1-Introduction to AI

8/2/2019 1-Introduction to AI

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Advanced Artificial IntelligenceAdvanced Artificial IntelligenceAdvanced Artificial IntelligenceAdvanced Artificial Intelligence

VV ThTh HngHng NhnNhn

([email protected])([email protected])

Faculty of Information TechnologyFaculty of Information Technology

University of Engineering & TechnologyUniversity of Engineering & Technology

VNU, HanoiVNU, Hanoi


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Objectives of this courseObjectives of this courseObjectives of this courseObjectives of this course

To introduce students to the field of AI

To explain the challenges inherent in building an intelligent system

To explain

12/10/201112/10/2011 Introduction to AIIntroduction to AI PagePage 22

e ey para gms, ore tec n ques, gor t ms

Understand the role of basics

Knowledge representation

Learning methods in AI, in engineering intelligent systems

Assess the applicability, strengths, and weakness of these methods in

solving particular engineering problems


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ScheduleScheduleScheduleSchedule

WeekWeek DayDay LectureLecture RemarkRemark

1 Dec. 07 Introduction to AI

2 Dec. 14 Intelligent agents

3 Dec. 21 Knowledge representation & Proposition Logic

4 Dec. 28 First order logic

5 Jan. 4 Rule-based system

6 Jan. 11 Rule based Expert System


.

8 Jan. 25 Mid-term

9 Feb. 01 Fuzzy reasoning Student seminar

10 Feb. 08 Introduction to learning

11 Feb. 15 Rule induction & Decision tree

12 Feb. 22 Probabilistic learning

13 Feb. 29 Neural network

14 Mar. 01 Natural language processing Student seminar

15 Mar. 08 Final


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Introduction to AIIntroduction to AIIntroduction to AIIntroduction to AI

1.1. What is AIWhat is AI

2.2. Example systemsExample systems

3.3. Approaches to AIApproaches to AI

4.4. A brief historyA brief history


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1. What is AI?1. What is AI?1. What is AI?1. What is AI?

Artificial intelligence

Is concerned with the design of intelligence in an artificial device

Its difficult to define the term AI simply & robustly


The term AI was coined by John McCarthy, 1956

The goal of AI is to develop machines that behave as though they were

intelligent

What is intelligence?


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1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)

What is intelligence? Humans?Humans?

If we take human beings to be intelligent,

AI is something that is characterized as humans or something that has behavior

like humans

Two school of thou hts


Systems/ Machines behave intelligently as a human

Humans dont believe intelligently all the time, AI concerns machines that behave

rationally

Two main types of behaviorsbehaviors

Thinking intelligently: reasoning intelligently and properly in order to come up

with a solution

Act/ behave intelligently


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1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)1. What is AI? (cont.)

Look at different ways of defining AI

Thought processes/reasoning vs. behavior

How to measure performance

Human-like performance vs. ideal performance


Behavior

Ideal

performance

(rationally)

Human-like

performance

A diagram that shows the 4 different definitions that emerge


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Approach to AIApproach to AIApproach to AIApproach to AI

Thought/reasoning

Ideal

Systems that think like

humans

(Alan Turing testAlan Turing test)

Systems that think

rationally

(Laws of thought/LogicLaws of thought/Logic)



Behavior

performance(rationally)

-performance

Systems that act

rationally

(Rational agentRational agent)

Systems that act like

human

(Cognitive scienceCognitive science )

A diagram that shows the 4 different definitions that emerge


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Turing TestTuring TestTuring TestTuring Test


The interrogator asks questions

The being inside the roombeing inside the room

processes the questionsprocesses the questions &

return answers

a computer

human


The interrogator receives the

answers on a screenanswers on a screen

He need to make out from the

answer whether the beingwhether the being

inside the room is computerinside the room is computer

or humanor human

an interrogator outside the

room doesnt know the

being inside the room is either

computer or human


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Turing Test (cont.)Turing Test (cont.)Turing Test (cont.)Turing Test (cont.)

The computer tries to convinces that it is human

The interrogator must decide who is human

If the interrogator cannot reliably distinguishcannot reliably distinguish the human from the


computer

Then the computercomputer does posses (artificial) intelligence



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Typical AI problemTypical AI problemTypical AI problemTypical AI problem


Intelligent entities (agent) need to be able to do both

Mundane & expert tasks

Mundane tasks

PlanningPlanning route, activity


RecognizingRecognizing people, objects (through vision)

CommunicatingCommunicating (through natural language)

NavigatingNavigating round obstacles on the street

Expert tasks Medical diagnosis

Mathematical problem solving


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Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)


Which of these problems are easy/hard?

Surprisingly, it has been

easier to mechanize many of the high-level tasks which are so-calledex ert tasks in the histor of AI


easier to solve the problem in the domain of expert

E.g.,

symbolic integration

Proving theorems

Playing chess

Medical diagnose


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Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)Typical AI problem (cont.)


AI doesnt have the same success in dealing with

mundane task


E.g., walking around without running into things

Catching prey and avoiding predators


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Intelligent behaviorIntelligent behaviorIntelligent behaviorIntelligent behavior

Perception

Reasoning


earn ng

Understanding language

Solving problems



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2. Example systems2. Example systems2. Example systems2. Example systems

Computer vision

Image recognition

Robotics

Natural language processing

Speech processing



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Practical impact of AIPractical impact of AIPractical impact of AIPractical impact of AI

AI components are embedded in numerous devices

E.g., copy/vending machines

AI systems are in everyday use

Detectin credit card fraud


Configuring products

Aiding complex planning tasks

Advising physicians

Intelligent tutoring systems

Provide students with personalized attentions



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2. Example systems (cont.)2. Example systems (cont.)2. Example systems (cont.)2. Example systems (cont.)

Machine translationMachine translation

Immediate translation between people speaking different languages

Would be a remarkable achievement of enormous economic and

cultural benefit

Autonomous agentsAutonomous agents

In space exploration, robotic space probes autonomously monitor their

surroundings, makes decisions & act to achieve their goals



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Internet agentsInternet agentsInternet agentsInternet agents

The explosive growth of the internet has also led to

growing interest in internet agent


Seek needed information

Learn which information is most useful



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3. Approaches to AI3. Approaches to AI3. Approaches to AI3. Approaches to AI

Strong AIStrong AI aims to build machines

that can truly reason & solve problem which is self-aware

& whose overall intellectual ability is distinguishable from that of a human being

Can be human-like

or non-human-like

When AI was first conceived in the 1950s and 1960s there were a huge

optimism about AI

A prediction that very soon AI systems will be able to overtake humans

Can do anything that humans can & can do much better

Even can do the task that humans cannot within a short time

But we now know the true difficulty that AI faces



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3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)

Weak AI:Weak AI: deals with the creation of some form of AI of computer-based

artificial intelligence

they cannot truly reason and solve problems, but can act as if they were

intelligent


Weak AI holds that

Suitably programmed machines can simulate human recognition

Strong AI really deal with

machines that have mental states that think, reason, understand

behaviors


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3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)3. Approaches to AI (cont.)

Applied AI

Aims to produce commercially viable smart systems

E.g., a security system that is able to recognize the faces of people who

are permitted to enter a particular building

12/10/201112/10/2011 IntroIntro uctionuction to AIto AI

Applied AI has already enjoyed considerable success

Cognitive AI

Computers are used to test theories about how the human mind works

E.g., theories about how we recognize faces & other objects, or about

how we solve abstract problem


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AI topicsAI topicsAI topicsAI topics


Core areasCore areas

Knowledge representation

Reasoning

Machine learning

General algorithmsGeneral algorithms

Search

Planning

Constraint satisfaction


PerceptionPerception

Vision

Natural language

Robotics UncertaintyUncertainty

Probabilistic approaches

ApplicationsApplications

Game playing

AI & education

Distributed agents Decision theoryDecision theory

Reasoning with symbolic dataReasoning with symbolic data


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Limits of AI todayLimits of AI todayLimits of AI todayLimits of AI today

Todays successful AI systems

Operate in well-defined domains

Employ narrow, specialized knowledge


Commonsense knowledge

Needed in complex, open-ended worlds

Understand unconstrained Natural Language



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4. AI history4. AI history4. AI history4. AI history

The dream of making a computer imitate us began many centuries

ago

Intellectual roots of AI stretch back thousands of years into the

The concept of intelligent machine is found in Greek mythology

8th century

Hephaestus created a huge robot, Talos to guard Crete inland



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FoundationsFoundationsFoundationsFoundations

4. AI history4. AI history4. AI history4. AI history

Psychology

Physiology Biology


Artificialintelligence

Mathematics

Economics Linguistics

Computerengineering


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The main movements of AIThe main movements of AIThe main movements of AIThe main movements of AI

4. The history of AI4. The history of AI4. The history of AI4. The history of AI



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The first beginningsThe first beginningsThe first beginningsThe first beginnings

In the 1930s, Godel, Church, & Turing

laid important foundations for logic and theoretical computer

science



In the 1940s, based on the results from neuroscience

McCulloch, Pitts, and Hebb designed the first mathematical

models of neural networks

Computers at that time lacked sufficient power to simulate

simple brains


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AI as a science of thought mechanization could begin once there

were programmable computers

In the 1950s

Logic solves almost all problemsLogic solves almost all problemsLogic solves almost all problemsLogic solves almost all problems


,

computers, which actually work with numbers, one can process symbols

McCarthy introduced a programming language with the language LISP,

esp. for the processing of symbolic structures

Both of these systems were introduced in 1956 at the Darthmouth

conference, which is considered the birthday of AI


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Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)Logic solves almost all problems (cont.)

In the 1970s, the logic programming language PROLOG was introduced

Offers the advantage of allowing direct programming using Horn clauses, a

subset of predicate logic

Until the 1980s

A breakthrough spirit dominated AI, esp. among logicians, thanks to the string of


impressive achievements in symbol processing

With the 5th Generation Computer System project in Japan & ESPRIT program in

Europe, heavy investment into the construction of intelligent computers

For small problemsFor small problems, automatic provers & other symbol processingsystems sometimes worked very well

But, the combinatorial explosion of the search space defined a narrow

window for these successes


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The new connectionismThe new connectionismThe new connectionismThe new connectionism

Computer scientist, physicians, and cognitive scientists showed that

Mathematically modeled neural networks are capable of learning using training

examples to perform tasks which previously required costly programming


-

patterns, considerable successes became possible, esp. in pattern recognition

The neural networks could acquire impressive capabilities

Attempts to combine neural networks with logical rules or the knowledge of

human experts met with great difficulties

No satisfactory to the structuring & modularization of the network was found


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Reasoning under uncertaintyReasoning under uncertaintyReasoning under uncertaintyReasoning under uncertainty

One of wishes to unite logics ability to explicitly represent knowledge with

neural networks strength in handling uncertainty

Several alternatives

The most promising, probabilistic reasoning, works with conditional probabilities


for propositional calculus formulas

Since then, many diagnostic & expert systems have been built for problems of

everyday reasoning using Baysian Networks

Since 1990, data mining has developed

as a subdiscipline of AI in the area of statistical data analysis for extraction of

knowledge from large databases

Bring no new techniques to AI, rather it introduces the requirement of using large

DB to gain explicit knowledge


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SummarySummarySummarySummary

Different definitions of AI

Thought/reasoning vs. behavior

Human-like performance vs. ideal performance (rationally)

Example systems

Approaches to solving AI problems

Strong AI, weak AI, applied AI, cognitive AI

Brief history



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QuestionsQuestionsQuestionsQuestions

1. Define intelligence

2. What are the different approaches in defining AI?

3. Suppose you design a machine to pass the Turing Test. What are the

capabilities such a machine must have?

4. Will building an artificially intelligent computer automatically shed light on

the nature of natural intelligence


Documents

1-Introduction to AI