive World Knowledge

Goal-Directed Behavior

Self Awareness


An Intelligent Entity

Has understanding/intentionality

Exhibits behavior

SeeHearTouchTasteSmell

INPUTS INTERNAL PROCESSES

OUTPUTS

Senses environment

Can Reason

Has knowledge


Commonly Accepted Definitions of Artificial Intelligence

Winston: “AI is the study of ideas which enable computers to do things which make people seem intelligent.”Steven Tanimoto, “Computational techniques for performing tasks that apparently require intelligence when performed by humans.”David Parnas, “Artificial intelligence is to artificial flowers as natural intelligence is to natural flowers.”Luger: The branch of computer science that is concerned with automation of intelligent behavior.Rich: “AI is the study of how to make computers do things which, at themoment, people do better.”

Fahlman: AI is the study of intelligence using the ideas and methods of computation.”

Found on the Web: AI is the reproduction of the methods or results of human reasoning or intuition.We can define it too: AI is a field of computer science that simulates human performance to make a computer reasons in a manner similar to humans.


Why Do Industry and Government Care about AI?

AI shows promise for handling many complex problems, saving lives and resources:

• Solving information overload problems.

• Intelligent search engines

• Operating in risky environments.

• Robots

• Distributing scarce commercial knowledge.

• Data-mining software sort through massive databases, looking for patterns that would take a human years to spot.

• Enhancing training through use of simulation.

• ES

• Adaptive Computer Based Tests


Main Areas of AI

Knowledge representation(including formal logic)Search, especially heuristic search (puzzles, games)PlanningReasoning under uncertainty, including probabilistic reasoningLearningAgent architecturesRobotics and perceptionNatural language processing

Search

Knowledgerep.Planning

Reasoning

Learning

Agent

RoboticsPerception

Naturallanguage Expert

Systems

Constraintsatisfaction

...


A Hierarchical Model of Intelligence

Wisdom

Knowledge

Information

Data Context+

Vision+Experience+


AI is a Multi-Disciplinary Field

ControlTheory

LinguisticsMathematics

Psychology

Artificial IntelligenceComputerScience

Philosophy

ComputerEngineering


AI is a Multi-disciplinary

Many disciplines contribute to the goal of creating/modeling intelligent

entities:

Computer Engineering (Building fast computers)

Psychology (Perceive, process information, represent knowledge.)

Philosophy (Logic, methods of reasoning, mind as physical system,

foundations of learning, etc)

Linguistics (Structure and meaning of language)

Human Biology (How brain works)

Mathematics (Formal representation and proof, algorithms, etc.)

Control theory (Design systems that maximize an objective function

over time)


Intelligent System Should do:

How can we make computer based systems more intelligent?

In practical terms, intelligent systems:

Should have the ability to automatically perform tasks that normally

require a human expert.

Should have more autonomy; less requirement for human intervention

or monitoring.

Should have Flexibility in dealing with variability in the environment

in an appropriate manner.

Are easier to use: able to understand what the user wants from limited

instructions.

Can improve their performance by learning from experience.


Typical AI Programs

Intelligent entities (or “agents”) need to be able to do both “ordinary” and “expert” tasks:

Ordinary tasks - consider going shopping:Planning a route, and sequence of shops to visit!Recognizing (through vision) buses, people.Communicating (through natural language).Navigating round obstacles on the street, and manipulating objects for purchase.

Expert tasks are things like:Medical diagnosis.Equipment repair.


How to Achieve AI?

How is AI research done?

AI research has both theoretical and experimental sides. The experimental side has both basic and applied aspects.

There are two main lines of research:One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology. The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of goals.

The two approaches interact to some extent, and both should eventually succeed. It is a race, but both racers seem to be walking. [John McCarthy]


What might be involved in building a “smart” computer?

What are the “components” that might be useful?Fast hardware?Foolproof [never makes error] software?Speech interaction?

Speech separation [segmentation/synthesis]Speech recognitionSpeech understanding

Image recognition and understanding?Learning?Planning and decision-making?


Can we build hardware as complex as the brain?

How complicated is our brain?A neuron, or nerve cell, is the basic information processing unitEstimated to be on the order of 1012 neurons in a human brainMany more synapses (1014) connecting these neuronsCycle time: 10-3 seconds (1 millisecond)

How complex can we make computers?106 or more transistors per CPU Supercomputer: hundreds of CPUs, 109 bits of RAM Cycle times: order of 10- 8 seconds

ConclusionYES: we can have computers with as many basic processing elements as our brain, but with

Far fewer interconnections (wires or synapses) than the brainMuch faster updates than the brain

But building hardware is very different from making a computer behave like a brain!


Must an Intelligent System be Foolproof?

A “foolproof” system is one that never makes an error:Types of possible computer errors

Hardware errors, e.g., memory errorsSoftware errors, e.g., coding bugs“Human-like” errors

Clearly, hardware and software errors are possible in practiceWhat about “human-like” errors?

An intelligent system can make errors and still be intelligentHumans are not right all of the timeWe learn and adapt from making mistakes

Conclusion:NO: intelligent systems will not (and need not) be foolproof


Can Computers Talk with Understanding?

This is known as “speech synthesis”Translate text to phonetic form

e.g., “fictitious” -> fik-tish-esUse pronunciation rules to map phonemes to actual sound

e.g., “tish” -> sequence of basic audio soundsDifficulties

Sounds made by this “lookup” approach sound unnaturalSounds are not independent

e.g., “act” and “action”A harder problem is emphasis, emotion, etc

Humans understand what they are sayingMachines don’t: so they sound unnatural

Conclusion: NO, for complete understanding, but YES for pronouncing and translating.


The End!!

Thank you

Any Questions?


Can Computers Recognize Speech?

Speech Recognition:Mapping sounds from a microphone into a list of words.Hard problem: noise, more than one person talking, speech variability,.. Even if we recognize each word, we may not understand its meaning.

Recognizing single words from a small vocabularySystems can do this with high accuracy (order of 99%)e.g., directory inquiries for phone companies

• Limited vocabulary (area codes, city names)• Computer tries to recognize you first, if unsuccessful hands you over to a human

operator• Saves millions of dollars a year for the phone companies

Recognizing normal speech is much more difficultSpeech is continuous: where are the boundaries between words?Large vocabularies

Can be many thousands of possible wordsWe can use context to help figure out what someone said

Background noise, other speakers, accents, colds, etcOn normal speech, modern systems are only about 60% accurate

Conclusion: NO/with little accuracy, normal speech is too complex to accurately recognize, but YES for restricted problems


Can Computers Understand speech?

Understanding is different to recognition:“Time flies like an arrow”

Assume the computer can recognize all the wordsBut how could it understand it?How could a computer figure this out?• Clearly humans use a lot of implicit common sense

knowledge in communication

Conclusion: NO with full semantic, much of what we say is beyond the capabilities of a computer to understand at present.


Can Computers Learn and Adapt ?

Learning and AdaptationConsider a computer learning to drive on the freewayWe could code lots of rules about what to doWe could let it drive and steer it back of course when it heads for the embankment

Systems like this are under development.Machine learning allows computers to learn to do things without explicit programming.

Conclusion: YES, computers can learn and adapt, when presented with information in the appropriate way.


Can Computers “see”?

Recognition v. Understanding (like Speech)Recognition and Understanding of Objects in a scene

Look around this roomYou can effortlessly recognize objectsHuman brain can map 2d visual image to 3d “map”

Why is visual recognition a hard problem?

Conclusion: Computers can partially “see” certain types of objects under limited circumstances: but YES/fully for certain constrained problems (e.g., face recognition).


Can Computers Plan and Make Decisions?

IntelligenceInvolves solving problems and making decisions and planse.g., you want to visit your cousin in Mekah

You need to decide on dates, flightsYou need to get to the airport, etcInvolves a sequence of decisions, plans, and actions

What makes planning hard?The world is not predictable:

Your flight might be canceled or there will be a backup.There are a potentially huge number of details

Do you consider all flights? all dates?• No: common sense constrains your solutions

AI systems are only successful in constrained planning problems

Conclusion: NO, real-world planning and decision-making is still beyond the capabilities of modern computers. But YES for very well-defined, constrained problems.


Intelligent Systems in Your Everyday Life

Post OfficeAutomatic address recognition and sorting of mail

BanksAutomatic check readers, signature verification systemsAutomated loan application classification

Telephone CompaniesAutomatic voice recognition for directory inquiriesAutomatic fraud detection,

Credit Card CompaniesAutomated fraud detection

Computer CompaniesAutomated diagnosis for help-desk applications


AI Applications: Consumer Marketing

Have you ever used any kind of credit/ATM/store card while shopping?If so, you have very likely been “input” to an AI algorithm

All of this information is recorded digitally

Companies gather this information weekly and search for patternsGeneral changes in consumer behaviorTracking responses to new productsIdentifying customer segments: targeted marketing, e.g., they find out that consumers with sports cars who buy textbooks respond well to offers of new credit cards.Currently a very hot area in marketing

How do they do this?Algorithms (“data mining”) search data for patternsBased on mathematical theories of learningCompletely impractical to do manually


AI Applications: Identification Technologies

ID cards e.g., ATM cardsCan be a security risk:

Cards can be lost, stolen, passwords forgotten, etc

Biometric IdentificationWalk up to a locked door

CameraFingerprint deviceMicrophone

Computer uses your biometric signature for identificationFace, eyes, fingerprints, voice pattern


AI Applications: Predicting the Stock Market

The Prediction ProblemGiven the past, predict the futureVery difficult problem!We can use learning algorithms to learn a predictive model from historical data

Prob (increase at day t+1 | values at day t, t-1,t-2....,t-k)Such models are routinely used by banks and financial traders to manage portfolios worth millions of dollars

Time in days

?

?

Value ofthe Stock


AI Brief History

1950: Alan Turing: Turing test

1950: Claude Shannon publishes a paper on chess playing. Shows that a game of chess involved about 10120 moves shows the need for heuristics

1943-56: McCulloch/Pitts: Research on the structure of the brain gives a model of neurons of the brain artificial neural networks

1951: von Neumann helps Minsky and Edmonds to build the first neural network computer.

1956: The first AI workshop sponsored by IBM Birth of AI

1958: McCarthy presents a paper “Program with common sense”.

1962: Rosenblatt proves the perception convergence theorem (learning algorithm)


AI Brief History

1965: Lotfy Zadeh introduces Fuzzy sets

Early 70s: shift from a general purpose, knowledge-sparse, weak methods to domain-

specific, knowledge-intensive techniques (ES)

Mycin: rule-based expert system for diagnosis of infectious blood diseases

Mid 80s: use of neural networks for machine learning.

Generalization of single-layer network: Hopfield network, back-propagation.

Knowledge engineering: use of Fuzzy logic improves computational power,

improves cognitive modeling, allows to represent multiple experts

In 1995 The emergence of intelligent agents


Can a machine be truly “intelligent”? : Turing’s Test

Alan Turing's 1950 article in Computing Machinery and Intelligence discussed conditions for considering a machine to be intelligentCan someone tell which is the machine, when communicating to human and to a machine in another room? If not, can we call the machine intelligent?If the computer succeeds in fooling the judge then it has managed to exhibit a human level of intelligence in the task of pretending to be a woman, the definition of intelligence the machine has shown itself to be intelligent.

Which one’s the computer?

A B


What would a computer need to pass the Turing test?

Passing Turing test requires the computer to have the following capabilities:1. NLP to communicate in English with the examiner2. Knowledge Representation to store information provided during the

test3. Automated reasoning to use stored information to answer questions

and draw conclusions. 4. Machine learning to adapt to new circumstances and to detect and

extrapolate patterns.5. Computer vision to recognize the examiner’s actions and various

objects presented by the examiner.5. Robotics to act upon objects as requested.


AI Technologies

Previous, Today, Future

Cognitive-Based AI

(has to percept, learn and reason)Expert SystemsDecision Support SystemsNatural Language ProcessingIntelligent AgentsCollaborative Intelligent Agent NetworksFuzzy Logic

Biologically-Based AINeural NetsGenetic AlgorithmsSpeech RecognitionComputer VisionEvolutionary (changeable) ProgrammingMachine LearningRobotics


The End!!

Thank you

Any Questions?

2 Lectures 2 4 AI Introduction

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