Used in optimization problems (minimization of costs, efficient scheduling, optimal jet engine design) in which hundreds or thousands of variables existAble to evaluate many solution alternatives quicklyFigure 11.6 The Components of a Genetic Algorithm (DIAGRAM IN CHAP 11)Machine LearningHow computer programs improve performance without explicit programmingRecognizing patternsExperiencePrior learnings (database)Supervised vs. unsupervised learningContemporary examplesGoogle searchesRecommender systems on Amazon, NetflixComputer Vision SystemsDigital 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 timeEvery image has a unique pattern of pixelsFacebook’s DeepFace can identify friends in photos across their system, and the entire webAutonomous vehicles can recognize signs, road markers, people, animals, and other vehicles with good reliabilityIndustrial machine (robot) vision
Passport control at airportsIdentifying people in crowdsRoboticsDesign, 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 disposalSurgical robots are expanding their capabilitiesIntelligent AgentsWork without direct human intervention to carry out repetitive, predictable tasksDeleting junk e-mailFinding cheapest airfareUse limited built-in or learned knowledge baseSome are capable of self-adjustment, for example: SiriChatbotsAgent-based modeling applications: Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemicsFigure 11.7 Intelligent Agents in P&G’s Supply Chain Network (DIAGRAM IN CHAP 11)Enterprise Content Management Systems
Help capture, store, retrieve, distribute, preserve documents and semistructured knowledgeBring in external sourcesNews feeds, researchTools for communication and collaborationBlogs, wikis, and so onKey problem: developing taxonomyDigital asset management systemsFigure 11.8 An Enterprise Content Management System (DIAGRAM IN CHAP 11)Locating and Sharing ExpertiseProvide online directory of corporate experts in well-defined knowledge domainsSearch tools enable employees to find appropriate expert in a companySocial networking and social business tools for finding knowledge outside the firmSavingTaggingSharing web pagesLearning Management Systems (L M S)Provide tools for management, delivery, tracking, and assessment of employee learning and trainingSupport multiple modes of learningC D-R O M, web-based classes, online forums, and so onAutomates selection and administration of coursesAssembles and delivers learning content
Measures learning effectivenessMassively open online courses (M O O C s) Web course open to large numbers of participantsKnowledge Workers and Knowledge WorkKnowledge workers
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