Knowledge Management and Specialized Information Systems Knowledge Management Systems 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 Knowledge management system (KMS): Organized collection of people, procedures, software, databases, and devices used to create, store, share, and use the organization’s knowledge and experience Overview of Knowledge Management Systems 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 Overview of Systems Data and Knowledge Management Workers and Communities of Practice Personnel involved in a KMS include: Data workers: secretaries, administrative assistants, bookkeepers, other dataentry personnel Knowledge workers: people who create, use, and disseminate knowledge Examples: professionals in science, engineering, and business; writers; researchers; educators; corporate designers Chief knowledge officer (CKO): top-level executive who helps the organization use a KMS to create, store, and use knowledge to achieve organizational goals Communities of practice (COP): group of people dedicated to a common discipline or practice, such as open-source software, auditing, medicine, or engineering Excel at obtaining, storing, sharing, and using knowledge Obtaining, Storing, Sharing, and Using Knowledge Figure 7.3: Knowledge Management System Technology to Support Knowledge Management Tools for capturing and using knowledge include: Data mining and business intelligence Enterprise resource planning tools, such as SAP Groupware Examples of specific KM products IBM’s Lotus Notes, Domino Microsoft’s Digital Dashboard, Web Store Technology, Access Workflow Designer An Overview of Artificial Intelligence Artificial intelligence (AI): ability of computers to mimic or duplicate the functions of the human brain AI-based computer systems have many applications in different fields, such as: Medical diagnoses Exploration for natural resources Determining what is wrong with mechanical devices Assisting in designing and developing other computer systems Artificial Intelligence in Perspective Artificial intelligence systems: people, procedures, hardware, software, data, and knowledge needed to develop computer systems and machines that demonstrate the characteristics of intelligence Ray Kurzweil on “Explosive Growth” http://www.youtube.com/watch?v=ovVIlxqAk8I The Nature of Intelligence Learn from experience and apply knowledge acquired from experience Example: computerized AI chess software Handle complex situations Solve problems when important information is missing Determine what is important React quickly and correctly to a new situation Understand visual images Perceptive system: approximates the way humans hear, see, or feel objects Process and manipulate symbols On a limited basis with machine-vision hardware and software Be creative and imaginative Example: writing short stories Use heuristics Obtaining good solutions (rather than the optimal) through approximation The Difference Between Natural and Artificial Intelligence Table 7.2: A Comparison of Natural and Artificial Intelligence The Major Branches of Artificial Intelligence Expert Systems Hardware and software that stores knowledge and makes inferences, similar to a human expert Used in many business applications Robotics Mechanical or computer devices that perform tasks requiring a high degree of precision or that 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 Doing the dishes http://www.youtube.com/watch?v=BE AmIGciSMI Vision Systems 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 Can be used in identifying people based on facial features Can be used with robots to give these machines “sight” Natural Language Processing and Voice Recognition Natural language processing: allows the computer to understand and react to statements and commands made in a “natural” language, such as English Voice recognition involves converting sound waves into words Learning Systems 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 Neural Networks Computer system that can simulate the functioning of a human brain Ability to retrieve information even if some of the neural nodes fail Fast modification of stored data as a result of new information Ability to discover relationships and trends in large databases Ability to solve complex problems for which all the information is not present WHAT IS A NEURAL NETWORK? A program that is constructed of multiple artificial neurons which interact with one another and "learn" a model used to take intelligent action Consists of three layers: Input, hidden, and output Network learns by adjusting the interconnection weights among the neurons Trained on data and, after linkages adjust weights to yield correct answers, used to predict. HOW DOES A NEURAL NETWORK FUNCTION? Identify and include variables that the designer believes will influence an outcome Network is "trained" using multiple sets of known input variables and associated outcomes Once trained, the network is presented with new data Some Neural Network Structures SOME USES IN BUSINESS: Identifying fraudulent credit card use Processing credit applications Allocating airline seats Rating bonds Signature verification Detecting explosives Evaluating electrocardiograms Detecting faulty paint finishes WHAT ARE THE IMPLICATIONS? Capable of discerning relationships from huge amounts of data and "learning" how they influence outcomes Capable of "learning on the fly"– model changes as assumptions change and old premises become invalid WHAT ABOUT THE FUTURE? Specialized neural network chips embedded in hardware Existing databases will be downloaded into neural networks – for data mining May be merged with expert systems – e.g., expert system could select -- neural network could monitor IS specialists may need to be proficient in neural network skills Neural network applications may increase with greater computing power Large interconnected neural network applications will be developed 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 The MIT Media Lab has a number of ongoing projects regarding software agents. http://www.media.mit.edu/research/ResearchPubWeb.pl?ID=23 An Overview of Expert Systems Like human experts, computerized expert systems use heuristics, or rules of thumb, to arrive at conclusions or make suggestions Used in many fields for a variety of tasks, such as: Designing new products and systems Developing innovative insurance products Increasing the quality of healthcare Determining credit limits for credit cards Determining the best fertilizer mix to use on certain soils When to Use Expert Systems Develop an expert system if it can do any of the following: Provide a high potential payoff or significantly reduce downside risk Capture and preserve irreplaceable human expertise Solve a problem that is not easily solved using traditional programming techniques Develop a system more consistent than human experts Develop an expert system if it can do any of the following-- Provide expertise needed at a number of locations at the same time or in a hostile environment that is dangerous to human health Provide expertise that is expensive or rare Develop a solution faster than human experts can Provide expertise needed for training and development to share the wisdom and experience of human experts with a large number of people Components of Expert Systems Figure 7.8: Components of an Expert System Participants in Developing and Using Expert Systems Domain expert: individual or group who has the expertise or knowledge one is trying to capture in the expert system Knowledge engineer: individual who has training or experience in the design, development, implementation, and maintenance of an expert system Knowledge user: individual or group who uses and benefits from the expert system Applications of Expert Systems and Artificial Intelligence Credit granting and loan analysis Stock picking Catching cheats and terrorists Gambling casinos Budgeting Prototype testing programs Games Crossword puzzles Information management and retrieval Uses bots AI and expert systems embedded in products Antilock braking system, television Plant layout and manufacturing Hospitals and medical facilities Probability of contracting diseases, lab analysis, home diagnosis, appointment scheduling Help desks and assistance Employee performance evaluation Virus detection Uses neural network technology Repair and maintenance Telephone networks, aerospace equipment Shipping and marketing Warehouse optimization Restocking, location Virtual Reality Virtual reality system: enables one or more users to move and react in a computer-simulated environment Immersive virtual reality: user becomes fully immersed in an artificial, three-dimensional world that is completely generated by a computer Experimental “gesture technology”: may have military applications Medicine: anxiety disorders, pain reduction Education and training: anatomy, history, military training Real estate marketing and tourism: virtual walkthroughs Entertainment: CGI movies and games