cs3014: artificial intelligence introduction to artificial...

Artificial Intelligence

Prof.E.Sreenivasa Reddy

Principal

University College of Engineering

Acharya Nagarjuna University

CS 561, Lecture 1

Why study AI?

Search engines

Labor

Science

Medicine/

Diagnosis

Appliances What

else?

CS 561, Lecture 1

Honda Humanoid Robot

Walk

Turn

Stair

s

http://world.honda.com/robot/

CS 561, Lecture 1

Sony AIBO

http://www.aibo.com

CS 561, Lecture 1

Natural Language Question

Answering

http://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.com

CS 561, Lecture 1

Robot Teams

USC robotics Lab

What is intelligence

Data

Information

Knowledge

Intelligence

How intelligence is developed

Learning

Experience

Reasoning

Intelligence vs Artificial Intelligence

Intelligence is a property/ability attributed to people, such as to

know, to think, to talk, to learn, to understand.

Intelligence = Knowledge + ability to perceive, feel, comprehend,

process, communicate, judge, learn.

Artificial Intelligence is an interdisciplinary field aiming at

developing techniques and tools for solving problems that people at

good at.

What is Artificial Intelligence ?

making computers that think?

the automation of activities we associate with human thinking, like

decision making, learning ... ?

the art of creating machines that perform functions that require

intelligence when performed by people ?

the study of mental faculties through the use of computational models ?

What is Artificial Intelligence ?

the study of computations that make it possible to perceive, reason

and act ?

a field of study that seeks to explain and emulate intelligent

behaviour in terms of computational processes ?

a branch of computer science that is concerned with the

automation of intelligent behaviour ?

anything in Computing Science that we don't yet know how to do

properly ? (!)

What is Artificial Intelligence ?

Systems that act

rationally

Systems that think

like humans

Systems that think

rationally

Systems that act

like humans

THOUGHT

BEHAVIOUR

HUMAN RATIONAL

Systems that act like humans:

Turing Test

“The art of creating machines that perform functions that

require intelligence when performed by people.” (Kurzweil)

“The study of how to make computers do things at which, at

the moment, people are better.” (Rich and Knight)

Systems that act like humans

You enter a room which has a computer terminal. You have a fixed period of time to type what you want into the terminal, and study the replies. At the other end of the line is either a human being or a computer system.

If it is a computer system, and at the end of the period you cannot reliably determine whether it is a system or a human, then the system is deemed to be intelligent.

?

Systems that act like humans

The Turing Test approach

a human questioner cannot tell if

there is a computer or a human answering his question, via teletype

(remote communication)

The computer must behave intelligently

Intelligent behavior

to achieve human-level performance in all cognitive tasks

Systems that act like humans

These cognitive tasks include:

Natural language processing

for communication with human

Knowledge representation

to store information effectively & efficiently

Automated reasoning

to retrieve & answer questions using the stored information

Machine learning

to adapt to new circumstances

The total Turing Test

Includes two more issues:

Computer vision

to perceive objects (seeing)

Robotics

to move objects (acting)

What is Artificial Intelligence ?

Systems that act

rationally

Systems that think

like humans

Systems that think

rationally

Systems that act

like humans

THOUGHT

BEHAVIOUR

HUMAN RATIONAL

Systems that think like humans:

cognitive modeling

Humans as observed from ‘inside’

How do we know how humans think? Introspection vs. psychological experiments

Cognitive Science

“The exciting new effort to make computers think … machines with minds in the full and literal sense” (Haugeland)

“[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning …” (Bellman)

What is Artificial Intelligence ?

Systems that act

rationally

Systems that think

like humans

Systems that think

rationally

Systems that act

like humans

THOUGHT

BEHAVIOUR

HUMAN RATIONAL

Systems that think ‘rationally’

"laws of thought"

Humans are not always ‘rational’

Rational - defined in terms of logic?

Logic can’t express everything (e.g. uncertainty)

Logical approach is often not feasible in terms of computation time (needs ‘guidance’)

“The study of mental facilities through the use of computational models” (Charniak and McDermott)

“The study of the computations that make it possible to perceive, reason, and act” (Winston)

What is Artificial Intelligence ?

Systems that act

rationally

Systems that think

like humans

Systems that think

rationally

Systems that act

like humans

THOUGHT

BEHAVIOUR

HUMAN RATIONAL

Systems that act rationally:

“Rational agent”

Rational behavior: doing the right thing

The right thing: that which is expected to maximize goal achievement, given the available information

Giving answers to questions is ‘acting’.

I don't care whether a system:

replicates human thought processes

makes the same decisions as humans

uses purely logical reasoning

Systems that act rationally

Logic only part of a rational agent, not all of

rationality

Sometimes logic cannot reason a correct conclusion

At that time, some specific (in domain) human knowledge or

information is used

Thus, it covers more generally different situations of

problems

Compensate the incorrectly reasoned conclusion

Systems that act rationally

Study AI as rational agent –

2 advantages:

It is more general than using logic only

Because: LOGIC + Domain knowledge

It allows extension of the approach with more scientific

methodologies

Rational agents

An agent is an entity that perceives and acts

This course is about designing rational agents

Abstractly, an agent is a function from percept histories to actions:

[f: P* A]

For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance

Caveat: computational limitations make perfect rationality unachievable design best program for given machine resources

Artificial

Produced by human art or effort, rather than originating

naturally.

Intelligence

is the ability to acquire knowledge and use it" [Pigford

and Baur]

So AI was defined as:

AI is the study of ideas that enable computers to be intelligent.

AI is the part of computer science concerned with design of

computer systems that exhibit human intelligence(From the

Concise Oxford Dictionary)

From the above two definitions, we can see that AI has two

major roles:

Study the intelligent part concerned with humans.

Represent those actions using computers.

An Intelligent Agent

Knowledge

representation

reasoningplanning

learning

inputNatural lang.

visioneffectors

Goals of AI

To make computers more useful by letting them take over

dangerous or tedious tasks from human

Understand principles of human intelligence

The Foundation of AI

Philosophy

At that time, the study of human intelligence began with no

formal expression

Initiate the idea of mind as a machine and its internal operations

The Foundation of AI Mathematics formalizes the three main area of AI: computation,

logic, and probability

Computation leads to analysis of the problems that can be computed complexity theory

Probability contributes the “degree of belief ” to handle uncertaintyin AI

Decision theory combines probability theory and utility theory (bias)

The Foundation of AI

Psychology

How do humans think and act?

The study of human reasoning and acting

Provides reasoning models for AI

Strengthen the ideas

humans and other animals can be considered as information processing

machines

The Foundation of AI Computer Engineering

How to build an efficient computer?

Provides the artifact that makes AI application possible

The power of computer makes computation of large and difficult problems more easily

AI has also contributed its own work to computer science, including: time-sharing, the linked list data type, OOP, etc.

The Foundation of AI

Control theory and Cybernetics

How can artifacts operate under their own control?

The artifacts adjust their actions

To do better for the environment over time

Based on an objective function and feedback from the environment

Not limited only to linear systems but also other problems

as language, vision, and planning, etc.

The Foundation of AI

Linguistics

For understanding natural languages

different approaches has been adopted from the linguistic work

Formal languages

Syntactic and semantic analysis

Knowledge representation

The main topics in AIArtificial intelligence can be considered under a number of headings:

Search (includes Game Playing).

Representing Knowledge and Reasoning with it.

Planning.

Learning.

Natural language processing.

Expert Systems.

Interacting with the Environment (e.g. Vision, Speech recognition, Robotics)

We won’t have time in this course to consider all of these.

more powerful and more useful computers

new and improved interfaces

solving new problems

better handling of information

relieves information overload

conversion of information into knowledge

Some Advantages of Artificial

Intelligence

The Disadvantages

increased costs

difficulty with software development - slow and expensive

few experienced programmers

few practical products have reached the market as yet.

Search Search is the fundamental technique of AI.

Possible answers, decisions or courses of action are structured into an abstract space, which we then search.

Search is either "blind" or “uninformed":

blind we move through the space without worrying about what is coming next, but

recognising the answer if we see it

informed we guess what is ahead, and use that information to decide where to look

next.

We may want to search for the first answer that satisfies our goal, or we may want

to keep searching until we find the best answer.

Knowledge Representation & Reasoning The second most important concept in AI

If we are going to act rationally in our environment, then we must have some way of

describing that environment and drawing inferences from that representation.

how do we describe what we know about the world ?

how do we describe it concisely ?

how do we describe it so that we can get hold of the right piece of knowledge

when we need it ?

how do we generate new pieces of knowledge ?

how do we deal with uncertain knowledge ?

Knowledge

Declarative Procedural

• Declarative knowledge deals with factoid questions

(what is the capital of India? Etc.)

• Procedural knowledge deals with “How”

• Procedural knowledge can be embedded in

declarative knowledge

Planning

Given a set of goals, construct a sequence of actions that achieves those goals:

often very large search space

but most parts of the world are independent of most other parts

often start with goals and connect them to actions

no necessary connection between order of planning and order of execution

what happens if the world changes as we execute the plan and/or our

actions don’t produce the expected results?

Learning

If a system is going to act truly appropriately, then it must be

able to change its actions in the light of experience:

how do we generate new facts from old ?

how do we generate new concepts ?

how do we learn to distinguish different situations

in new environments ?

Interacting with the Environment

In order to enable intelligent behaviour, we will have to interact with our environment.

Properly intelligent systems may be expected to: accept sensory input

vision, sound, … interact with humans

understand language, recognise speech, generate text, speech and graphics, …

modify the environment

robotics

History of AI AI has a long history

Ancient Greece

Aristotle

Historical Figures Contributed

Ramon Lull

Al Khowarazmi

Leonardo da Vinci

David Hume

George Boole

Charles Babbage

John von Neuman

As old as electronic computers themselves (c1940)

The ‘von Neuman’ Architecture

History of AI Origins

The Dartmouth conference: 1956 John McCarthy (Stanford)

Marvin Minsky (MIT)

Herbert Simon (CMU)

Allen Newell (CMU)

Arthur Samuel (IBM)

The Turing Test (1950)

“Machines who Think”

By Pamela McCorckindale

Periods in AI

Early period - 1950’s & 60’s

Game playing brute force (calculate your way out)

Theorem proving symbol manipulation

Biological models neural nets

Symbolic application period - 70’s

Early expert systems, use of knowledge

Commercial period - 80’s

boom in knowledge/ rule bases

Periods in AI cont’d

? period - 90’s and New Millenium

Real-world applications, modelling, better evidence, use of theory, ......?

Topics: data mining, formal models, GA’s, fuzzy logic, agents, neural nets, autonomous systems

Applications visual recognition of traffic

medical diagnosis

directory enquiries

power plant control

automatic cars

Fashions in AIProgress goes in stages, following funding booms and crises: Some examples:

1. Machine translation of languages

1950’s to 1966 - Syntactic translators

1966 - all US funding cancelled

1980 - commercial translators available

2. Neural Networks

1943 - first AI work by McCulloch & Pitts

1950’s & 60’s - Minsky’s book on “Perceptrons” stops nearly all work on nets

1986 - rediscovery of solutions leads to massive growth in neural nets research

The UK had its own funding freeze in 1973 when the Lighthill report reduced AI work severely -

Lesson: Don’t claim too much for your discipline!!!!

Look for similar stop/go effects in fields like genetic algorithms and evolutionary computing. This is a

very active modern area dating back to the work of Friedberg in 1958.

Symbolic and Sub-symbolic AI

Symbolic AI is concerned with describing and manipulating our

knowledge of the world as explicit symbols, where these symbols

have clear relationships to entities in the real world.

Sub-symbolic AI (e.g. neural-nets) is more concerned with

obtaining the correct response to an input stimulus without

‘looking inside the box’ to see if parts of the mechanism can be

associated with discrete real world objects.

This course is concerned with symbolic AI.

Symbolic vs. Subsymbolic AI

Subsymbolic AI: Model

intelligence at a level similar to

the neuron. Let such things as

knowledge and planning emerge.

Symbolic AI: Model such

things as knowledge and

planning in data structures that

make sense to the

programmers that build them.

(blueberry (isa fruit)

(shape round)

(color purple)

(size .4 inch))

Consider what might be involved in

building a “smart” computer….

What are the “components” that might be useful?

Fast hardware?

Foolproof software?

Chess-playing at grandmaster level?

Speech interaction?

speech synthesis

speech recognition

speech 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 unit

estimated to be on the order of 10 12 neurons in a human brain

many more synapses (10 14) connecting these neurons

cycle time: 10 -3 seconds (1 millisecond)

How complex can we make computers?

106 or more transistors per CPU

supercomputer: hundreds of CPUs, 10 9 bits of RAM

cycle times: order of 10 - 8 seconds

Conclusion

YES: in the near future we can have computers with as many basic processing

elements as our brain, but with

far fewer interconnections (wires or synapses) than the brain

much 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 errors

software errors, e.g., coding bugs

“human-like” errors

Clearly, hardware and software errors are possible in practice

what about “human-like” errors?

An intelligent system can make errors and still be intelligent

humans are not right all of the time

we learn and adapt from making mistakes

e.g., consider learning to surf or ski

we improve by taking risks and falling

an intelligent system can learn in the same way

Conclusion:

Can Computers Talk? This is known as “speech synthesis”

translate text to phonetic form

e.g., “fictitious” -> fik-tish-es

use pronunciation rules to map phonemes to actual sound

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

Difficulties

sounds made by this “lookup” approach sound unnatural

sounds are not independent

e.g., “act” and “action”

modern systems (e.g., at AT&T) can handle this pretty well

a harder problem is emphasis, emotion, etc

humans understand what they are saying

machines don’t: so they sound unnatural

Conclusion: NO, for complete sentences, but YES for individual words

Can Computers Recognize

Speech? Speech Recognition:

mapping sounds from a microphone into a list of words.

Hard problem: noise, more than one person talking,

occlusion, speech variability,..

Even if we recognize each word, we may not understand its meaning.

Recognizing single words from a small vocabulary systems can do this with high accuracy (order of 99%)

e.g., directory inquiries

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 human speech (ctd.)

Recognizing normal speech is much more difficult

speech is continuous: where are the boundaries between words?

e.g., “John’s car has a flat tire”

large vocabularies

can be many thousands of possible words

we can use context to help figure out what someone said

try telling a waiter in a restaurant:

“I would like some dream and sugar in my coffee”

background noise, other speakers, accents, colds, etc

on normal speech, modern systems are only about 60% accurate

Conclusion: NO, normal speech is too complex to accurately recognize, but YES for

restricted problems

(e.g., recent software for PC use by IBM, Dragon systems, etc)

Can Computers Understand

speech?

Understanding is different to recognition:

“Time flies like an arrow”

assume the computer can recognize all the words

but how could it understand it?

1. time passes quickly like an arrow?

2. command: time the flies the way an arrow times the flies

3. command: only time those flies which are like an arrow

4. “time-flies” are fond of arrows

only 1. makes any sense, but how could a computer figure this out?

clearly humans use a lot of implicit commonsense knowledge in

communication

Conclusion: NO, much of what we say is beyond the

capabilities of a computer to understand at present

Can Computers Learn and Adapt ? Learning and Adaptation

consider a computer learning to drive on the freeway

we could code lots of rules about what to do

or we could let it drive and steer it back on course when it heads

for the embankment

systems like this are under development (e.g., Daimler Benz)

e.g., RALPH at CMU

in mid 90’s it drove 98% of the way from Pittsburgh to San Diego

without any human assistance

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

Recognition v. Understanding (like Speech)

Recognition and Understanding of Objects in a scene

look around this room

you can effortlessly recognize objects

human brain can map 2d visual image to 3d “map”

Why is visual recognition a hard problem?

Can Computers “see”?

Can Computers plan and make

decisions? Intelligence

involves solving problems and making decisions and plans

e.g., you want to visit your cousin in Boston

you need to decide on dates, flights

you need to get to the airport, etc

involves a sequence of decisions, plans, and actions

What makes planning hard?

the world is not predictable:

your flight is canceled or there’s a backup on the 405

there are a potentially huge number of details

do you consider all flights? all dates?

no: commonsense 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

exception: very well-defined, constrained problems: mission planning for satelites.

Summary of State of AI Systems in

Practice Speech synthesis, recognition and understanding

very useful for limited vocabulary applications

unconstrained speech understanding is still too hard

Computer vision

works for constrained problems (hand-written zip-codes)

understanding real-world, natural scenes is still too hard

Learning

adaptive systems are used in many applications: have their limits

Planning and Reasoning

only works for constrained problems: e.g., chess

real-world is too complex for general systems

Overall:

many components of intelligent systems are “doable”

Intelligent Systems in Your

Everyday Life

Post Office

automatic address recognition and sorting of mail

Banks

automatic check readers, signature verification systems

automated loan application classification

Telephone Companies

automatic voice recognition for directory inquiries

automatic fraud detection,

classification of phone numbers into groups

Credit Card Companies

automated fraud detection, automated screening of 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 like Nielsen gather this information weekly and

search for patterns

general changes in consumer behavior

tracking responses to new products

identifying 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?

AI Applications: Identification

Technologies

ID cards

e.g., ATM cards

can be a nuisance and security risk:

cards can be lost, stolen, passwords forgotten, etc

Biometric Identification

walk up to a locked door

camera

fingerprint device

microphone

computer uses your biometric signature for identification

face, eyes, fingerprints, voice pattern

AI Applications: Predicting the

Stock Market

The Prediction Problem

given the past, predict the future

very 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 of

the Stock

AI-Applications: Machine Translation Language problems in international business

e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common

language

or: you are shipping your software manuals to 127 countries

solution; hire translators to translate

would be much cheaper if a machine could do this!

How hard is automated translation

very difficult!

e.g., English to Russian

“The spirit is willing but the flesh is weak” (English)

“the vodka is good but the meat is rotten” (Russian)

not only must the words be translated, but their meaning also!

Nonetheless....

commercial systems can do alot of the work very well (e.g.,restricted vocabularies in

software documentation)

algorithms which combine dictionaries, grammar models, etc.

see for example babelfish.altavista.com

AI Applications

Autonomous Planning &

Scheduling:

Autonomous rovers.

AI Applications

Autonomous Planning & Scheduling:

Telescope scheduling

AI Applications

Autonomous Planning & Scheduling:

Analysis of data:

AI Applications

Medicine:

Image guided surgery

AI Applications

Medicine:

Image analysis and enhancement

AI Applications

Transportation:

Autonomous

vehicle control:

AI Applications

Transportation:

Pedestrian detection:

AI Applications

Games:

AI Applications

Games:

AI Applications

Robotic toys:

Thank You?

Any Queries? You can clarify your doubts at

Email:esreddy67@gmail.com

M:7893111985