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STIM5013 IT For Manager

Discovering Computers 2010: Living in a Digital World

Chapter 141

Topic Eight

Knowledge Management and IntelligentSupport Systems

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Content

Knowledge Management

Artificial Intelligence & Intelligent Behavior

A.I: The Brief History of Time

Knowledge & Artificial Intelligence

The Commercial AI Field

Expert System

Natural Language Processing [NLP]

Speech Recognition & Understanding

Neural Computing Hybrid Systems

Intelligent Agent

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Knowledge Management (KM)

KM is a process that helps organizations identify, select,organize, disseminate, and transfer knowledge and expertisethat are part of the organizations memory.

KM is the process of systematically and actively managing andleveraging stores of knowledge in an organization

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Data, Information and Knowledge

Data: raw facts

Information: collection of facts organized so that they have additional

value beyond the value of the facts themselves

Knowledge: awareness and understanding of a set of information and

the ways that information can be made useful to support a specific

task or reach a decision

Principles of InformationSystems, Eighth Edition 4

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Principles of InformationSystems, Eighth Edition 5

Knowledge Management Systems (KMS)

KMS is an organized collection of people, procedures, software,

databases, and devices

Used to create, store, share, and use the organizationsknowledge and experience

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Principles of Information

Systems, Eighth Edition 6

Types ofKnowledge

A KMS can involve different types of knowledge:

Explicit knowledge: objective; can be measured and documented

in reports, papers, and rules

Tacit knowledge: hard to measure and document; typically not

objective or formalized

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Types ofKnowledge

Explicit knowledge

Objective, rational, technical

Policies, goals, strategies, papers, reports

Codified

Leaky knowledge

Tacit knowledge

Subjective, cognitive, experiential learning

Highly personalized Difficult to formalize

Sticky knowledge

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Why Knowledge Management?

Organizations are realizing how important it is to "know what

they know" and be able to make maximum use of the

knowledge.

Preventing reinvent the wheel many times.

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Thus, organizations need to know:

what their knowledge assets are;

how to manage and make use of these assets to get

maximum return.

KM can improve organization efficiency by providing

framework, tools and techniques to reuse captured intellectual

assets.

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We have four people in Boston who know how to solve thisproblem. How can we get them to help our team in Korea?

"People are leaving the company with a lifetime's experience. Howcan we capture and re-use that?"

"We had a team that did a successful proposal for aerospace fiveyears ago. Why did they make the decisions they did? How did theydeal with the customer? What made the team tick?"

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"How do we start learning from our experiences and help ourpeople stop repeating others' mistakes?"

"We're involved in an exciting project with four other companies.

How can we all learn how these virtual teams tick?"

"Needs change often these days and we're always bringing newpeople into projects. How can we get them up to speed andcontributing quickly?"

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Successful companies of the 21Successful companies of the 21stst century will be thosecentury will be those

who do the best ofwho do the best ofcapturing, storingcapturing, storing, and, and leveragingleveraging

what their employees knowwhat their employees know

(CEO, Hewlett Packard)(CEO, Hewlett Packard)

Quote:Quote:

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Obtaining, Storing, Sharing, and Using

Knowledge



Figure 11.3: Knowledge Management System

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Technology to Support Knowledge

Management

An effective KMS is based on learning new knowledge and

changing procedures and approaches as a result of:

Organizational learning

Organizational change

Data mining and business intelligence are important in capturing

and using knowledge

Enterprise resource planning tools include knowledgemanagement features



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Management

Groupware can help capture, store, and use knowledge

Hardware, software, databases, telecommunications, and the

Internet are needed to support most knowledge management

systems Examples of knowledge management products and services

IBMs Lotus Notes and Domino

Microsofts Digital Dashboard, Web Store Technology, and

Access Workflow Designer

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Management

Table 11.1: Additional Knowledge Management Organizations and

Resources

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Artificial Intelligence & Intelligent Behavior

Artificial Intelligence (AI) is

behavior by a machine that, if

performed by a human being,

would be called intelligent.

AIs ultimate goal is to build

machines that will mimic human

intelligence.

AI is concerned with two basicideas:

It involves studying the thought

processes of humans;

It deals with representingthose processes via machines

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A.I: The Brief History of Time

Deep Blue beats human Chess master

The Birth ofAnalytical Engine

The Boolean Logic

1st Electronic Comp.Neural Computation

The Turing Test

1832

1940

Dartmouth Conference - The Birth of AI1951

Expert Systems

Machine Vision, Natural Language Processing

Machine Learning, Speech Recognition

Tutoring System, Fuzzy System, ommercial I system

Data Mining, Face Recognition, Decision Support System

1970

1980

1990

Hybrid technology, Intelligent Agent,Collaborative Intelligence, Humanoid,

Sociable Machines

2002

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Knowledge & Artificial Intelligence

AI is frequently associated with the concept ofknowledge

Such knowledge consists of facts, concepts, theories, heuristic

methods, procedures, and relationships.

Knowledge Base: an organized & stored collection of knowledge

related to a specific problem (or an opportunity) to be used in an

intelligent system.

Organizational Knowledge Base: The collection of knowledge

related to the operation of an organization.

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The Commercial AI Field

Expert Systems (ESs) arecomputerized advisory programsthat attempt to imitate thereasoning processes of experts

in solving difficult problems.

Natural language processing(NLP) gives computer users theability to communicate with the

computer in human languages.

Speech (Voice) Understanding isthe recognition andunderstanding by a computer ofa spoken language.

Robotics refers to a broadcategory of systems thatcombines sensory systems such

as vision systems with AI.

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The Commercial AI Field (cont.)

Visual recognition has been

defined as the addition ofsome form of computer

intelligence and decision

making to digitized visualinformation received from amachine sensor.

Intelligent computer-aided

instruction (ICAI) refers to thework of machines that can

tutor humans.

Machine learning refers to a

set of methods that attempt

to teach computers to solve

problems or to support

problem solving by analyzing(learning from) historical

cases.

Handwriting recognition is

supported by technologies

such as expert systems andneural computing and is

available in some pen-basedcomputers.

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Expert Systems

Expert Systems (ES) are an attempt tomimic human experts.

Expert systems can either support

decision makers or completelyreplace them.

Expert systems are the most widely

applied & commercially successful

AI technology.

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Expertise & Knowledge

Expertise is the extensive, task-specific knowledge acquired

from training, reading, and experience.

The transfer of expertise from an expert to a computer and

then to the user involves four activities:

knowledge acquisition from experts or other sources.

knowledge representation in the computer.

knowledge inferencing, resulting in a recommendationfor novices.

knowledge transfer to the user.

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Components ofExpert Systems

The knowledge base contains

knowledge necessary for

understanding, formulating, and

solving problems.

The blackboard is an area of

working memory set aside for

the description of a currentproblem, as specified by the

input data.

The brain of the ES is the

inference engine, a computer

program that provides a

methodology for reasoning &

formulating conclusions.

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Components ofExpert Systems (cont.)

The user interface allows for

user-computer dialogue,

which can be best carried out

in a natural language, usually

presented in a Q&As format

and sometimes supplemented

by graphics.

The explanation subsystem cantrace responsibility and explainthe ESs behavior by

interactively answeringquestions.

A knowledge-refining systemenables the system to analyze

its performance, learn from it,and improve it for futureconsultations.

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26

MYCIN

Developed at Stanford University in the mid 1970's

The first large expert system that perform at the level ofhuman expert and use as benchmark by expert system

developers.

Provide consultative advise about bacteremia andmeningitis.

Bacteremia is an infections that involve bacteria in theblood.

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MYCIN

Meningitis is an infection which is an inflammation of the

membranes that envelop the brain and spinal cord.

Can be fatal, thus need quick response, but positiveidentification normally takes 24 - 48 hours.

Normally doctors must begin treatment in the absence of lab

results.

Very complex and doctor need advice of an expert of

nbacteremia and meningitis.

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How MYCIN works?

MYCIN begin by initiating a dialogue.

Physician response to the questions

MYCIN provides:

a. diagnosisb. Prescription

MYCIN reason by the following ways:

o Laboratory results of body fluid analyses

o Symptoms that patient is displaying

o Characteristics of the patient such as age, sex etc.

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MYCIN

MYCIN consultative proceeds in 2 phases:

a. Diagnosis is made to identify the most likely infection

organismsb. Prescribe one or more drugs (antibiotics)

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MYCIN: Example of a rule

IF the stain of the organism is gram-positive

AND the morphology of the organism is coccus,

AND the growth conformation of the organism is clumps,

THEN

there is suggestive evidence (0.7) that the identity of the

organism is staphyloccus

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A consultation With MYCIN

1. Please enter information about the patient.

Name Age Sex Race

Sally 42 years Female Melayu

2. Are there any positive cultures obtained from SALLY?

No

3. Are there any pending cultures of SALLY?

Yes

4. Site: date collected: stain examined: method

CSF 3-Jun-75 Yes N/A

5. Stain of smear:

(type NONE if no organism was seen on the smear)None

(Unable to reach conclusion from lab test, like physician continue asking

questions.)

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A consultation With MYCIN (cont..)

6. Has Sally recently had symptoms of persistent headache or otherabnormal neurologic symptoms(dizziness, lethargy etc.)

Yes

7. Has SALLY recently had objective evidence of abnormal neurologic signsdocumented by physician observation of examination?

Yes

Questions 6 and 7 traditional approach to diagnose by symptoms.

Conclude from the questions by giving first conclusion.

Conclusion: The CSF cultures will be considered to be associated

with MENINGITIS.

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The following questions will identify particular organism that cause theinfections.

8. Is the meningitis a hospital-acquired infection?

Yes

9. Is SALLY clinical history consistent with EPIGLOTTITIS?

No

10. Is Sally clinical history consistent with OTITIS-MEDIA?

No

11. Has the patient ever been to an area that is endemic for

coccidiomycoses?

Unknown12. Is Sally a burn patient?

Yes (Why)

Conclusion: The type of infection may be bacterial .

A consultationWith MYCIN (cont..)

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The final phase, prescription of antibiotics.

13. Does SALLY have clinically significant allergic reaction to anyantimicrobial agent?

No

14. Is Sally pregnant of breast feeding?

No

15. Do you have reason to suspect that SALLY may have impaired renalfunctions?

No

16. SALLY weight in kilograms?

51.4

A consultationWith MYCIN (cont..)

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A consultationWith MYCIN

My preferred therapy recommendation is as follows:

Give the following combination:

1. ETHAMBUTAL

Dose: 1.28g (13.0 100mg tablets) q24h PO for 60 daysthen 770 mg (7.5 100 mg tablets) q24h PO.

Comments: periodic vision screening tests are recommendedfor optic neuritis.

2. INH

Dose: 513 mg (5.0 100mg-tablets) q24h PO

3. RIFAMPINDose: 600 mg PO q24hComments: Administer dose on empty stomach.

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HOWGOODANEXPERT IS MYCIN?

In one complex evaluation, 8 independent evaluators evaluate MYCIN

with 9 others human diagnostician for 10 difficult cases of meningitis.

The task used was the selection of drugs for cases of meningitis before

causative agents had been identified.

Two phases of the evaluation:

a. MCYIN and 9 human experts evaluate 10 cases

b. Each of them prescribe medications

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Two evaluative criteria was used to see whether the prescriptions:

a. Would be effective against the actual bacteria after it was finally

identified.

b. Adequately covered for other possible bacteria while avoiding over-

prescribing.

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Result:

Criteria 1: MYCIN and 3 other humans expert consistently prescribe

therapy that would have been effective for all 10 cases.

Criteria 2: MYCIN received higher ratings. 65% correct in all the cases

whereas human expert 42.5% to 62.5%.

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MYCIN strengths is based on 4 factors:

a. MYCIN's knowledge base is extremely detail because acquiredfrom the best human practitioners.

b. MYCIN do not overlook anything or forget any details. It considersevery possibility.

c. MYCIN never jumps to conclusions of fails to ask for key pieces ofinformation.

d. MYCIN is maintained at a major medical center and consequently,completely current.

MYCIN represents 50 man-years of effort.

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Using Expert Systems on the Net

The widespread availability and use of the Internet and intranets nowprovide the opportunity to disseminate expertise and knowledge to massaudiences.

ESs can be transferred over the Net not only to human users, but also toother computerized systems, including DSS, robotics, and databases.

The Web also can support the spread of multimedia-based expert systems.

Such systems, referred to as Intellimedia Systems, support the integration

of extensive multimedia applications and ES.

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Natural Language Processing [NLP]

Natural Language Understanding

Investigates methods of allowinga computer to comprehendinstructions given in ordinary

English, via the keyboard or byvoice, so that computers areable to understand people.

Natural Language Generation

Strives to allow computers toproduce ordinary Englishlanguage, on the screen or by

voice (known as voice synthesis),so people can understandcomputers more easily.

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Applications ofNLP

Natural language processing programs have been applied in

several areas.

The most important are:

o Humancomputer interfaces (mainly to databases)

o Abstracting and summarizing text

o Grammar analysis

o Translation of a natural language to another natural language

o Translation of a computer language to another computer language

o Speech understandingo Composing letters by machine.

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ELIZA

ELIZAis one of the first AI programs employing NLP Written to be a therapist

ELIZA A computer program for the study of natural languagecommunication between man and machine.

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ELIZA (cont..)

Person : Men are all alike.

ELIZA : In what way?

Person : Theyre always bugging us about something or other.

ELIZA : Can you think of a specific example?

Person : Well, my boyfriend made me come here.

ELIZA : Your boyfriend made you come here?

Person : He says Im depressed much of the time.

ELIZA : I am sorry to hear that you are depressed.

Person : Its true. I am unhappy.

ELIZA : Do you think coming here will help you not to be unhappy?

Person : I need some help, that much seems certain.

ELIZA : What would it mean to you if you got some help?

Person : Perhaps I could learn to get along with my mother.

ELIZA : Tell me more about your family

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ELIZA (cont..)

(X me Y) p (X you Y)

(I remember X) p (Why do you remember X just now?)

(My {family-member} is Y) p (Who else in your family is Y?)

(X {family-member} Y) p (Tell me more about your family)

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SpeechRecognition & Understanding

SPEECHRECOGNITION

is a process that allows us to

communicate with a computer by

speaking to it. The computerrecognizes words that have been

spoken without necessarily

interpreting their meanings.

SPEECH UNDERSTANDING

refers to the second part of the

communication process, where

the meaning of the speech isascertained.

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Neural Computing

Neural Computing or Artificial Neural Networks (ANN), a field 3 in AI thatmimics certain processing capabilities of the brain.

The results are:

knowledge representations and processing based on massiveparallel processing,

fast retrieval of large amounts of information, and

the ability to recognize patterns based on experiences.

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Benefits ofNeural Systems

Pattern recognition. Neural

networks can analyze large

quantities of data to establish

patterns/ characteristics in

situations where the logic or rules

are not known.

Fault tolerance. If there are many

processing nodes, damage to a few

nodes or links does not bring the

system to a halt.

Generalization. When a neural

network is presented with an

incomplete or previously unseen

input, it can generalize to produce a

reasonable response.

Adaptability. The network learns in

new environments.

Forecasting capabilities. Similar to

statistics, prediction is made based on

historical data.

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Suitable Business Areas for ANNs

Data mining

Tax fraud

Financial services

Loan application evaluationSolvency prediction

New product analysis

Airline fare management

Resource allocation

Identifying takeover targets

Signature validation

Prediction

Insurance fraud detection

Credit card fraud detection

Evaluation of personnel & jobcandidates

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Case: Visa Cracks Down on Fraud

Problem:Only 0.2% of Visa Internationals turnover in 1995 was lost to fraud,

but at $655 million it is a loss well worth addressing.

Solution:

Visa invested in a cardholder risk identification system (CRIS)designed to notice inconsistent use, such as sudden expensive non-

essentials.

Results:

Visas participating banks believe the neural technology has been

successful. Bank of America has cut fraudulent card use by two-thirds.

By 1995, Visa member banks loss to counterfeiters dropped by more

than 16 percent.

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Hybrid Systems

Intelligent systems are frequently integrated with other intelligent systemsor with conventional systems such as decision support systems.

These form what is known as Hybrid Systems.

The integrated technologies and their roles are:

Neural Networks. These are used to predict future market share andgrowth.

Expert Systems. These provide intelligent advice on developing marketstrategy to individuals and to the planning team.

Fuzzy Logic. This helps deal with uncertainties by simulating the

process of human reasoning, allowing the computer to behave less

precisely and logically than conventional computers do.

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Intelligent Agents

Intelligent agents are software entities that carry out some set of operationson behalf of a user or another program with some degree of independenceor autonomy, and in so doing, employ some knowledge or representation ofthe user's goals or desires.

An intelligent agent may also be referred to as a "knowbot or "bot" (shortfor robot).

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Applications of Intelligent Agents

User Interface. Intelligent agenttechnology allows systems to monitorthe users actions, develop models ofuser abilities, and automatically helpout when interface problems arise.

Operating Systems Agents. Agentscan assist in the use of operatingsystems.

Spreadsheet Agents. Spreadsheet

agents make the software morefriendly.

Workflow & Task Management

Agents. Intelligent agents can be

used to ascertain, then automate,

user wishes or business processes.

Negotiation in e-Commerce. A

challenging system is one in which

agents need to negotiate with each

other. Such systems are especially

applicable to EC.

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Managerial Issues

Acquiring knowledge. Intelligentsystems are built up on expertsknowledge. How can an expert bemotivated to contribute his or herknowledge?

Cost-benefit and justification. Whilesome of the benefits of intelligentsystems are tangible, it is difficult toput a dollar value on the intangiblebenefits of many intelligent systems.

Heightened expectations. Whenthere is too much expectation andhope associated with intelligenttechnologies, management may get

discouraged.

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Managerial Issues (cont.)

Embedded technologies. Intelligent

systems are expected to be

embedded in at least 20 % of all IT

applications in about ten years.

Ethical issues. Finally, there are

several issues related to the use of

intelligent systems. The actions

performed by an ES can be

unethical, or even illegal. There is

also the issue of using knowledge

extracted from people and

replacing people with machines.

System acceptance. The acceptanceof intelligent systems by the ISdepartment and the integration ofsuch systems with mainstream IT is

a critical success factor.

System integration. Intelligentsystems can succeed as standalonesystems, but they have a broaderarea of applications whenintegrated with other computer-based information systems.

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