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Chapter 7:

Please turn your cell phone off. Chapter 7:. Topics:. Artificial Intelligence Expert Systems Virtual Reality Other Specialized Systems. Specialized Information Systems. Chapter 7.1. An Overview of Artificial Intelligence. Key Terms. Vision systems Natural language processing

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Chapter 7:

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  1. Please turn your cell phone off. Chapter 7: Topics: Artificial Intelligence Expert Systems Virtual Reality Other Specialized Systems Specialized Information Systems

  2. Chapter 7.1 An Overview of Artificial Intelligence 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

  3. 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

  4. Artificial Intelligence Systems • People, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that demonstrate characteristics of “intelligence”

  5. 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

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

  7. 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?).

  8. The Difference Between Natural & Artificial Intelligence

  9. The Major Branches of AI

  10. The Major Branches of AIExpert Systems • Hardware and software that stores knowledge and makes inferences, similar to a human expert • Used in many business applications

  11. The Major Branches of AIRobotics • 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

  12. The Major Branches of AIRobotics 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.

  13. The Major Branches of AIVision 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

  14. The Major Branches of AINatural 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

  15. The Major Branches of AINatural 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

  16. The Major Branches of AINatural 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.

  17. The Major Branches of AILearning 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

  18. The Major Branches of AINeural 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

  19. 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

  20. Chapter 7.2 An Overview of Expert Systems Key Terms • Knowledge engineer • Knowledge user • Backward chaining • Forward chaining • Explanation facility • Knowledge acquisition • facility • Domain • Expert system shell • Knowledge base • If-then statements • Fuzzy logic • Rule • Inference engine

  21. 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

  22. 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

  23. 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

  24. Components of an Expert System

  25. 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

  26. 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

  27. 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

  28. 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

  29. The Inference Engine Figure 7.4: Rules for a Credit Application

  30. 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

  31. 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

  32. 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.

  33. 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

  34. Expert Systems Development Figure 7.6: Steps in the Expert System Development Process

  35. Participants in Expert System Development

  36. 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

  37. Chapter 7.3 Virtual Reality Key Terms • Virtual reality system

  38. 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

  39. Chapter 7.4 Other Specialized Systems Key Terms • Game theory • Informatics

  40. 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

  41. ? Questions? ? ? ? ?

  42. Interesting Statistics • Average Pentium PC executes 100 megaflops (millions of operations per second) • FSU’s super computer can carry out 2.5 teraflops (trillion operations per second) • Fastest supercomputers in 2004 • IBM’s BlueGene/L - 70.72 teraflops • NASA’s Columbia - 51.87 teraflops • NEC’s Earth Simulator - 35.86 teraflops • To achieve anything even approaching human intelligence, a computer must carry out 100 teraflops • Example: Computer speech recognition

  43. 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). Lenat’s 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 – Lenat’s 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.”

  44. 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 can’t 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/

  45. Case Study: IBM’s 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 administrator’s 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

  46. 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

  47. Expert Systems Development Alternatives

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