Lecture 8: Managing Knowledge and Artificial Intelligence PDF

Lecture 8 BHMS4472 ICT in Business Managing Knowledge and Artificial Intelligence ICT (Information, Communication, and Technology) Learning Objectives What is the role of knowledge management systems in business? What is Knowl...

Lecture 8 BHMS4472 ICT in Business Managing Knowledge and Artificial Intelligence ICT (Information, Communication, and Technology) Learning Objectives What is the role of knowledge management systems in business? What is Knowledge Management Value Chain What are artificial intelligence (AI) and machine learning? How do businesses use AI? What types of systems are used for enterprise-wide knowledge management, and how do they provide value for businesses? What are the major types of knowledge work systems, and how do they provide value for firms? What is the Role of Knowledge Management Systems in Business? Knowledge management systems among fastest growing areas of software investment Information economy: production and distribution of information and knowledge a major source of wealth and prosperity Substantial part of a firm’s stock market value is related to intangible assets: knowledge, brands, reputations, and unique business processes Well-executed knowledge-based projects can produce extraordinary ROI Important Dimensions of Knowledge (1 of 2) Data, information, knowledge, and wisdom Tacit knowledge and explicit knowledge Important dimensions of knowledge – Knowledge is a firm asset – Knowledge has different forms – Knowledge has a location – Knowledge is situational Important Dimensions of Knowledge (2 of 2) Knowledge-based core competencies – Key organizational assets Knowing how to do things effectively and efficiently in ways others cannot duplicate is a prime source of profit and competitive advantage – Example: Having a unique build-to-order production system Organizational learning – Process in which organizations gain experience through collection of data, measurement, trial and error, and feedback The Knowledge Management Value Chain (1 of 10) Knowledge management “Set of business processes developed in an organization to create, store, transfer, and apply knowledge” – Knowledge management increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes Knowledge management value chain – Each stage adds value to raw data and information as they are transformed into usable knowledge 1) Knowledge acquisition 2) Knowledge storage 3) Knowledge dissemination 4) Knowledge application The Knowledge Management Value Chain (2 of 10) Knowledge acquisition 1) Knowledge Acquisition Methods: Organizations gather knowledge in various ways, tailored to the specific type of knowledge they need. 2) Initial Knowledge Management Systems: Early systems focused on creating corporate repositories that included documents, reports, presentations, and best practices. 3) Inclusion of Unstructured Data: Knowledge management efforts have expanded to encompass unstructured documents, such as emails, enhancing the breadth of stored knowledge. The Knowledge Management Value Chain (3 of 10) Knowledge acquisition 4) Online Expert Networks: Organizations facilitate knowledge acquisition by developing networks that allow employees to connect with in-house experts who possess specific knowledge. 5) Data Pattern Discovery: Firms leverage machine learning techniques (like neural networks and natural language processing) and knowledge workstations to identify new patterns and knowledge within corporate data. The Knowledge Management Value Chain (4 of 10) Knowledge storage 1) Knowledge Storage: After discovery, documents, patterns, and expert rules need to be stored in a database for easy retrieval and use by employees. 2) Document Management Systems: These systems digitize, index, and tag documents, creating large databases that effectively manage collections of information. 3) Expert Systems: These systems help organizations preserve acquired knowledge by integrating it into their processes and culture. The Knowledge Management Value Chain (5 of 10) Knowledge storage 4) Management Support: Leadership must endorse the creation of structured knowledge storage systems and promote uniform indexing schemas across the organization. 5) Employee Incentives: Organizations should reward employees for updating and properly storing documents to foster a culture of knowledge sharing and management. The Knowledge Management Value Chain (6 of 10) Knowledge dissemination 1) Collaboration Tools: A variety of tools, including portals, email, instant messaging, wikis, and social business platforms, enhance collaboration by facilitating the sharing of calendars, documents, data, and graphics. 2) Information Overload: The advancement of technology has led to an overwhelming amount of information and knowledge, making it challenging for individuals to manage what is relevant. 3) Training Programs: Structured training initiatives help employees navigate the vast array of information, focusing their skills on essential knowledge and practices. The Knowledge Management Value Chain (7 of 10) Knowledge dissemination 4) Informal Networks: Informal networks play a vital role in knowledge sharing, allowing employees to exchange insights and experiences outside formal structures. 5) Supportive Culture: A culture that encourages open communication and shared management experiences aids managers in prioritizing critical information amid the deluge of data. The Knowledge Management Value Chain (8 of 10) Knowledge application 1) Importance of Shared Knowledge: Knowledge that remains unshared and unused does not contribute to business value, regardless of the knowledge management system in place. 2) Return on Investment: For organizational knowledge to yield returns, it must be systematically integrated into management decision-making and aligned with decision support systems. 3) Incorporation into Business Processes: New knowledge should be embedded within a firm's business processes and key application systems, including those for managing internal operations and relationships with customers and suppliers. The Knowledge Management Value Chain (9 of 10) Knowledge application 4) Role of Management: Leadership plays a critical role in transforming new knowledge into actionable business practices, innovative products and services, and exploring new market opportunities. 5) Continuous Improvement: By fostering a culture of knowledge application, management enables ongoing enhancements to business strategies and operations, leading to sustained competitive advantage. The Knowledge Management Value Chain (10 of 10) Major Types of Knowledge Management Systems Types of Knowledge Management Systems Enterprise-wide knowledge management systems – General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge – These systems include capabilities for searching for information, storing both structured and unstructured data, and locating employee expertise within the firm. – They also include supporting technologies such as portals, search engines, collaboration and social business tools, and learning management systems. Example Microsoft SharePoint: A widely used platform that offers document management, collaboration tools, and intranet capabilities, enabling organizations to manage knowledge effectively. Types of Knowledge Management Systems Knowledge work systems (K W S) – Specialized systems built for engineers, scientists, other knowledge workers charged with discovering and creating new knowledge – The development of powerful networked workstations and software for assisting engineers and scientists in the discovery of new knowledge has led to the creation of knowledge work systems such as computer-aided design (CAD), visualization, simulation, and virtual reality systems. Types of Knowledge Management Systems Intelligent techniques – Knowledge management also includes a diverse group of “intelligent” techniques, such as data mining, expert systems, machine learning, neural networks, natural language processing, computer vision systems, robotics, genetic algorithms, and intelligent agents. – These techniques have different objectives, from a focus on discovering knowledge (data mining and neural networks) to distilling knowledge in the form of rules for a computer program (expert systems) to discovering optimal solutions for problems (genetic algorithms) What Is Artificial Intelligence? Artificial intelligence (AI): a form of intelligent technique Grand vision – Computer hardware and software systems that are as “smart” as humans – So far, this vision has eluded computer programmers and scientists Narrower, more realistic vision – Systems that take data inputs, process them, and produce outputs (like all software programs) and that can perform many complex tasks that would be difficult or impossible for humans to perform. https://www.youtube.com/watch?v=ad79nYk2keg Major Types of AI Expert systems Machine learning Neural networks and deep learning networks Genetic algorithms Natural language processing Computer vision Robotics Expert Systems Capture tacit knowledge in very specific and limited domain of human expertise Capture knowledge as set of rules Typically perform limited tasks – Diagnosing malfunctioning machine – Determining whether to grant credit for loan Used for discrete, highly structured decision making Knowledge base: Set of hundreds or thousands of rules Inference engine: Strategy used to search knowledge base – Forward chaining – Backward chaining Rules in an Expert System Machine Learning (1 of 2) “Machine Learning (ML) focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions”. Instead of being programmed to follow specific rules, machine learning systems learn from data, identify patterns, and make decisions based on that learning. A subset of artificial intelligence (AI) that Used by neural networks, deep learning networks, and genetic algorithms. Contemporary examples – Facebook ad display – Netflix recommender system Machine Learning (2 of 2) Supervised learning – System “trained” by providing examples of desired inputs and outputs identified by humans in advanced – One technique used to develop autonomous vehicles Unsupervised learning – Same procedures as used with supervised learning, but humans do not provide examples – “Cat Paper” Neural Networks (1 of 3) Find patterns and relationships in massive amounts of data too complicated for humans to analyze “Learn” patterns by searching for relationships, building models, and correcting over and over again Humans “train” network by feeding it data inputs for which outputs are known, to help neural network learn solution by example from human experts Used in medicine, science, and business for problems in pattern classification, prediction, financial analysis, and control and optimization How a Neural Network Works Neural Networks (2 of 3) Deep learning neural networks – More complex, with many layers of transformations of input data to produce target output – Used almost exclusively for pattern detection on unlabeled data (unsupervised learning) – Some believe these come closest to “grand vision” of AI A Deep Learning Network Neural Networks (3 of 3) Limitations of neural networks and machine learning – Require very large data sets to identify patterns – Patterns may not “make sense: or may be ephemeral – How system arrived at a particular solution often cannot be explained – Most useful for classifying digital assets into binary categories (yes or no), but most real-world problems do not have binary solutions – No sense of ethics, so may recommend actions that are illegal or immoral Genetic Algorithms Useful for finding optimal solution for specific problem by examining very large number of possible solutions for that problem Conceptually based on process of evolution – Search among solution variables by changing and reorganizing component parts using processes such as inheritance, mutation, and selection Used in optimization problems (minimization of costs, efficient scheduling, optimal jet engine design) in which hundreds or thousands of variables exist Able to evaluate many solution alternatives quickly The Components of a Genetic Algorithm Natural Language Processing Software that can process voice or text command using natural human language Typically based on machine learning, including deep learning Examples: Google search; spam filtering systems; text mining sentiment analysis; customer call center interactions Computer Vision Systems Emulate human visual system to view and extract information from real-world images Examples: – Facebook’s DeepFace can identify friends in photos across their system and the entire web – Autonomous vehicles can recognize signs, road markers, people, animals, and other vehicles with good reliability Computer Vision Systems Facebook’s DeepFace Robotics “Design, construction, and operation of movable machines that can substitute for humans, along with computer systems for their control, sensory feedback, and information processing”. Generally programmed to perform specific and detailed actions in limited domains, e.g. robots spray paint autos, and assemble certain parts, welding, heavy assembly movement Used in dangerous situations like bomb disposal, delivering medical supplies to coronavirus-contaminated locations Surgical robots are expanding their capabilities Intelligent Agents Work without direct human intervention to carry out repetitive, predictable tasks – Deleting junk e-mail – Finding cheapest airfare Use limited built-in or learned knowledge base – Some are capable of self-adjustment, for example: Siri Chatbots Agent-based modeling applications: – Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics Intelligent Agents in P&G’s Supply Chain Network The World of Artificial Intelligence https://www.youtube.com/watch?v=qYNweeDHiyU What Types of Systems Are Used for Enterprise-Wide Knowledge Management? Three major types of knowledge in an enterprise – Structured documents  Reports, presentations  Formal rules – Semistructured documents  E-mails, videos – Unstructured, tacit knowledge 80% of an organization’s business content is semistructured or unstructured Enterprise Content Management Systems Help capture, store, retrieve, distribute, preserve documents and semi structured knowledge Bring in external sources – News feeds, research Tools for communication and collaboration – Blogs, wikis, and so on Key problem: developing taxonomy Digital asset management systems An Enterprise Content Management System Locating and Sharing Expertise Provide online directory of corporate experts in well-defined knowledge domains Search tools enable employees to find appropriate expert in a company Social networking and social business tools for finding knowledge outside the firm – Saving – Tagging – Sharing web pages Learning Management Systems (LM S) “Provide tools for management, delivery, tracking, and assessment of employee learning and training”. Support multiple modes of learning – web-based classes, online forums, and so on Automates selection and administration of courses Assembles and delivers learning content Measures learning effectiveness Massively open online courses (MOOC s) – Web course open to large numbers of participants Knowledge Workers and Knowledge Work Knowledge workers – Researchers, designers, architects, scientists, engineers who create knowledge for the organization – Perform key roles critical to organization and managers who work within organization Knowledge work systems – Systems for knowledge workers to help create new knowledge and integrate that knowledge into business Requirements of Knowledge Work Systems Sufficient computing power for graphics, complex calculations Communications and document management Access to external databases User-friendly interfaces Optimized for tasks to be performed (design engineering, financial analysis) Requirements of Knowledge Work Systems Examples of Knowledge Work Systems CAD (computer-aided design) – Creation of engineering or architectural designs – 3D printing Virtual Reality Systems – Simulate real-life environments Augmented Reality (AR) Systems – Enhance visualization by overlaying digital data and images onto physical real-world environment https://www.youtube.com/watch?v=d1RfVHK3Vt8 49