Artificial Intelligence Edited 972003

8/3/2019 Artificial Intelligence Edited 972003

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INTRODUCTION

Artificial intelligence (AI) is the intelligence of machines

and the branch of computer science that aims to createit. AI textbooks define the field as "the study anddesign of intelligent agents" where an intelligent agentis a system that perceives its environment and takesactions that maximize its chances of success. JohnMcCarthy, who coined the term in 1956, defines it as"the science and engineering of making intelligentmachines."

The field was founded on the claim that a centralproperty of humans, intelligence - the sapience ofHomo sapiens - can be so precisely described that itcan be simulated by a machine. This raisesphilosophical issues about the nature of the mind andthe ethics of creating artificial beings, issues whichhave been addressed by myth, fiction and philosophysince antiquity. Artificial intelligence has been thesubject of optimism, but has also suffered setbacksand, today, has become an essential part of thetechnology industry, providing the heavy lifting formany of the most difficult problems in computerscience.

AI research is highly technical and specialized, anddeeply divided into subfields that often fail to

communicate with each other. Subfields have grown uparound particular institutions, the work of individualresearchers, the solution of specific problems,longstanding differences of opinion about how AI shouldbe done and the application of widely differing tools.General intelligence (or "strong AI") is still among thefield's long term goals.

HISTORY

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The history of artificial intelligence began in antiquity,with myths, stories and rumors of artificial beingsendowed with intelligence or consciousness by mastercraftsmen.

The field of AI research was founded at a conference onthe campus of Dartmouth College in the summer of1956. The attendees including John McCarthy, MarvinMinsky, Allen Newell and Herbert Simon, became theleaders of AI research for many decades. They andtheir students wrote programs that were, to mostpeople, simply astonishing: computers were solving

word problems in algebra, proving logical theorems andspeaking English.

By the middle of the 1960s, research in the U.S. washeavily funded by the Department of Defense andlaboratories had been established around the world.AI's founders were profoundly optimistic about thefuture of the new field. Herbert Simon predicted that

"machines will be capable, within twenty years, ofdoing any work a man can do" and Marvin Minskyagreed, writing that "within a generation the problem ofcreating 'artificial intelligence' will substantially besolved".

In the early 1980s, AI research was revived by thecommercial success of expert systems, a form of AIprogram that simulated the knowledge and analytical

skills of one or more human experts.

By 1985 the market for AI had reached over a billiondollars. At the same time, Japan's fifth generationcomputer project inspired the U.S and British

governments to restore funding for academic researchin the field.

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However, beginning with the collapse of the LispMachine market in 1987, AI once again fell intodisrepute, and a second, longer lasting AI winter began.

On 11 May 1997, Deep Blue became the first computerchess-playing system to beat a reigning world chesschampion, Garry Kasparov.

In 2005, a Stanford robot won the DARPA GrandChallenge by driving autonomously for 131 miles alongan unrehearsed desert trail.

In February 2011, in a Jeopardy! quiz show exhibition

match, IBM's question answering system, Watson,defeated the two greatest Jeopardy! champions, BradRutter and Ken Jennings, by a significant margin.

AI applications are no longer the exclusive domain ofDepartment of defense R&D, but are now commonplace consumer items and inexpensive intelligent toys.

In common usage, the term "AI" no longer seems to

apply to off-the-shelf solved computing-science

problems, which may have originally emerged out of

years of AI research.

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PROBLEMS

The general problem of simulating (or creating)intelligence has been broken down into a number ofspecific sub-problems. These consist of particular traitsor capabilities that researchers would like an intelligentsystem to display. The traits described below havereceived the most attention.

Deduction, Reasoning, Problemsolving

For problems, most of algorithms can require enormouscomputational resources most experience a"combinatorial explosion". The amount of memory orcomputer time required becomes astronomical whenthe problem goes beyond a certain size. The search for

more efficient problem solving algorithms is a highpriority for AI research.

Human beings solve most of their problems using fast,intuitive judgments rather than the conscious, step-by-step deduction that early AI research was able tomodel. AI has made some progress at imitating thiskind of "sub-symbolic" problem solving: embodiedagent approaches emphasize the importance ofsensormotor skills to higher reasoning; neural net researchattempts to simulate the structures inside human andanimal brains that give rise to this skill.

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Knowledge Representation

Many of the problems machines are expected to solvewill require extensive knowledge about the world.Among the things that AI needs to represent are:objects, properties, categories and relations betweenobjects; situations, events, states and time; causes andeffects; knowledge about knowledge and many other,less well researched domains. Among the most difficultproblems in knowledge representation are:

Many of the things people know take the form of

"working assumptions." For example, if a bird comes upin conversation, people typically picture an animal thatis fist sized, sings, and flies. None of these things aretrue about all birds. John McCarthy identified thisproblem in 1969 as the qualification problem: for anycommonsense rule that AI researchers care torepresent, there tend to be a huge number ofexceptions. Almost nothing is simply true or false in the

way that abstract logic requires. AI research hasexplored a number of solutions to this problem.

Planning

Intelligent agents must be able to set goals and achievethem. They need a way to visualize the future and beable to make choices that maximize the utility (or

"value") of the available choices.

In classical planning problems, the agent can assumethat it is the only thing acting on the world and it canbe certain what the consequences of its actions maybe.

However, if this is not true, it must periodically check if

the world matches its predictions and it must change

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its plan as this becomes necessary, requiring the agentto reason under uncertainty.

Learning

Machine learning has been central to AI research fromthe beginning. In 1956, at the Dartmouth AI summerconference, Ray Solomonoff wrote a report onunsupervised probabilistic machine learning: "AnInductive Inference Machine". Unsupervised learning isthe ability to find patterns in a stream of input.Supervised learning includes both classification and

numerical regression. Classification is used todetermine what category something belongs in, afterseeing a number of examples of things from severalcategories. Regression takes a set of numericalinput/output examples and attempts to discover acontinuous function that would generate the outputsfrom the inputs. In reinforcement learning the agent isrewarded for good responses and punished for badones. These can be analyzed in terms of decisiontheory, using concepts like utility. The mathematicalanalysis of machine learning algorithms and theirperformance is a branch of theoretical computerscience known as computational learning theory.

Natural Language Processing

ASIMO uses sensors and intelligent algorithms to avoid

obstacles and navigate stairs. Natural language

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processing gives machines the ability to read andunderstand the languages that humans speak. Manyresearchers hope that a sufficiently powerful naturallanguage processing system would be able to acquireknowledge on its own, by reading the existing textavailable over the internet. Some straightforwardapplications of natural language processing includeinformation retrieval (or text mining) and machinetranslation.

Natural language processing is a very attractivemethod of humancomputer interaction. Naturallanguage understanding is sometimes referred to as anAI-complete problem because it seems to requireextensive knowledge about the outside world and theability to manipulate it.

Motion and Manipulation

The field ofrobotics is closely related to AI. Intelligence

is required for robots to be able to handle such tasks asobject manipulation and navigation, with sub-problemsof localization (knowing where you are), mapping(learning what is around you) and motion planning(figuring out how to get there).

Perception

Machine perception is the ability to use input fromsensors (such as cameras, microphones, sonar andothers more exotic) to deduce aspects of the world.

Computer vision is the ability to analyze visual input. A

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few selected sub-problems are speech recognition,facial recognition and object recognition.

Social Intelligence

Kismet, a robot with rudimentary social skills. Emotionand social skills play two roles for an intelligent agent.First, it must be able to predict the actions of others, byunderstanding their motives and emotional states. (Thisinvolves elements ofgame theory, decision theory, aswell as the ability to model human emotions and theperceptual skills to detect emotions.) Also, for good

human-computer interaction, an intelligent machinealso needs to display emotions. At the very least itmust appear polite and sensitive to the humans itinteracts with. At best, it should have normal emotionsitself.

Creativity

A sub-field of AI addresses creativity both theoretically(from a philosophical and psychological perspective)and practically (via specific implementations of systemsthat generate outputs that can be considered creative,or systems that identify and assess creativity). Arelated area of computational research is ArtificialIntuition and Artificial Imagination.

General IntelligenceMost researchers hope that their work will eventuallybe incorporated into a machine with generalintelligence (known as strong AI), combining all theskills above and exceeding human abilities at most orall of them. A few believe that anthropomorphicfeatures like artificial consciousness or an artificial

brain may be required for such a project.

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Many of the problems above are considered AI-complete: to solve one problem, you must solve themall. For example, even a straightforward, specific tasklike machine translation requires that the machinefollow the author's argument (reason), know what isbeing talked about (knowledge), and faithfullyreproduce the author's intention (social intelligence).Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well ashumans can do it.

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APPROACHES

There is no established unifying theory or paradigmthat guides AI research. Researchers disagree aboutmany issues. A few of the most long standing questionsthat have remained unanswered are these: shouldartificial intelligence simulate natural intelligence, bystudying psychology or neurology? Or is human biologyas irrelevant to AI research as bird biology is to

aeronautical engineering? Can intelligent behavior bedescribed using simple, elegant principles (such aslogic or optimization)? Or does it necessarily requiresolving a large number of completely unrelatedproblems? Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does itrequire "sub-symbolic" processing? John Haugeland,who coined the term GOFAI, also proposed that AI

should more properly be referred to as syntheticintelligence, a term which has since been adopted bysome non-GOFAI researchers.

Cybernetics And Brain Simulation

In the 1940s and 1950s, a number of researchersexplored the connection between neurology,information theory, and cybernetics. Some of thembuilt machines that used electronic networks to exhibitrudimentary intelligence, such as W. Grey Walter'sturtles and the Johns Hopkins Beast. Many of theseresearchers gathered for meetings of the TeleologicalSociety at Princeton University and the Ratio Club inEngland. By 1960, this approach was largelyabandoned, although elements of it would be revived inthe 1980s.

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Symbolic

When access to digital computers became possible inthe middle 1950s, AI research began to explore thepossibility that human intelligence could be reduced tosymbol manipulation. The research was centered inthree institutions: CMU, Stanford and MIT, and each onedeveloped its own style of research. John Haugelandnamed these approaches to AI "good old fashioned AI"or "GOFAI"

Types of symbolics are:

Cognitive simulation

Logic-based

"Anti-logic" or "scruffy"

Knowledge-based

Sub-Symbolic

During the 1960s, symbolic approaches had achievedgreat success at simulating high-level thinking in smalldemonstration programs. Approaches based oncybernetics or neural networks were abandoned orpushed into the background.[92] By the 1980s, however,progress in symbolic AI seemed to stall and many

believed that symbolic systems would never be able toimitate all the processes of human cognition, especiallyperception, robotics, learning and pattern recognition.A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.

1. Bottom-up, embodied, situated, behavior-based ornouvelle AI

Researchers from the related field of robotics, such asRodney Brooks, rejected symbolic AI and focused on

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the basic engineering problems that would allow robotsto move and survive.[93] Their work revived the non-symbolic viewpoint of the early cybernetics researchersof the 50s and reintroduced the use ofcontrol theory inAI. This coincided with the development of theembodied mind thesis in the related field of cognitivescience: the idea that aspects of the body (such asmovement, perception and visualization) are requiredfor higher intelligence.

2. Computational IntelligenceInterest in neural networks and "connectionism" wasrevived by David Rumelhart and others in the middle1980s. These and other sub-symbolic approaches, suchas fuzzy systems and evolutionary computation, arenow studied collectively by the emerging discipline ofcomputational intelligence.

Statistical

In the 1990s, AI researchers developed sophisticatedmathematical tools to solve specific subproblems.

These tools are truly scientific, in the sense that theirresults are both measurable and verifiable, and theyhave been responsible for many of AI's recentsuccesses. The shared mathematical language has alsopermitted a high level of collaboration with moreestablished fields (like mathematics, economics or

operations research). Stuart Russell and Peter Norvigdescribe this movement as nothing less than a"revolution" and "the victory of the neats."

Integrating the Approaches

1. Intelligent agent paradigmAn intelligent agent is a system that perceives its

environment and takes actions which maximizes itschances of success. The simplest intelligent agents are

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programs that solve specific problems. Morecomplicated agents include human beings andorganizations of human beings (such as firms). Theparadigm gives researchers license to study isolatedproblems and find solutions that are both verifiable anduseful, without agreeing on one single approach. Anagent that solves a specific problem can use anyapproach that works some agents are symbolic andlogical, some are sub-symbolic neural networks andothers may use new approaches. The paradigm alsogives researchers a common language to communicatewith other fieldssuch as decision theory andeconomicsthat also use concepts of abstract agents.

The intelligent agent paradigm became widelyaccepted during the 1990s.

2. Agent architectures and cognitive architecturesResearchers have designed systems to build intelligentsystems out of interacting intelligent agents in a multi-agent system. A system with both symbolic and sub-symbolic components is a hybrid intelligent system,and the study of such systems is artificial intelligencesystems integration. A hierarchical control systemprovides a bridge between sub-symbolic AI at itslowest, reactive levels and traditional symbolic AI at itshighest levels, where relaxed time constraints permitplanning and world modelling. Rodney Brooks'subsumption architecture was an early proposal forsuch a hierarchical system

TOOLS AND METHODS

Logical AI

What a program knows about the world in general thefacts of the specific situation in which it must act, andits goals are all represented by sentences of some

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mathematical logical language. The program decideswhat to do by inferring that certain actions areappropriate for achieving its goals. The first articleproposing this was [McC59]. [McC89] is a more recentsummary. [McC96b] lists some of the concepts involvedin logical aI. [Sha97] is an important text.

Search

AI programs often examine large numbers ofpossibilities, e.g. moves in a chess game or inferencesby a theorem proving program. Discoveries are

continually made about how to do this more efficientlyin various domains.

Pattern Recognition

When a program makes observations of some kind, it isoften programmed to compare what it sees with apattern. For example, a vision program may try to

match a pattern of eyes and a nose in a scene in orderto find a face. More complex patterns, e.g. in a naturallanguage text, in a chess position, or in the history ofsome event are also studied. These more complexpatterns require quite different methods than do thesimple patterns that have been studied the most.

Representation

Facts about the world have to be represented in someway. Usually languages of mathematical logic are used.

Inference

From some facts, others can be inferred. Mathematical

logical deduction is adequate for some purposes, butnew methods of non-monotonic inference have been

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added to logic since the 1970s. The simplest kind ofnon-monotonic reasoning is default reasoning in whicha conclusion is to be inferred by default, but theconclusion can be withdrawn if there is evidence to thecontrary. For example, when we hear of a bird, we maninfer that it can fly, but this conclusion can be reversedwhen we hear that it is a penguin. It is the possibilitythat a conclusion may have to be withdrawn thatconstitutes the non-monotonic character of thereasoning. Ordinary logical reasoning is monotonic inthat the set of conclusions that can the drawn from aset of premises is a monotonic increasing function ofthe premises. Circumscription is another form of non-monotonic reasoning.

Common Sense Knowledge AndReasoning

This is the area in which AI is farthest from human-level, in spite of the fact that it has been an activeresearch area since the 1950s. While there has been

considerable progress, e.g. in developing systems ofnon-monotonic reasoning and theories of action, yetmore new ideas are needed. The Cyc system contains alarge but spotty collection of common sense facts.

Learning From Experience

Programs do that. The approaches to AI based on

connectionism and neural nets specialize in that. Thereis also learning of laws expressed in logic. [Mit97] is a

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comprehensive undergraduate text on machinelearning. Programs can only learn what facts orbehaviors their formalisms can represent, andunfortunately learning systems are almost all based onvery limited abilities to represent information.

Planning

Planning programs start with general facts about theworld (especially facts about the effects of actions),facts about the particular situation and a statement of agoal. From these, they generate a strategy for

achieving the goal. In the most common cases, thestrategy is just a sequence of actions.

Epistemology

This is a study of the kinds of knowledge that arerequired for solving problems in the world.

Ontology

Ontology is the study of the kinds of things that exist.In AI, the programs and sentences deal with variouskinds of objects, and we study what these kinds are andwhat their basic properties are. Emphasis on ontologybegins in the 1990s.

Heuristics

A heuristic is a way of trying to discover something oran idea imbedded in a program. The term is usedvariously in AI. Heuristic functions are used in someapproaches to search to measure how far a node in asearch tree seems to be from a goal. Heuristicpredicates that compare two nodes in a search tree tosee if one is better than the other, i.e. constitutes an

advance toward the goal, may be more useful.

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Genetic Programming

Genetic programming is a technique for gettingprograms to solve a task by mating random Lispprograms and selecting fittest in millions ofgenerations. It is being developed by John Koza's groupand here's a tutorial.

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APPLICATIONS OF ARTIFICIAL

INTELLIGENCEAlmost every branch of science and engineeringcurrently shares the tools and techniques available inthe domain of artificial intelligence. However, for thesake of the convenience, we mention here a fewapplications, where AI plays a significant and decisiverole in engineering automation.

Image Understanding and ComputerVision

Adigital image can be regarded as a two-dimensionalarray of pixels containing gray levels corresponding tothe intensity of the reflected illumination received by avideo camera. For interpretation of a scene, its image

should be passed through three basic processes: low,medium and high level vision .

Speech and Natural Language

Understanding

In speech analysis, the main problem is to separate thesyllables of a spoken word and determine features like

amplitude, and fundamental and harmonic frequenciesof each syllable. The words then could be identifiedfrom the extracted features by pattern classificationtechniques. A robot capable of understanding speech ina natural language will be of immense importance. Thephonetic typewriter, which prints the words pronouncedby a person, is another recent invention where speechunderstanding is employed in a commercial application.

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while it is possible to instruct some computers usingspeech.

Scheduling

In a scheduling problem, one has to plan the timeschedule of a set of events to improve the timeefficiency of the solution. Flowshop schedulingproblems are a NP complete problem anddetermination of optimal scheduling (for minimizing themake-span) thus requires an exponential order of timewith respect to both machine-size and job-size. Finding

a sub-optimal solution is thus preferred for suchscheduling problems. Recently, artificial neural netsand genetic algorithms have been employed to solvethis problem. The heuristic search, to be discussedshortly, has also been used for handling this problem.

Intelligent Control

In process control, the controller is designed from theknown models of the process and the required controlobjective. When the dynamics of the plant is notcompletely known, the existing techniques forcontroller design no longer remain valid. Rule-basedcontrol is appropriate in such situations.

Games Playing

Programming computers to play games such as chessand checkers . You can buy machines that can playmaster level chess for a few hundred dollars. There issome ai in them, but they play well against peoplemainly through brute force computation looking athundreds of thousands of positions. To beat a worldchampion by brute force and known reliable heuristicsrequires being able to look at 200 million positions per

second.

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Expert Systems

A ``knowledge engineer'' interviews experts in acertain domain and tries to embody their knowledge ina computer program for carrying out some task. Howwell this works depends on whether the intellectualmechanisms required for the task are within thepresent state of AI. When this turned out not to be so,there were many disappointing results. One of the firstexpert systems was MYCIN in 1974, which diagnosed

bacterial infections of the blood and suggestedtreatments. It did better than medical students orpracticing doctors, provided its limitations wereobserved.

Neural Networks

Artificial neural networks may either be used to gain an

understanding of biological neural networks, or forsolving artificial intelligence problems withoutnecessarily creating a model of a real biologicalsystem. The real, biological nervous system is highlycomplex: artificial neural network algorithms attempt toabstract this complexity and focus on what mayhypothetically matter most from an informationprocessing point of view.

Robotics

Robotics is the branch oftechnology that deals with thedesign, construction, operation, structural disposition,manufacture and application of robots. Robotics isrelated to the sciences of electronics, engineering,mechanics, and software

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Driverless Car

A driverless car is a vehicle equipped with an autopilotsystem, which is capable of driving from one point toanother without input from a human operator. Car havecapability to park itself without drivers help.

Heuristic Classification

One of the most feasible kinds of expert system giventhe present knowledge of AI is to put some informationin one of a fixed set of categories using several sources

of information. An example is advising whether toaccept a proposed credit card purchase. Information isavailable about the owner of the credit card, his recordof payment and also about the item he is buying andabout the establishment from which he is buying it(e.g., about whether there have been previous creditcard frauds at this establishment).

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EXPERT SYSTEMExpert Systems are computer programs that arederived from a branch of computer science researchcalled Artificial Intelligence (AI). AI's scientific goal is tounderstand intelligence by building computer programsthat exhibit intelligent behavior. It is concerned withthe concepts and methods of symbolic inference, or

reasoning, by a computer, and how the knowledgeused to make those inferences will be representedinside the machine.

Expert systems aim to mimic human reasoning. The

methods and techniques used to build these programs

are the outcome of efforts in a field of computer

science known as Artificial intelligence

In conventional computer programs, problem-solving

knowledge is encoded in program logic and program-

resident data structures. Expert systems differ from

conventional programs both in the way problem

knowledge is stored and used.

Expert systems are especially important to

organizations that rely on people who possessspecialized knowledge of some problem domain,

especially if this knowledge and experience cannot be

easily transferred. Artificial intelligence methods and

techniques have been applied to a broad range of

problems and disciplines, some of which are esoteric

and others which are extremely practical.

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EXAMPLES OF PROBLEMS WHERE

AN EXPERT ASSISTANT MAYHELP

Productivity

Bottlenecks result where there is only one or a fewexperienced personnel who spend much of their time

helping others rather than applying their expertise tofuture planning or higher level tasks. An ExpertAssistant can reduce the demands on their time as wellas raising the knowledge level of the junior employees.

Diagnosis Of Problems

Many diagnostic systems have been implemented asExpert Systems. The help desk of a computer facility

lends itself to implementation as an Expert Assistant.Relevant facts can be gathered about the problem byclerical staff and the system can recommend theperson most able to cope with that type of problem.Routinely occurring problems can have solutionssuggested by the Expert Assistant.

Distribution of policy, knowledge or

information

With an Expert Assistant, all users in an organisation

can have access to the knowledge. Moreover, the

knowledge will be made available in more than . One

location at a time, and when and where needed.

Manuals of policy, procedures or regulations are oftendaunting to many employees. As the information

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becomes more complex, there is an increasedprobability of incorrect information being given toclients because the information is so difficult or timeconsuming to locate. These documents are easilyrepresented as a knowledge base. Having theknowledge captured in a central location improves theease and speed with which these manuals can beupdated.

Loss of expertise from employeeturnover or retirement

Long serving employees take expertise from thecompany when they retire. Developing an ExpertSystem can retain this expertise which would otherwisebe lost.

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WHEN TO USE AN EXPERT

SYSTEM

1. Provide a high potential payoff or significantlyreduced downside risk

2. Capture and preserve irreplaceable humanexpertise

3.Provide expertise needed at a number of locationsat the same time or in a hostile environment that isdangerous to human health

4. Provide expertise that is expensive or rare

5. Develop a solution faster than human experts can

6. Provide expertise needed for training anddevelopment to share the wisdom of humanexperts with a large number of people

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ADVANTAGES OF EXPERTSYSTEMS

Permanence

Expert systems do not forget, but human experts may

Reproducibility

Many copies of an expert system can be made, buttraining new human experts is time-consuming and

expensive. If there is a maze of rules (e.g. tax andauditing), then the expert system can "unravel" themaze

Efficiency

Expert system can increase throughput and decreasepersonnel costs. Although expert systems are

expensive to build and maintain, they are inexpensiveto operate. Development and maintenance costs canbe spread over many users. The overall cost can bequite reasonable when compared to expensive andscarce human experts. It is Cost savings:-Wages (elimination of a room full of clerks)-Other costs (minimize loan loss)

ConsistencyWith expert systems similar transactions handled in thesame way. The system will make comparablerecommendations for like situations. Humans areinfluenced by recency effects and primacy effects.

Documentation

An expert system can provide permanentdocumentation of the decision process

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Completeness

An expert system can review all the transactions, ahuman expert can only review a sample

Timeliness

Fraud and/or errors can be prevented. Information isavailable sooner for decision making

Breadth

The knowledge of multiple human experts can becombined to give a system more breadth that a singleperson is likely to achieve

Entry barriers

Expert systems can help a firm create entry barriers forpotential competitors

DifferentiationIn some cases, an expert system can differentiate aproduct or can be related to the focus of the firm.Computer programs are best in those situations wherethere is a structure that is noted as previously existingor can be elicited

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DISADVANTAGES OF EXPERTSYSTEMS

Common Sense

In addition to a great deal of technical knowledge,human experts have common sense. It is not yet knownhow to give expert systems common sense.

Creativity

Human experts can respond creatively to unusualsituations, expert systems cannot.

Learning

Human experts automatically adapt to changingenvironments; expert systems must be explicitlyupdated. Case-based reasoning and neural networksare methods that can incorporate learning.

Sensory Experience

Human experts have available to them a wide range of

sensory experience; expert systems are currentlydependent on symbolic input.

Degradation

Expert systems are not good at recognizing when noanswer exists or when the problem is outside their areaof expertise.

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APPLICATION OF ARTIFICIAL IN

BUSINESS AND COMMERCE

WORLD

Are e-commerce and e-business having relation withArtificial Intelligence or AI? AI is the science andengineering of making intelligent machines, especiallyintelligent computer programs. It is related to thesimilar task of using computers to understand humanintelligence, but AI does not have to confine itself tomethods that are biologically observable. Then, whatabout the relation between e-business, e-commerce,and AI? AI techniques are extensively used in thedevelopment of e-commerce systems. The field of e-commerce can be classified as B2C e-commerce andB2B e-commerce, in terms of AI techniques involved inthis field. In B2C e-commerce, AI is used primarily forproduct selection and recommendation, negotiation,auctions, solving real-world scheduling problems andenhancing servers scalability, generating automatedresponses, and decisions on bundling and pricing ofgoods, etc. In B2B e-commerce, AI is used mainly forsupply chain management.

Business applications utilise the specific technologiesto try and make better sense of potentially enormous

variability (for example, unknown patterns/relationshipsin sales data, customer buying habits, and so on).However, within the corporate world, AI is widely usedfor complex problem-solving and decision-supporttechniques in real-time business applications. Thebusiness applicability of AI techniques is spread acrossfunctions ranging from finance management toforecasting and production.

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ARTIFICIAL INTELLIGENCE (AI) INBUSINESS

Artificial Intelligence (AI) has been used in businessapplications since the early eighties. As with alltechnologies, AI initially generated much interest, butfailed to live up to the hype. However, with the adventof web-enabled infrastructure and rapid strides madeby the AI development community, the application of AItechniques in real-time business applications haspicked up substantially in the recent past.

Computers are fundamentally well suited to performingmechanical computations, using fixed programmedrules. This allows artificial machines to perform simplemonotonous tasks efficiently and reliably, whichhumans are ill-suited to. For more complex problems,things get more difficult. Unlike humans, computershave trouble understanding specific situations, andadapting to new situations. Artificial Intelligence aims

to improve machine behavior in tackling such complextasks.

IMPORTANCE OF ARTIFICIALINTELLIGENCE (AI)

Enterprises that utilize AI-enhanced applications are

expected to become more diverse, as the needs for theability to analyze data across multiple variables, frauddetection and customer relationship managementemerge as key business drivers to gain competitiveadvantage.Artificial Intelligence is a branch of Science which dealswith helping machines, finds solutions to complexproblems in a more human-like fashion. This generally

involves borrowing characteristics from human

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intelligence, and applying them as algorithms in acomputer friendly way.

ADVANTAGES

1. Increase efficiency and quality by using optimalsettings from past production.

2.Artificial Intelligence can optimize your schedulebeyond normal human capabilities.

3. Increase productivity by eliminating downtime dueto unpredictable changes in the schedule.

DISADVANTAGES

Limited sensory input. Compared to a biologicalmind, an artificial mind is only capable of taking in asmall amount of information. This is because of theneed for individual input devices. The most important

input that we humans take is the condition of ourbodies. Because we feel what is going on with ourown bodies, we can maintain them much moreefficiently than an artificial mind. At this point, it isunclear whether that would be possible with acomputer-system.

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ADVANTAGES ANDDISADVANTAGES OF ARTIFICIAL

INTELLIGENCE

Without getting into too many technical specifics, hereare some advantages-

Artificial intelligence would notneed any sleep.

This would be an advantage because it would not beinterrupted from its tasks for sleep, as well as other

issues that plague biological minds like restroombreaks and eating.

Unemotional consideration ofproblems.

While an artificial mind could theoretically haveemotions, it would be better for performance if it were

programmed for unemotional reasoning. When peoplemake decisions, sometimes those decisions are basedon emotion rather than logic. This is not always thebest way to make decisions.

Copied very easily

Once an artificial mind is trained in a task, that mind

can then be copied very easily, compared to thetraining of multiple people for the same task.

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There are some disadvantages to the artificial mind aswell

Limited Sensory Input

Compared to a biological mind, an artificial mind isonly capable of taking in a small amount ofinformation. This is because of the need for individualinput devices. The most important input that wehumans take in is the condition of our bodies.

Because we feel what is going on with our ownbodies, we can maintain them much more efficientlythan an artificial mind. At this point, it is unclearwhether that would be possible with a computersystem.

The Inability to Heal.

Biological systems can heal with time and treatment.For minor conditions, most biological systems cancontinue normally with only a small drop inperformance. Most computer systems, on the otherhand, often need to be shut down for maintenance.

Replacement of Human

If robots start replacing human resources in every field,we will have to deal with serious issues likeunemployment in turn leading to mental depression,poverty and crime in the society. Human beingsdeprived of their work life may not find any means tochannelize their energies and harness their expertise.Human beings will be left with empty time.

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Absence of Human Touch

Replacing human beings with robots in every field maynot be a right decision to make. There are many jobsthat require the human touch. Intelligent machines willsurely not be able to substitute for the caring behaviourof hospital nurses or the promising voice of a doctor.Intelligent machines may not be the right choice forcustomer service.

Cannot Be Human

One of the major disadvantages of intelligent machinesis that they cannot be human. We might be able tomake them think. But will we be able to make themfeel?

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THE FUTURE OF ARTIFICIALINTELLIGENCE

In spite of its great advances and strong promise, AI, in name, has

suffered from low esteem in both academic and corporate settings.AI

unfavourably associated with impractical chess playing computers

and recluse professors trying to build a "thinking machine." As a

result, many developers of Al theories and applications consciouslyshun the moniker, preferring instead to use the newer jargon of fuzzy

applications, flexible software, and data-mining tools. In avoiding the

label Al, they have found more receptive audiences among corporate

decision-makers and private investors for their Al-inspired

technologies.

Thus, while the practices and ideas known as Al are hardly dead, the

name itself is drifting toward obscurity. This is true not only becauseof the perceived stigma, but also as a consequence of the diversity and

heterogeneity of ways in which Al concepts have been implemented.

Furthermore, these concepts are verging on ubiquity in software

applications programming. Such disparate objectives as building a

customer order system, implementing a self-diagnostic manufacturing

system, designing a sophisticated search engine, and adding voice-

recognition capabilities to applications all employ AI theories and

methods. Indeed, Ford Motor Company was slated to implement an

engine-diagnostic neural network in its car computers beginning in

the 2001 model year. With Al so entrenched in modern software

development, it has lost many of its distinctions from software

generally.

Artificial Intelligence is a common topic in both science fiction and

projections about the future of technology and society. The existence

of an artificial intelligence that rivals human intelligence raises

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difficult ethical issues, and the potential power of the technology

inspires both hopes and fears.

In fiction, Artificial Intelligence has appeared fulfilling many roles,

including a servant (R2D2 in Star Wars), a law enforcer (K.I.T.T."Knight Rider"), a comrade (Lt. Commander Data in Star Trek: The

Next Generation), a conqueror/overlord (The Matrix), a dictator (With

Folded Hands), an assassin (Terminator), a sentient race (Battlestar

Galactica/Transformers), an extension to human abilities (Ghost in the

Shell) and the savior of the human race (R. Daneel Olivaw in the

Asimov's Robot Series).

Mary Shelley's Frankenstein considers a key issue in the ethics ofartificial intelligence: if a machine can be created that has

intelligence, could it also feel? If it can feel, does it have the same

rights as a human? The idea also appears in modern science fiction,

including the films I Robot, Blade Runner and A.I.: Artificial

Intelligence, in which humanoid machines have the ability to feel

human emotions. This issue, now known as "robot rights", is currently

being considered by, for example, California's Institute for the Future,

although many critics believe that the discussion is premature.[155]

Thesubject is profoundly discussed in the 2010 documentary film Plug &

Pray.[156]

Martin Ford and others argue that specialized artificial intelligence

applications, robotics and other forms of automation will ultimately

result in significant unemployment as machines begin to match and

exceed the capability of workers to perform most routine and

repetitive jobs. Ford predicts that many knowledge-based occupations

and in particular entry level jobs will be increasingly susceptible to

automation via expert systems and other AI-enhanced applications.

AI-based applications may also be used to amplify the capabilities of

low-wage offshore workers, making it more feasible to outsource

knowledge work.

Joseph Weizenbaum wrote that AI applications can not, by definition,

successfully simulate genuine human empathy and that the use of AI

technology in fields such as customer service orpsychotherapy was

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deeply misguided. Weizenbaum was also bothered that AI researchers

(and some philosophers) were willing to view the human mind as

nothing more than a computer program (a position now known as

computationalism). To Weizenbaum these points suggest that AI

research devalues human life.

Many futurists believe that artificial intelligence will ultimately

transcend the limits of progress. Ray Kurzweil has used Moore's law

which describes the relentless exponential improvement in digital

technology, to calculate that desktop computers will have the same

processing power as human brains by the year 2029. He also predicts

that by 2045 artificial intelligence will reach a point where it is able to

improve itself at a rate that far exceeds anything conceivable in thepast, a scenario that science fiction writer Vernor Vinge named the

"singularity".

Robot designer Hans Moravec, cyberneticist Kevin Warwick and

inventorRay Kurzweil have predicted that humans and machines will

merge in the future into cyborgs that are more capable and powerful

than either. This idea, called transhumanism, which has roots in

Aldous Huxley and Robert Ettinger, has been illustrated in fiction aswell, for example in the manga Ghost in the Shell and the science-

fiction series Dune.

Edward Fredkin argues that "artificial intelligence is the next stage in

evolution," an idea first proposed by Samuel Butler's "Darwin among

the Machines" (1863), and expanded upon by George Dyson in his

book of the same name in 1998.

Pamela McCorduckwrites that all these scenarios are expressions ofthe ancient human desire to, as she calls it, "forge the gods".

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INDEX

S.No

Particulars Pg.no

1 Introduction2 History3 Problems4 Approaches

5 Tools And Methods6 Applications Of Artificial Intelligence7 Expert System8 Examples Of Problems Where An Expert

Assistant May Help9 When To Use An Expert System10 Advantages Of Expert Systems11 Disadvantages Of Expert Systems12 Application Of Artificial In Business And

Commerce World13 Advantages And Disadvantages Of Artificial

Intelligence14 The Future Of Artificial Intelligence

Documents

Artificial Intelligence Edited 972003