Specialized Information Systems

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Chapter 7

• Specialized Information Systems

Topics
Artificial Intelligence Expert Systems Virtual
Reality Other Specialized Systems
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An Overview of Artificial Intelligence
Chapter 7.1
Key Terms

• Vision systems
• Natural language processing
• Learning systems
• Neural network
• Genetic algorithm
• Intelligent agent

• Artificial intelligence
• Artificial intelligence
• systems
• Intelligent behavior
• Perceptive system
• Expert system
• Robotics

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Artificial Intelligence

• AI
• The ability of computers to mimic or duplicate
  the functions of the human brain

• Mobile AI
• http//www.artificial-life.com/

• Customer Service Agents
• http//www.conversagent.com

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Artificial Intelligence Systems

• People, procedures, hardware, software, data, and
  knowledge needed to develop computer systems and
  machines that demonstrate characteristics of
  intelligence

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Intelligent Behavior

• The ability to
• learn from experience
• apply knowledge acquired from experience
• handle complex situations
• solve problems when important information is
  missing
• determine what is important
• react quickly and correctly to a new situation
• And understand visual images

Perceptive Systeman AI system that approximates
human senses
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Perceptive System

• A system that approximates the way a human sees,
  hears, and feels objects.

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Interesting Statistics

• It has been estimated that computers that can
  exhibit humanlike intelligence (including musical
  and artistic aptitude, creativity, physical
  movement physically, and emotional
  responsiveness) require processing power of 20
  million billion calculations per second (by the
  year 2030?).

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The Difference Between Natural Artificial
Intelligence
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The Major Branches of AI
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The Major Branches of AI Expert Systems

• Hardware and software that stores knowledge and
  makes inferences, similar to a human expert
• Used in many business applications

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The Major Branches of AI Robotics

• Mechanical or computer devices that perform tasks
  that either require a high degree of precision or
  are tedious or hazardous for humans
• Contemporary robotics combines high-precision
  machine capabilities with sophisticated
  controlling software
• Many applications of robotics exist today
• Research into robots is continuing

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The Major Branches of AI Robotics
Robots can be used in situations that are
hazardous or inaccessible to humans. The Rover
was a remote-controlled robot used by NASA to
explore the surface of Mars.
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The Major Branches of AI Vision Systems

• The hardware and software that permit computers
  to capture, store, and manipulate visual images
  and pictures
• Used by the U.S. Justice Department to perform
  fingerprint analysis
• Used for identifying people based on facial
  features

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The Major Branches of AI Natural Language
Processing

• Processing that allows the computer to understand
  and react to statements and commands made in a
  natural language, such as English
• Three levels of voice recognition
• Command recognition of dozens to hundreds of
  words
• Discrete recognition of dictated speech with
  pauses between words
• Continuous recognition of natural speech

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The Major Branches of AI Natural Language
Processing

• Processing that allows the computer to understand
  and react to statements and commands made in a
  natural language, such as English
• Three levels of voice recognition
• Command recognition of dozens to hundreds of
  words
• Discrete recognition of dictated speech with
  pauses between words
• Continuous recognition of natural speech

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The Major Branches of AI Natural Language
Processing
Dragon Systems Naturally Speaking 7 Essentials
uses continuous voice recognition, or natural
speech, allowing the user to speak to the
computer at a normal pace without pausing between
words. The spoken words are transcribed
immediately onto the computer screen.
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The Major Branches of AI Learning Systems

• A combination of software and hardware that
  allows the computer to change how it functions or
  reacts to situations based on feedback it
  receives
• Learning systems software requires feedback on
  the results of actions or decisions
• Feedback is used to alter what the system will do
  in the future
• Java Whale Watcher
• 20 Questions

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The Major Branches of AI Neural Networks

• A computer system that can simulate the
  functioning of a human brain
• The ability to retrieve information even if some
  of the neural nodes fail
• Fast modification of stored data as a result of
  new information
• The ability to discover relationships and trends
  in large databases
• The ability to solve complex problems for which
  all the information is not present

Face Detection
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Other Artificial Intelligence Applications

• Genetic algorithm an approach to solving large,
  complex problems in which a number of related
  operations or models change and evolve until the
  best one emerges
• Intelligent agent programs and a knowledge base
  used to perform a specific task for a person, a
  process, or another program

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An Overview of Expert Systems
Chapter 7.2
Key Terms

• Backward chaining
• Forward chaining
• Explanation facility
• Knowledge acquisition
• facility
• Domain

• Knowledge engineer
• Knowledge user

• Expert system shell
• Knowledge base
• If-then statements
• Fuzzy logic
• Rule
• Inference engine

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Characteristics and Limitations of an Expert
System

• Can explain its reasoning or suggested decisions
• Can display intelligent behavior
• Can draw conclusions from complex relationships
• Can provide portable knowledge
• Can deal with uncertainty

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Characteristics and Limitations of an Expert
System

• Not widely used or tested
• Difficult to use
• Limited to relatively narrow problems
• Cannot readily deal with mixed knowledge
• Possibility of error

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Characteristics and Limitations of an Expert
System

• Cannot refine its own knowledge
• Difficult to maintain
• May have high development costs
• Expert system shell
• A collection of software packages and tools used
  to develop expert systems
• Raises legal and ethical concerns

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Components of an Expert System
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Components of an Expert System

• Knowledge Base
• Stores all relevant information, data, rules,
  cases, and relationships used by the expert
  system.
• Uses
• Rules
• If-then Statements
• Fuzzy Logic

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The Knowledge Base

• Stores all relevant information, data, rules,
  cases, and relationships used by the expert
  system
• Assembling human experts
• Use of fuzzy logic
• A special research area in computer science that
  allows shades of gray and does not require
  everything to be simple black/white, yes/no, or
  true/false
• Use of rules
• Conditional statement that links given conditions
  to actions or outcomes
• E.g. if-then statements
• Use of cases

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Components of an Expert System

• Inference Engine
• Seeks information and relationships from the
  knowledge base and provides answers, predictions,
  and suggestions the way a human expert would.
• Uses
• Backward Chaining
• Forward Chaining

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The Inference Engine

• Seeks information and relationships from the
  knowledge base and provides answers, predictions,
  and suggestions the way a human expert would
• Backward chaining
• Starting with conclusions and working backward to
  the supporting facts
• Forward chaining
• Starting with the facts and working forwards to
  the conclusions
• Comparison of backward and forward chaining

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The Inference Engine
Figure 7.4 Rules for a Credit Application
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Components of an Expert System
Explanation Facility Allows a user to understand
how the expert system arrived at certain
conclusions or results. For example it allows a
doctor to find out the logic or rationale of the
diagnosis made by a medical expert system
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The Explanation Facility

• Allows a user or decision maker to understand how
  the expert system arrived at certain conclusions
  or results
• For example it allows a doctor to find out the
  logic or rationale of the diagnosis made by a
  medical expert system

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Components of an Expert System
Knowledge acquisition facility Provide convenient
and efficient means of capturing and storing all
the components of the knowledge base. Acts as an
interface between experts and the knowledge
base.
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Components of an Expert System
User Interface Specialized user interface
software employed for designing, creating,
updating, and using expert systems. The main
purpose of the user interface is to make the
development and use of an expert system easier
for users and decision makers
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Expert Systems Development
Figure 7.6 Steps in the Expert System
Development Process
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Participants in Expert System Development
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Participants in Expert System Development

• Domain
• The area of knowledge addressed by the expert
  system
• Domain Expert
• The individual or group who has the expertise or
  knowledge one is trying to capture in the expert
  system
• Knowledge Engineer
• An individual who has training or expertise in
  the design, development, implementation, and
  maintenance of an expert system
• Knowledge User
• The individual or group who uses and benefits
  from the expert system

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Virtual Reality
Chapter 7.3
Key Terms

• Virtual reality system

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Virtual Reality System

• A system that enables one or more users to move
  and react in a computer-simulated environment

secondlife.com
www.worlds.com
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Other Specialized Systems
Chapter 7.4
Key Terms

• Game theory
• Informatics

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Other Specialized Systems

• Game theory
• The use of information systems to develop
  competitive strategies for people, organizations,
  or even countries.
• Informatics
• A specialized system that combines traditional
  disciplines, such as science and medicine, with
  computer systems and technology

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Questions?
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Interesting Statistics

• Average Pentium PC executes 100 megaflops
  (millions of operations per second)
• FSUs super computer can carry out 2.5 teraflops
  (trillion operations per second)
• Fastest supercomputers in 2004
• IBMs BlueGene/L - 70.72 teraflops
• NASAs Columbia - 51.87 teraflops
• NECs Earth Simulator - 35.86 teraflops
• To achieve anything even approaching human
  intelligence, a computer must carry out 100
  teraflops
• Example Computer speech recognition

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Some Current Research

• www.cyc.com
• In 1984 AI Pioneer Doug Lenat began formalizing
  human common sense and entering it into a
  computer program he named Cyc (short for
  encyclopedia). Lenats goal was to develop a
  rational computer program that could make
  independent assertions. He has labored years to
  codify facts such as "Once people die, they stop
  buying things." He uses a form of symbolic logic
  called "predicate calculus" to classify and show
  the properties of information in a standard way.
  Now, 19 years later, with over 600 person-years
  and 60 million invested, the Cyc knowledge base
  contains over 3 million rules that the average
  person knows about the world, plus about 300,000
  terms or concepts Lenats intelligent child is
  ready to begin earning its keep.
• What service can Cyc provide to businesses? I
  see this more as a power source rather than a
  single application. Lenat states. For any
  given application, you need common-sense
  knowledge and domain knowledge. We are building
  in the common-sense knowledge.

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Case Study Transko and Gensym

• Complex volatile systems, such as manufacturing
  and production systems, telecommunications
  systems, supply-chain systems, and distribution
  systems, typically require technicians to
  continuously monitor them in order to safeguard
  against unexpected problems. Failure to catch
  tell-tale signs of trouble, in some cases, could
  lead to disaster. Take for example Transko, the
  company responsible for delivering natural gas to
  over 20 million industrial, commercial and
  domestic customers in the UK.
• Transko maintains over 275,000 km of natural gas
  pipeline, comprising high pressure national and
  regional transmission systems and lower pressure
  distribution systems. Gas is pumped through the
  network by 24 compressor stations located around
  the country. Each compressor station is staffed
  with a team of technicians that monitor the
  pressure within the system watching for increases
  in pressure, that could lead to explosions, or
  decreases in pressure which could indicate a
  leakage of the poisonous gas.
• Such work is tedious and tiring. The stream of
  data to monitor is continuously varying with
  compensating adjustments needed with each
  fluctuation. Operators cant afford a lapse in
  concentration, since failure in the system would
  be disastrous. This scenario is ripe for
  automation. Enter Gensym.
• http//www.gensym.com/

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Case Study IBMs eLiza

• IBM has launched project eLiza to automate many
  system administrator duties and save their
  customers big bucks. Project eLiza is an ongoing
  effort to create servers that respond to
  unexpected capacity demands and system glitches
  without human intervention. The goal new highs
  in reliability, availability and serviceability,
  and new lows in downtime and cost of ownership.
• IBM has classified a system administrators
  duties into four areas system configuration,
  maintenance, security, and efficiency. By
  analyzing the details involved in each of these
  areas, IBM has been able to automate many of
  these tasks in order to create servers that are
  smart enough to care for themselves. The goal
  is to create severs that are

• Self configuring the ability for servers to
  define themselves "on-the fly". This aspect of
  self-managing means that new features, software,
  and servers can be dynamically added to the
  enterprise infrastructure with no disruption of
  services.
• Self-healing the ability to recover from a
  failing components by first detecting and
  isolating the failed component, taking it
  off-line, fixing or isolating the failed
  component , and reintroducing the fixed or
  replacement component into service without any
  application disruption.
• Self-protecting the ability to define and manage
  the access from users to all the resources within
  the enterprise, protect against unauthorized
  resource access, detect intrusions and report
  these activities as they occur, and provide
  backup/recovery capabilities which are as secure
  as the original resource management systems.
• Self-optimizing the ability to efficiently
  maximize resource utilization to meet the end
  user needs with no human intervention required

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Expert System

• Characteristics
• Can explain their reasoning or suggested
  decisions
• Can display intelligent behavior
• Can draw conclusions from complex relationships
• Can provide portable knowledge
• Can deal with uncertainty
• Java Whale Watcher

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Expert Systems Development Alternatives