Used in optimization problems minimization of costs efficient scheduling

Used in optimization problems minimization of costs

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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 Figure 11.6 The Components of a Genetic Algorithm (DIAGRAM IN CHAP 11) Machine Learning How computer programs improve performance without explicit programming Recognizing patterns Experience Prior learnings (database) Supervised vs. unsupervised learning Contemporary examples Google searches Recommender systems on Amazon, Netflix Computer Vision Systems Digital image systems that create a digital map of an image (like a face, or a street sign), and recognize this image in large data bases of images in near real time Every image has a unique pattern of pixels 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 Industrial machine (robot) vision
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Passport control at airports Identifying people in crowds Robotics Design, construction, and operation of machines that can substitute for humans in many factory, office, and home applications (home vacuums). 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 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 Figure 11.7 Intelligent Agents in P&G’s Supply Chain Network (DIAGRAM IN CHAP 11) Enterprise Content Management Systems
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Help capture, store, retrieve, distribute, preserve documents and semistructured 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 Figure 11.8 An Enterprise Content Management System (DIAGRAM IN CHAP 11) 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 (L M S) Provide tools for management, delivery, tracking, and assessment of employee learning and training Support multiple modes of learning C D-R O M, web-based classes, online forums, and so on Automates selection and administration of courses Assembles and delivers learning content
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Measures learning effectiveness Massively open online courses (M O O C s) Web course open to large numbers of participants Knowledge Workers and Knowledge Work Knowledge workers
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