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1. Definitions of Artificial Intelligence

I. Artificial Intelligenceis a branch of Science which deals with helping machines to find solutions to complex

problems in a more human-like fashion1.

This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in

a computer friendly way.

II. Turing's test2: the begin of AI

We place something behind a curtain and it speaks with us. If we cant make difference between it and a human

being then it will be !I.

!I is knowledge gained through experiences.

III. Then how about newly born baby??

Intelligent thing is that it may know nothing but it can learn.

IV. "Artificial intelligence is the study of ideas to bring into being machines that respond to stimulationconsistent with traditional responses from humans, given the human capacity for contemplation, "udgment and

intention. #ach such machine should engage in critical appraisal and selection of differing opinions within itself.

$roduced by human skill and labor, these machines should conduct themselves in agreement with life, spirit and

sensitivity, though in reality, they have limitations.%3

!ll pursuits of !I involve the construction of a machine, where a machine may be a robot, a computer, a

program or a system of machines whose essence these days is assumed to be rooted in digital computer

technology &though biological machines or combined biological and digital machines may be possible in thefuture &'night and Sussman, ())*+. The construction of a machine reuires hardwiring, which is the

knowledge, expertise or know-how that is incorporated a priori into the machine. While self-refinement within

the machine is possible such as modifying internal state, ad"usting parameters, updating data structures, or even

modifying its own control structure, hardwiring concerns the construction of the initial machine itself. achinesare hardwired to conduct one or more tasks.

5. Artificial intelligence is the study of ideas to bring into being machines that perform behavior or thinking

tasks ideally or like humans4.

So,Artificial Intelligence &!I+ is a term that encompasses many definitions. owever, the most experts agree

that !I is concerned with two basic ideas/

It involves studying thought processes of humans &understanding what intelligence is+

It deals with representing these processes via machines &such as computer, robots etc.+

!I is the part of computer science concerned with designing intelligent computer systems, that is, computer

systems that exhibit the characteristics we associate with intelligence in human behavior - understandinglanguage, learning, reasoning and solving problems.

V.What do we mean by Intelligent beha!ior?

(0romhttp://ai-depot.com/Intro.html

12omputing achinery and Intelligence, !lan Turing &()34+

56atanya Sweeney&())1+,http://privacy.cs.cmu.edu/people/sweeney/aidef.html

78ussell, S. and 9orvig, $.Artificial intelligence: a modern approach. #nglewood 2liffs/ $rentice-all, ())3.

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Several abilities are considered as signs of intelligence/

6earning or understanding from experience

aking sense out of ambiguous or contradictory message

8esponding uickly and successfully to a new situation

:sing reasoning in solving problems and directing conduct effectively

;ealing with perplexing situations

:nderstanding and inferring in ordinary rational ways

!pplying knowledge for manipulate the environment

Thinking and reasoning

8ecogniohn c2arthy, ())? @ 2oncept of

6ogical !I+

II. Search

!I programs often examine large numbers of possibilities.

III. $attern 8ecognition

When a program makes observations of some kind, it is often programmed to compare what it sees with apattern.

IA. 8epresentation

0acts about a system or world are represented in some way, usually using languages of mathematical logic

&expert system+.

A. Inference &8easoning+

The strategy concerns the problem of how a reasonable conclusion &action+ should be made with respect to an

incoming situation.

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AI. euristic &opposite of analytic+

AII. Benetic programming

3. Areas of AI

!rtificial intelligence includes games playing/ programming computers to play games such as chess and checkers

expert systems / programming computers to make decisions in real-life situations &for example, some

expert systems help doctors diagnose diseases based on symptomsC fault diagnosis in industrial

applications+

natural language / programming computers to understand natural human languages

neural networks / Systems that simulate intelligence by attempting to reproduce the types of physical

connections that occur in animal brains

robotics / programming computers to see and hear and react to other sensory stimuli

0ew examples of application ilitary

Industry

ospitals

=anks

Insurance 2ompanies

$. Artificial !erses %atural Intell igence

!I is more permanent &mDc donEt forget like human workers, and workers may switch the working place or

"obs+.

!I offers ease of duplication and dissemination. Transferring knowledge from human to human to

computer is not as easy as transfer of files or memory device of intelligent machines.

!I can be less expensive than natural intelligence

!I can be well documented.

!I can execute certain tasks much faster than a human can.

!I can perform certain tasks better than many or even most people.

owever

!I is not as creative as uman &9atural Intelligence+.

9atural intelligence enables people to benefit from and use sensory experience directly, whereas !Isystems must work with symbolic input and representations.

9I i.e.human reasoning uses a wide context of experiences.

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Approaches in "ystem Analysis

! system can be defined as a set of elements standing in interrelations. There exist models, principle, and lawsthat apply to generali

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So, the ob"ective of expert system is considered as transfer of expertise from an expert to a computer system and

then on to other humans &novice+. This process involves four activities/

'nowledge acuisition &from expert or other sources e.g. books, records etc.+

'nowledge representation &coding in the computer+

'nowledge inferencing, and

'nowledge transfer to user.

The components of the expert system are as shown in 0igure below.

0ig. Structure of an #xpert System

(nowledge basecontains all the rules &if-then+

)atabasegives the context of the problem domain and is generally considered to be a set of useful facts. Theseare the facts that satisfy the condition part of the action rules.

Inference enginecontrols overall execution of the rules. It searches through the knowledge base, attempting to

pattern match facts or knowledge present in memory to the antecedents of rules. If a rules antecedent issatisfied, the rule is ready to fire. When a rule is ready to fire it means that since the antecedent is satisfied, the

conseuent can be executed.

*achine +earning

#$pert "ystem

%nowledge !ase

(Set of rules&

Inference #ngine

&8ule Interpreter+

Data !ase

'Set of facts&

I/(In

terface

(Knowledge

acquisition)

)ser

#$pert

*

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'nowledge acuisition is a uite labor intensive process. Two ma"or participants of the knowledge acuisition

process are the knowledge engineer who works to acuire the domain knowledge, and the expert who could be

too busy personnel or very expensive recordsDdocuments describing the problem situations. Therefore, manual

and even semiautomatic elicitation methods are both slow and expensive. Thus, it makes sense to developknowledge acuisition methods that will reduce or even eliminate the need for these two participants. These

methods are computer aided knowledge acuisitions, or automated knowledge acuisition. This is also called

machine learning.

,eresenting -ncertainty

! rule or fact is usually assumed as whether it is true or false. owever, human knowledge is often inexact.Sometimes, we will be partly sure about the truth of a statement and still have to make educated guesses to

solve problems.

Some concepts or words are inherently inexact. 0or instance, how can we determine exactly whether someone is

tallH The concept tall has a built-in form of inexactness.

oreover, we sometimes have to make decisions based on partial or incomplete data.

eaning of uncertainty/ doubtful, dubious, uestionable, not sure, or problematic.

In knowledge-based &expert+ system, it is necessary to understand how to process uncertain knowledge. Inaddition, there is a need for inexact inference methods in !I because there do exit many inexact pieces of data

and knowledge that must be combined.

In a numeric context, uncertainty can be viewed as a value with a known error margin. When the possible range

of value is symbolic rather than numeric, the uncertainty can be represented in terms of ualitative expressions

or by using fu

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#eature e$traction/ It pulls out specified data that is significant in some particular context e.g. feature

selection, principle component analysis &$2!+. 0eature selection simply selects important set of data

from the data matrix. $2! is useful tool capable of compressing data and reducing its dimensionality

so that essential information is retained and easier to analy

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Kbservational data &may not be experimental+ -L historical data or data that have already been collected

for some purpose other than data mining analysis.

$roblem faced due to large data sets ousekeeping issues/ how to store and access the data

ow to determine the representative ness of the data &sampling, does the sample represent the data in

generalH+ ow to analy

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The sample mean has the property that it is the value that is FcentralE in the sense that it minimi

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#ach eigenvector represents a principle component. $2( &$rinciple 2omponent (+, is defined as the eigenvector

with the highest corresponding eigenvalue. The individual eigenvalues are numerically related to the variance

they capture via $2s - the higher the value, the more variance they have captured.

Partial 1east Squares (P1S)regression is based on linear transition from a large number of original descriptors

to a new variable space based on small number of orthogonal factors &latent variables+. In other words, factors

are mutually independent &orthogonal+ linear combinations of original descriptors. :nlike some similar

approaches &e.g. principal component regression $28*+, latent variables are chosen in such a way as to providemaximum correlation with dependent variableC thus, $6S model contains the smallest necessary number of

factors &oskuldsson, ()PP+

This concept is illustrated by 0ig. ( representing a hypothetical data set with two independent variables $2 and$3 and one dependent variable '. It can be easily seen that original variables $2 and $3here are strongly

correlated. 0rom them, we change to two orthogonal factors &latent variables+ t2 and t3 that are linear

combinations of original descriptors. !s a result, a single-factor model can be obtained that relates activity'to

the first latent variable t2.

=asic algorithm of $6S method artens Q 9aes, ()P)J for the step of building -th factor/

Where, % @ number of compounds &samples+,* @ number of descriptors &variables+56%7*8 - descriptor matrixy6%8 @ activity vector,W6*8 @ auxiliary weight vectort6%8 @ factor coefficient vector 6*8 @ loading vector,4 @ scalar coefficient of relationship between factor and activity

!ll vectors are columns, entities without index %&42+% are for the current &-th+ factor.atent variales are the linear cominations of original descriptors 'with coefficients represented y

loading vector p&.

3To performa principal component analysis of the X matrix and then use the principalcomponents of X as regressors on Y.The orthogonality of the principal componentseliminates the multicolinearity problem. Here, nothing guarantees that the principalcomponents, which explain X are relevant for Y. By contrast, P! regression "ndscomponents from X that are also relevant for Y. !peci"cally, P! regression searches fora set of components #called latent vectors$ that performs a simultaneous decompositionof X and Y with the constraint that these components explain as much as possible of thecovariance between X and Y. This step generali%es P&'. (t is followed by a regressionstep where the decomposition of X is used to predict Y.

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#ig. 2 ransformation of original descriptors to latent -ariables (a) and construction of acti-it' modelcontaining one P1S factor (b).

artens ., 9aes T. ultivariate 2alibration. 2hichester etc./ Wiley, ()P).

Rskuldsson !. $6S regression methods. >. 2hemometrics., ()PP, 1&5+ 1((-11

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