DSS Presentation.pptx DSS SLIDE FILE CHAPTER

DSS Presentation.pptx DSS SLIDE FILE CHAPTER

1. Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition Chapter 11 Group Decision Making, Collaborative Systems, and A I Support Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F77

2. Making Decisions in Groups: Characteristics, Process, Benefits, And Dysfunctions Groupwork : the work done by two or more people together A group performs a task Members may be located in different places Group members may work at different times Group members may work for the same organization or for different organizations A group can be permanent or temporary A group can be at one managerial level or span several levels …

3. Why Groupwork / Collaborate?

4. Characteristics of Group Work Group member located in different places/organizations Work at different times Group work can be permanent or temporary Group members can be from different managerial levels Group can create synergy (or not!) Process gains versus process losses Group work may lead to (or required to be completed in a very short time (more people = less time to finish?) Physical meetings may be cost prohibitive, …. more

5. Group Decision-Making Process Types of Decisions Made by Groups Groups are usually involved in two major types of decision making: Making a decision together. Supporting activities or tasks related to the decision-making process. For example, the group may select criteria for evaluating alternative solutions, …

6. Overview of Group Support Systems Technology that helps groups to collaborate effectively These technologies are called G S S (Group Support Systems) G S S enablers: Internet and its derivatives (intranets, internet of things [ IoT ], and extranets) The Web is the common infrastructure for G S S Recently communication and collaboration tools have received more attention due to Their increased capabilities Save time and money Expedite decision making

7. Time/Place Framework (1 of 2) Same time / same place. Participants meet face-to-face, as in a traditional meeting, or decisions are made in a specially equipped decision room. Same time / different place. Participants are in different places, but they communicate at the same time (e.g., with videoconferencing or I M). Different time / same place. People work in shifts. One shift leaves information for the next shift. Different time / different place . Participants are in different places, and they send and receive information at different times.

8. Time/Place Framework (2 of 2)

9. Electronic Support for Group Communication and Collaboration Groupware products provide a way for groups to share resources and opinions Synchronous or Asynchronous Examples dropbox.com drive.google.com office.microsoft.com … See Table 11.1 for a list of examples

10. Groupware Virtual Meeting Systems Webex.com , GoTomeeting.com , Skype.com , … GroupSystems ( Groupsystems.com ) Collaborative Workflow Web 2.0 Search, links, authoring, tags, extensions, signals Collaborative Networks Synchronous versus asynchronous systems

11. Groupware Products Synchronous versus Asynchronous (1 of 3) Table 11.1 Groupware Products and Features. General (Can Be Either Synchronous or Asynchronous) Built-in e-mail, messaging system Browser interface Joint Web page creation Active hyperlink sharing File sharing (graphics, video, audio, or other) Built-in search functions (by topic or keyword) Workflow tools Corporate portals for communication, collaboration, and search Shared screens Electronic decision rooms Peer-to-peer networks

12. Groupware Products Synchronous versus Asynchronous (2 of 3) Table 11.1 Groupware Products and Features. Synchronous (Same Time) I M Videoconferences, multimedia conferences Audioconferences Shared whiteboard, smart whiteboard Instant videos Brainstorming Polling (voting) and other decision support (activities such as consensus building, scheduling) Chats with people Chats with bots

13. Groupware Products Synchronous versus Asynchronous (3 of 3) Table 11.1 Groupware Products and Features. Asynchronous (Different Times) Virtual workspaces Tweets Ability to receive/send e-mail, SMS Ability to receive notification alerts via e-mail or SMS Ability to collapse/expand discussion threads Message sorting (by date, author, or read/unread) Auto responders Chat session logs Electronic bulletin boards, discussion groups Blogs and wikis Collaborative planning and/or design tools

14. Collaborative Workflow (1 of 2) Software products that address project-oriented and collaborative business processes Administered centrally, and accessed/used by participants at different locations (and times) Goal: empower knowledge workers through communication, negotiation, and collaboration within an integrated environment Collaborative workplace: moved from a conference room to a virtual place for teams to work together Virtual collaborative workplace – support by digital enablers and computerized tools

15. Collaborative Workflow (2 of 2) Major Vendors of Virtual Workspace Google Cloud Platform (deployed on the “cloud,” so it is offered as a platform-as-a-service (PaaS) Citrix Workspace Cloud (deployed on the “cloud” and Citrix is known for its GoToMeeting collaboration tool) Microsoft Workspace (a part of Office 365) Cisco’s Webex (a popular collaboration package including Meeting). Slack workspace (a very popular workspace) A digital space where teammates share, communicate, and collaborate on work [supported with many components]

16. Collaborative Networks and Hubs Traditionally, supply-chain member close to each other (supplier and manufacturer; distributor and retailer) communicate to share information on product flow Vertically integrated supply-chain Nowadays, the whole supply chain can communicate and collaborate on collaborative planning, forecasting, and replenishment Multi-node, network-based integration of supply-chain Collaborative hub: a center point for group collaboration Example: Surface Hub for Business by Microsoft

17. Social Collaboration Collaboration conducted within and between socially oriented groups Group interactions and information/knowledge sharing to attain common goals Done on social media sites, and it is enabled by the Internet, IoT , and social collaboration software Collaboration in Social Networks Facebook – Facebook workspace LinkedIn – LinkedIn Lookup Social collaboration software for teams Wrike , Ryver , Azendoo , Zimbra social platform, Samepage , Zoho , Asana, Jive, Chatter, and Social Tables

18. Popular Collaboration Software Communication tools: Yammer (social collaboration), Slack, Skype, Google Hangouts, GoToMeeting Design tools: InVision , Mural, Red Pen, Logo Maker Documentation tools: Office Online, Google Docs, Zoho File-sharing tools: Google Drive, Dropbox, Box Project management tools: Asana, Podio , Trello, WorkflowMax , Kanban Tool, Software tools: GitHub, Usersnap,Workflow tools: Integrity, B P Logix

19. Other Tools that Support Collaboraiton and/or Communication Notejoy (makes collaborative notes for team) Kahootz (brings stakeholders together to form communities of interest) Nowbridge (offers team connectivity, ability to see participants) Walkabout Workplace (is a 3D virtual office for remote teams). RealtimeBoard (is a enterprise visual collaboration). Quora (is a popular place for posting questions to the crowd). Pinterest (allows collection of text and images on selected topics). I B M connection closed (offers a comprehensive communication and collaboration tool set). Skedda (schedules space for coworking ) Zinc (is a social collaboration tool) Scribblar (is an online collaboration room for virtual brainstorming) Collokia (is a machine learning platform for workflow)

20. Direct Computerized Support for Group Decision Making Decisions are frequently made at meetings Some are one-time critical/strategic decision Often complex and controversial decisions Process dysfunctions can significant affect the decision outcomes Computerized support has often been suggested to mitigate these controversies These systems are usually called group decision support systems ( G D S S), group support systems ( G S S), electronic meeting systems ( E M S)

21. Group Decision Support Systems ( G D S S) It is an interactive computer-based system that facilitates the solution of semistructured or unstructured problems by a group of decision makers Goal – support group decision making A specially designed I S to enhance collaborative decision processes It encourages generation of ideas, freedom of expression, and resolution of conflicts First generation G D S S: face-to-face in the same room Decision room Today’s G D S S: virtual, over the Web

22. Decision Rooms Expensive, customized, purposeful facilities 12 to 30 networked computers Usually recessed into the desktop Server and special software Large-screen projection system Breakout rooms Need a trained facilitator for success

23. Supporting the Entire Decision-Making Process - Stormboard (1 of 2) Provides support for different brainstorming and group decision-making configurations Sequence of activities Define the problem and the users’ objectives Brainstorm ideas Organize the ideas in groups of similar flavor, look for patterns, and select only viable ideas Collaborate, refine concepts, and evaluate (using criteria) the meeting’s objectives…

24. Supporting the Entire Decision-Making Process - Stormboard (2 of 2) Sequence of activities (cont.) The software enables users to prioritize proposed ideas by focusing on the selection criteria. It lets all participants express their thinking and directs the team to be cohesive. It presents a short list of superior ideas The software suggests the best idea and recommends implementation It plans the project implementation. It manages the project. It periodically reviews progress.

25. Online Brainstorming Service and Tool Providing Companies eZ Talks Meetings . Cloud-based tool for brainstorming and idea sharing. Bubbl.us . Visual thinking machine that provides a graphical representation of ideas and concepts, helps in idea generation, and shows where ideas and thoughts overlap. Mindomo . Tool for real-time collaboration that offers integrated chat capability. Mural. Tool that enables collecting and sorting of ideas in rich media files. It is designed as a Pinboard that invites participants. iMindQ . Cloud-based service that enables creating mind maps and basic diagrams.

26. Group Support Systems ( G S S) Includes all forms of communication and collaborative activities including collaborative computing It used to be a specialize software, now it is embedded into standard I T productivity tools Microsoft Office 365 includes Microsoft Teams (opening vignette) How G S S improves group work Improve productivity and effectiveness Streamline and speed-up the process Improving the quality of the outcomes Increase process gains and reduce process losses Helps in working simultaneously and with anonymity

27. Collective Intelligence and Collaborative Intelligence (1 of 4) Collective intelligence ( C I) refers to the total intelligence of a group. It is also refers to as the wisdom of the crowd M I T center for collective intelligence ( cci.mit.edu ) Benefits of C I, see 50Minutes.com Types of Collective Intelligence Cognition Cooperation, and Coordination Computerized support to collective intelligence

28. Collective Intelligence and Collaborative Intelligence (2 of 4) Example 1: The Carnegie University Foundation Supports Network Collaboration Content is stored and shared in one place (the “cloud”) Asynchronous conversations using discussion boards Facilitating social collaboration and problem solving Example 2: How Governments Tap IoT for Collective Intelligence Governments are using IoT to support decision making and policy creation The IoT collect ideas and aspirations of the citizens How Collective Intelligence May Change Work and Life

29. Collective Intelligence and Collaborative Intelligence (3 of 4) Having people to collaborate is difficult Collaborative intelligence requires: (1) willingness to share, (2) knowing how to share, (3) being willing to collaborate, (4) knowing what to share, (5) knowing how to build trust, (6) understanding team dynamics, (7) using correct hubs for networking, (8) mentoring and coaching properly, (9) being open to new ideas, and (10) using computerized tools and technology. For another list of success factors, see thebalancecareers.com/collaboration-skills-with-examples-2059686

30. Collective Intelligence and Collaborative Intelligence (4 of 4) How to Create Business Value from Collaboration: The I B M Study Groups and team members provide ideas and insights. The study presents three major points: Enhances organizational outcomes by correctly tapping the knowledge and experience of working groups (e.g., customers, partners, and employees). It is crucial to target and motivate the appropriate participants. Needs to address the issue of participants’ resistance to change.

31. Crowdsourcing as a Method for Decision Support Crowdsourcing - outsourcing tasks to a large group of people (crowd). Goal – to leverage the wisdom of a crowd Viewed as a method of collective intelligence Essentials of crowdsourcing… Tutorial on crowdsourcing and examples, watch the video youtube.com/ watch?v = lXhydx S S N O Y

32. Major Types of Crowdsourcing Collective intelligence (or wisdom). People in crowds are solving problems and providing new insights. Crowd creation. People are creating various types of content and sharing it with others (for pay or free). Crowd voting. People are giving their opinions and ratings on ideas, products, or services, as well as evaluating and filtering information presented to them. Crowd support and funding. People are contributing and supporting endeavors for social or business causes, such as offering donations, and micro-financing new ventures.

33. The Process of Crowdsourcing (1 of 2) Identify the problem and the task(s) to be outsourced. Select the target crowd (if not an open call). Broadcast the task to the crowd. Engage the crowd in accomplishing the task (e.g., idea generation, problem solving). Collect user-generated content. Have the quality of submitted material evaluated by the management, by experts, or by a crowd. Select the best solution (or a short list). Compensate the crowd (e.g., the winning proposal). Implement the solution.

34. The Process of Crowdsourcing (2 of 2)

35. A I Support of Group Decision Making A major objective of A I is to automate decision making and/or to support its process A I can help in the following steps in the process: Meeting preparation Problem identification Idea generation Idea organization Group interaction and collaboration Predictions Multilingual group communications …

36. Swarm Intelligence and Swarm A I (1 of 2) Swarm intelligence refers to the collective behavior of decentralized, self organized systems, natural or artificial Such systems consist of things (e.g., ants, people) interacting with each other and their environment A swarm’s actions are not centrally controlled, but they lead to intelligent behavior In contrast with animals and other species whose interactions among group members are natural, people need technology to exhibit swarm intelligence Example - Oxford university study on english premier league Swarm A I technology Algorithms for creating the human swarm

37. Swarm Intelligence and Swarm A I (2 of 2) Swarm A I Predictions - Swarm A I was used by Unanimous A I for making predictions in difficult-to-assess situations. Examples include: Predicting Super Bowl #52 number of points scored Predicting winners in the regular N F L season. Predicting the top four finishers of the 2017 Kentucky Derby. Predicting the top recipients of the Oscars in 2018.

38. Human-Machine Collaboration and Teams of Robots Human–Machine Collaboration in Cognitive Jobs Top Management Jobs Robots as coworkers – Challenges Designing a human–machine team that capitalizes on the strength of each partner. Exchanging information between humans and robots. Preparing company employees for the collaboration Changing business processes to accommodate human–robot collaboration Ensuring the safety of robots and employees that work together.

39. Human-Machine Collaboration and Teams of Robots Team of Robots Prepares to Go to Mars Figure 11.4 Team of Robots Prepares to Go to Mars. Source: C.Kang .

40. End of Chapter 11

41. Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots , Virtual Personal Assistants, and Robo Advisors Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved

42. Concepts of Expert Systems ( E S) (1 of 6) E S is a computer-based information system Emulates the decision making and/or problem solving abilities of human experts in complex areas One of the earliest success application areas of A I Expert systems use started in research institutions in 1960s Goal – help nonexperts to make decisions and solve problems that usually require expertise Works well in narrowly defined domains

43. Concepts of Expert Systems ( E S) (2 of 6) Expert - A person who has the special knowledge, judgment, experience, and skills to provide sound advice and solve complex problems in a narrowly defined area. To be called an expert, one must be able to solve a problem and achieve a performance level that is significantly better than an average person An expert at one time or in one region may not be an expert in another time or region. E.g., a legal expert in New York is not a expert in Beijing Experts have expertise that can help solve problems and explain certain obscure phenomena only within a specific domain

44. Concepts of Expert Systems ( E S) (3 of 6) Typically, human experts are capable of doing the following: Recognizing and formulating a problem Solving a problem quickly and correctly Explaining a solution Learning from experience Restructuring knowledge Breaking rules and norms, if necessary Determining relevance and associations Can E S do these? Can a machine help a nonexpert perform like an expert? Real experts are rare and hard to find

45. Concepts of Expert Systems ( E S) (4 of 6) Expertise - The extensive, task-specific knowledge that experts possess. The level of expertise determines the success of a decision made by an expert. Expertise is often acquired through training, learning, and experience in practice. Expertise includes explicit knowledge, such as theories learned from a textbook or a classroom and implicit knowledge gained from experience.

46. Concepts of Expert Systems ( E S) (5 of 6) Knowledge types (expertise) used in E S applications Theories about the problem domain Rules and procedures regarding the general problem domain Heuristics about what to do in a given problem situation Global strategies for solving of problems amenable to expert systems Meta knowledge (i.e., knowledge about knowledge) Facts about the problem area These types of knowledge enable experts to make better and faster decisions than nonexperts .

47. Concepts of Expert Systems ( E S) (6 of 6) Expertise often includes the following characteristics: It is usually associated with a high degree of intelligence, but it is not always associated with the smartest person It is usually associated with a vast quantity of knowledge It is based on learning from past successes and mistakes It is based on knowledge that is well stored, organized, and quickly retrievable from an expert who has excellent recall of patterns from previous experiences.

48. Benefits of E S Perform routine tasks (e.g., diagnosis, candidate screening, credit analysis) that require expertise much faster than humans. Reduce the cost of operations. Improve consistency and quality of work, reduce human errors. Speed up decision making and make consistent decisions. May motivate employees to increase productivity. Preserve scarce expertise of retiring employees. Help transfer and reuse knowledge. Reduce employee training cost by using self-training. Solve complex problems without experts and solve them faster. See things that even experts sometimes miss. Combine expertise of several experts. Centralize decision making (e.g., by using the “cloud”). Facilitate knowledge sharing.

49. Structure and Process of E S Consultation Environment (use of E S via G U I) Development Environment Component of an E S Knowledge acquisition (from humans and others) Knowledge representation (if-then-else rules) Knowledge base (knowledge repository) Inference engine (control/search structure) User interface Justifier/explanation module Knowledge refinement system

50. General Architecture of an E S

51. Recommendation Systems Recommendation system, also known as recommender system or recommendation engine Recommending/suggesting one-to-one targeted products or services Predict the importance (rating or preference) that a user will attach to a product or service Based on the prediction, specific products and services are recommended to the user Top applications include movies, music, and books. However, there are also systems for travel, restaurants, insurance, and online dating.

52. Benefits of Recommendation Systems Benefits to customer: Personalization Discovery Customer satisfaction Reports Increased dialog with seller Benefits to seller: Higher conversion rate Increased cross-sell Increased customer loyalty Enabling mass customization

53. Methods for Recommendation Systems Collaborative filtering Building a model that summarizes the past behavior of shoppers in a multi-dimensional manner Makes recommendations on the new customers based on the similarity to previous shoppers Uses A I/machine learning to predict the preferences Content-based filtering Allows vendors to identify customer preferences by the attributes of the product(s) that customers have bought Recommend new products with similar attributes Several other filtering methods also exists

54. Chatbots Chatbots (chat robots) emerged in the last decade A computerized service that enables easy conversations between humans and humanlike computerized robots or image characters Some chatbots are equipped with N L P abilities for better understanding, and some with A I/machine learning for learning and improving Chatbot services are often available messaging services such as Facebook Messenger or WeChat , and on Twitter

55. Types of Bots Regular bots. These are essentially conversational intelligent agents (Chapter 2). They can do simple, usually repetitive, tasks for their owners, such as showing their bank’s debits, helping them to purchase goods online, and to sell or buy stocks online. Chatbots . In this category, we include more capable bots, for example, those that can stimulate conversations with people. Intelligent bots. These have a knowledge base that is improving with experience. That is, these bots can learn, for example, a customer’s preferences (e.g., like Alexa and some robo advisors).

56. Process of Chatting with a Chatbots

57. Chatbots Representative Chatbots from Around the World RoboCoke , Kip, Walnut, Taxi Bot, ShopiiBot , B O.T, Hazie , Green Card, Zoom, Akita, … For more, please see chatbots.org/ and botlist.co/bots/ Major Categories of Chatbots ’ Applications Chatbots for enterprise activities, including communication, collaboration, customer service, and sales (such as in the opening vignette) Chatbots that act as personal assistants Chatbots that act as advisors, mostly on finance-related topics These are explained in the following sections

58. Technology Insights 12.1 Chatbots ’ Platform Providers Popular vendors: ChettyPeople Kudi Twyla The most popular platforms: I B M Watson Microsoft’s Bot Framework

59. Virtual Personal Assistants (1 of 2) Assistant for Information Search If You Were Mark Zuckerberg , Facebook C E O While Siri and Alexa were in development he develop his own personal assistant to help him run his home and his work Amazon’s Alexa and Echo Alexa can do many things… Alexa can be taught/customized for individualized skills Amazon Echo, Echo Dot, and Echo Tap Alexa for Enterprise …

60. Virtual Personal Assistants (2 of 2) Apple Siri Siri : Speech Interpretation and Recognition Interface V I V: developed in 2016, by Dag Kittlaus , the creator of Siri , as “an intelligent Interface for everything” Goggle Assistant Other personal assistants Microsoft Cortana ( Cortana with Bing) Samsung Bixby Competition Among Large Tech Companies Knowledge for Virtual Personal Assistants

61. Chatbots Implementation Issues Technology issues Disadvantages and limitations of bots Inferior performance Virtual assistants under attack Quality of Chatbots Quality of robo advisors Microsoft’s Tay (Twitter based chatbot ) Constructing Bots Using Microsoft’s Azure bot service

62. End of Chapter 12

63. Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition Chapter 13 The Internet of Things as a Platform for Intelligent Applications Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7

64. Essentials of Internet of Things ( IoT ) (1 of 2) IoT refers to a computerized network that connects many objects (people, animals, devices, sensors, buildings, items) each with embedded microprocessor Connections are made wirelessly via Internet IoT allows communication and exchange of data among the object and their environment Connections are made anytime, anyplace IoT uses ubiquitous computing

65. Essentials of Internet of Things ( IoT ) (2 of 2) Analysts predicts that by 2025, more than 50 Billion objects (devices) will be connected to the Internet, creating the backbone of IoT applications It is a disruptive technology Changing the business models Join the conversations at iotcommunity.com Allows extensive communication and collaboration between users and items Devices can connect each other directly Increasing productivity and automation Unlimited use cases…

66. Definitions and Characteristics of IoT (1 of 3) “The Internet of Things means sensors connected to the Internet and behaving in an Internet-like way by making open, ad hoc connections, sharing data freely, and allowing unexpected applications, so computers can understand the world around them and become humanity’s nervous system.” Kevin Ashton, Creator of the term Internet of Things “The IoT is a network of connected computing devices including different types of objects (e.g., digital machines). Each object in the network has a unique identifier ( U I D), and it is capable of collecting and transferring data automatically across the network.” Our working definition

67. Definitions and Characteristics of IoT (2 of 3) IoT allows people and things to interact and communicate at any time, any place, regarding any business topic or service. IoT Characteristics (Miller, 2015) Large numbers of objects (things) can be connected. Each thing has a unique definition/ I D ( I P address). Each thing has the ability to receive, send, and store data automatically. Each thing is delivered mostly over the wireless Internet. Each thing is built upon machine-to-machine (M2M) communication. Internet connects people to each other using computing technology, while IoT connects “things” (physical devices and people) to each other and to sensors that collect data

68. IoT Ecosystem The IoT ecosystem refers to all components that enable users to create IoT applications E.g., gateways, analytics, A I algorithms, servers, data storage, security, and connectivity devices Platforms Software, hardware, connectivity, … Building blocks Interfaces, platforms, 3D, … Applications Personal, home, vehicle, industrial, enterprise See Figure 13.1 for a full picture of the IoT ecosystem

69. IoT Ecosystem Figure 13.1 The IoT 2016 (Ecosystem).

70. Structure of IoT Systems (1 of 2) IoT Technology Infrastructure (four major blocks) Hardware physical devices, sensors, and actuators where data are produced and recorded Connectivity Via hubs, gateways and Internet/Cloud) Software backend The logic/process implementation that manages data, often in the cloud) Applications The use of the generates data  information for some specific of purposes

71. Structure of IoT Systems (2 of 2) Figure 13.2 The Building Blocks of IoT . Implementations often utilize IoT Platforms Amazon A W S IoT , Microsoft Azure IoT suite, Predix IoT Platform by General Electric ( G E), I B M Watson IoT platform Teradata Unified Data Architecture

72. How IoT Works (1 of 2) IoT is not an application. It is an infrastructure, platform, or framework that is used to support applications. A simple view to hot IoT works: The Internet ecosystem includes a large number of things Sensors and other devices collect information from the ecosystem The collected information can be displayed, stored, and processed analytically (e.g., by data mining) This analysis converts the information into knowledge and/or intelligence Expert systems or machine learning may help in turning the knowledge into decision support (made by people and/or machines), which is evidenced by improved actions and results… leading to new applications and use cases.

73. How IoT Works (2 of 2) Figure 13.3 The Process of IoT .

74. Sensors and Their Role in IoT How Sensors Work with IoT In large-scale applications, sensors collect data that are transferred to processing in the “cloud” Sensor Applications and Radio-Frequency Identification ( R F I D) Sensors Sensors can measure many things: humidity, temperature, etc. A well-known type of sensor that plays an important role in IoT is radio-frequency identification R F I D in conjunction with other sensors play a major role in IoT applications

75. Technology Insight 13.1 R F I D Sensors R F I D - a generic technology that uses of radio-frequency waves to identify objects Part of a family of automatic identification technologies that also includes ubiquitous barcodes and magnetic strips R F I S stores richer identification data Use of R F I D spread by retailers’ supply-chains R F I D works with tags and readers Active vs passive tags (long/short range)

76. Use of R F I D and Smart Sensors in IoT Basic R F I D tags, active or passive, are not sensors Purpose: determine the location of the object, couple it with the time of detection R F I D sensors – tags enhanced with on-board sensors Purpose: determine the location, time, and measurements of the environmental conditions Smart Sensor - Senses the environment and processes the input it collects by using its built-in computing capabilities

77. Smart Homes and Appliances A smart home is a home with automated components that are interconnected such as lights, appliances, security, and entertainment that are able to communicate each other Designed to provide their dwellers with comfort, security, low energy cost, and convenience Most existing home are not smart, but the can inexpensively be equipped with partial smartness See techterms.com\definition\ smart_home Protocols: X I O, U P B, Z-Wave, EnOcean , … These products offer scalability, so more devices can be connected

78. Smart Homes and Appliances Typical Components of Smart Homes Lighting and T V Energy management (e.g., Nest) Water control ( watercop.com ) Smart speaker and chatbots (e.g., Alexa ) Home entertainment Alarm clock Vacuum cleaner Camera Refrigerator (and other appliances) Home security and safety …

79. The Components of a Smart Home

80. Smart Homes and Appliances Example: iHealthHome Provides real-time information to caregivers and physicians (and loved ones) Reminds seniors of daily appointments and when to take their medicine Smart appliances are appliances enhanced with sensor and communication technologies They comminute with other devises and people through the home network and Internet Google Nest, and other Nest products ( nest.com ) Popular kits for smart homes include Amazon Eco, Google Home

81. Smart Cities and Factories Smart cities use digital technologies (mostly mobile based) to facilitate better public services for citizens, better utilization of resources, and less negative environmental impact. Smart Buildings: From Automated to Cognitive Buildings I B M’s Cognitive Building learns the behavior of a building’s system in order to optimize it Doing so autonomously by integrating with the IoT devices and sensors Uses IoT and sensors to monitor, analytics to learn, robots to act …

82. Smart Building - Example Figure 13.5 I B M’s Cognitive Building Maturity Framework. Hong Kong has a project called a smart mobility for the improvement of road safety. A consortium of private and public organizations has introduced Intelligent Transport Source : I B M. “Embracing the Internet of Things in the new era of cognitive buildings.” I B M Global Business Services, White Paper, 2016. Courtesy of International Business Machines Corporation, © International Business Machines Corporation.Used with permission.

83. Smart Factories Figure 13.6 Five Key Characteristics of a Smart Factory (Deloitte). Source: Burke, Hartigan , Laaper , Martin, Mussomeli , Sniderman , “The smart factory: Responsive, adaptive, connected manufacturing,” Deloitte Insights (2017), https://www.deloitte.com/insights/us/en/focus/industry-4-0/smart-factory-connected-manufacturing.html . Used with permission.

84. Smart Cities (1 of 3) Improving Transportation in the Smart City A major problem in many cities is the increased number of vehicles and the inability to accommodate all of them effectively Solutions include building more roads, public transportation, smart traffic via IoT+Sensors+Analytics Example 1 Smart studs transmits information of what they sense Smart studs + autonomous vehicle = feature of traffic Example 2 Hong Kong parking, collision warning, and alerts for speeders and lane changing violators

85. Smart Cities (2 of 3) Example: The S A S Analytics Model for Smart Cities Sense Understand the signals in the data Act Bill Gates’ Futuristic Smart City In November 2017, Bill Gates purchased 60,000 acres of land west of Phoenix, Arizona, where he plans to construct a futuristic city from scratch Technology Support for Smart Cities Technology support by Bosch Corp., and Others

86. Smart Cities (3 of 3) Figure 13.7 S A S Supports the Full IoT Analytics Life Cycle for Smart Cities ( S A S). Source: Courtesy of S A S Institute Inc. Used with permisison .

87. Autonomous Self-Driving Vehicles Autonomous vehicles (driverless cars, robot-driven cars, self-driving cars, and autonomous cars) are already on the roads in several places The Developments of Smart Vehicles Google in the 1990s – Waymo TECHNOLOGY INSIGHTS 13.2 Toyota and Nvidia Corp. Plan to Bring Autonomous Driving to the Masses See blogs.nvidia.com/blog/2016/09/28/Xavier/ . Flying Cars?

88. Implementing IoT and Managerial Considerations (1 of 2) Major Implementation Issues Organizational alignment Interoperability challenges Security Additionally … Privacy Connection of the silos of data Preparation of existing I T architectures Management Connected customers

89. Implementing IoT and Managerial Considerations (2 of 2) Strategy for Turning Industrial IoT into Competitive Advantage Specify the business goals Express an analytic strategy Evaluate the needs for edge analytics Select appropriate analytics solutions Continues improvement closes the loop Future of IoT Larger, more connected/networked, smarter, … A I enhancement of IoT

90. End of Chapter 13

91. Analytics, Data Science and A I: Systems for Decision Support Eleventh Edition Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F77

92. Implementing Intelligent Systems What can you do with the output of analytics? Implement them! Some results are “good to know” and done Most require perpetual use (as a D S S) in the organizaiton Implementing A I/analytic solution is not easy... In addition to common issues related to any computer based system implementation, A I/analytics implementation has specific issues to deal with Before talking about the issues, let us first look at the process…

93. The Intelligent Systems Implementation Process Five major steps of implementation Step 1. Need assessment (business case) Step 2. Preparation (readiness) Step 3. System acquisition (in-house/outsource) Step 4. System development Step 5. Impact assessment

94. The impact of Intelligent Systems Figure 14.2 Impact Landscape. Drawn by E. Turban

95. Legal, Privacy and Ethical Issues (1 of 12) As data science, analytics, cognitive computing, and A I grow in reach and pervasiveness, everyone may be affected by these applications Just because something is doable through technology does not make it appropriate, legal, or ethical Data science and A I professionals/manager must be aware of these concerns Legality versus Privacy versus Ethics Something legal may not be ethical…

96. Legal, Privacy and Ethical Issues (2 of 12) Legal Issues What is the value of an expert opinion in court? Who is liable for wrong advice (or information) provided by an intelligent application? What happens if a manager enters an incorrect judgment value into an intelligent application and the result is damage or a disaster? Who owns the knowledge in a knowledge base (e.g., the knowledge of a chatbot )? Can management force experts to contribute their expertise to an intelligent system? … See the example on “Intellectual Property Protection”

97. Legal, Privacy and Ethical Issues (3 of 12) A I and Law A I applications to the legal profession/problems Analyzing legal-related data (e.g., regulatory conflicts) to detect pattern Providing legal advice to consumers (e.g., see DoNotPay.com ). Document review Analyzing contracts Supporting legal research Predicting results (e.g., likelihood to win) A I impact on the legal profession.

98. Legal, Privacy and Ethical Issues (4 of 12) Privacy Issues Privacy: the right to be left alone and the right to be free from unreasonable personal intrusions Related to legal, ethical, and social issues in many countries. It recognized today by federal government and by every state in the U S either by statute or by common law Two rules that applies to interpretation of privacy The right of privacy is not absolute (needs to be balanced against the needs of the society) The public’s right to know is superior to the individual’s right to privacy It is difficult to determine/enforce privacy regulations

99. Legal, Privacy and Ethical Issues (5 of 12) Privacy Issues Collecting information about individuals Target marketing… Internet is the enabler of new face of data collection Virtual personal assistants Amazon Echo/ Alexa … listening all the time Mobile user privacy Tracking through the smartphones – not just the cell-phone providers but potentially many apps on your phone Privacy in IoT networks Recent technology issues in privacy and analytics “What They Know” ( WallStreetJournal.com , 2016). See Rapleaf , Qualia ( qualia.com ), reputation.com , …

100. Legal, Privacy and Ethical Issues (6 of 12) Privacy Example: Using Sensors and IoT to Observe Bankers at Barclays Bank Using heat and motion sensors, Barclays tracks how long its bankers are at their desks Other issues of potential privacy violation Delaware police are using A I dashcams to look for fugitives in passing cars Facebook’s face recognition systems create concerns regarding privacy protection Epicenter offers its employees a microchip implant. It acts like a swipe card, …

101. Legal, Privacy and Ethical Issues (7 of 12) Privacy Who own our private data? You or the technology creators? A new car with sensors to collect data and connected to the Internet to disseminate it … New battle between car manufacturer and Apple, Google, … as to who can access this data Apps collect data abut the users Google’s Waze Yelp… Spotify … …

102. Legal, Privacy and Ethical Issues (8 of 12) Ethical Issues Not necessarily illegal, matter of personal values Example: Facebook’s experiment to present different News Feeds to the users and monitor their emotional reactions as measured by replies, likes, sentiment analysis, and so on. … Running this experiment without the users’ informed consent was viewed as unethical Transparency on what A I does for both vendors and customers is needed in order to stay ethical This way people can stay honest and adhere to the goals of A I, so it can play a significant role in our life and work.

103. Legal, Privacy and Ethical Issues (9 of 12) Ethical Issues of Intelligent Systems What are their impact on jobs? How do machines affect our behavior and interactions? How can wealth created by intelligent machines be distributed? How can intelligent applications mistakes be guarded against? Can intelligent systems be fair and unbiased? How can bias in creation and operation of A I systems be eliminated? How can intelligent applications be keep safe from adversaries? How can systems be protected against unintended consequences (e.g., accidents in robot operations)? …

104. Legal, Privacy and Ethical Issues (10 of 12) Additional Ethical Issues of Intelligent Systems Electronic surveillance. Ethics in business intelligence ( B I) and A I systems design. Software piracy. Invasion of individuals’ privacy. Use of proprietary databases and knowledge bases. Use of personal intellectual property, and benefits. Accuracy of data, information, and knowledge. Protection of the rights of users. Accessibility to information by A I users. The amount of decision making to delegate to intelligent machines (how A I can fail due to inappropriate ethics).

105. Legal, Privacy and Ethical Issues (11 of 12) Other Topics in Intelligent Systems Ethics Machine ethics is a part of the ethics of A I that is concerned with the moral behavior of artificially intelligent beings. Robotics is concerned with the moral behavior of designers, builders, and users of robots. Microsoft’s Tay chatbot was closed due to its inability to understand many irrelevant and offending comments. Some are afraid that algorithm-based technologies, including A I, may become racists. Self-driving cars may one day face a decision of whom to save and whom to kill. Voice technologies enable the identification of callers to A I machines. Good, but also creates privacy concerns. …

106. Impact on Jobs and Work (1 of 9) Generally agreed upon that Intelligent systems will create many new jobs as automation always has. There will be a need to retrain many people. The nature of work will be changed. Polarization of the labor market (in the future) Most jobs lost will be in the middle—middle skills Are intelligent systems going to take jobs—my job? Example: Pilots at FedEx Three pilot operating 1000 airplanes by 2020

107. Impact on Jobs and Work (2 of 9) Intelligent systems may create massive job losses They are moving very fast. They may take a large variety of jobs, including many white-collar and nonphysical jobs. Their comparative advantage over manual labor is very large and growing rapidly They are already taking some professional jobs Financial advisors, paralegals, medical specialists... The capabilities of A I are growing rapidly. In Russia, robots are already teaching mathematics in schools

108. Impact on Jobs and Work (3 of 9) Which Jobs Are Most in Danger? Which Ones Are Safe? Table 14.1 Ten Top Safe and at Risk Occupations. Probability of Job Loss Low-Risk Jobs 0.0036 First-Line supervisors of firefighting and prevention workers 0.0036 Oral and maxillofacial surgeons 0.0035 Healthcare social workers 0.0035 Orthotists and prosthetists 0.0033 Audiologists 0.0031 Mental health and substance abuse social workers 0.0030 Emergency management directors 0.0030 First-Line supervisors of mechanics, installers, and repairers 0.0028 Recreational therapists

109. Impact on Jobs and Work (4 of 9) Which Jobs Are Most in Danger? Which Ones Are Safe? Table 14.1 Ten Top Safe and at Risk Occupations. Probability of Job Loss High-Risk Jobs 0.99 Telemarketers 0.99 Title examiners, abstractors, and searchers 0.99 Sewers, hand 0.99 Mathematical technicians 0.99 Insurance underwriters 0.99 Watch repairer 0.99 Cargo and freight agents 0.99 Tax preparers 0.99 Photographic process workers and processing machine operators 0.99 New account clerks Source: Based on Straus (2014) Straus, R.R. “Will You Be Replaced by a Robot? We Reveal the 100 Occupations Judged Most and Least at Risk of Automation.” ThisisMoney.com , May 31, 2014. thisismoney.co.uk/money/news/article-2642880/Table-700-jobs-reveals-professions-likely-replaced-robots.html

110. Impact on Jobs and Work (5 of 9) Intelligent systems may actually add jobs P w C – robots will create 7 million new jobs in U K I B M new deep learning service saves I T jobs Automation will fill unfilled 50K truck driver jobs Gartner Inc. predicts that by 2020, A I will create more jobs than it eliminates New categories of human jobs that have been created by A I Some believe that there will be a total of increase in jobs due to A I-induced innovations…

111. Impact on Jobs and Work (6 of 9) Jobs and the Nature of Work Will Change While you may not lose your job, intelligent applications may change it. Moving low-skilled to high skilled jobs for humans Example: Skills of Data Scientists Will Change Shortage of 250,000 data scientists by 2024 Need to keep-up with the advancements… Executives think.. 85% - intelligent technologies will impact their workforce within five years 79% - the current skill sets to be restructured

112. Impact on Jobs and Work (7 of 9) A McKinsey study of 3,000 executives Digital capabilities need to come before A I. Machine learning is powerful, but it is not the solution to all problems. Do not put technology teams solely in charge of intelligent technologies. Adding a business partner may help with A I-based projects. Prioritize a portfolio approach to A I initiatives. The biggest challenges will be people and business processes. ...

113. Impact on Jobs and Work (8 of 9) Dealing with the changes - suggestions Use learning and education to facilitate the change. Involve the private sector in enhancing retraining. Have governments provide incentives to the private sector to improve human capital. Encourage private and public sectors to create appropriate digital infrastructure. Innovative income and wage schemes need to be developed. Carefully plan the transition to the new work. Deal properly with displaced employees. ...

114. Impact on Jobs and Work (9 of 9) Conclusion: Let’s Be Optimistic!.. Replacing many human jobs and reducing wages are [hopefully] exaggerated Yes, there will be some jobs replaced, but also new jobs and job types will be created ... Instead, intelligent technologies will clearly contribute to shorter work time for humans. Today, most people work long hours just for survival. ...

115. Potential Dangers f Robots, A I, and Analytical Modeling (1 of 3) Position of A I Dystopia Elon Musk: “We need to be super careful with A I. Potentially more dangerous than nukes.” See video at youtube.com/ watch?v =SYqCbJ0AqR4 Bill Gates: “I am in the camp that is concerned about super intelligence. Musk and some others are on this and I don’t understand why some people are not concerned.” Stephen Hawking: The late scientist stated, “The development of full artificial intelligence could spell the end of the human race.” Watch the T E D: youtube.com/ watch?v =MnT1xgZgkpk What do you think?

116. Potential Dangers f Robots, A I, and Analytical Modeling (2 of 3) The A I Utopia’s Position Watch the 26 min. documentary video on the future of A I at youtube.com/ watch?v =UzT3Tkwx17A Crime fighting in Santa Cruz, California Prediction of the probability that a song will be a hit Finding the perfect match for dating in a population of 30,000 Idea: A I will partner and support humans to innovate Some issues related to utopia People will have a problem of what to do with their free time The road to A I Utopia could be rocky (impact on jobs) Everything will be different - one day we will not drive anymore and there may not be human financial advisors

117. Potential Dangers f Robots, A I, and Analytical Modeling (3 of 3) The Open A I Project and the Friendly A I Open A I, a non-profit organization Created by Elon Mask and others to prepare against the unintended action of robotics and A I Safe artificial general intelligence ( A G I) See Open A I.com The friendly A I A I benefiting humans rather than harming them Watch youtube.com/ watch?v =EUjc1WuyPT8 The O’Neil Claim of Potential Analytics’ Dangers Book: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” See author’s blog site at mathbabe.org

118. Relevant Technology Trends (1 of 4) Gartner’s Top Strategic Technology Trends for 2018 and 2019 A I Foundation and Development Intelligent Apps and Analytics Intelligent and Autonomous Things Digital Twin (real-world objects and systems) Empowered Cloud (Cloud to the Edge) Conversational Human-Machine Platforms Immersive Experience Blockchain Augmented Analytics …

119. Relevant Technology Trends (2 of 4) Figure 14.3 Predict the future of A I.

120. Relevant Technology Trends (3 of 4) Ambient Computing (Intelligence) Electronic environments (e.g., network devices such as sensors) that are sensitive and responsive to people and their environments Potential benefits of ambient computing Recognize individuals and other “things” and their context at any given time and place. Integrate into the environment and existing systems. Anticipate people’s desires and needs without asking. Deliver targeted services based on people’s needs. Be flexible (i.e., can change their actions in response to people’s needs or activities). Be invisible.

121. Relevant Technology Trends (4 of 4) Figure 14.4 Future of Analytics. Source: “Analytics and B I Trends”, Datapine , in Top 10 Analytics and Business Intelligence Trends for 2018, Business Intelligence, Dec 13th 2017, © 2017, Used with permission.

122. Future of Intelligent Systems (1 of 2) What Are the Major U.S. High-Tech Companies Doing in the Intelligent Technologies Field? Google (Alphabet) … Apple … Facebook … Microsoft … I B M … A I Research Activities in China TENCENT BAIDU ALIBABA

123. Future of Intelligent Systems (2 of 2) The U.S. – China Competition Who will control A I? At the moment, U.S. companies are ahead of Chinese companies, but the future is anybody’s guess The Largest Opportunity in Business Tech companies has been the beneficiary of A I Despite their rivalry, Facebook, Amazon, Google, I B M, and Microsoft partner to advance practices in A I Impact on Business Impact on Quality of Life