Assignment

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11 C H A P T E R

I n this chapter, we present several topics related to group decision support and col-laboration. People work together, and groups (or teams) make many of the complex decisions in organizations. The increase in organizational decision-making complex- ity drives the need for meetings and group work. Supporting group work in which team members may be in different locations and working at different times emphasizes the important aspects of communications, computer-mediated collaboration, and workplace methodologies. Group support is a critical aspect of decision support systems (DSS). Effective computer-supported group support systems have evolved to increase gains and decrease losses in task performance and underlying processes. New tools and methodol- ogy are used to support teamwork. These include collective intelligence, crowdsourcing, and different types of AI. Finally, human–machine and machine–machine collaboration

■■ Understand the basic concepts and processes of group work, communication, and collaboration

■■ Describe how computer systems facilitate team communication and collaboration in an enterprise

■■ Explain the concepts and importance of the time/ place framework

■■ Explain the underlying principles and capabilities of groupware, such as group support systems (GSS)

■■ Understand how the Web enables collaborative computing and group support of virtual meetings

■■ Describe collective intelligence and its role in decision making

■■ Define crowdsourcing and explain how it supports decision making and problem solving

■■ Describe the role of AI in supporting collaboration, group work, and decision making

■■ Describe human–machine collaboration ■■ Explain how teams of robots work

LEARNING OBJECTIVES

Group Decision Making, Collaborative Systems, and AI Support

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are increasing the power of collaboration and problem solving. All these are presented in the following sections:

11.1 Opening Vignette: Hendrick Motorsport Excels with Collaboration Teams 611

11.2 Making Decisions in Groups: Characteristics, Processes, Benefits, and Dysfunctions 613

11.3 Supporting Group Work and Team Collaboration with Computerized Systems 616

11.4 Electronic Support to Group Communication and Collaboration 619 11.5 Direct Computerized Support for Group Decision Making 623 11.6 Collective Intelligence and Collaborative Intelligence 629 11.7 Crowdsourcing as a Method for Decision Support 633 11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration

and Group Decision Making 636

11.9 Human–Machine Collaboration and Teams of Robots 640

11.1 OPENING VIGNETTE: Hendrick Motorsports Excels with Collaborative Teams

Hendrick Motorsports (HMS) is a leading car racing company (with more than 500 employees) that competes in the Monster Energy NASCAR Cup Series. HMS’s major objective is to win as many races as possible each year. The company enters four race cars and their teams. HMS also builds its race cars. This includes building or rebuilding 550 car engines every year. In this kind of business, teamwork is critical because many different people with different skills and knowledge and several professional teams contribute to the success of the company.

THE OPERATIONS

HMS is engaged in car races all over the United States during the racing season (38 weeks a year). The company moves to a different racetrack every week. During the off-season time (14 weeks), the company analyzes the data obtained, and lessons learned during the latest racing seasons, and prepares for the following season. The company’s headquarters contains 19 buildings scattered over 100 acres.

THE PROBLEMS DURING THE RACING SEASON

The company needs to make quick decisions during races—some in real time, sometimes in a split second. Different team members need to participate, and they are in different locations. Communication and collaboration are critical.

Car racing is based on teamwork, drivers, engineers, planners, mechanics, and others who participate. Members must communicate and collaborate to make decisions.

The environment is too noisy to talk during a race. However, team members need to share data, graphs, and images, and chat quickly. Several decisions need to be made in real time that will help win races (e.g., how much fuel to add in the next few seconds to a car in the middle of the race). Team members must communicate and share data, including visual. It takes about 45–50 seconds for a car to complete a 2.5-mile lap at Daytona 500. During the race, top engineers need to communicate constantly with the fuelers. Last- minute data are common during the racing session.

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Any knowledge acquired in each lap can be used to improve the next one. In races, fueling decisions are critical. There are many other decisions to be made during the racing season. For example, after each race, the company needs to move a large crew with equipment and supplies from one location to the next (38 different venues). Moves need to be fast, efficient, and economical. Again, teamwork, as well as coordination, is needed.

OFF-SEASON PROBLEMS

There are 14 weeks to prepare for the next season. In addition, there is a considerable amount of data to analyze, simulate, discuss, and manipulate. For this, people need not only communication and collaboration tools but also analytics of different types.

THE SOLUTION

HMS decided to use Microsoft Teams, which is a chat-based platform, for team workspace in Microsoft Office 365. This platform is used as a communication hub for team members at the race tracks and at any other location in the organization.

Microsoft Teams stores data in different formats in its Teams workspace. Therefore, car crews, engineers, and mechanics can make split-second decisions that may help win races. This also enables computational analysis in a central place.

Microsoft Teams includes several subprograms and is easily connected to other soft- ware in Office 365. Office 365 provides several other tools that increase collaboration (e.g., SharePoint). For example, in the HSM solution, there is a working link to Excel as well as to SharePoint. Also, One Note of Teams is used to share meeting notes. Before Teams, the company used Slack (Section 11.4), but Slack did not provide enough security and functions.

Members need to share and discuss the massive amount of data accumulated during the racing season. Note that several employees have multiple skills and tasks. The solution included the creation of a collaboration hub for concurrent projects. Note that each different project may require different talents and data, depending on the project’s type. Also, the solution involves other information technology (IT) tools. For example, HMS uses Power BI dashboard to com- municate data visually. Some data can be processed as Excel-based spreadsheets.

Microsoft Teams is also available as a mobile app. Each team’s data file is available on the track at home and even under a car. So, the software package is able to respond to important situations right away.

The Results

The major results were improved productivity, smoother communication, easier collabora- tion, and reduction of the need for the time consumed in face-to-face meetings. People can chat online, seeing their partners without leaving their physical workplace. The company admits that without Teams, it would not have been able to accomplish its success. Today, Teams has everything the company needs at its fingertips.

u QUESTIONS FOR THE OPENING VIGNETTE

1. What were the major drivers for the use of Microsoft’s Teams? 2. List some discussions held during the racing season, and how they were supported

by the technology.

3. List decisions held during the off-season, and how they were supported by the technology.

4. Discuss why Microsoft Teams was selected, and explain how it supports teamwork group decision making.

5. Trace communication and collaboration within and between groups.

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6. Specify the function of Microsoft Teams workspace. 7. Watch the video at youtube.com/watch?time_continue=108&v=xnFdM9IOaTE

and summarize its content.

WHAT WE CAN LEARN FROM THIS VIGNETTE

The first lesson is that many tasks today must be done by collaborating teams in order to succeed. Second, time is critical; therefore, companies must use technology to speed opera- tions and facilitate communication and collaboration in teamwork. Third, it is possible to use existing software for support, but it is better to use a major vendor that has additional products that can supplement the collaboration/communication software. Fourth, chat- ting can expedite communication, and visual technology support can be useful. Fifth, team members belong to diverse units and have diverse skills. The software brings them together. Team members should have clear goals and understand how to achieve them. Finally, collaboration can be both within and between groups.

Sources: Compiled from Ruiz-Hopper (2016) and Microsoft (2017).

11.2 MAKING DECISIONS IN GROUPS: CHARACTERISTICS, PROCESS, BENEFITS, AND DYSFUNCTIONS

Managers and other knowledge workers continuously make decisions, design products, develop policies and strategies, create software systems, and so on. Frequently they do it in groups. When people work in groups (i.e., teams), they perform group work or teamwork. Group work refers to work done by two or more people together. One aspect of group work is group decision making.

Group decision making refers to a situation in which people make decisions together. Let’s first look at the characteristics of group work.

Characteristics of Group Work

The following are some of the functions and characteristics of group work:

• Group members may be located in different places. • Group members may work at different times. • Group members may work for the same organization or different organizations. • A group can be permanent or temporary. • A group can be at one managerial level or span several levels. • A group can create synergy (leading to process and task gains) or result in conflict. • A group can generate productivity gains and/or losses. • A group’s task may have to be accomplished very quickly. • It may be impossible or too expensive for all team members to meet in one place

at the same time, especially when the meeting is called for emergency purposes. • Some of the groups’ needed data, information, or knowledge may be located in

several sources, some of which may be external to the organization. • The expertise of a group’s team members may be needed. • Groups perform many tasks; however, groups of managers and analysts frequently

concentrate on decision making or problem solving. • The decisions made by a group are easier to implement if supported by all (or at

least most) members. • Group work has many benefits and, unfortunately, some possible dysfunctions. • Group behaviors are influenced by several factors and may affect group decisions.

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Types of Decisions Made by Groups

Groups are usually involved in two major types of decision making:

1. Making a decision together. 2. Supporting activities or tasks related to the decision-making process. For example,

the group may select criteria for evaluating alternative solutions, prioritizing possible ones, and helping design strategy to implement them.

Group Decision-Making Process

The process of group decision making is similar to that of the general decision-making process described in Chapter 1 but it has more steps. Steps of the group decision-making process are illustrated in Figure 11.1.

Step 1. Prepare for meetings regarding the agenda, time, place, participants, and schedule. Step 2. Determine the topic of the meeting (e.g., problem definition). Step 3. Select participants for the meeting. Step 4. Select criteria for evaluating the alternatives and the selected solution. Step 5. Generate alternative ideas (brainstorm). Step 6. Organize the ideas generated into similar groups. Step 7. Evaluate the ideas, discuss, and brainstorm.

FIGURE 11.1 The Process of Group Decision Making.

Preparation, schedule, agenda

Select participants

Define the problem

Select evaluation criteria

Idea generation, alternative solution

Organize submitted ideas

Idea evaluation, discussion

Select or find idea or shortlist of ideas

Make a choice, recommendations

Plan implementation

Implement solutions

1

2

3

4

5

6

7

8

9

10

11

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Step 8. Select a short list (finalists). Step 9. Select a recommended solution. Step 10. Plan implementation of the solution. Step 11. Implement the solution.

The process is shown as sequential, but as shown in Figure 11.1, some loops are possible. Also, if no solution is found, the process may start again.

GROUP DECISION FACTS When a group is going through the steps shown in Figure 11.1, the following is usually true:

• The decisions made need to be implemented. • Group members are typically of equal or nearly equal status. • The outcome of a meeting depends partly on the knowledge, opinions, and judg-

ments of its participants and the support they give to the outcome. • The outcome of a meeting depends on the composition of the group and on the

decision-making process it uses. • Group members settle differences of opinions either by the ranking person present

or through negotiations or arbitration. • The members of a group can be in one place, meeting face-to-face, or they can be

a virtual team, in which case they are in different places meeting electronically. They can also meet at different times.

Benefits and Limitations of Group Work

Some people endure meetings (the most common form of group work) as a necessity; oth- ers find meetings to be a waste of time. Many things can go wrong in a meeting. Participants may not clearly understand its purpose, may lack focus, or may have hidden agendas. Many participants may be afraid to speak up, or a few may dominate the discussions. Misunder- standings occur because of different interpretations of language, gestures, or expression. Technology Insight 11.1 provides a list of factors that can hinder the effectiveness of a manually managed meeting. Besides being challenging, teamwork is also expensive. A meeting of several top managers or executives can cost thousands of dollars.

Group work may have potential benefits (process gains) or drawbacks (process losses). Process gains are the benefits of working in groups. The unfortunate dysfunc- tions that may occur when people work in groups are called process losses. Examples of each are listed in Technology Insight 11.1.

TECHNOLOGY INSIGHT 11.1 Benefits and Dysfunctions of Working in Groups

The following are the possible major benefits and dysfunctions of group works.

Benefits of Working in Groups (Process Gains) Dysfunctions of Face-to-Face Group Process

(Process Losses)

• It provides learning. Groups are better than individuals at understanding problems. They can teach each other.

• Social pressures of conformity may result in groupthink (i.e., people begin to think alike and not tolerate new ideas; they yield to conformance pressure).

• People readily take ownership of problems and their solutions.

• It is a time-consuming, slow process. • Some relevant information could be missing.

• Group members have their egos embedded in the final decision, so they are committed it.

• A meeting can lack coordination, have a poor agenda, or be poorly planned.

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Benefits of Working in Groups (Process Gains) Dysfunctions of Face-to-Face Group Process

(Process Losses)

• Groups are better than individuals at catching errors.

• A meeting may be dominated by time, topic, opinion of one or a few individuals, or fear of contributing because of the possibility of conflicts.

• A group has more information and knowledge than any one member does. Members can combine their knowledge to create new knowledge. More and more creative alternatives for problem solving can be generated, and better solutions can be derived (e.g., through brainstorming).

• Some group members can tend to influence the agenda while some try to rely on others to do most of the work (free riding). The group may ignore good solutions, have poorly defined goals, or be composed of the wrong participants.

• A group may produce synergy during problem solving, therefore the effectiveness and/or quality of group work can be greater than the sum of what individual members produce.

• Some members may be afraid to speak up. • The group may be unable to reach consensus. • The group may lack focus.

• Working in a group may stimulate the creativity of the participants and the process.

• There can be a tendency to produce poor- quality compromises.

• Working together could allow a group to have better and more precise communication.

• There is often nonproductive time (e.g., socializing, preparing, waiting for latecomers).

• Risk propensity is balanced. Groups moderate high-risk takers and encourage conservatives.

• There can be a tendency to repeat what has already been said (because of failure to remember or process).

• Meeting costs can be high (e.g., travel, participation time spent).

• There can be incomplete or inappropriate use of information.

• There can be too much information (i.e., information overload).

• There can be incomplete or incorrect task analysis.

• There can be inappropriate or incomplete representation in the group.

• There can be attention or concentration blockage.

u SECTION 11.2 REVIEW QUESTIONS

1. Define group work. 2. List five characteristics of group work. 3. Describe the steps of group decision making. 4. List the major activities that occur in group work. 5. List and discuss five benefits of group work. 6. List and discuss five dysfunctions of group-made decisions.

11.3 SUPPORTING GROUP WORK AND TEAM COLLABORATION WITH COMPUTERIZED SYSTEMS

When people work in teams, especially when the members are in different locations and may work at different times, they need to communicate, collaborate, and access a diverse set of information sources in multiple formats. This makes meetings, especially virtual ones, complex with an increased chance for process losses. Therefore, it is important to follow certain processes and procedures for conducting meetings.

Group work may require different levels of coordination. Sometimes a group oper- ates at the individual work level with members making individual efforts that require

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 617

no coordination. As with a team of sprinters representing a country participating in a 100-meter dash, group productivity is simply the best of the individual results. At other times, group members may interact in coordination. At this level, as with a team in a relay race, the work requires careful coordination between otherwise independent individual efforts. Sometimes a team may operate at the concerted work level. As in a rowing race, teams working at this level must make a continuous concerted effort to be successful. Different mechanisms support group work at different levels of coordination.

Most organizations, small and large, use some computer-based communication and collaboration methods and tools to support people working in teams or groups. From e-mails to mobile phones and Short Message Service (SMS), as well as conferencing tech- nologies, such tools are an indispensable part of today’s work life. We next highlight some related technologies and applications.

Overview of Group Support Systems (GSS)

For groups to collaborate effectively, appropriate communication methods and technolo- gies are needed. We refer to these technologies as group support systems (GSS). The Internet and its derivatives (i.e., intranets, Internet of Things [IoT], and extranets) are the infrastructures on which much communication and collaboration occurs. The Web supports intra- and inter-organizational collaborative decision making.

Computers have been used for several decades to facilitate group work and decision making. Lately, collaborative tools have received more attention due to their increased capabilities and ability to save time and money (e.g., on travel cost) and to expedite deci- sion making. Computerized tools can be classified according to time and place categories.

Time/Place Framework

The tools used to support collaboration, groups, and the effectiveness of collaborative com- puting technology depend on the location of the group members and on the time that shared information is sent and received. DeSanctis and Gallupe (1987) proposed a framework for classifying IT communication support technologies. In this framework, communication is divided into four cells, which are shown with representative computerized support technolo- gies in Figure 11.2. The four cells are organized along two dimensions—time and place.

When information is sent and received almost simultaneously, the communication is in synchronous (real-time) mode. Telephones, instant messaging (IM), and face-to-face meet- ings are examples of synchronous communication. Asynchronous communication occurs when the receiver gets (or views) the information, such as an e-mail, at a different time than it was sent. The senders and the receivers can be in the same place or in different places.

As shown in Figure 11.2, time and place combinations can be viewed as a four-cell matrix, or framework. The four cells of the framework are as follows:

• Same time/same place. Participants meet face-to-face, as in a traditional meeting, or decisions are made in a specially equipped decision room. This is still an impor- tant way to meet even when Web-based support is used because it is sometimes critical for participants to leave their regular workplace to eliminate distractions.

• Same time/different place. Participants are in different places, but they com- municate at the same time (e.g., with videoconferencing or IM).

• Different time/same place. People work in shifts. One shift leaves information for the next shift.

• Different time/(any place) different place (any place). Participants are in different places, and they send and receive information at different times. This occurs when team members are traveling, have conflicting schedules, or work in different time zones.

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Groups and group work in organizations are proliferating. Consequently, groupware continues to evolve to support effective group work, mostly for communication and col- laboration (Section 11.4).

Group Collaboration for Decision Support

In addition to making decisions, groups also support decision-making subprocesses such as brainstorming. Collaboration technology is known to be the driving force for productivity increase and boosting people and organizational performance. Groups collaborate to make decisions in several ways. For example, groups provide assistance for the steps in Figure 11.1. Groups can help to identify problems, to assist in choosing criteria for selecting solutions, generating solutions (e.g., brainstorming), evaluating alternatives, and assisting in the selection of the best solution and implementing it. The group can be involved in one step or in several steps. In addition, it can collect the necessary data.

Many technologies can be used for collaboration; several of them are computerized and are described in several sections in this chapter.

Studies indicate that adopting collaboration technologies increases productiv- ity: for example, visual collaborative solutions increase employees’ satisfaction and productivity.

COMPUTERIZED TOOLS AND PLATFORMS We divide the computerized support into two parts. In Section 11.4, we present the major support of generic activities in com- munication and collaboration. Note that hundreds, maybe thousands, of commercial products are available to support communication and collaboration. We cover only a sample here.

FIGURE 11.2 The Time/Place Framework.

Same Time

• Instant Messaging • Chatting, decision room • Web-based GSS • Multimedia presentation system • Whiteboard • Document sharing • Workspace

• GSS in a decision room • Web-based GSS • Workflow management system • Document sharing • E-mail, V-mail • Videoconferencing playback

• Web-based GSS • Virtual whiteboard • Document sharing • Videoconferencing • Audio-conferencing • Computer conferencing • E-mail, V-mail • Virtual workspace

• Web-based GSS • Virtual whiteboard • Document sharing • E-mail, V-mail • Workflow management system • Computer conferencing with memory • videoconferencing playback • Voice memo

Different Time

Same Place

Different Place

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Section 11.5 covers direct support of decision making, both to the entire process and to the major steps in the process. Note that some products, such as Microsoft Teams, which is cited in the opening vignette, support both generic activities and those in the decision-making process.

u SECTION 11.3 REVIEW QUESTIONS

1. Why do companies use computers to support group work? 2. What is GSS? 3. Describe the components of the time/place framework. 4. Describe the importance of collaboration for decision making.

11.4 ELECTRONIC SUPPORT FOR GROUP COMMUNICATION AND COLLABORATION

A large number of tools and methods are available to facilitate group work, e-collaboration, and communication. The following sections present only some tools that support the process. Our attention here is on indirect support to decision making. In Section 11.5, we cover direct support.

Groupware for Group Collaboration

Many computerized tools have been developed to provide group support. These tools are called groupware because their primary objective is to support group work indirectly as described in this section. Some e-mail programs, chat rooms, IM, and teleconferences provide indirect support.

Groupware provides a mechanism for team members to share opinions, data, infor- mation, knowledge, and other resources. Different computing technologies support group work in different ways depending on the task and size of the group, the security required, and other factors.

CATEGORIES OF GROUPWARE PRODUCTS AND FEATURES Many groupware products to enhance the collaboration of a small and large number of people are available on the Inter- net or intranets. A prime example is Microsoft’s Teams (opening vignette). The features of groupware products that support commutation, collaboration, and coordination are listed in Table 11.1. What follows are brief definitions of some of those features.

Synchronous versus Asynchronous Products

The products and features described in Table 11.1 may be synchronous or asynchronous. Web conferencing and IM, as well as voice-over IP (VoIP), are associated with the syn- chronous mode. Methods associated with asynchronous modes include e-mail and online workspaces where participants can collaborate while working at different times. Google Drive (drive.google.com) and Microsoft SharePoint (http://office.microsoft.com/en-us/ SharePoint/collaboration-software-SharePoint-FX103479517.aspx) allow users to set up online workspaces for storing, sharing, and working collaboratively on different types of documents. Similar products are Google Cloud Platform and Citrix Workspace Cloud.

Companies such as Dropbox.com provide an easy way to share documents. Similar systems, such as photo sharing (e.g., Instagram, WhatsApp, Facebook), are evolving for consumer home use.

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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

Synchronous (Same Time)

• IM • 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

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

Groupware products are either stand-alone, supporting one task (such as videoconfer- encing), or integrated, including several tools. In general, groupware technology products are fairly inexpensive and can easily be incorporated into existing information systems.

Virtual Meeting Systems

The advancement of Web-based systems opens the door for improved electronically sup- ported virtual meetings with the virtual team members in different locations, even in different countries. Online meetings and presentation tools are provided by tools such as webex, GoToMeeting.com, Skype.com, and many others. These systems feature Web

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seminars (popularly called Webinars), screen sharing, audioconferencing, videoconferenc- ing, polling, question–answer sessions, and so on. Microsoft Office 365 includes a built-in virtual meeting capability. Even smartphones now have sufficient interaction capabilities to allow live meetings through applications such as FaceTime.

COLLABORATIVE WORKFLOW Collaborative workflow refers to software products that address project-oriented and collaborative processes. They are administered centrally yet are capable of being accessed and used by workers from different departments and from different physical locations. The goal of collaborative workflow tools is to empower knowl- edge workers. The focus of an enterprise solution for collaborative workflow is on allowing workers to communicate, negotiate, and collaborate within an integrated environment. Some leading vendors of collaborative workflow applications are FileNet and Action Tech- nologies. Collaborative workflow is related to but different than collaborative workspace.

DIGITAL COLLABORATIVE WORKSPACE: PHYSICAL AND VIRTUAL A collaborative work- space is where people can work together from any location at the same or at a different time. Originally, it was a physical conference room that teams used for conducting meet- ings. It was expanded to be a shared workspace, also known as “coworking space.” Some of these are in companies; others are offered for rent. Different computerized technologies are available to support group work in a physical structure. For 12 benefits of collaborative workspace, see Pena (2017).

A virtual collaboration workspace is an environment equipped with digital support by which group members who are in different locations can share information and col- laborate. A simple example is Google Drive, which enables sharing spreadsheets.

Collaborative workspace enables tech-savvy employees to access systems and tools from any device they need. People can work together in a secure way from anywhere. The digital workspace increases team productivity and innovation. It empowers employees and unlocks innovation. It allows workers to reach other people for collaborative work. For details and other collaboration technologies, see de Lares Norris (2018).

Example

PricewaterhouseCoopers (PwC) built an ideation war room in its Paris office as a large, immersive collaboration facility to support customer meetings.

MAJOR VENDORS OF VIRTUAL WORKSPACE Products by five major vendors follow:

• Google Cloud Platform is deployed on the “cloud,” so it is offered as a platform-as-a- service (PaaS). Google is also known for its Flexible Workspace product.

• Citrix Workspace Cloud is also deployed on the “cloud.” Citrix is known for its GoToMeeting collaboration tool. Citrix Workspace Cloud users can manage secure digital workplaces on Google Cloud.

• Microsoft Workspace is part of Office 365. • Cisco’s Webex, a popular collaboration package including Meeting. • Slack workspace is a very popular workspace.

ESSENTIALS OF SLACK Slack workspace is a digital space on which teammates share, communicate, and collaborate on work. It can be in one organization, or large organiza- tions may have multiple interconnected Slack spaces.

Each workspace includes several topical channels. These can be organized as pub- lic, private, or shared. The remaining components of Slack are messages, searches, and notifications. There are four groups of people involved with Slack: workspace owners,

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workspace administrators, members, and guests. For a Slack Guide, see get.slack.help/ hc/en-us/articles/115004071768-What-is-Slack-.

Slack has many key features and can deliver secure virtual apps to almost any device.

Collaborative Networks and Hubs

Traditionally, collaboration has taken place among supply chain members, frequently those that were close to each other (e.g., a manufacturer and its distributor or a distributor and a retailer). Even if more partners were involved, the focus was on the optimization of information and product flow between existing nodes in the traditional supply chain. Advanced methods, such as collaborative planning, forecasting, and replenishment (CPFR), do not change this basic structure.

Traditional collaboration results in a vertically integrated supply chain. However, Web technologies can fundamentally change the shape of the supply chain, the number of play- ers in it, and their individual roles. In a collaborative network, partners at any point in the network can interact with each other, bypassing what are traditional partners. Interaction may occur among several manufacturers or distributors as well as with new players, such as software agents that act as aggregators.

Collaborative Hubs

The purpose of a collaborative hub is to be a center point for group collaboration. Collaborative hub platforms need to enable participants’ interactions to unfold in

various forms online.

Example: Surface Hub for Business by Microsoft

This product connects individuals wherever they are and whenever they want to use a digital whiteboard and integrating software and apps. It helps to create a collaboration workplace where multiple devices are connected wirelessly to create a powerful work environment.

Social Collaboration

Social collaboration refers to collaboration conducted within and between socially ori- ented groups. It is a process of group interactions and information/knowledge sharing while attempting to attain common goals. Social collaboration is usually done on social media sites, and it is enabled by the Internet, IoT, and diversified social collaboration software. Social collaboration groups and schemes can take many different shapes. For images, conduct a Google search for “images of social collaboration.”

COLLABORATION IN SOCIAL NETWORKS Business-related collaboration is most evidenced on Facebook and LinkedIn. However, Instagram, Pinterest, and Twitter support collabora- tion as well.

• Facebook. Facebook’s Workspace facebook.com/workspace is used by hundreds of thousands of companies utilizing its features, such as “groups,” to support team members. For example, 80 percent of Starbucks store managers use this software.

• LinkedIn. LinkedIn provides several collaboration tools to its members. For exam- ple, LinkedIn Lookup provides several tools. Also, LinkedIn is a Microsoft company and it provides some integrated tools. The creation of subgroups of interest is a useful facilitator.

SOCIAL COLLABORATION SOFTWARE FOR TEAMS In addition to the generic collabora- tion software that can be used by two people and by teams, there are software platforms specifically for forming teams and supporting their activities. A few popular examples

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according to collaboration-software.financesonline.com/c/social-collaboration- software/ are Wrike, Ryver, Azendoo, Zimbra social platform, Samepage, Zoho, Asana, Jive, Chatter, and Social Tables. For viewing the best social collaboration software by category, see technologyadvice.com/social-collaboration-software/.

Sample of Popular Collaboration Software

As noted earlier, there are hundreds or maybe thousands of communication and collabora- tion software products. Furthermore, their capabilities are ever changing. Given that our major interest is decision-making support, we provide only a small sample of these tools. We use the classification and example of Time Doctor, using the 2018 list (see Digneo, 2018).

• 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, BP Logix

OTHER TOOLS THAT SUPPORT COLLABORATION 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 (provides an e-commerce workspace that allows collection of text and images on selected topics). IBM 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) For additional tools, see Steward (2017).

u SECTION 11.4 REVIEW QUESTIONS

1. Define groupware. 2. List the major groupware tools and divide them into synchronous and asynchronous types. 3. Identify specific tools for Web conferencing and their capabilities. 4. Describe collaborative workflow. 5. What is collaborative workspace? What are its benefits? 6. Describe social collaboration.

11.5 DIRECT COMPUTERIZED SUPPORT FOR GROUP DECISION MAKING

Decisions are made frequently at meetings, some of which are called in order to make a one-time specific decision. For example, directors are elected at shareholder meetings, orga- nizations allocate budgets in meetings, cities decide which candidates to hire for their top

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positions, and the U.S. federal government meets periodically to set the short-term interest rate. Some of these decisions are complex; others can be controversial, as in resource alloca- tion by a city government. Process dysfunctions can be significantly large in such situations; therefore, computerized support has often been suggested to mitigate these controversies. These computer-based support systems have appeared in the literature under different names, including group decision support systems (GDSS), group support systems (GSS), computer- supported collaborative work (CSCW), and electronic meeting systems (EMS). These systems are the subject of this section. In addition to supporting entire processes, there are tools that support one or several activities in the group decision-making process (e.g., brainstorming).

Group Decision Support Systems (GDSS)

During the 1980s, researchers realized that computerized support to managerial decision making needed to be expanded to groups, because major organizational decisions are made by groups, such as executive committees and special task forces. The result was the creation of the group decision support systems methodology.

A group decision support system (GDSS) is an interactive computer-based sys- tem that facilitates the solution of semistructured or unstructured problems by a group of decision makers. The goals of GDSS are to improve the productivity of decision-making meetings by speeding up the decision-making process and/or to increase the quality of the resulting decisions.

MAJOR CHARACTERISTICS AND CAPABILITIES OF A GDSS GDSS characteristics follow:

• It supports the process of group decision makers mainly by providing automation of subprocesses (e.g., brainstorming) and using information technology tools.

• It is a specially designed information system, not merely a configuration of already existing system components. It can be designed to address one type of problem or make a variety of group-level organizational decisions.

• It encourages generation of ideas, resolution of conflicts, and freedom of expres- sion. It contains built-in mechanisms that discourage development of negative group behaviors, such as destructive conflict, miscommunication, and groupthink.

The first generation of GDSS was designed to support face-to-face meetings in a decision room. Today, support is provided mostly over the Web to virtual teams. A group can meet at the same time or at different times. GDSS is especially useful when controver- sial decisions have to be made (e.g., resource allocation, determining which individuals to lay off). GDSS applications require a facilitator for one physical place or a coordinator or leader for online virtual meetings.

GDSS can improve the decision-making process in various ways. For one, GDSS gen- erally provides structure to the meeting planning process, which keeps a group meeting on track, although some applications permit the group to use unstructured techniques and methods for idea generation. In addition, GDSS offers rapid and easy access to external and stored information needed for decision making. It also supports parallel processing of information and idea generation by participants and allows asynchronous computer discus- sion. GDSS makes possible larger group meetings that would otherwise be unmanageable; having a larger group means that more complete information, knowledge, and skills can be represented in the meeting. Finally, voting can be anonymous with instant results, and all information that passes through the system can be recorded for future analysis (producing organizational memory).

Over time, it became clear that supporting teams needed to be broader than GDSS has beed supported in a decision room. Furthermore, it became clear that what was really

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 625

needed was support for virtual teams, both in different place/same time and different place/different time situations. Also, it became clear that teams needed indirect support in most decision-making cases (e.g., help in searching for information or in collaboration) rather than direct support for the decision-making process. Although GDSS expanded to virtual team support, it was unable to meet all the other needs. In addition, the traditional GDSS was designed to deal with contradictory decisions when conflicts were likely to arise. Thus, a new generation of GDSS that supports collaboration work was needed. As we will see later, products such as Stormboard provide those needs.

Characteristics of GDSS

There are two options for deploying GDSS technology: (1) in a special-purpose decision room and (2) as Internet-based groupware with client programs running wherever the group members are.

DECISION ROOMS The earliest GDSS was installed in expensive, customized, special- purpose facilities called decision rooms (or electronic meeting rooms) that had PCs and a large public screen at the front of each room. The original idea was that only executives and high-level managers would use the expensive facility. The software in an electronic meeting room usually ran over a local area network (LAN), and these rooms were fairly plush in their furnishings. Electronic meeting rooms were structured in different shapes and sizes. A common design was a room equipped with 12 to 30 networked PCs, usually recessed into the desktop (for better participant viewing). A server PC was attached to a large screen projection system and connected to the network to display the work at indi- vidual workstations and aggregated information from the facilitator’s workstation. Breakout rooms equipped with PCs connected to the server, in which small subgroups could consult, were sometimes located adjacent to the decision room. The output from the subgroups was able to be displayed on the large public screen. A few companies offered such rooms for a daily rent. Only a few upgraded rooms are still available today, usually for high rent.

INTERNET-BASED GROUPWARE Since the late 1990s, the most common approach to GSS and GDSS delivery has been to use an Internet-based groupware that allows group mem- bers to work from any location at any time (e.g., WebEx, GoToMeeting, Adobe Connect, IBM Connections, Microsoft Teams). This groupware often includes audio conferencing and videoconferencing. The availability of relatively inexpensive groupware, as described in Section 11.4, combined with the power and low cost of computers and the availability of mobile devices, makes this type of system very attractive.

Supporting the Entire Decision-Making Process

The process that was illustrated in Figure 11.1 can be supported by a variety of software products. In this section, we provide an example of one product, Stormboard, that sup- ports several aspects of that process.

Example: Stormboard

Stormboard stormboard.com provides support for different brainstorming and group decision-making configurations. The following is the product’s sequence of activities:

1. Define the problem and the users’ objectives (what they are hoping to achieve). 2. Brainstorm ideas (to be discussed later). 3. Organize the ideas in groups of similar flavor, look for patterns, and select only

viable ideas.

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4. Collaborate, refine concepts, and evaluate (using criteria) the meeting’s objectives. 5. The software enables users to prioritize proposed ideas by focusing on the selec-

tion criteria. It lets all participants express their thinking and directs the team to be cohesive.

6. It presents a short list of superior ideas. 7. The software suggests the best idea and recommends implementation. 8. It plans the project implementation. 9. It manages the project.

10. It periodically reviews progress.

For a video, see youtube.com/watch?v=0buRzu4rhJs.

COMPREHENSIVE GROUPWARE TOOLS INCLUDING THINKTHANK Although many capabili- ties that enable group decision support are embedded in common software tools for office productivity such as Microsoft Office 365, it is instructive to learn about specific software that illustrates some of groupware’s unique capabilities. MeetingRoom was one of the first comprehensive, same time/same place electronic meeting packages. Its follow-up product, GroupSystems OnLine, offered similar capabilities, and it ran in asynchronous mode (any- time/anyplace) over the Web (MeetingRoom ran only over a LAN). GroupSystems’ latest product is ThinkTank, a suite of tools that facilitate the various group decision-making activities. For example, it shortens cycle time for brainstorming. ThinkTank improves the collaboration of face-to-face or virtual teams through customizable processes toward the groups’ goals faster and more effectively than in previous product generations. ThinkTank offers the following:

• It can provide efficient participation, workflow, prioritization, and decision analysis. • Its anonymous brainstorming for ideas and comment generation is an ideal way to

capture the participants’ creativity and experience. • The product’s enhanced Web 2.0 user interface ensures that participants do not

need special training to join, so they can focus 100 percent on solving problems and making decisions.

• With ThinkTank, all of the knowledge shared by participants is captured and saved in documents and spreadsheets, automatically converted to the meeting minutes, and made available to all participants at the end of the session.

Examples: ThinkTank Use (thinktank.net/case-study)

The following are two examples of ThinkTank’s use.

• It enables transformational collaboration between supply chain partners. Their meet- ing was supported by collective intelligence tools and procedures. Partners agreed on how to cut costs, speed processes, and improve efficiencies. In the past, there had been no progress on these issues.

• The University of Nebraska and the American College of Cardiology collaborated using ThinkTank tools and procedures to rethink how electronic health records could be reorganized to help medical consultants save time. Patients’ appointment times were shortened by 5 to 8 minutes. Other improvements also were achieved. Both patient care and large monetary savings were achieved.

OTHER DECISION-MAKING SUPPORT The following is a list of other types of support pro- vided by intelligent systems:

• Using knowledge systems and a product called Expert Choice Software for dealing with multiple-criteria group decision making.

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• A mediating group decision-making method for infrastructure asset management was proposed by Yoon et al. (2017).

• For a group decision-making support system in logistics and supply chain manage- ment, see Yazdani et al. (2017).

Brainstorming for Idea Generation and Problem Solving

A major activity in group decision making is idea generation. Brainstorming is a process for generating creative ideas. It involves freewheeling group discussions and spontaneous contribution of ideas for solving problems and making strategy and resource allocation. Contributors’ ideas are discussed by the members. An attempt is made to generate as many ideas as possible, no matter how bizarre they look. Generated ideas are discussed and evaluated by the group. There is evidence that groups not only generate more ideas but also better ones (McMahon et al., 2016). Manually managed brainstorming has some of the limitations of group work described in Section 11.2. Therefore, computer support is frequently recommended.

COMPUTER-SUPPORTED BRAINSTORMING Computer programs can support the various brainstorming activities. The support is usually for online brainstorming, synchronously or asynchronously. Hopefully, electronic brainstorming eliminates many of the process dysfunctions cited in Section 11.2 and helps in the generation of many new ideas. Brain- storming software can stand alone or be a part of a general group support package. The major features of software packages follow:

• Creation of a large number of ideas. • Large group participation. • Real-time updates. • Information color coding. • Collaborative editing. • Design of brainstorming sessions. • Idea sharing. • People participation. • Idea mapping (e.g., create mind maps). • Text, video, documents, etc. posting. • Remote brainstorming. • Creation of an electronic archive. • Reduction of social loafing.

The major limitations of electronic software support are increased cognitive load, fear of using new technology, and need for technical assistance.

COMPANIES THAT PROVIDE ONLINE BRAINSTORMING SERVICES AND SUPPORT FOR GROUP WORK Some companies and the services and support they provide follow:

• 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 (visually, in colors).

• 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.

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For an evaluation of 28 online brainstorming tools, see blog.lucidmeetings.com/ blog/25-tools-for-online-brainstorming-and-decision-making-in-meetings/.

ARTIFICIAL INTELLIGENCE SUPPORTS BRAINSTORMING In Chapter 12, we will introduce the use of bots. Some software allows users to create and post a bot (or avatar) that rep- resents people in order to communicate anonymously. Artificial intelligence (AI) can also be used for pattern recognition and identifying ideas that are similar to each other. AI is also used in crowdsourcing (Section 11.7), which is used extensively for idea generation and voting.

Group Support Systems

A group support system (GSS), which was discussed earlier, is any combination of hardware and software that enhances group work. GSS is a generic term that includes all forms of communication and collaborative computing. It evolved after information technology researchers recognized that technology could be developed to support many activities that normally occur at face-to-face meetings when they occur in virtual meetings (e.g., idea generation, consensus building, anonymous ranking). Also, a focus was made on collaboration rather than on minimizing conflicts.

A complete GSS is considered a specially designed information system software, but today, its special capabilities have been embedded in standard IT productivity tools. For example, Microsoft Office 365 includes Microsoft Teams (opening vignette). It also includes the tools for Web conferences. Also, many commercial products have been developed to support only one or two aspects of teamwork (e.g., videoconferencing, idea generation, screen sharing, wikis).

HOW GSS IMPROVES GROUP WORK The goal of GSS is to provide support to participants in improving the productivity and effectiveness of meetings by streamlining and speed- ing up the decision-making process and/or by improving the quality of the results. GSS attempts to increase process and task gains and decrease process and task losses. Overall, GSS has been successful in doing just that. Improvement is achieved by providing support to group members for the generation and exchange of ideas, opinions, and preferences. Specific features such as the ability of participants in a group to work simultaneously on a task (e.g., idea generation or voting) and anonymity produce improvements. The following are some specific GSS support activities:

• Supporting parallel processing of information and idea generation (brainstorming). • Enabling the participation of larger groups with more complete information, knowl-

edge, and skills. • Permitting the group to use structured or unstructured techniques and methods. • Offering rapid, easy access to external information. • Allowing parallel computer discussions. • Helping participants frame the big picture. • Providing anonymity, which allows shy people to contribute to the meeting (i.e.,

to get up and do what needs to be done). • Providing measures that help prevent aggressive individuals from controlling a

meeting. • Providing multiple ways to participate in instant anonymous voting. • Providing structure for the planning process to keep the group on track. • Enabling several users to interact simultaneously (i.e., conferencing). • Recording all information presented at a meeting (i.e., providing organizational

memory).

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 629

For GSS success stories, look for sample cases at vendors’ Web sites. As you will see in many of these cases, collaborative computing led to dramatic process improvements and cost savings.

Note that only some of these capabilities are provided in a single package from one vendor.

u SECTION 11.5 REVIEW QUESTIONS

1. Define GDSS and list the limitations of the initial GSS software. 2. List the benefits of GDSS. 3. List process gains made by GDSS. 4. Define decision room. 5. Describe Web-based GSS. 6. Describe how GDSS supports brainstorming and idea generation.

11.6 COLLECTIVE INTELLIGENCE AND COLLABORATIVE INTELLIGENCE

Groups or teams are created for several purposes. Our book concentrates on support for decision making. This section deals with the collective intelligence and collaborative intel- ligence of groups.

Definitions and Benefits

Collective intelligence (CI) refers to the total intelligence of a group. It is also refers to as the wisdom of the crowd. People in a group are using their skills and knowledge for solving problems and providing new insights and ideas. The major benefits are the ability to solve com- plex problems and/or design new products and services that result from innovations. A major research center on collective intelligence (CI) is the MIT Center for Collective Intelligence (CCI) (cci.mit.edu). A major study aspect of CCI is how people and computers can work together so that teams can be more innovative than any individual, group, or computer can be alone. CI appears in several disciplines ranging from sociology to political science. Our interest here is in CI as it relates to computerized decision making. We cover CI here and in Section 11.7 where we present the topic of crowdsourcing. In Section 11.8, we present swarm intelligence, which is also an application of CI. For the benefits of CI, see 50Minutes.com (2017).

TYPES OF COLLECTIVE INTELLIGENCE One way to categorize CI is to divide it into three major areas of applications: cognition, cooperation, and coordination. Each of these can be further divided. For an overview, see collective intelligence on Wikipedia. Our inter- est is in applications by which the group synergy helps in problem solving and decision making. People contribute their experience and knowledge, and the group interactions and the computerized support help in making better decisions.

Thomas W. Malone, the founder and director of CCI at MIT, considers CI as a broad umbrella. He views collective intelligence as “groups of individuals act- ing collectively in ways that seem intelligent.” The CCI work, known as the Edge, is available at the Edge video (31:45 minutes) available at edge.org/conversation/ thomas_w__malone-collective-intelligence.

Computerized Support to Collective Intelligence

Collective intelligence can be supported by many of the tools and platforms described in Sections 11.4 and 11.5. In addition, the Internet, intranet, and the IoT (Chapter 13) play a major role in facilitating CI by enabling people to share knowledge and ideas.

630 Part IV • Robotics, Social Networks, AI and IoT

Example 1: The Carnegie University Foundation Supports Network Collaboration

The Carnegie Foundation was looking for ways to have people work together collab- oratively in order to accelerate improvements and to share data and learning across its networks of people. The solution is an online workspace called the Carnegie Hub, which serves as an access point to resources and enables engagement in group work and collaboration.

The Hub uses several software products, some of which were described in Section 11.4, such as Google Drive, creating a collaborative workspace. The major aspects of the Carnegie Collection Intelligence project follow:

1. Content is shared in one place (the “cloud”) for everyone to view, edit, or contribute even at the same time.

2. All data and knowledge are stored in one location on the Web. Discovery is easy. 3. Asynchronous conversations using discussion boards are easy; all notes are publicly

displayed, documented, and stored. 4. These aspects facilitate social collaboration, commitment to problem solving, and

peer support. The Carnegie University faculty is now a community of practice, using collective intelligence to plan, create, and solve problems together. For details, see Thorn and Huang (2014).

Example 2: How Governments Tap IoT for Collective Intelligence

According to Bridgwater (2018), governments are using IoT to support decision making and policy creation. Governments are trying to collect information and knowledge from people and increasingly do so via IoT. Bridgwater cites the government of the United Arab Emirates that uses IoT to enhance public decision making. The IoT systems collect ideas and aspirations of the citizens. The collective intelligence platform allows the targeting of narrowly defined groups. Real estate plans are subjected to the opinion of residents in the vicinity of proposed developments. The country’s project of smart cities is combined with CI (Chapter 13). In addition to IoT, there are activities in CI and networks as shown in Application Case 11.1.

Introduction

Water management is one of the most important chal- lenges for many communities. In general, the demand for water is growing while the supply could shrink (e.g., due to pollution). Managing water requires the involvement of numerous stakeholders ranging from consumers and suppliers to local governments and sanitation experts. The stakeholders must work together. The objective is to have responsible water use and water preservation. The accounting office of PwC published report 150CO47, “Collaboration: Preserving

Water Through Partnership That Works” available at pwc.com/hu/hu/kiadvanyok/assets/pdf/pwc_ water_collaboration.pdf. It describes the problem and its benefits and risks. The report shares the differ- ent stakeholders’ perspectives, identifies the success factors of collaboration, and weighs the trade-offs for evaluating alternative solutions for the water manage- ment issue. An interesting framework for a solution is the collaborative modeling developed at Oregon State University in collaboration with Indiana University- Purdue University.

Application Case 11.1 Collaborative Modeling for Optimal Water Management: The Oregon State University Project

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 631

The Challenge

Planning and managing water conservation activities are not simple tasks. The idea is to develop a user- friendly tool that will enable all stakeholders to par- ticipate in these activities. It is necessary to involve the stakeholder communities in using scientifically developed guidelines for designing water conserva- tion practices. Here are some of the requirements of the desired tool:

• The tool needs to be interactive and human guided and operated.

• It needs to be Web-based and user friendly. • Both individuals and groups should be able to

use it. • It should enable users to view and evaluate

solution designs based on both quantitative and qualitative criteria.

The Solution: WRESTORE

Watershed Restoration Using Spatio-Temporal Optimi- zation (WRESTORE) is a Web-based tool that meets the preceding requirements. It is based on AI and ana- lytical optimization algorithms. The algorithms process dynamic simulation models and allow users to spatially optimize the location of new water conservations. In addition to using the dynamic simulation models, users are able to include their own personal subjective views and qualitative criteria. WRESTORE generates alterna- tive practices that users can discuss and evaluate.

Incorporation of human preferences to com- puter solutions makes the solutions more accept- able. The AI part of the project includes machine learning and crowdsourcing (Section 11.7) to solicit

information from the crowd. The reason for the par- ticipative collaboration is that water is an essential resource and should not be only centrally controlled. The AI technologies “democratize” water manage- ment while harnessing the power of people and com- puters to solve difficult water management problems.

The machine-learning algorithms learn from what people are doing. Human feedback helps AI to iden- tify best solutions and strategies. Thus, humans and machines are combined to solve problems together.

The Results

WRESTORE developers are experimenting with the technology in several places and so far have achieved full collaboration from participating stakeholders. Initial results indicate the creation by WRESTORE of innova- tive ideas for developing water resources and distribu- tion methods that save significant amounts of water.

Questions for Case 11.1 1. Crowdsourcing is used to find information from a

crowd. Why is it needed in this case? (see Section 11.7 if you are not familiar with crowdsourcing).

2. How does WRESTORE act as a CI tool?

3. Debate centralized control versus participative col- laboration. Cite the pros and cons of each.

4. Why it is difficult to manage water resources?

5. How can an optimization/simulation/AI model support group work in this case?

Sources: Compiled from Basco-Carrera et al. (2017), KTVZ.com (Channel 21, Oregon, March 21, 2018), and Babbar-Sebens et al. (2015).

How Collective Intelligence May Change Work and Life

For several decades, researchers studied the relationship of CI and work. For example, Doug Engebert, a pioneer in CI, describes how people work together in response to a shared challenge and how they can leverage their collective memory, perception, planning, reasoning, and so on into powerful knowledge. Since Engebert’s pioneering work, the impact of technology is increasing organizations’ CI and building collaborative communi- ties of knowledge. In summary, CI attempts to augment human intelligence to solve busi- ness and social problems. This basically means that CI allows more people to have more engagement and involvement in organizational decision making. At MIT’s CCI, research is done on how people and computers can work together to improve work (see also Sec- tion 11.9). MIT’s CCI focuses on the role of networks, including the Internet, intranets, and IoT. Researchers there found that organizations’ structures tend to be flatter, and more

632 Part IV • Robotics, Social Networks, AI and IoT

decisions are delegated to teams. All this results in decentralized workplaces. For further discussion on MIT’s CCI, see MIT’s blog of April 3, 2016, at executive.mit.edu/blog/will- collective-intelligence-change-the-way-we-work/. For a comprehensive view on how CI can change the entire world, see Mulgan (2017).

A major thrust in CI is the collaboration efforts within a group, as described next.

Collaborative Intelligence

Placing people in groups and expecting them to collaborate with the help of technology may be wishful thinking. Management and behavioral researchers study the issue of how to make people collaborate in groups.

Called by some collaborative intelligence, Coleman (2011) stipulates that group col- laboration has the following 10 components: (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 com- puterized tools and technology. A similar list is provided at thebalancecareers.com/ collaboration-skills-with-examples-2059686.

Computerized tools and technologies are critical enablers of communication, col- laboration, and people’s understanding of each other.

How to Create Business Value from Collaboration: The IBM Study

Groups and team members provide ideas and insights. To excel, organizations must utilize people’s knowledge, some of which is created by collective intelligence. One way to do this is provided by a study of collective intelligence conducted by the IBM Institute for Business Value. The study is available (free) at www-935.ibm.com/services/us/gbs/ thoughtleadership/ibv-collective-intelligence.html. There is also a free executive sum- mary. The study presents three major points:

1. CI can enhance organizational outcomes by correctly tapping the knowledge and experience of working groups (including customers, partners, and employees).

2. It is crucial to target and motivate the appropriate participants. 3. CI needs to address the issue of participants’ resistance to change. All in all, IBM

concludes that “Collective intelligence is a powerful resource for creating value using the experience and insights of vast numbers of people around the world.”

Access the untapped knowledge of your networks, IBM. (www-935.ibm.com/ services/us/gbs/thoughtleadership/ibv-collective-intelligence.html)

An offshoot of CI is crowdsourcing, the topic of the next section (11.7).

u SECTION 11.6 REVIEW QUESTIONS

1. What is collective intelligence (CI)? 2. List the major benefits of CI. 3. How is CI supported by computers? 4. How can CI change work and life? 5. How can CI impact organization structure and decision making? 6. The Carnegie case described how standard collaboration tools create a collective intel-

ligence infrastructure. The WRESTORE case described a modeling analytical framework that enables stakeholders to collaborate. What are the similarities and differences between the two cases?

7. Describe collaborative intelligence. 8. How do you create business value from collective intelligence?

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 633

11.7 CROWDSOURCING AS A METHOD FOR DECISION SUPPORT

Crowdsourcing refers to outsourcing tasks to a large group of people (crowd). One of the major reasons for doing so is the potential for the wisdom of a crowd to improve decision making and assist in solving difficult problems; see Power (2014). Therefore, crowdsourc- ing can be viewed as a method of collective intelligence. This section is divided into three parts: The essentials of crowdsourcing, crowdsourcing as a decision support mechanism, and implementing crowdsourcing for problem solving.

The Essentials of Crowdsourcing

Crowdsourcing has several definitions because it is used for several purposes in a number of fields. For a tutorial on crowdsourcing and examples, view the video (14:51 min.) at youtube.com/watch?v=lXhydxSSNOY. Crowdsourcing means that an organization is outsourcing or farming out work for several reasons: Necessary skills may not be available internally, speed of execution is needed, problems are too complex to solve, or special innovation is needed.

SOME EXAMPLES

• Since 2005, Doritos Inc. has run a “Crash the Super Bowl” contest for creating a 30-second video for the Super Bowl. The company has given $7 million in prizes in the last 10 years for commercials composed by the public.

• Airbnb is using user-submitted videos (15 seconds each) that describe travel sites. • Dell’s Idea Storm (ideastorm.com) enables customers to vote on features of Idea

Storm the customers prefer, including new ones. Dell is using a technically oriented crowd, such as the Linux (linux.org) community. The crowd submits ideas and sometimes members of the community vote on them.

• Procter & Gamble’s researchers post their problems at innocentive.com and ninesigma.com, offering cash rewards to problem solvers. It uses other crowdsourc- ing service providers such as yourencore.com.

• The LEGO company has a platform called LEGO Ideas through which users can submit ideas for new LEGO sets and vote on submitted ideas by the crowd. Accepted ideas generate royalties to those who proposed them if the ideas are commercialized.

• PepsiCo solicits ideas regarding new potato chip flavors for the company’s Lay’s brand. Over the years, the company has received over 14 million suggestions. The estimated contribution to sales increase is 8 percent.

• Cities in Canada are creating real-time electronic city maps to inform cyclists about high-risk areas to make the streets safer. Users can mark the maps when they expe- rience a collision, bike theft, road hazard, and so on. For details, see Keith (2018).

• U.S. intelligence agencies have been using ordinary people (crowds) to predict world events ranging from the results of elections to the direction of prices.

• Hershey crowdsourced potential solutions of how to ship chocolate in warm climates. For how this was done, see Dignan (2016). The winning prize was $25,000.

These examples illustrate some of the benefits of crowdsourcing, such as wide exposure to expertise, increased performance and speed, and improved problem-solving and innova- tion capabilities. These examples also illustrate the variety of applications.

MAJOR TYPES OF CROWDSOURCING Howe (2008), a crowdsourcing pioneer, divided the crowdsourcing applications into the following types (or models):

1. Collective intelligence (or wisdom). People in crowds are solving problems and providing new insights and ideas leading to product, process, or service innovations.

2. Crowd creation. People are creating various types of content and sharing it with others (for pay or free). The created content may be used for problem

634 Part IV • Robotics, Social Networks, AI and IoT

Another way to classify crowdsourcing is by the type of work it does. Some examples with a crowdsourcing vendor for each follow:

• Logo design—Design Bill • Problem solving—InnoCentive, NineSigma, IdeaConnection • Business innovation—Chardix • Brand names—Name This • Product and manufacturing design—Pronto ERP • Data cleansing—Amazon Mechanical Turk • Software testing—uTest • Trend watching—TrendWatching • Images—Flickr Creative Commons

For a compressive list of crowdsourcing, collective intelligence, and related compa- nies, see boardofinnovation.com.

THE PROCESS OF CROWDSOURCING The process of crowdsourcing differs from applica- tion to application, depending on the nature of the specific problem to be solved and the method used. However, the following steps exist in most enterprise crowdsourcing applications, even though the details of the execution may differ. The process is illustrated in Figure 11.3.

1. Identify the problem and the task(s) to be outsourced. 2. Select the target crowd (if not an open call). 3. Broadcast the task to the crowd (or to an unidentified crowd in an open call). 4. Engage the crowd in accomplishing the task (e.g., idea generation, problem

solving). 5. Collect user-generated content. 6. Have the quality of submitted material evaluated by the management that initiated

the request, by experts, or by a crowd. 7. Select the best solution (or a short list). 8. Compensate the crowd (e.g., the winning proposal). 9. Implement the solution.

Note that we show the process as sequential, but there could be loops returning to previ- ous steps.

Crowdsourcing for Problem-Solving and Decision Support

Although there are many potential activities in crowdsourcing, major ones are support- ing the managerial decision-making process and/or providing a solution to a problem. A complicated problem that is difficult for one decision maker or a small group to solve may be solved by a crowd, which can generate a large number of ideas for solving a

solving, advertising, or knowledge accumulation. Content creation can also be done by splitting large tasks into small segments (e.g., contributing content to create Wikipedia).

3. Crowd voting. People are giving their opinions and ratings on ideas, products, or services, as well as evaluating and filtering information presented to them. An example is voting in American Idol competitions.

4. Crowd support and funding. People are contributing and supporting endeavors for social or business causes, such as offering donations, and micro-financing new ventures.

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 635

problem. However, inappropriate use of crowdsourcing could generate negative results (e.g., see Grant, 2015). On how to avoid the potential pitfalls of crowdsourcing, see Bhandari et al., 2018.

THE ROLE OF CROWDSOURCING IN DECISION MAKING Crowds can provide ideas in a col- laborative or a competitive mode. However, the crowd’s role may differ at different stages of the decision-making process. We may use a crowd to decide how to respond to a com- petitor’s act or to help us decide whether a proposed design is useful. Chiu et al. (2014) adopted Herbert Simon’s decision-making process model to outline the potential roles of a crowd. Simon’s model includes three major phases before implementation: intelligence (information gathering and sharing for the purpose of problem solving or opportunity exploitation, problem identification, and determination of the problem’s importance), design (generating ideas and alternative solutions), and choice (evaluating the generated alternatives and then recommending or selecting the best course of action). Crowdsourc- ing can provide different types of support to this managerial decision-making process. Most of the applications are in the design phase (e.g., idea generation and co-creation) and in the choice phase (voting). In some cases, support can be provided in all phases of the process.

Implementing Crowdsourcing for Problem Solving

While using an open call to the public can be done fairly easily by the problem owner, people who need to solve difficult problems usually like to reach experts for solving problems (solvers). For a company to obtain assistance in finding such experts, especially externally, it can use a third-party vendor. Such vendors have hundreds of thousands or even millions of preregistered solvers. Then, the vendor can do the job as illustrated in Application Case 11.2.

FIGURE 11.3 The Crowdsourcing Process.

Problem owner

Problem

Preparation, specific task(s) to outsource

Crowd membersCrowd Selection

Ideas, solutions submitted

Broadcasting task

Crowd perform work

Idea evaluation

Recommended solution

Activities

Components

636 Part IV • Robotics, Social Networks, AI and IoT

GlaxoSmithKline (GSK) is a UK-based global phar- maceutical/healthcare company, with over 100,000 employees. The company strives on innovations. However, despite its mega size and global presence, it has problems that it needs outside expertise to solve.

The Problem

The company researched a potentially disruptive technology that promised cure to difficult diseases. The company wanted to discover which disease to use as a test bed for the potential innovative treatments. It was necessary to make sure that the selection will cover a disease where every aspect of the new treatment is checked. Despite its large size, GSK wanted some outside expertise to sup- port and check the in-house research efforts.

The Solution

GSK decided to crowdsource the problem solution to experts, using InnoCentive Corp. (Innocentive. com). InnoCentive is a US-based global crowdsourc- ing company. The company receives challenges from client companies like GSK. These challenges are posted for solvers to see with the potential rewards, in InnoCentive’s Challenge Center. Solvers that think they want to participate follow instructions and may

sign an agreement. The solutions submitted are eval- uated, and awards are provided to the winners.

The GSK Situation

In total, 397 solvers engaged in this challenge, even the reward was minimal ($5000). The solvers resided in several countries. The solvers submitted 66 pro- posed solutions. The entire process lasted 75 days.

The Results

The winning solution proposed a new area that was not considered by GSK teams. The proposer was a Bulgarian who based his idea on a Mexican publi- cation. Several other winning proposals contributed useful ideas. Also, the process enabled collaboration between the GSK team and the winning researchers.

Questions for Case 11.2 1. Why did GSK decide to crowdsource?

2. Why did the company use InnoCentive?

3. Comment on the global nature of the case.

4. What lessons did you learn from this case?

5. Why do you think a small $5000 reward is sufficient?

Sources: Compiled from InnoCentive Inc. Case Study GlaxoSmithKline. Waltham, MA., GSK Corporate Information (gsk. com) and InnoCentive.com/our-solvers/.

Application Case 11.2 How InnoCentive Helped GSK Solve a Difficult Problem

CROWDSOURCING FOR MARKETING More than 1 million customers are registered at Crowd Tap, the company that provides a platform named Suzy that enables marketers to conduct crowdsourcing studies.

u SECTION 11.7 REVIEW QUESTIONS

1. Define crowdsourcing. 2. Describe the crowdsourcing process. 3. List the major benefits of the technology. 4. List some areas for which crowdsourcing is suitable. 5. Why may you need a vendor to crowdsource the problem-solving process?

11.8 ARTIFICIAL INTELLIGENCE AND SWARM AI SUPPORT OF TEAM COLLABORATION AND GROUP DECISION MAKING

AI, as seen in Chapter 2, is a diversified field. Its technologies can be used to support group decision making and team collaboration.

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 637

AI Support of Group Decision Making

A major objective of AI is to automate decision making and/or to support its process. This objective holds also for decisions made by groups. However, we cannot automate a decision made by a group. All we can do is to support some of the steps in a group’s decision-making process.

A logical place to start is Figure 11.1. We can examine the different steps of the pro- cess and see where AI can be used.

1. Meeting preparation. AI is used to find a convenient time for meetings to take place. AI can assist in scheduling meetings so that all can participate.

2. Problem identification. AI technologies are used for pattern recognition that can identify areas that need attention. AI can be used in other types of analysis to identify potential or difficult to pinpoint problems.

3. Idea generation. AI is known for its quest for creativity. Team members can increase their creativity when they use AI for support.

4. Idea organization. Natural language processing (NLP) can be used to sort ideas and organize them for improved evaluation.

5. Group interaction and collaboration. AI can facilitate communication and collabo- ration among group members. This activity is critical in the process of arriving at a consensus. Also, Swarm AI (see the end of this section) is designed to increase interactions among group members so their combined wisdom is elevated.

6. Predictions. AI supports predictions that are required to assess the impact of the ideas generated regarding performance and/or impacts in the future. Machine learn- ing, deep learning, and Swarm AI are useful tools in this area.

7. Multinational groups. Collaboration among people located in different countries is on the rise. AI enables group interaction of people who speak different languages, in real time.

8. Bots are useful in supporting meetings. Group members may consult Alexa and other bots. Chatbots can provide answers to queries in real time.

9. Other advisors. IBM Watson can provide useful advice during meetings, supple- menting knowledge provided by participants and by Alexa.

Example

In 2018, Amazon.com was looking for a site for its second headquarters. A robot named Aiera from Wells Fargo Securities used deep learning to predict that the winning site would be Boston (Yurieff, 2018a). (When this chapter was written, the decision had not been made.)

For an academic approach on how to improve group decision making by AI, see Xia (2017).

AI Support of Team Collaboration

Organizations today are looking for ways to increase and improve collaboration with employees, business partners, and customers. To gain insight into how AI may impact collaboration, Cisco Systems sponsored a global survey, AI Meets Collaboration (Morar HPI, 2017), regarding the impact of AI, including the use of virtual assistants in the work space. The major findings of this survey are:

1. Virtual assistants increase employees’ productivity, creativity, and job satisfaction. Bots also enable employees to focus on high-value tasks.

2. Bots are accepted as part of workers’ teams.

638 Part IV • Robotics, Social Networks, AI and IoT

3. Bots improve conference calls. They also can take meetings notes and schedule meetings.

4. AI can use facial recognition to sign in eligible people to meetings. 5. Personal characteristics are likely to influence how people feel about AI in the

workplace. 6. Employees in general like to have AI in their teams. 7. Security is a major concern when AI, such as virtual assistants, is used in teams. 8. The major AI tools that are most useful are NLP and voice response; AI can also

summarize the key topics of meetings and understand participants’ needs. AI can be aware of organizational goals and workers’ skills and can make suggestions accordingly.

For how virtual meetings are supported with AI by Cisco Systems in their leading products, see Technology Insight 11.2.

TECHNOLOGY INSIGHT 11.2 How Cisco Improves Collaboration with AI

Cisco Systems is well known for its collaboration products such as Spark and Webex. The first step in introducing AI was to acquire MindMeld’s AI platform for use in Cisco’s collaboration products. The project’s objective was to improve the conversational interferences for any application or device so users could better understand the context of conversations. MindMeld uses machine learning to improve the accuracy of voice and text communication. To do so, it uses NLP and five varieties of machine learning. Cisco is also integrating IBM Watson into its enterprise collabora- tion solutions. As you may recall from Chapter 6, Watson is a powerful advisor. AI collaboration tools can increase efficiency, speed idea generation, and improve the quality of decisions made by groups. The improved Cisco’s technology will be used in conference rooms and everywhere else. One of the major AI projects is the assistant to Spark.

Monica, a Digital Assistant to the Spark Collaboration Platform Monica is trained to answer users’ queries by employing machine learning. Furthermore, users can use Monicait to interact with the Spark collaboration platform using natural language com- mands. It is an enterprise assistant similar to Alexa and Google Assistant (Chapter 12). Cisco’s Monica is the world’s first enterprise-ready voice assistant specifically designed to support meetings. The bot has deep-domain conversational AI that adds cognitive capabilities to the Spark platform.

Monica can assist users in several of the steps of Figure 11.1, such as:

• Organize meetings. • Provide information to participants before and during meetings. • Navigate and control Spark’s devices. • Help organizers find a meeting room and reserve it. • Help share screens and bring up a whiteboard. • Take meeting notes and organize them.

In the near future, Monica will know about participants’ internal and external activities and will schedule meetings using this information. Additional functions to support more steps of the pro- cess in Figure 11.1 will be added in the future.

For more about the assistant, see youtube.com/watch?v=8OcFSEbR_6k (5:10 minutes).

Note: Cisco Spark will become Webex Teams with more AI functionalities. In addition, Webex meetings will include videoconferencing for collaboration and other supports to meetings.

Sources: Compiled from Goecke (2017), Finnegan (2018), and Goldstein (2017).

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 639

Swarm Intelligence and Swarm AI

The term swarm intelligence refers to the collective behavior of decentralized, self- organized systems, natural or artificial (per Wikipedia). 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 nature, there are many examples (e.g., ant colonies, fish schools) of such behaviors.

Natural groups were observed to amplify their group intelligence by forming swarms. Social creatures, including people, can improve the performance of their individual mem- bers when working together as a unified system. In contrast with animals and other species whose interactions among group members are natural, people need technology to exhibit swarm intelligence. This concept is used in studies and implementation of AI and robotics. The major applications are in the area of predictions.

Example

A study at Oxford University (United Kingdom) involved predicting the results of all 50 English Premier League soccer games over five weeks. A group of independent judges scored 55 percent accuracy when working alone. However, when predicting using an AI swarm, their prediction success increased to 72 percent (an improvement of 31 percent). Similar improvement was recorded in several other studies.

In addition to improved prediction accuracy, studies show that using swarm AI results in more ethical decisions than that of individuals (Reese, 2016).

SWARM AI TECHNOLOGY Swarm AI (or AI swarm) provides the algorithms for the inter- connections among people creating the human swarm. These connections enable the knowledge, intuition, experience, and wisdom of individuals to merge into single improved swarm intelligence. Results of swarm intelligence can be seen in the TED presentation (15:58 min.) at youtube.com/watch?v=Eu-RyZt_Uas. Swarm AI is used by several third- party companies (e.g., Unanimous.aI, as illustrated in Application Case 11.3.

XPRIZE is a nonprofit organization that allocates prizes via competitions to promote innovations that have the potential to change the world for the bet- ter. The main channel for designing prizes that solve humanity’s grandest challenges is called Visioneer- ing. It attempts to harness the power of the global crowd to develop solutions to important challenges. The organization’s major event is an annual summit meeting where prizes are designed and proposals are evaluated. The experts at XPRIZE develop concepts and turn them into incentivized competitions. Prizes are donated by leading corporations.

For example, in 2018, IBM Watson donated a $5 million prize called “AI approaches and collabora- tion.” The competition had 142 registered teams, and 62 were left in round 2 in June 2018. The teams are

invited to create their own goals and solutions to a grand challenge.

The Problem

Every year, there is a meeting of 250 members of “Visioneers Summit Ideation” where top experts (entrepreneurs, politicians, scientists, etc.), partici- pate to discover and prioritize topics for the XPRIZE agenda.

Finding the top global problems can be a very complex challenge due to a large number of vari- ables. In just a few days, top experts need to use their collective wisdom to agree on the next year’s XPRIZE top challenges. The method used to support the group’s decision is a critical success factor.

(Continued )

Application Case 11.3 XPRIZE Optimizes Visioneering

640 Part IV • Robotics, Social Networks, AI and IoT

The Solution

In the 2017 annual meeting for determining what challenge to use for 2018, the organization used the swarm AI platform (from Unanimous AI). Several small groups (swarms) moderated by AI algorithms were created to discover challenging topics. The mis- sion was to explore ideas and agree on preferred solutions. The objective was to use the talents and brainpower of the participants.

In other words, the objective was to use the thinking together feature of swarm AI to generate each group’s synergy with the AI algorithms acting as moderators. This way, smarter decisions were generated by the groups than its individual par- ticipants. The different groups examined six pre- selected topics: energy and infrastructure, learning human potential, space and new frontiers, plant and environment, civil society, and health and well-being. The groups brainstormed the issues. Then, each participant created a customized evalu- ation table. The tables were combined and ana- lyzed by algorithms.

Application Case 11.3 (Continued)

The Swarm AI replaced traditional voting meth- ods by optimizing the detailed contribution of each participant.

The Results

Use of swarm AI did the following:

• Supported the generation of optimized answers and enabled fast buy-in from the participants.

• Enabled all participants to contribute. • Provided a better voting system than in previ-

ous years.

Questions for Case 11.3 1. Why is the group discussion in this case complex?

2. Why is getting a consensus when top experts are involved more difficult than when non-experts are involved?

3. What was the contribution of swarm AI?

4. Compare simple voting to swarm AI voting.

Sources: Compiled from Unanimous AI (2018), xprize.org, and xprize.org/about.

SWARM AI FOR PREDICTIONS Swarm AI was used by Unanimous AI for making predic- tions in difficult-to-assess situations. Examples are:

• Predicting Super Bowl #52 number of points scored (used for spread waging). • Predicting winners in the regular NFL season. • Predicting the top four finishers of the 2017 Kentucky Derby. • Predicting the top recipients of the Oscars in 2018.

u SECTION 11.8 REVIEW QUESTIONS

1. Relate the use of AI to the activities in Figure 11.1. 2. Discuss the different ways that AI can facilitate group collaboration. 3. How can AI support group evaluation of ideas? 4. How can AI facilitate idea generation? 5. What is the analogy of swarm AI to swarms of living species? 6. How is swarm AI used to improve group work and to initiate group predictions?

11.9 HUMAN–MACHINE COLLABORATION AND TEAMS OF ROBOTS

Since the beginning of the Industrial Revolution, people and machines have worked together. Until the late 1900s, the collaboration was in manufacturing. But since then, due to advanced technology and changes in the nature of work, human–machine collabora- tion has spread to many other areas, including performing mental and cognitive work

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 641

and collaborating on managerial and executive work. According to Nizri (2017), human and AI collaboration will shape the future of work (see also Chapter 14).

Humans and machines can collaborate in many ways, depending on the tasks they perform. The collaboration with robots in the manufacturing scenario is an extension of the older model in which humans and robots collaborated with humans controlling and monitoring production and robots doing physical work that requires speed, power, accuracy, or nonstop attention. Robots are also doing work in hazardous environments. In general, robots complement human capabilities. An example is Amazon’s distribution centers where over 50,000 mobile robots do a variety of tasks, mostly in hauling materials and helping to fulfill customer orders. The robotic technology enables fully collaborative solutions. For details, watch the video at Kuka kuka.com/en-us/technologies/human- robot-collaboration. Kuka’s system allows the execution of complex jobs that can be done cost effectively.

Another collaborative human-robotic system is called YuMi. To see this sys- tem (from ABB Robotics) at work, watch the 4:38 min. video at youtube.com/ watch?v=2KfXY2SvlmQ. Notice that the robot has two arms.

Human–Machine Collaboration in Cognitive Jobs

Advancement in AI enables the automation of nonmanual activities. While some intelligent systems are fully automated (see automated decision making in Chapter 2 and chatbots in Chapter 12), there are many more examples of human–machine collaboration in cognitive jobs (e.g., in marketing and finance). An example is in investment decisions. A human asks the computer for advice concerning investments, and after receiving the advice, can ask more questions, changing some of the input. The difference from the past is that today the computers (machines) can provide much more accurate suggestions, by using machine learning and deep learning. Another collaboration example involves medical diagnoses of complex situations. For example, IBM Watson provides medical advice, which permits doctors and nurses to significantly improve their jobs. Actually, the entire field of machines advising humans is reaching new heights. For more on the increasing collaborative power of AI, see Carter (2017).

TOP MANAGEMENT JOBS A major task of managers is decision making, which has become one area of human–machine collaboration. Use of AI and analytics has improved decision making considerably, as illustrated throughout this book. For an overview, see Wladawsky-Berger (2017).

McKinsey & Company and MIT are two major players in researching the topic of col- laboration between managers and machines. For example, Dewhurst and Wilmott (2014) report on its increased use of man-machine collaboration, using deep learning. A Hong Kong company even appointed a decision-making algorithm to its board of directors. Com- panies are using crowdsourcing advice to support complex problem solving, as illustrated in Section 11.7.

Robots as Coworkers: Opportunities and Challenges

Sometime in the future, walking and talking humanoid robots will socialize with humans during breaks from work. Someday, robots will become cognitive coworkers and help people be more productive (as long as people do not talk too much with the robots).

According to Tobe (2015), a study at a BMW factory found that human–robot col- laboration could be more productive than either humans or robots working by themselves. Also, the study found that collaboration reduced idle time by 85 percent. This is because people and machines capitalize on the strengths of each (Marr, 2017).

642 Part IV • Robotics, Social Networks, AI and IoT

The following challenges must be considered:

• Designing a human–machine team that capitalizes on the strength of each partner. • Exchanging information between humans and robots. • Preparing company employees in all departments for the collaboration (Marr,

2017). • Changing business processes to accommodate human–robot collaboration (Moran,

2018). • Ensuring the safety of robots and employees that work together.

TECHNOLOGIES THAT SUPPORT ROBOTS AS COWORKERS Yurieff (2018b) lists the following examples of facilitating or considering robots as coworkers.

1. Virtual reality can be used as a powerful training tool (e.g., for safety). 2. A robot is working with an ad agency in Japan to generate ideas. 3. A robot can be your boss. 4. Robots are coworkers in providing parts out of bins in assembly lines and can check

quality together with humans. 5. AI tools measure blood flow and volume of the cardiac muscles in seconds (instead

of minutes when done completely by a radiologist). This information facilitates the decisions made by radiologists.

BLENDING HUMANS AND AI TO BEST SERVE CUSTOMERS Genesys Corp. commissioned Forrester Research Company to conduct a global study in 2017 to find how companies are using AI to improve customer service. The study, titled “Artificial Intelligence with the Human Touch,” is available at no charge from genesys.com/resources/artificial- intelligence-with-the-human-touch. A related video is available at youtube.com/ watch?v=NP2qqwGTNPk.

The study revealed the following:

1. “AI is already transforming enterprises by increasing worker efficiency and produc- tivity, delivering better customer experiences and uncovering new revenue streams” (from the Executive Summary).

2. A major objective of man–machine collaboration is to improve the satisfaction of both customers and companies’ agents rather than reduce cost.

3. Human agents’ ability to connect emotionally with customers for the increased satisfaction of themselves and customers is superior to that of service provided by AI.

4. By blending the strengths of humans and AI, companies achieve better customer service satisfaction of customers (71 percent) and agents (69 percent).

Note that AI excels in the support of marketing and advertising as illustrated in Chapter 2. See also Loten (2018) for the use of AI to support customer relationship management (CRM) and of crowdsourcing and collective intelligence to support marketing.

COLLABORATIVE ROBOTS (CO-BOTS) Collaborative robots (co-bots) are designed to work with people, assisting in executing various tasks. These robots are not very smart, but their low cost and high usability make them popular. For details, see Tobe (2015).

Teams of collaborating Robots

One of the future directions in robotics is creating teams of robots that are designed to do complex work. Robot teams are common in manufacturing where they serve each other or join a robot group in simple assembly jobs. An interesting example is the use of a team of robots in preparation to land on Mars.

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 643

Example: Teams of Robots to Explore Mars

Before people land on Mars, scientists need to know more about the “Red Planet.” The idea was to use teams of robots. The German Research Centers for Artificial Intelligence (DFKI) conducted simulation experiments in the desert of Utah. The details of this simulation are described by Staff Writers (2016). The process is illustrated in a 4:54 min. video at youtube. com/watch?v=pvKIzldni68/ showing robots’ collaboration. For more information, see robotik.dfki-bremen.de/en/research/projects/ft-utah.html.

DFKI is not the only entity that plans to explore the surface of Mars. NASA plans to send swarms of robot bees with flapping wings called Marsbees that will operate in a group to explore the land and air of the Red Planet. The reason for the flapping wings structure is to enable low-energy flights (like bumblebees). Each robot is the size of a bee. Part of a wireless communication network, Marsbees will together create networks of sensors. Information will be delivered to a mobile base (see Figure 11.4, showing one robot) that will be the main communication center and a recharging station for the Marsbees. For more information, see Kang (2018).

Getting robots to work together is being researched at MIT. They use their per- ception system to sense the environment, and then they communicate their findings to each other and coordinate their work. For example, a robot can open a door for another robot. Read about how this is done and watch a video at ft.com/video/ ea2d4877-f3fb-403d-84a8-a4d2d4018c5e.

Example

Alibaba.com is using teams of robots in its smart warehouses where robots do 70 percent of the work. This is shown in a video at youtube.com/watch?v=FBl4Y55V2Z4.

Social collaboration of robots is being investigated by watching the behavior of swarms of ants and other species to learn how to design robots to work in teams. Watch the TED presentation at youtube.com/watch?v=ULKyXnQ9xWA on how to design a robot collaboration.

Having robots collaborate involves several issues such as making sure they do not col- lide with each other. This is a part of the safety issue regarding robotics. Finally, you can build your own team of robots with LEGO’s Mindstorms. For details, see Hughes and Hughes (2013).

FIGURE 11.4 Team of Robots Prepares to Go to Mars. Source: C. Kang.

644 Part IV • Robotics, Social Networks, AI and IoT

Chapter Highlights

• Groupware refers to software products that pro- vide collaborative support to groups (including conducting meetings).

• Groupware can support decision-making and problem solving directly or indirectly by improv- ing communication between team members.

• People collaborate in their work (called group work). Groupware (i.e., collaborative computing software) supports group work.

• Group members may be in the same organiza- tion or in different organizations in the same or in different locations and may work at the same or different times.

• The time/place framework is a convenient way to describe the communication and collaboration pat- terns and support of group work. Different tech- nologies can support different time/place settings.

• Working in groups can result in many benefits, including improved decision making, increased productivity and speed, and cost reductions.

• Communication can be synchronous (i.e., same time) or asynchronous (i.e., sent and received at different times).

• The Internet, intranets, and IoT support virtual meetings and decision making through collabora- tive tools and access to data analysis, information, and knowledge.

• Groupware for direct support typically contains capabilities for brainstorming, conferencing, scheduling group meetings; planning; resolving conflicts; videoconferencing; sharing electronic documents; voting; formulating policy; and ana- lyzing enterprise data.

• A GDSS is any combination of hardware and software that facilitates decision-making meet- ings. It provides direct support in face-to-face settings and in virtual meetings, attempting to increase process gains, and reducing process losses of group works.

• Collective intelligence is based on the premise that the combined wisdom of several collabo- rating people is greater than that of individuals working separately.

• Each of the several configurations of collective intelligence can be supported differently by technology.

• Several collaboration platforms, such as Micro- soft Teams and Slack, can facilitate collective intelligence.

• Idea generation and brainstorming are key activ- ities in group work for decision making. Several collaboration software and AI programs are sup- porting these activities.

• Crowdsourcing is a process of outsourcing work to a crowd. Doing so can improve problem solving, idea generation, and other innovative activities.

• Crowdsourcing can be used to make predictions by groups of people, including crowds. Results have shown better predictions, especially when communication is used among the predictors than when no communication was enabled.

• One method of communication in crowdsourc- ing is based on swarm intelligence. A technol- ogy known as swarm AI has had significant success.

• AI can support many activities in group deci- sion making.

• Human–machine collaboration can be a major method of work in the future.

• Machines that once supported manufacturing work are used now also in support of cogni- tive, including managerial, work.

• For people and machines to work in teams, it is necessary to make special preparations.

• Robots may work in exclusive teams. They do so in manufacturing and possibly in other activities (e.g., explore Mars) as they become more intelligent.

u SECTION 11.9 REVIEW QUESTIONS

1. Why is there an increase in human–machine collaboration? 2. List some benefits of such collaboration. 3. Describe how collaborating robotics can be used in manufacturing. 4. Discuss the use of teams of robots. 5. What will do robots on Mars?

Chapter 11 • Group Decision Making, Collaborative Systems, and AI Support 645

Exercises

1. Go to realtimeboard.com. How can the site support idea creation and brainstorming?

2. Investigate how researchers are trying to develop collab- orative computer systems that portray or display nonver- bal communication factors (e.g., images).

3. For each of the software packages Skype Business and WebEx, check the trade literature and the Web for details and explain how each includes computerized collabora- tive support system capabilities.

4. Compare Simon’s four-phase decision-making model to the steps in using GDSS.

5. A major claim in favor of wikis is that they can replace e-mail, eliminating its disadvantages (e.g., spam). Go to socialtext.com and review such claims. Find other sup- porters of switching to wikis. Then find counter argu- ments and conduct a debate on the topic.

6. Search the Internet to identify sites that describe methods for making meetings more effective and efficient.

7. Enter MIT Center for CI and review some of its recent activities. Write a report.

8. Debate the issue of the quality of crowdsourc- ing results. Start by viewing youtube.com/ watch?v=JJHAHQmiI3c.

9. Find information about Yammer (a Microsoft company). Why is it considered a social collaboration tool? Why is it popular? Write a report.

10. Enter Dropbox.com and find its collaboration tools. Write a summary.

11. Read Pena (2017). Examine the 12 benefits of collabora- tion. Which are related to social collaboration?

12. Compare Microsoft’s Universal Translator to Google’s Translator. Concentrate on face-to-face conversation in real time.

13. Write a report on the issue of whether crowdsourcing produces superior decisions. Use Quora for help. Find other sources.

14. Investigate the status of IBM Connections Cloud. Exam- ine all the collaboration and communication features. How does the product improve productivity? Write a report.

Key Terms

Questions for Discussion

1. Explain why it is useful to describe group work in terms of the time/place framework.

2. Describe the kinds of support that groupware can pro- vide to decision makers.

3. Explain why most groupware is deployed today over the Web.

4. Explain in what ways physical meetings can be ineffi- cient. Explain how technology can make meetings more effective.

5. Explain how GDSS can increase some benefits of col- laboration and decision making in groups and eliminate or reduce some losses.

6. The initial term for group support system (GSS) was group decision support system (GDSS). Why was the

word decision dropped? Does this make sense? Why, or why not?

7. Discuss why Microsoft SharePoint is considered a work- space. What kind of collaboration does it support?

8. Reese (2017) claims that swarm AI can be used instead of polls for market research. Discuss the advantages of swarm AI. In what circumstances would you prefer each method? (Read “Polls vs. Swarms” at Unanimous AI.)

9. What is a collaborative robot? What is an uncollabora- tive one?

10. Discuss the ways in which social collaboration can improve work in a digital workplace.

11. Provide an example of using analytics to improve deci- sion making in sport.

asynchronous brainstorming collective intelligence collaborative workspace crowdsourcing decision room

group decision making group decision support

system (GDSS) group support system

(GSS) groupthink

groupware group work idea generation online workspace process gain process loss

swarm intelligence synchronous (real-time) virtual meeting virtual team

646 Part IV • Robotics, Social Networks, AI and IoT

15. Compare Microsoft Teams to Spark Teams. Write a report.

16. Enter crowdtap.com and read Kurzer (2018) paper. Explain how the platforms work. Relate the material

about crowdsourcing and collective intelligence. Write a report.

17. Go to technologyreview.com and look at the May 8, 2017, video (17:42 min.) “Next Generation Human- Machine Collaboration.” Write a report.

References

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C H A P T E R

12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors

LEARNING OBJECTIVES

■■ Describe recommendation systems ■■ Describe expert systems ■■ Describe chatbots ■■ Understand the drivers and capabilities of chatbots and their use

■■ Describe virtual personal assistants and their benefits

■■ Describe the use of chatbots as advisors ■■ Discuss the major issues related to the implementation of chatbots

Advancement in artificial intelligence (AI) technologies and especially natural lan-guage processing (NLP), machine and deep learning and knowledge systems, coupled with the increased quality and functionalities of other intelligent systems, and mobile devices and their apps, have driven the development of chatbots (bots) for inexpensive and fast execution of many tasks related to communication, collaboration, and information retrieval. The use of chatbots in business is increasing rapidly, partly be- cause of their fit with mobile systems and devices. As a matter of fact, sending messages is probably the major activity in the mobile world.

In the last two to three years, many thousands of bots have been placed into ser- vice worldwide by both organizations (private and public) and individuals. Many people refer to these phenomena as the chatbot revolution. Chatbots today are much more so- phisticated than those of the past. They are extensively used, for example, in marketing; customer, government, and financial services; healthcare; and in manufacturing. Chatbots make communication more personal than faceless computers and excel in data gathering. Chatbots can stand alone or be parts of other knowledge systems.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 649

We divide the applications in this chapter into four categories: expert systems, chat- bots for communication and collaboration, virtual personal assistants (native products, such as Alexa), and chatbots that are used as professional advisors. Some implementation topics of intelligent systems are described last.

This chapter has the following sections:

12.1 Opening Vignette: Sephora Excels with Chatbots 649 12.2 Expert Systems and Recommenders 650 12.3 Concepts, Drivers, and Benefits of Chatbots 660 12.4 Enterprise Chatbots 664 12.5 Virtual Personal Assistants 672 12.6 Chatbots as Professional Advisors (Robo Advisors) 676 12.7 Implementation Issues 680

12.1 OPENING VIGNETTE: Sephora Excels with Chatbots

THE PROBLEM

Sephora is a French-based cosmetics/beauty products company doing business globally. It has its own stores and sells its goods in cosmetic and department stores. In addition, Sephora sells online on Amazon and on its online store. The company sells hundreds of brands, including many of its own. It operates in a very competitive market where cus- tomer care and advertising are critical. Sephora sells some products for men, but most beauty products are targeted to women.

THE SOLUTION

Sephora’s first use of chatbots occurred through messaging services. The purpose of the first bot was to search for information for the company’s resources such as videos, images, tips, and so on. This bot operates in a question-and-answer (Q&A) mode. It rec- ommends relevant content based on customers’ interests. The company aims to appeal to young customers messaging on Kik.

Sephora researchers found that customers conversing with the Kikbot were engaged deeply in the dialog. Then the bot encouraged them to explore new products. Sephora’s newer bot called Reservation Assistant was placed on Facebook Messenger. It enables customers to book or reschedule makeover appointments.

Another Sephora bot delivered on Kik is Shade-Matching. It matches lips colors to photos (face and lips) uploaded by users and recommends the best match to them. The bot also lets users try on photos of recommended colors, using Sephora Virtual Artist that runs on Facebook Messenger. Bots are deployed as mobile apps. If users like the recom- mendation, they are directed to the company’s Web store to buy the products. Users can upload photos taken with selfies so that the program can do the matching. Over 4 million visitors tried 90 million shades in the first year of Virtual Artist’s operation.

The Q&A collection of the knowledge base was built by connecting it with store experts. Knowledge acquisition techniques (Chapter 2) were used for this purpose. The company’s bots use NLPs that were trained to understand the typical vocabulary of users.

THE RESULTS

The company’s customers loved the bots. In addition, Sephora learned the importance of providing assistance and guidance to users who are motivated to return (at a reasonable cost!), happier, and more engaged.

650 Part IV • Robotics, Social Networks, AI and IoT

Sephora’s bot asks users questions to find their tastes and preferences. Then it acts like a recommendation system (Section 12.2), offering products. Kik and Messenger users can purchase items without leaving the messaging service.

Finally, the company has improved the bots’ knowledge over time and plans new bots for additional tasks.

Note: Sephora was selected by Fast Company Magazine, March/April 2018, as one of the “World’s Most Innovative Companies.” Sephora is known for its digital transformation and innovation (Rayome, 2018). Also, Sephora’s bots are considered among the top marketing chatbots (Quoc, 2017).

Sources: Compiled from Arthur (2016), Rayome (2018), and Taylor (2016), theverge.com/2017/3/16/14946086/ sephora-virtual-assistant-ios-app-update-ar-makeup/, and sephora.com/.

u QUESTIONS FOR THE OPENING VIGNETTE

1. List and discuss the benefits of bots to the company. 2. List and discuss the benefits of bots to customers. 3. Why were the bots deployed via Messenger and Kik? 4. What would happen to Sephora if competitors use a similar approach?

WHAT WE CAN LEARN FROM THIS VIGNETTE

In the highly competitive world of retail beauty products, customer care and marketing are critical. Using only live employees can be very expensive. In addition, customers are shopping 24/7, and physical stores are open during limited hours and days. In addi- tion, there are large combinations of certain beauty products (e.g., many shades/colors) available. Sephora decided to use chatbots on Facebook Messenger and Kik to engage its customers. Chatbots, the subject of this chapter, are available 24/7 at a lower cost and are delivered via mobile devices. Bots deliver information to customers consistently and quickly direct customers to easy online shopping. Sephora placed its chatbots on messag- ing services. The logic was that people like to chat with friends on messaging services, and they may also like to chat with businesses.

In addition to several services to customers, using chatbots helps Sephora learn about customers. This type of chatbot is the most common type for customer care and market- ing. In this chapter, we cover several other types of knowledge systems, including the pioneering expert systems, recommenders, virtual personal assistants offered by several large technology companies, and robo advisors.

12.2 EXPERT SYSTEMS AND RECOMMENDERS

In Chapter 2 we introduced the reader to the concept of autonomous decision systems. An expert system is a category of autonomous decision systems and are considered the earliest applications of AI. Expert systems use started in research institutions in the early and mid- 1960s (e.g., Stanford University, IBM) and was adopted commercially during the 1980s.

Basic Concepts of Expert Systems (ES)

The following are the major concepts related to ES technology.

DEFINITIONS There are several definitions of expert systems. Our working definition is that an expert system is a computer-based system that emulates decision making and/or problem solving of human experts. These decisions and problems are in complex areas

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 651

that require expertise to solve. The basic objective is to enable nonexperts to make deci- sions and solve problems that usually require expertise. This activity is usually performed in narrowly defined domains (e.g., making small loans, providing tax advice, analyzing reasons for machine failure). Classical ES use “what-if-then” rules for their reasoning.

EXPERTS An expert is a person who has the special knowledge, judgment, experience, and skills to provide sound advice and solve complex problems in a narrowly defined area. It is an expert’s job to provide the knowledge about how to perform a task so that a nonexpert will be able to do the same task assisted by ES. An expert knows which facts are important and understands and explains the dependent relationships among those facts. In diagnosing a problem with an automobile’s electrical system, for example, an expert car mechanic knows that a broken fan belt can be the cause for the battery to discharge.

There is no standard definition of expert, but decision performance and the level of knowledge a person has are typical criteria used to determine whether a particular person is an expert as related to ES. Typically, experts must be able to solve a problem and achieve a performance level that is significantly better than average. An expert at one time or in one region may not be an expert in another time or region. For example, a legal expert in New York may not be one in Beijing, China. A medical student may be an expert compared to the general public but not in making a diagnosis or performing sur- gery. Note that experts have expertise that can help solve problems and explain certain obscure phenomena only within a specific domain. 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 (i.e., going outside the general norms) if necessary. • Determining relevance and associations.

Can a machine help a nonexpert perform like an expert? Can a machine make autonomous decisions that experts make? Let us see. But first, we need to explore what expertise is.

EXPERTISE An expertise is 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. It includes ex- plicit knowledge, such as theories learned from a textbook or a classroom and implicit knowledge gained from experience. The following is a list of possible knowledge types used in ES 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.

652 Part IV • Robotics, Social Networks, AI and IoT

Expertise often includes the following characteristics:

• It is usually associated with a high degree of intelligence, but it is not always as- sociated 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.

Characteristics and Benefits of ES

ES were used during the period 1980 to 2010 by hundreds of companies worldwide. However, since 2011, their use has declined rapidly, mostly due to the emergence of bet- ter knowledge systems, three types of which are described in this chapter. It is important, however, to understand the major characteristics and benefits of expert systems since many of them evolved evidenced newer knowledge systems.

The major objective of ES is the transfer of expertise to a machine. The expertise will be used by nonexperts. A typical example is a diagnosis. For example, many of us can use self-diagnosis to find (and correct) problems in our computers. Even more than that, computers can find and correct problems by themselves. One field in which such ability is practiced is medicine, as described in the following example:

Example: Are You Crazy?

A Web-based ES was developed in Korea for people to self-check their mental health sta- tus. Anyone in the world can access it and get a free evaluation. The knowledge for the system was collected from a survey of 3,235 Korean immigrants. The results of the survey were analyzed and then reviewed by experts via focus group discussions. For more infor- mation, see Bae (2013).

BENEFITS OF ES Depending on the mission and structure of ES, the following are their capabilities and potential benefits:

• 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 (e.g., 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.

These benefits can provide a significant competitive advantage to companies that use ES. Indeed, some companies have saved considerable amounts of money using them.

Despite these benefits, the use of ES is on the decline. The reasons for this and the related limitations are discussed later in this section.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 653

Typical Areas for ES Applications

ES have been applied commercially in a number of areas, including the following:

• Finance. Finance ES include analysis of investments, credit, and financial reports; evaluation of insurance and performance; tax planning; fraud prevention; and finan- cial planning.

• Data processing. Data processing ES include system planning, equipment selec- tion, equipment maintenance, vendor evaluation, and network management.

• Marketing. Marketing ES include customer relationship management, market research and analysis, product planning, and market planning. Also, presale advice is provided for prospects.

• Human resources. Examples of human resource ES are planning, performance evaluation, staff scheduling, pension management, regulatory advising, and design of questionnaires.

• Manufacturing. Manufacturing ES include production planning, complex prod- uct configuration, quality management, product design, plant site selection, and equipment maintenance and repair (including diagnosis).

• Homeland security. These ES include terrorist threat assessment and terrorist finance detection.

• Business process automation. ES have been developed for desk automation, call center management, and regulation enforcement.

• Healthcare management. ES have been developed for bioinformatics and other healthcare management issues.

• Regulatory and compliance requirements. Regulations can be complex. ES are using a stepwise process to ensure compliance.

• Web site design. A good Web site design requires paying attention to many vari- ables and ensures that performance is up to standard. ES can lead to a proper design process.

Now that you are familiar with the basic concepts of ES, it is time to look at the internal structure of ES and how their goals are achieved.

Structure and Process of ES

As you may recall from Section 2.5 and Figure 2.5, the process of knowledge extraction and its use is divided into two distinct parts. In ES we refer to these as the development environment and the consultation environment (see Figure 12.1). An ES builder builds the necessary ES components and loads the knowledge base with appropriate repre- sentation of expert knowledge in the development environment. A nonexpert uses the consultation environment to obtain advice and solve problems using the expert knowledge embedded into the system. These two environments are usually separated.

MAJOR COMPONENTS OF ES The major components in typical expert systems include:

• Knowledge acquisition. Mostly from human experts, is usually obtained by knowledge engineers. This knowledge, which may derive from several sources, is integrated, validated, and verified.

• Knowledge base. This is a knowledge repository. The knowledge is divided into knowledge about the domain and knowledge about problem solving and solu- tion procedures. Also, the input data provided by the users may be stored in the knowledge base.

• Knowledge representation. This is frequently organized as business rules (also known as production rules).

654 Part IV • Robotics, Social Networks, AI and IoT

These major components of ES generate useful solutions in many areas. Remember that these areas need to be well structured and in fairly narrow domains. Less common is a justifier/explanation subsystem that shows users of rule-based systems the chains of rules used to arrive at conclusions. Also, least common is a knowledge refining subsystem that helped to improve knowledge (e.g., rules) when new knowledge is added.

A major provider of expert systems technologies was Exsys Inc. While the company is no longer active in this business, its Web site (Exsys.com) is. It contains tutorials and a large number of cases related to its major software product, Exsys Corvid. Application Case 12.1 is one example.

Human Expert(s)

Other Knowledge

Sources

Knowledge Base(s)

(Long Term)

Information Gathering

Knowledge Elicitation

De ve

lop m en

t

En vir

on m en

t

Co ns

ult at

ion

En vir

on m en

t

Knowledge Engineer

Knowledge Rules

Inference Engine

Explanation Facility

Knowledge Refinement

Blackboard (Workspace)

Inferencing Rules

Rules Firing

Refined Rules

Working Memory

(Short Term)

External Data Sources

Data/InformationFacts

User User Interface

Facts

Questions/ Answers

FIGURE 12.1 General Architecture of Expert Systems.

• Inference engine. Also known as the control structure or the rule interpreter, this is the “brain” of ES. It provides the reasoning capability, namely the ability to answer users’ questions, provide recommendations for solutions, generate predic- tions, and conduct other relevant tasks. The engine manipulates the rules by either forward chaining or backward chaining. In 1990s ES started to use other inference methods.

• User interface. This component allows user inference engine interactions. In clas- sical ES, this was done in writing or by using menus. In today’s knowledge sys- tems, it is done by natural languages and voice.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 655

Terrorist attacks using chemical, biological, or radio- logical (CBR) agents are of great concern due to their potential for leading to large loss of life. The United States and other nations have spent billions of dollars on plans and protocols to defend against acts of terrorism that could involve CBR. However, CBR covers a wide range of input agents with many specific organisms that could be used in multiple ways. Timely response to such attacks requires rapid identification of the input agents involved. This can be a difficult process involving different methods and instruments.

The U.S. Environmental Protection Agency (EPA) along with Dr. Lawrence H. Keith, president of Instant Reference Sources Inc. and other consultants, have incorporated their knowledge, experience, and expertise as well as information in publicly available EPA documents to develop the CBR Advisor using Exsys Inc.’s Corvid software.

One of the most important parts of the CBR Advisor is providing advice in logical step-by- step procedures to determine the identity of a toxic agent when little or no information is avail- able, which is typical at the beginning of a terror- ist attack. The system helps response staff proceed according to a well-established action plan even in such a highly stressful environment. The sys- tem’s dual screens present three levels of informa- tion: (1) a top/executive level with brief answers, (2) an educational level with in-depth information, and (3) a research level with links to other docu- ments, slide shows, forms, and Internet sites. CBR Advisor’s content includes:

Restricted content includes CBR agents and methods for analyzing them. The CBR Advisor can be used for incident response and/or training. It has two different menus, one for emergency response and another, longer menu for training. It is a restricted software program and is not publicly available.

Questions for Case 12.1

1. How can the CBR Advisor assist in making quick decisions?

2. What characteristics of the CBR Advisor make it an expert system?

3. What could be other situations in which similar expert systems can be employed?

Expert systems are also used in high-pressure situa- tions in which human decision makers often need to take split-second actions involving both subjective as well as objective knowledge in responding to emer- gency situations.

Sources: www.exsys.com “Identification of Chemical, Biological and Radiological Agents” http://www.exsyssoftware.com/CaseStudy Selector/casestudies.html. April 2018. (Publicly available informa- tion.) Used with permission.

Application Case 12.1 ES Aid in Identification of Chemical, Biological, and Radiological Agents

Why the Classical Type of ES Is Disappearing

The large benefits described earlier drove the implementation of many ES worldwide. However, like many other technologies, the classical ES have been replaced by better sys- tems. Let us first look at some of the limitations of ES that contributed to its declining use.

1. The acquisition of knowledge from human experts has proven to be very expensive due to the shortage of good knowledge engineers as well as the possible need to interview several experts for one application.

2. Any acquired knowledge needed to be updated frequently at a high cost. 3. The rule-based foundation was frequently not robust and not too reliable or flexible

and could have too many exceptions to the rules. Improved knowledge systems use

• How to classify threat warnings. • How to conduct an initial threat evaluation. • What immediate response actions to take. • How to perform site characterization. • How to evaluate the initial site and safe entry

to it. • Where and how to best collect samples. • How to package and ship samples for analysis.

656 Part IV • Robotics, Social Networks, AI and IoT

data-driven and statistical approaches to make the inferences with better success. In addition, case-based reasoning could work better only if a sufficient number of similar cases were available. So, usually it cannot support ES.

4. The rule-based user-interface needed to be supplemented (e.g., by voice communi- cation, image maps). This could make ES too cumbersome.

5. The reasoning capability of rule-based technology is limited compared to use of newer mechanisms such as those used in machine learning.

NEW GENERATION OF EXPERT SYSTEMS Instead of using the old knowledge acquisi- tion and representation system, newer ES based on machine learning algorithms and other AI technologies are deployed to create better systems. An example is provided in Application Case 12.2.

VisiRule is an older ES company that remodeled its business over time. VisiRule (of the United Kingdom) provides easy-to-use diagramming tools to facilitate the construction of ES. Diagramming allows easier extraction and use of knowledge in expert systems.

The process of building the knowledge base can be seen on the left side of Figure 12.2. On the left-hand side, you can see the hybrid creation. Using a decision tree, the domain experts can cre- ate additional rules directly from relevant data (e.g.,

historical). In addition, rules can be created by machine learning (lower left side).

The right-hand side (upper corner) illustrates the hybrid delivery (consultation). Using interac- tive questions and answers the system can gener- ate advice. In addition, rules can be used to process data remotely and update the data repository. Note that the dual delivery option is based on machine learning’s ability to discover hidden patterns in data that can be used to form predictive decision models.

Application Case 12.2 VisiRule

Domain Expert draws rules as

decision tree using VisiRule Author

Expert Systems deployed using

interactive questionnaire

Rules are created from data using Machine Learning

Rules are used to process data remotely and

update database

Human Expert Interactive

Machine Learning

Data- Driven

Hybrid Creation

Hybrid DeliveryVisiRule

FIGURE 12.2 The Process of Recommendation Systems.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 657

Three major AI types of applications that overcome the earlier discussed limitations of RS are chatbots, virtual personal assistants, and robo advisors, which are presented next in this chapter. Other AI technologies that perform similar activities are presented in Chapters 4 to 9. Most notable is IBM Watson (Chapter 6); some of its advising capabilities are similar to those of ES but are much superior.

Another similar AI technology, the recommendation system, is presented next. Its newer variations use machine learning and IBM Watson Analytics.

Recommendation Systems

A heavily used knowledge system for recommending one-to-one targeted products or services is the recommendation system, also known as recommender system or recommendation engine. Such a system tries to predict the importance (rating or pref- erence) that a user will attach to a product or service. Once the rating is known, a vendor knows users’ tastes and preferences and can match and recommend a prod- uct or service to the user. For comprehensive coverage, see Aggarwal (2016). For a comprehensive tutorial and case study, see analyticsvidhya.com/blood/2015/10/ recommendation-engines/.

Recommendation systems are very common and are used in many areas. Top appli- cations include movies, music, and books. However, there are also systems for travel, res- taurants, insurance, and online dating. The recommendations are typically given in rank order. Online recommendations are preferred by many people over regular searches, which are less personalized, slower, and sometimes less accurate.

BENEFITS OF RECOMMENDATION SYSTEMS Using these systems may result in substan- tial benefits both to buyers and sellers (see Makadia, 2018).

VisiRule also provides chatbots for improving the interactive part of the process and supplies an interactive map. According to the company’s Web site visirule.co.uk/, the major benefits of the product are:

All-in-all, VisiRule provides a comprehensive AI-based expert system.

Source: Courtesy of VisiRule Corp. UK. Used with permission.

Questions for Case 12.2

1. Which of the limitations of early ES have been solved by the VisiRule system?

2. Compare Figures 12.2 and 12.1. What are the dif- ferences between the creation (Fig. 12.2) and the development (Fig. 12.1) subsystems?

3. Compare Figures 12.2 and 12.1. What are the dif- ferences between the delivery (Fig. 12.2) and the consultation (Fig. 12.1) subsystems?

4. Identify all AI technologies and list their contri- bution to the VisiRule system.

5. List some benefits of this ES to users.

• It is code-free; no programming is needed. • The diagrams are drawn by human experts or

induced automatically from data. • It contains self-assessment tools with report

generation and document production. • The generated knowledge can be easily ex-

ecuted as XML code. • It provides explanation and justification. • The interactive expert advice attracts new

customers. • It can be used for training and advising em-

ployees. • Companies can easily access the corporate

knowledge repository. • The charts to use VisiRule authoring tools

are created with ease using flowcharting and decision trees.

• The charts allow creation of models that can be immediately executed and validated.

658 Part IV • Robotics, Social Networks, AI and IoT

Benefits to customers are:

According to ir.netflix.com, Netflix is (Spring 2018 data) the world’s leading Internet television network with more than 118 million members in over 190 countries enjoying more than 150 million hours of

TV shows and movies per day, including original series, documentaries, and feature films. Members can view unlimited shows without commercials for a monthly fee.

Application Case 12.3 Netflix Recommender: A Critical Success Factor

• Personalization. They receive recommendations that are very close to fulfilling what they like or need. This depends, of course, on the quality of the method used.

• Discovery. They may receive recommendations for products that they did not even know existed but were what they really need.

• Customer satisfaction. With repeated recommendations tends to increase. • Reports. Some recommenders provide reports and others provide explanations

about the selected products. • Increased dialog with sellers. Because recommendations may come with expla-

nations, buyers may want more interactions with the sellers.

Benefits to sellers are:

• Higher conversion rate. With personalized product recommendations, buyers tend to buy more.

• Increased cross-sell. Recommendation systems can suggest additional products. Amazon.com, for example, shows other products that “people bought together with the product you ordered.”

• Increased customer loyalty. As benefits to customers increase, their loyalty to the seller increases.

• Enabling of mass customization. This provides more information on potential customized orders.

Several methods are (or were) used for building recommendation systems. Two classic methods are collaborative filtering and content-based filtering.

COLLABORATIVE FILTERING This method builds a model that summarizes the past be- havior of shoppers, how they surf the Internet, what they were looking for, what they have purchased, and how much they like (rate) the products. Furthermore, collaborative filtering considers what shoppers with similar profiles bought and how they rated their purchases. From this, the method uses AI algorithms to predict the preference of both old and new customers. Then, the computer program makes a recommendation.

CONTENT-BASED FILTERING This technique allows vendors to identify preferences by the attributes of the product(s) that customers have bought or intend to buy. Knowing these preferences, the vendor recommends to customers products with similar attributes. For instance, the system may recommend a text-mining book to a customer who has shown interest in data mining, or action movies after a consumer has rented one in this category.

Each of these types has advantages and limitations (see example at en.wikipedia.org/ wiki/Recommender_system). Sometimes the two are combined into a unified method.

Several other filtering methods exist. Examples include rule-based filtering and activity-based filtering. Newer methods include machine learning and other AI technolo- gies, as illustrated in Application Case 12.3.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 659

The Challenges

Netflix has several million titles and now produces its own shows. The large titles inventory often creates a problem for customers who have difficulty determin- ing which offerings they want to watch. An additional challenge is that Netflix expanded its business from the United States and Canada to 190 other countries. Netflix operates in a very competitive environment in which large players such as Apple, Amazon.com, and Google operate. Netflix was looking for a way to distinguish itself from the competition by making useful recommendations to its customers.

The Original Recommendation Engine

Netflix originally was solely a mail-order business for DVDs. At that time, it encountered inventory prob- lems due to its customers’ difficulties in determining which DVDs to rent. The solution was to develop a recommendation engine (called Cinematch) that told subscribers which titles they probably would like. Cinematch used data mining tools to sift through a database of billions of film ratings and customers’ rental histories. Using proprietary algorithms, it recom- mended rentals to customers. The recommendation was accomplished by comparing an individual’s likes, dislikes, and preferences against those of people with similar tastes, using a variant of collaborative filtering. Cinematch was like the geeky clerk at a small movie store who sets aside titles he knows you will like and suggests them to you when you visit the store.

To improve Cinematch’s accuracy, Netflix began a contest in October 2016, offering $1 million to the first person or team that will write a program that would increase Cinematch’s prediction accuracy by at least 10 percent. The company understood that this would take quite some time; therefore, it offered a $50,000 Progress Prize each year in which the contest was conducted. After more than two years of com- petition, the grand prize went to Bellkor’s Pragmatic Chaos team, a combination of two runner-up teams.

To learn how the movie recommendation algo- rithms work, see quora.com/How-does-the-Netflix- movie-recommendation-algorithm-work/.

The New Era

As time passed, Netflix moved to the streaming business and then to Internet TV. Also, the spread of cloud technology enabled improvement in the

recommendation system. The new system stopped making recommendations based on what people have seen in the past. Instead, it is using Amazon’s cloud to mimic the human brain in order to find what people really like in their favorite movies and shows. The system is based on AI and its technology of deep learning. The company can now visualize Big Data and draw insights for the recommenda- tions. The analysis is also used in creating the com- pany’s productions. Another major change dealt with the transformation to the global arena. In the past, recommendations had been based on information collected in the country (or region) where users live. The recommendations were based on what other people in the same country enjoyed. This approach did not work well in the global environment due to cultural, political, and social differences. The modi- fied system considers what people who live in many countries view and their viewing habits and likes.

Implementation of the new system was dif- ficult, especially when a new country or region was added. Recommendations were initially made without knowing much about the new customers. It took 70 engineers and a year of work to modify the recommendation system. For details, see Popper (2016).

The Results

As a result of implementing its recommender sys- tem, Netflix has seen very fast growth in sales and membership. The benefits include the following:

• Effective recommendations. Many Netflix members select their movies based on recom- mendations tailored to their individual tastes.

• Customer satisfaction. More than 90 per- cent of Netflix members say they are so satisfied with the Netflix service that they recommend it to family members and friends.

• Finance. The number of Netflix members has grown from 10 million in 2008 to 118 million in 2018. Its sales and profits are climbing steadily. In spring 2018, Netflix stock sold for over $400 per share compared with $140 a year earlier.

Sources: Based on Popper (2016), Arora (2016), and StartUp (2016).

(Continued )

660 Part IV • Robotics, Social Networks, AI and IoT

Questions for Case 12.3

1. Why is the recommender system useful? (Relate it to one-to-one targeted marketing.)

2. Explain how recommendations are generated.

3. Amazon disclosed its recommendation algo- rithms to the public but Netflix did not. Why?

4. Research the research activities that attempt to “mimic the human brain.”

5. Explain the changes due to the globalization of the company.

Application Case 12.3 (Continued)

u SECTION 12.2 REVIEW QUESTIONS

1. Define expert systems. 2. What is the major objective of ES? 3. Describe experts. 4. What is expertise? 5. List some areas especially amenable to ES. 6. List the major components of ES and describe each briefly. 7. Why is ES usage on the decline? 8. Define recommendation systems and describe their operations and benefits. 9. How do recommendation systems relate to AI?

12.3 CONCEPTS, DRIVERS, AND BENEFITS OF CHATBOTS

The world is now infested with chatbots. According to 2017 data (Knight, 2017c), 60 percent of millennials have already used chatbots and 53 percent of those who have not used them are interested in doing so. Millennials are not the only generation using chat- bots, although they may use them more than others. What chatbots are and what they do is the subject of this section.

What Is a Chatbot?

Short for chat robot, a chatbot, also known as a “bot” or “robo,” is a computerized service that enables easy conversations between humans and humanlike computerized robots or image characters, sometimes over the Internet. The conversations can be in writing, and more and more are by voice and images. The conversations frequently involve short questions and answers and are executed in a natural language. More intel- ligent chatbots are equipped with NLPs, so the computer can understand unstructured dialog. Interactions also can occur by taking or uploading images (e.g., as is done by Samsung Bixby on the Samsung S8 and 8). Some companies experiment with learning chatbots, which gain more knowledge with their accumulated experience. The ability of the computer to converse with a human is provided by a knowledge system (e.g., rule- based) and a natural language understanding capability. The service is often available on messaging services such as Facebook Messenger or WeChat, and on Twitter.

Chatbot Evolution

Chatbots originated decades ago. They were simple ES that enabled machines to answer questions posted by users. The first known such machine was Eliza (en.wikipedia. org/wiki/ELIZA). Eliza and similar machines were developed to work in Q&A mode.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 661

The machine evaluated each question, usually to be found in a bank of FAQs, and gener- ated an answer matched to each question. Obviously, if the question was not in the FAQ collection, the machine provided irrelevant answers. In addition, because the power of the natural language understanding was limited, some questions were misunderstood and the answers were at times at best entertaining. Therefore, many companies opted to use live chats, some with inexpensive labor, organized as call centers around the globe. For more about Eliza’s current generation, and how to build it, see search.cpan.org/dist/ Chatbot-Eliza/Chatbot/Eliza.pm/. Chatbot use and reputation are rapidly increasing globally.

Example

Sophia is a chatbot created in Hong Kong and was awarded citizenship by Saudi Arabia in October 2017. Because she is not a Muslim, she is not wearing a hijab. She can answer many questions. For details, see newsweek.com/Saudi-arabia-robot-sophia-muslim-694152/.

TYPES OF BOTS Bots can be classified by their capabilities; three classes follow:

1. 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.

2. Chatbots. In this category, we include more capable bots, for example, those that can stimulate conversations with people. This chapter deals mainly with chatbots.

3. 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).

A major limitation of the older types of bots was that updating their knowledge base was both slow and expensive. They were developed for specific narrow domains and/or specific users. It took many years to improve the supporting technology. NLP has become better and better. Knowledge bases are updated today in the “cloud” in a central location; the knowledge is shared by many users so the cost per user is reduced.

The stored knowledge is matched with questions asked by users. The answers by the machines have improved dramatically. Since 2000, we have seen more and more capable AI machines for Q&A dialogs. Around 2010, conversational AI machines were named chatbots and later were developed into virtual personal assistants, championed by Amazon’s Alexa.

DRIVERS OF CHATBOTS The major drivers are:

• Developers are creating powerful tools to build chatbots quickly and inexpensively with useful functionalities.

• The quality of chatbots is improving, so conversations are getting more useful to users.

• Demand for chatbots is growing due to their potential cost reduction and improved customer service and marketing services, which are provided 24/7.

• Use of chatbots allows rapid growth without the need to hire and train many cus- tomer service employees.

• Using chatbots, companies can utilize the messaging systems and related apps that are the darlings for consumers, especially younger ones.

662 Part IV • Robotics, Social Networks, AI and IoT

Components of Chatbots and the Process of Their Use

The major components of chatbots are:

Messenger, Webpage,

Mobile

Platforms

Voice, texting, video, VR

Mode of communication Question,

Order, Menu

Men– Machine Interface

NLP

Robot Chatbot

Natural Language Generation

Cloud services

Analytics Data Knowledge

Response

FIGURE 12.3 The Process of Chatting with Chatbots.

• A person (client). • A computer, avatar, or robot (the AI machine). • A knowledge base that can be embedded in the machine or available and con-

nected to the “cloud.” • A human-computer interface that provides the dialog for written or voice modes. • An NLP that enables the machine to understand natural language.

Advanced chatbots can also understand human gestures, cues, and voice variations.

PERSON-MACHINE INTERACTION PROCESS The components just listed provide the framework for people-bot conversation. Figure 12.3 shows the conversation process.

• A person (left side of the figure) needs to find some information, or need some help. • The person asks a related question from the bot by voice, texting, and so on. • NLP translates the question to machine language. • The chatbot transfers the question to cloud services. • The cloud contains a knowledge base, business logic, and analytics (if appropri-

ate) to craft a response to the question. • The response is transferred to a natural language generation program and then to

the person who asked the question in the preferred mode of dialog.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 663

Drivers and Benefits

Chatbot use is driven by the following forces and benefits:

• The need to cut costs. • The increasing capabilities of AI, especially NLP and voice technologies. • The ability to converse in different languages (via machine translation). • The increased quality and capability of captured knowledge. • The push of devices by vendors (e.g., virtual personal assistants such as Alexa

from Amazon and Google Assistant from Alphabet). • Its use for providing superb and economic customer service and conducting mar-

ket research. • Its use for text and image recognition. • Its use to facilitate shopping. • Its support of decision making.

Chatbots and similar AI machines have been improved over time. Chatbots are beneficial to both users and organizations. For example, several hospitals employ robot reception- ists to direct patients to their place of treatment. Zora Robotics created a robot named Nao to act as a chatting companion for people who are sick or elderly. The bot acts, for example, as a form of therapy for those suffering from dementia.

Note: For some limitations of chatbots, see Section 12.7.

Representative Chatbots from Around the World

For a chatbot directory of the more than 1,250 bots in 53 countries as of April 2018, see chatbots.org/ and at botlist.co/bots/. Examples of chatbots and what they can do from chatbot.org/ are provided here:

• RoboCoke. This is a party and music recommendation bot created for Coca-Cola in Hungary.

• Kip. This shopping helper is available on Slack (a messaging platform). Tell Kip what you want to buy, and Kip will find it and even buy it for you.

• Walnut. This chatbot can discover skills relevant to you and help you learn them. It analyzes a large set of data points to discover the skills.

• Ride sharing by Taxi Bot. If you are not sure whether Uber, Lyft, Grab, or Comfort DelGro is the cheapest service, you can ask this bot. In addition, you can get current promo codes.

• ShopiiBot. When you send a picture of a product to this bot, it will find similar ones in seconds. Alternatively, tell ShopiiBot what kind of product you are looking for at what price, and it will find the best one for you.

• Concerning desired trips. It can answer questions regarding events, restaurants, and attractions in major destinations.

• BO.T. The first Bolivian chatbot, it talks to you (in Spanish) and answers your questions about Bolivia, its culture, geography, society, and more.

• Hazie. She is your digital assistant that aims to close the gap between you and your next career move. Job seekers can converse directly with Hazie just as they do with a job placement agent or friends.

• Green Card. This Visabot product helps users to properly file requests for Green Cards in the United States.

• Zoom. Zoom.ai (botlist.co/bots/369-zoomai), an automated virtual assistant, is for everyone in the workplace.

• Akita. This chatbot (botlist.co/bots/1314-akita) can connect you to businesses in your area.

664 Part IV • Robotics, Social Networks, AI and IoT

As you can see, chatbots can be used for many different tasks. Morgan (2017) classifies bots into the following categories: education, banking, insurance, retail, travel, health- care, and customer experience.

MAJOR CATEGORIES OF CHATBOTS’ APPLICATIONS Chatbots are used today for many purposes and in many industries and countries. We divide the applications into the fol- lowing categories:

• Chatbots for enterprise activities, including communication, collaboration, cus- tomer service, and sales (such as in the opening vignette). These are described in Section 12.4.

• Chatbots that act as personal assistants. These are presented in Section 12.5. • Chatbots that act as advisors, mostly on finance-related topics (Section 12.6).

For a discussion of these categories, see Ferron (2017).

u SECTION 12.3 REVIEW QUESTIONS

1. Define chatbots and describe their use. 2. List the major components of chatbots. 3. What are the major drivers of chatbot technology? 4. How do chatbots work? 5. Why are chatbots considered AI machines?

12.4 ENTERPRISE CHATBOTS

Chatbots play a major role in enterprises, both in external and internal applications. Some believe that chatbots can fundamentally change the way that business is done.

The Interest of Enterprises in Chatbots

The benefits of chatbots to enterprises are increasing rapidly, making dialog less expen- sive and more consistent. Chatbots can interact with customers and business partners more efficiently, are available anytime, and can be reached from anywhere. Businesses are clearly paying attention to the chatbot revolution. According to Beaver (2016), busi- nesses should look at enterprise bots for the following reasons:

• “AI has reached a stage in which chatbots can have increasingly engaging and human conversations, allowing businesses to leverage the inexpensive and wide- reaching technology to engage with more consumers.

• Chatbots are particularly well suited for mobile-perhaps more so than apps. Messaging is at the heart of the mobile experience, as the rapid adoption of chat app demonstrates.

• The chatbot ecosystem is already robust, encompassing many different third-party chat bots, native bots, distribution channels, and enabling technology companies.

• Chatbots could be lucrative for messaging apps and the developers who build bots for these platforms, similar to how app stores have developed into moneymaking ecosystems.”

A study conducted in 2016 found that 80 percent of businesses want chatbots by 2020 businessinsider.com/80-of-businesses-want-chatbots-by-2020-2016-12. For more opportunities in marketing, see Knight (2017a).

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 665

Enterprise Chatbots: Marketing and Customer Experience

As we saw in the opening vignette to this chapter and will see in in the several ex- amples later in this chapter, chatbots are very useful in providing marketing and customer service (e.g., Mah, 2016), obtaining sales leads, persuading customers to buy products and services, providing critical information to potential buyers, opti- mizing advertising campaigns (e.g., a bot named Baroj; see Radu, 2016), and much more. Customers want to do business on the app they are already in. For this rea- son, many bots are on Facebook Messenger, Snapchat, WhatsApp, Kik, and WeChat. Using voice and texting, it is possible to provide personalization as well as superb customer experience. Chatbots can enable vendors to improve personal relationships with customers.

In addition to the marketing areas, plenty of chatbots are in areas such as finan- cial (e.g., banks) and HRM services as well as production and operation management for communication, collaboration, and other external and internal enterprise business processes. In general, enterprises use chatbots on messaging platforms to run mar- keting campaigns (e.g., see the opening vignette) and to provide superb customer experience.

IMPROVING THE CUSTOMER EXPERIENCE Enterprise chatbots create improved customer experience by providing a conversation platform for quick and 24/7 contact with en- terprises. When customers benefit from the system, they are more inclined to buy and promote a specific brand. Chatbots can also supplement humans in providing improved customer experience.

EXAMPLES OF ENTERPRISE CHATBOTS Schlicht (2016) provides a beginner’s guide to chatbots. He presents the following hypothetical example about today’s shopping at Nordstrom (a large department store) versus the use of chatbots.

If you wanted to buy shoes from Nordstrom online, you would go to their Web site, look around until you find the shoes you wanted, and then you would purchase them. If Nordstrom makes a bot, which I am sure they will, you would simply be able to message Nordstrom on Facebook. It would ask you what you are looking for and you would simply . . . tell it.

Instead of browsing a Web site, you will have a conversation with the Nordstrom bot, mirroring the type of experience you would get when you go into the retail store.

Three additional examples follow:

Example 1: LinkedIn

LinkedIn is introducing chatbots that conduct tasks such as comparing the calendars of people participating in meetings and suggesting meeting times and places. For details, see CBS News (2016).

Example 2: Mastercard

Mastercard has two bots based on massaging platforms, one bot for banks and another bot for merchants.

666 Part IV • Robotics, Social Networks, AI and IoT

Example 3: Coca-Cola

Customers worldwide can chat with Coca-Cola bots via Facebook Messenger. The bots make users feel good with conversations that are increasingly becoming personalized. The bots collect customers’ data, including their interests, problems, local dialect, and at- titudes and then can target advertisements tailored to each user.

A 5-min. video about Facebook is available at cnbc.com/2016/04/13/ why-facebook-is-going-all-in-on-chatbots.html. It provides a Q&A session with David Marcus describing Facebook’s increasing interest in chatbots.

WHY USE MESSAGING SERVICES? So far, we have noted that enterprises are using mes- saging services such as Facebook Messenger, WeChat, Kik, Skype, and WhatsApp. The reason is that in 2017, more than 2.6 billion people were chatting on messaging services. Messaging is becoming the most widespread digital behavior. WeChat of China was the first to commercialize its service by offering “chat with business” capabilities as illustrated in Application Case 12.4.

FACEBOOK’S CHATBOTS Following the example of WeChat, Facebook launched users’ conversations with businesses’s chatbots on a large scale on Messenger, suggesting that users could message a business just the way they would message a friend. The service allows businesses to conduct text exchanges with users. In addition, the bots have a

WeChat is a very large comprehensive messaging service in China and other countries with about 1 billion members in early 2018. It pioneered the use of bots in 2013 (see mp.weixin.qq.com). Users can use the chatbot for activities such as the following:

Griffiths (2016) has provided information con- cerning a Chinese online fashion flash sales com- pany, Meici. The company used its WeChat account to gather information related to sales. Each time new users followed Meici’s account, a welcome mes- sage instructed them on how to trigger resources. WeChat is available in English and other languages worldwide due to its usefulness. Facebook installed similar capabilities in 2015.

Questions for Case 12.4

1. Find some recent activities that WeChat does.

2. What makes this chatbot so unique?

3. Compare the bot of WeChat to bots offered by Facebook.

Application Case 12.4 WeChat’s Super Chatbot

• Conduct market research. • Get information and recommendations on

products and services. • Launch a start-up on WeChat (you can make

your own bot on WeChat for this purpose).

• Hail a taxi. • Order food to be delivered. • Buy movie tickets and other items. • Customize and order a pair of Nikes. • Send an order to the nearest Starbucks. • Track your daily fitness progress. • Shop Burberry’s latest collection. • Book doctor appointments. • Pay your water bill. • Host a business conference call. • Send voice messages, emoticons, and snap-

shots to friends. • Send voice messages to communicate with

businesses. • Communicate and engage with customers. • Provide a framework for teamwork and col-

laboration.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 667

learning ability that enables them to accurately analyze people’s input and provide cor- rect responses. Overall, as of early 2018, there were more than 30,000 company bots on Facebook Messenger. Some companies use Messenger bots to recognize faces in pictures, suggesting recipients for targeted ads. According to Guynn (2016), Facebook allows soft- ware developers access to its tools that build its personal assistant called “M,” which com- bines AI with a human touch for tasks such as ordering food or sending flowers. Using the M tools, developers can build applications for Messenger that can have an increased understanding of requests made in natural languages. A major benefit of these bots for Facebook is their collection of data and creation of profiles of users.

The following is another example of how the use of chatbots is facilitating customer service and marketing (Application Case 12.5).

Vera Gold Mark is a real estate developer of luxury high rises in Punjab, India.

The Problem

Vera Gold Mark (VGM) is active in a very competi- tive market. As a developer of luxury apartments, which are usually expensive, it must try to attract many potential buyers and thus needs as many sales leads as possible at a reasonable cost. Chatting live with potential customers can be expensive since it requires very knowledgeable and courteous agents available 24/7. VGM has a large inventory of units that must be sold as soon as possible.

The Solution

VGM decided to use chatbots to supplement or replace expensive manual live chats. These work in the fol- lowing ways. Buyers may click on the “chat with the robot” button on the company’s Facebook page, and receive any information they need. The chat helps VGM promote its available products. When they click, users are able to chat and get information about pric- ing, delivery dates, construction sites, and much more for VGM projects. Users can also tweet. The chatbots provide answers about the projects. Facebook pro- vides VGM access to potential buyers’ profiles (with users’ permission), which VGM sales teams can use to refine sales strategies. The system is available 24/7. Voice communication is coming soon (2018).

The Results

VGM is now viewed in a very positive way and is considered to be very professional. VGM is getting good reviews for its customer service. The builder is considered more honest and unbiased because it provides written answers and promises to cus- tomers. Salespeople at VGM get an increased num- ber of sales leads, and because they know more about prospective customers, they can better align them with units (optimal fit). The system is also able to attract international buyers without increas- ing cost. Because the system is available 24/7, global buyers can easily evaluate VGM’s available condominiums.

The chatbot is also used as a teaching tool for new employees. At the time that this case was writ- ten, no financial data were available.

The technology is available to other build- ers from Kenyt Technologies of India kenyt.com, which provides the smart real estate chatbot.

Sources: Based on Garg (2017) and facebook.com/ veragoldmark/ (accessed April 2018).

Questions for Case 12.5

1. List the benefits to VGM.

2. List the benefits to buyers.

3. What is the role of Kenyt Technologies?

Application Case 12.5 How Vera Gold Mark Uses Chatbots to Increase Sales

Chatbots Magazine provides a three-part overview on the use of chatbots for re- tail and e-commerce. For details, see chatbotsmagazine.com/chatbots-for-retail-and- e-commerce-part-three-c112a89c0b48.

668 Part IV • Robotics, Social Networks, AI and IoT

Enterprise Chatbots: Financial Services

The second area in which enterprise bots are active is financial services. Here we briefly discuss their use in banking. In Section 12.6, we present the robo financial advisors for investment.

BANKING A 2017 survey (Morgan, 2017) found that most people in the United States will bank via chatbots by 2019. Chatbots can use predictive analytics and cognitive messaging to perform tasks such as making payments. They can inform customers about personalized deals. Banks’ credit cards can be advertised via chatbots on Facebook Messenger. It seems that customers prefer to deal with chatbots rather than with salespeople who can be pushy.

Examples

POSB of Singapore has an AI-driven bot on Facebook Messenger. The bot was created with the help of Kasisto, Inc. of the United States. Using actual Q&A sessions, it took IT workers 11,000 hours to create the bot. Its knowledge base was tested and verified. The bot can learn to improve its performance. Known as POSB digi-bank virtual assistant, the service is accessed via Messenger. Customers save time rather than waiting for human customer service. In the future, the service will be available on other messaging plat- forms. For details, see Nur (2017).

A similar application in Singapore is used by Citi Bank (by Citi Group). It can an- swer FAQs about people’s accounts in a natural language (English). The bank is adding progressively more capabilities to its bot.

A generic banking bot is Verbal Access (from North Side Co.) that provides recom- mendations for banking services (see Hunt, 2017).

Enterprise Chatbots: Service Industries

Chatbots are used extensively in many services. We provide several examples in the fol- lowing sections.

HEALTHCARE Chatbots are extremely active in the healthcare area, helping millions of people worldwide (Larson, 2016). Here are a few examples:

• Robot receptionists direct patients to departments in hospitals. (Similar services are available at airports, hotels, universities, government offices, and private and other public organizations.)

• Several chatbots are chatty companions for people who are elderly and sick (e.g., Zora Robotics).

• Chatbots are used in telemedicine; patients converse with doctors and healthcare professionals who are in different locations. For example, the Chinese company Baidu developed the Melody chatbot for this purpose.

• Chatbots can connect patients quickly and easily with information they need. • Important services in the healthcare field are currently provided by IBM Watson

(Chapter 6).

For more on bots for healthcare, see the end of Section 12.6.

EDUCATION Chatbot tutors are used in several countries to teach subjects ranging from English (in Korea) to mathematics (in Russia). One thing is certain: The chatbot treats all students equally. Students like the chatbots in online education as well. Machine transla- tion of languages will enable students to take online classes in languages other than their own. Finally, chatbots can be used as private tutors.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 669

GOVERNMENT According to Lacheca (2017), chatbots are spreading in government as a new dialog tool for use by the public. The most popular use is in providing access to government information and answering government-related questions.

TRAVEL AND HOSPITALITY Chatbots are working as tour guides in several countries (e.g., Norway). They are not only cheaper (or free) but also may know more than some human guides. Chatbots work as guides in several hotels in Japan. In hotels, they act as concierges, providing information and personalized recommendations (e.g., about res- taurants). Chatbots can arrange reservations for hotel rooms, meals, and events. In busy hotels, there is frequently a wait for human concierges; chatbots are available on smart- phones all the time. As with other computer services, the chatbots are fast, inexpensive, easy to reach, and always nice. They give excellent customer experience.

An example of external travel service is given in Application Case 12.6.

Chatbot Platforms

CHATBOTS INSIDE ENTERPRISES So far we have seen chatbots that are working in the external side of enterprises, mostly in customer care and marketing (e.g., the opening vignette). However, companies lately have started to use chatbots to automate tasks for supporting internal communication, collaboration, and business processes. According to

Background

The air travel business is very competitive, espe- cially in Europe. There is a clear trend for younger customers to use wireless devices as well as social media sites and chatting. Customers like to com- municate with travel businesses by using their pre- ferred technology via their preferred platforms. Most popular is Facebook Messenger, where over 1.2 bil- lion people chat, many times via their smartphones. These users today interact not only among them- selves but also with the business world.

Messaging platforms such as Messenger, WhatsApp, and WeChat are becoming the norm for this customer group. Vendors are building smart apps for the messaging platforms including bots.

Transavia’s Bot

Learning from other companies, Transavia decided to create a bot on Facebook Messenger. To do so, it hired the IT consultant Cognizant Digital Business unit, called Mirabean, which specializes in conversa- tion interfaces, especially via bots. Transavia’s activi- ties business processes, marketing, and customer

care were combined with Mirabean’s technological experience to enable a quick deployment of the bot in weeks. It now enables real-time dialog with customers. The first application is Transavia Flight Search, which provides flight information as well as the ability to buy tickets. The system is now inte- grated with business processes that facilitate other transactions via the bot. Giving customers their digi- tal tool of choice enables Transavia to increase mar- ket share and to drive growth.

Note that KLM, the owner of Transavia, was the first European airline that implemented a similar chatbot on Facebook Messenger in 2016.

Sources: Compiled from Cognizant (2017) and transavia.com.

Questions for Case 12.6

1. What drives consumer preference for mobile devices and chat?

2. Why was the bot placed on Facebook Messenger?

3. What were the benefits of using Cognizant?

4. What is the advantage of buying a ticket from a bot rather than from an online store?

Application Case 12.6 Transavia Airlines Uses Bots for Communication and Customer Care Delivery

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Hunt (2017), “Enterprise and internal chatbots are revolutionizing the way companies do business.” Chatbots in enterprises can do many tasks and support decision-making activi- ties. For examples, see Newlands (2017a). Chatbots can cut costs, increase productivity, assist working groups, and foster relationships with business partners. Representative examples of chatbot tasks are:

• Help with project management. • Handle data entry. • Conduct scheduling. • Streamline payments with partners. • Advise on authorization of funds. • Monitor work and workers. • Analyze internal Big Data. • Find discounted and less expensive products. • Simplify interactions. • Facilitate data-driven strategy. • Use machine learning.

Facilitate and manage personal finance.

Given the large number of bots, it is not surprising that many developers started to offer tools and platforms to assist in building chatbots as discussed in Technology Insights 12.1.

TECHNOLOGY INSIGHTS 12.1 Chatbots’ Platform Providers

Several companies provide platforms for building enterprise chatbots. The companies can con- struct chatbots fairly easily using these tools for their entry into popular messaging platforms or for their Web sites. Some of the tools have machine-learning capability to ensure that the bots learn with every interaction. According to Hunt (2017), these are some popular vendors:

1. ChattyPeople. This chatbot builder assists in creating bots requiring minimal program- ming skills. It simply allows a business to link its social media pages to its ChattyPeople account. The created bot can: • Arrange for payments to or from social media contacts. • Use major payment providers such as Apple Pay and PayPal. • Recognize variations in keywords. • Support messaging.

2. Kudi. This financial helper allows people to make payments to vendors directly from their messaging apps, specifically, Messenger, Skype, and Telegram and through an Internet browser. Using the bot, users can: • Pay bills. • Set bill payment reminders. • Transfer money by sending text messages.

The bot is safe and it protects users’ privacy. Vendors can easily install it for use. 3. Twyla. This chatbot building platform is for improving existing customer care and of-

fering live chats. It acts as a messaging platform for customers who prefer to use chatting. The major objective is to free humans in HR departments from routine tasks.

The most popular platforms are:

• IBM Watson. This package uses a neural network of 1 billion words for excellent understanding of natural languages (e.g., English, Japanese). Watson provides free devel- opment tools, such as Java SDK, Node SDK, Pyton SDK, and iOS SDK.

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For additional information about chatbot platforms for building enterprise chatbots, see entrepreneur.com/article/289788.

INDUSTRY-SPECIFIC BOTS As we have seen, bots can be specialists (e.g., for investment ad- vice, customer service) or industry-specific experts (e.g., banking, airlines). An interesting bot for the waste industry is Alto (from Bio Hi Tech Global), which enables users to communicate intelligently with industrial equipment. This helps owners of the equipment make decisions that improve performance levels, smooth maintenance routines, and facilitate communication.

Knowledge for Enterprise Chatbots

Knowledge for chatbots depends on their tasks. Most marketing and customer care bots require proprietary knowledge, which is usually generated and maintained in-house. This knowledge is similar to that of ES; in many cases, enterprise chatbots operate very simi- larly to ES except that the interface occurs in a natural language and frequently by voice. For example, the knowledge of Sephora’s bot (opening vignette) is specific to that com- pany and its products and is organized in a Q&A format.

On the other hand, chatbots that are used within the enterprise (e.g., to train em- ployees or to provide advice on security or compliance with government regulations) may not be company specific. A company can buy this knowledge and modify it to fit local situations and its specific needs (as is done in ES; e.g., see Exsys Inc.). Newer chat- bots use machine learning to extract knowledge from data.

PERSONAL ASSISTANTS IN THE ENTERPRISE Enterprise chatbots can also be virtual per- sonal assistants as will be described in Section 12.5. For example, these bots can answer work-related queries and help in increasing employees’ decision-making capabilities and productivity.

u SECTION 12.4 REVIEW QUESTIONS

1. Describe some marketing bots. 2. What can bots do for financial services? 3. How can bots assist shoppers? 4. List some benefits of enterprise chatbots. 5. Describe the sources of knowledge for enterprise chatbots.

• Microsoft’s Bot Framework. Similar to IBM, Microsoft offers a variety of tools translat- able into 30 languages. It is an open source. The system has three parts, Bot Connector, Developer Portal, and Bot Directory and is interconnected with Microsoft Language Understanding Intelligent Service (LUIS) that understands users’ intent. The system also includes active learning technology. A simplified tool is AZURE; see Section 12.7 and Afaq (2017). For a comparative table of 25 chatbots platforms, see Davydova (2017). For a list of other platforms, see Ismail (2017).

Sources: Compiled from Hunt (2017) and Davydova (2017).

DisCussion Questions

1. What is the difference between a regular enterprise bot and a platform?

2. Discuss the benefits of ChattyPeople.

3. Discuss the need for Kudi.

4. Discuss the reasons for consumers to prefer messaging platforms.

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12.5 VIRTUAL PERSONAL ASSISTANTS

In the previous section, we introduced enterprise chatbots that can be used to conduct conversations. In marketing and sales, they can facilitate customer relationship manage- ment (CRM, execute searches for customers, provide information, and execute many specific tasks in organizations for their customers and employees. For comprehensive coverage, including research issues, see Costa et al. (2018)).

An emerging type of chatbot is designed as a virtual personal assistant for both individuals and organizations. Known as a virtual personal assistant (VPA), this software agent helps people improve their work, assist in decision making, and fa- cilitate their lifestyle. VPAs are basically extensions of intelligent software agents that interact with people. VPAs are chatbots whose major objective is to help people better perform certain tasks. At this time, millions of people are using Siri with their Apple products, Google Assistant, and Amazon’s Alexa. The assistants’ knowledge bases are usually universal, and they are maintained centrally in the “cloud,” which makes them economical for a large number of users. Users can get assistance and advice from their virtual assistants anytime. In this section, we provide some interesting applications. The first set of applications involves virtual personal assistants, notably Amazon’s Alexa and Apple’s Siri and Google Assistant. O’Brien (2016) provides a discussion of what personal assistant chatbots can do for business. The second set (presented in Section 12.6) is about computer programs that act mostly as advisors on specific topics (mostly investments).

Assistant for Information Search

A major task of virtual personal assistants is to help users conduct a search by voice for information. Without the assistant, users need to surf the Internet to find information and many times abandon the search. In business situations, users can call a live cus- tomer service agent for assistance. This may be an expensive service for the vendors. Delegating the search to a machine may save sellers considerable money and make customers happy by not having to wait for the service. For example, Lenovo uses the noHold assistant in its Single Point of Search service to help customers find answers to their questions.

If You Were Mark Zuckerberg, Facebook CEO

While Siri and Alexa were in development, Zuckerberg decided to develop his own personal assistant to help him run his home and his work as the CEO of Facebook. He viewed this assistant as Jarvis from Iron Man. Zuckerberg trained the bot to recognize his voice and understand basic commands related to home appliances. The assistant can rec- ognize the faces of visitors and monitor the movement of Zuckerberg’s young daughter. For details, see Ulanoff (2016).

The essentials of this assistant can be seen in a 2:13 min. video at youtube.com/ watch?v=vvimBPJ3XGQ and one (5:01 min.) at youtube.com/watch?v=vPoT2vdVkVc, with the narration by Morgan Freeman. Today, similar assistants are available for a mini- mal fee or even for free. The most well-known such assistant is Amazon’s Alexa.

Amazon’s Alexa and Echo

Of the several virtual personal assistants, the one considered the best in 2018 was Alexa. She was developed by Amazon to compete with Apple’s Siri and is a superior prod- uct. (See Figure 12.4.) Alexa works with a smart speaker, such as Amazon’s Echo (to be described later).

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Amazon’s Alexa is a cloud-based virtual personal voice assistant that can do many things such as:

FIGURE 12.4 Amazon’s Echo and Alexa. Source: McClatchy-Tribune/Tribune Content Agency LLC/ Alamy Stock Photo

• Answer questions in several domains. • Control smartphone operations with voice commands. • Provide real-time weather and traffic updates. • Control smart home appliances and other devices by using itself as a home auto-

mation hub. • Make to-do lists. • Arrange music in Playbox. • Set alarms. • Play audio books. • Control home automation devices, as well as home appliances (e.g., a microwave). • Analyze shopping lists. • Control a car’s devices. • Deliver proactive notification. • Shop for its user. • Make phone calls and send text messages.

Alexa has the ability to recognize different voices, so it can provide personalized responses. Also, she uses a mix of speech and touch to deliver news, hail an Uber, and play games. As time passes, her capabilities and skill grow. For more capabilities, which are ever-increasing, see Johnson (2017). For what Alexa can hear and remember and how she learns, see Oremus (2018).

Watch the 3:55 min. video of how Alexa works at youtube.com/ watch?v=jCtfRdqPlbw. For more tasks, see cnet.com/pictures/what-can- amazon-echo-and-alexa-do-pictures/, Mangalindan (2017), and tomsguide.com/ us/pictures-story/1012-alexa-tricks-and-easter-eggs.html.

674 Part IV • Robotics, Social Networks, AI and IoT

ALEXA’S SKILLS In addition to the standard (native) capabilities listed, people can use Alexa apps (referred to as Skills) to download customized capabilities to Alexa (via your smartphone). Skills are intended to teach Alexa something new.

The following are examples of Alexa’s Skills (Apps):

• Call Uber and find the cost of a ride. • Order a pizza. • Order take-out meals. • Obtain financial advice. • Start a person’s Hyundai Genesis car from inside her or his house (Korosec, 2016).

These skills are provided by third-party vendors; they are required to activate invo- cation commands. There are tens of thousands of them.

For example, a person can say, “Alexa, call Uber to pick me up at my office at 4:30 p.m.” For more on Amazon’s Alexa, see Kelly (2018); for its benefits, see Reisinger (2016).

Alexa is equipped with NLP user interface, so it can be activated by providing a voice command. This is done by combining the Alexa software with Amazon’s intelligent speaker, Echo.

ALEXA’S VOICE INTERFACE AND SPEAKERS Amazon has a family of three speakers (or voice communication devices for Alexa: Echo, Dot, and Tag. Alexa can be accessed by a Fire TV line and some non-Amazon devices. For the relationship between Alexa and Echo, see Gikas (2016).

AMAZON’S ECHO Echo is a hands-free intelligent (or smart) wireless speaker that is controlled by voice. It is the hardware companion of Alexa (a software product), so the two operate hand in hand. Echo is always on, always listening. When Echo hears a question, command, or request, it sends the audio to Alexa and from there up to the cloud. Amazon’s servers match responses to the questions, delivering them to Alexa as “responses to questions” in a split second. Amazon’s Alexa/Echo is now available in some Ford vehicles.

Amazon Echo Dot Amazon Echo Dot is the “little brother” of Echo. It offers full Alexa functionality but has only one very small speaker. It can be linked to any existing speaker systems to provide an Echo-like experience.

Amazon Echo Tap Amazon Echo Tap is another “little brother” of Echo that can be used on the go. It is completely wireless and portable and can be charged via a charg- ing dock.

Both Dot and Tap are less expensive than Echo, but they offer fewer functional- ities and lower quality. However, people who already have good home speakers can use Dot with them. For a discussion about the three speakers, see Trusted Review at trustedreviews.com/news/amazon-echo-show-vs-echo-2948302.

Note: Non-Amazon speakers for Alexa are available now (e.g., Eufy Genie, from third-party vendors); some are inexpensive.

Note: Alexa was smart enough earlier to admit that she did not know an answer, but today, she will make references to third-party sources for an answer she cannot make. For details and examples, see uk.finance.yahoo.com/news/alexa-recommend- third-party-skills-192700876.html.

ALEXA FOR THE ENTERPRISE While the initial use of Alexa was for individual consumers, her use for business has increased. WeWork Corp. developed a platform for helping com- panies to integrate an Alexa skill in meeting rooms, for example. For details, see Crook (2017), and yahoo.com/news/destiny-2-alexa-skills-let-140946575.html/.

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Apple’s Siri

Siri (short for Speech Interpretation and Recognition Interface) is an intelligent virtual personal assistant and knowledge navigator. It is a part of Apple’s several operating systems. It can answer questions, make recommendations, and perform some actions by delegating requests to a set of Web services in the “cloud.” The software can adapt itself to the user’s individual language, search preferences with continuing use, and return per- sonalized results. Siri is available for free to iPhone and iPad users.

Siri can be integrated into Apple’s Siri Remote. Using CarPlay, Siri is available in some auto brands where it can be controlled by iPhone (5 and higher). Siri 2 is the 2017–2018 model.

VIV In 2016, Dag Kittlaus, the creator of Siri, introduced Viv, “an intelligent Interface for everything.” Viv is expected to be the next generation of intelligent virtual interac- tions (for details, see Matney, 2016). In contrast with other assistants, Viv is open to all developers (third-party ecosystem products). Viv is now a Samsung Company. In 2017, Samsung launched its own personal assistant for the Galaxy S8.

Google Assistant

Competition regarding virtual personal assistants is increasing with the improved ca- pabilities of Google Assistant, which was developed as a competitor to Siri to fit Android smartphones. An interesting demonstration of it is available at youtube.com/ watch?v=WTMbF0qYWVs; some advanced capabilities are illustrated in the video at youtube.com/watch?v=17rY2ogJQQs. For details, see Kelly (2016). The product im- proved dramatically in 2018 as shown in CES 2018 Conference.

Other Personal Assistants

Several other companies have virtual personal assistants. For example, Microsoft Cortana is well known. In September 2016, Microsoft combined Cortana and Bing (see Hachman, 2016). Alexa and Cortana now work together. Note that it is estimated that by the year 2022, voice-enabled personal assistants will reach 55 percent of all U.S. households. For this and the future of personal assistants, see Perez (2017).

Competition Among Large Tech Companies

Apple and Google have provided their personal assistants to hundreds of million users of their mobile devices. Microsoft has equipped over 250 million PCs with its personal assistant. Amazon’s Alexa/Echo sells many more assistants than others. The competition is on voice-controlled chatbots. Their competitors view them as “the biggest thing since the iPhone.”

Knowledge for Virtual Personal Assistants

As indicated earlier, the knowledge for virtual personal assistants is kept in the “cloud.” The reason is that the assistants are commodities, available to millions of users, and need to provide dynamic, updated information (e.g., weather conditions, news, stock prices). When the knowledge base is centralized, its maintenance is performed in one place. This is in contrast with the knowledge of many enterprise bots, for which updating is decen- tralized. Thus, Siri on an iPhone will always be updated for its general knowledge by AAPL. Knowledge for the skills of Alexa has to be maintained locally or by the third-party vendors that create them.

676 Part IV • Robotics, Social Networks, AI and IoT

u SECTION 12.5 REVIEW QUESTIONS

1. Describe an intelligent virtual personal assistant. 2. Describe the capabilities of Amazon’s Alexa. 3. Relate Amazon’s Alexa to Echo. 4. Describe Echo Dot and Tap. 5. Describe Apple’s Siri Google’s Assistant. 6. How is the knowledge of personal assistants maintained? 7. Explain the relationship between virtual personal assistants and chatbots.

12.6 CHATBOTS AS PROFESSIONAL ADVISORS (ROBO ADVISORS)

The personal assistants described in Section 12.5 can provide much information and ru- dimentary advice. A special category of virtual personal assistants is designed to provide personalized professional advice in specific domains. A major area for their activities is investment and portfolio management where robo advisors operate.

Robo Financial Advisors

It is known that the vast majority of “buy” and “sell” decisions of stock trading on the major exchanges, especially by financial institutions, are made by computers. However, computers can also manage an individual’s accounts in a personalized way.

According to an A. T. Kearney’s survey (reported by Regan, 2015), robo advisors are defined as online providers that offer automated, low-cost, personalized investment advisory services, usually through mobile platforms. These robo advisors use algorithms that allocate, deploy, rebalance, and trade investment products. Once enrolled for the robo service, individuals enter their investment objectives and preferences. Then, using advanced AI algorithms, the robo will offer alternative personalized investments for in- dividuals to choose from funds or exchange-traded funds [ETFs]. By conducting a dialog with the robo advisor, an AI program will refine the investment portfolio. This is all done digitally without having to talk to a live person. For details, see Keppel (2016).

Evolution of Financial Robo Advisors

The pioneering emergence of Betterment Inc. in 2010 (described later) was followed by several other companies (Future Advisor and Hedgeable in 2010 and Personal Capital, Wealthfront, and SigFig in 2011 and 2012). Other well-known companies (Schwab Intelligent Portfolios, Acorns, Vanguard RAS, and Ally) joined the crowd in 2014 and 2015. In 2016 and 2017, the brokerage houses of E*Trade and TD Ameritrade joined, as did Fidelity and Merrill Edge. There is no question that robo advisors are game-changing phenomena for the wealth management business, even though their performance so far has not been much different from that of traditional, manual, and financial services.

Robo advising companies try to cut costs by using ETFs, whose commission fees are significantly lower than that of mutual funds. Annual fees vary as does the minimum amount of required assets. Premium services are more expensive since they offer the op- portunity to consult human experts (advisors 2.0), which are described next.

Robo Advisors 2.0: Adding the Human Touch

As robo advisors matured, it became clear that sometimes they could not do an effective job by themselves. Therefore, in late 2016, several of the fully automated advisors started to add what they call the human touch (e.g., see Eule, 2017; Huang, 2017). Companies

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are adding a human option, or partner with another company. For example, UBS Wealth Management Americas has partnered with pure robo advisor SigFig.

Robo advisors with human additions vary in expertise. For example, Betterment (Plus and Permission options), Schwab Intelligent Advisory, and Vanguard Personal Advisor Service use certified financial planners (CFPs); other companies offer less exper- tise. For details, see Huang (2017).

Application Case 12.7 describes how Betterment has added the human touch.

QUALITY OF ADVICE PROVIDED BY ROBO ADVISORS You may wonder how good the advice from robo advisors is. The answer is that it depends on their knowledge, the type of investments involved, the inference engine of the AI machine, and so on. However, remember that the robots are not biased and are consistent. They may prove to be even

As the pioneer of financial robo advisors in 2010, Betterment created an automated platform for wealth management. Since then, it has played a leading role in a growing industry. In 2017, the company controlled more than $9 billion in assets, yielding over an 11 percent return to its 200,000 members. Like other robo advisors, Betterment appeals to investors who do not want to manage their portfolio by themselves or pay the 2 to 3 percent annual fee charged by human advisors.

The company advertises the following benefits:

Premium Service—Adding the Human Touch

Like Amazon.com and Expedia, which started as pure online companies and later added physical commerce, in 2017 Betterment added what it calls a human touch; its Plus service is offered to custom- ers with assets of over $100,000 who are willing to pay an annual fee of 0.4 percent for this service. Using it, customers can interact with human advi- sors in addition to the automated bot. An even bet- ter service is the company’s Premium level, which requires $250,000 in assets and charges 0.5 percent in fees.

While the quality of the automated service is getting better with added knowledge (machine learning), complex situations that require human intervention still remain. This is where the Plus and Premium services enter the picture. Several com- petitors also have added the human touch to their offering.

Sources: Compiled from O’Shea (2017), Eule (2017), and betterment.com (accessed April 2018).

Questions for Case 12.7

1. What are Betterment’s benefits to investors?

2. Compare Betterment to its major competitors (see Eule, 2017).

3. What are the benefits of adding the human touch (i.e., compared to pure automation and only human service)?

4. Find some new information about Betterment. Write a report.

Application Case 12.7 Betterment, the Pioneer of Financial Robo Advisors

• Provides unlimited professional expert advice (by the bot) anytime and anywhere.

• Provides advice from bots that contain the knowledge of human investment advisors.

• Assists investors in making decisions of how much to invest.

• Helps investors figure out how much risk to take.

• Helps in lowering investment-related tax. • Provides actionable answers to questions. • Advises on college savings. • Helps plan for retirement. • Assists in mortgage management (e.g., refi-

nance). • Provides personalized service via the use of

investors’ goal-based analysis.

Betterment has no account minimum (compet- itors require up to $100,000).

Each investor’s portfolio is automatically adjusted to market conditions to meet his or her goals. All portfolios are built and managed by AI algorithms.

678 Part IV • Robotics, Social Networks, AI and IoT

better than humans at one of the most important aspects in investment advising: know how to legally minimize the related tax. This implies that institutional-grade tax-loss harvesting is now within the reach of all investors. By contrast, some people believe that it is difficult to replace investment brokers with robots. De Aenlle (2018) believes that humans are still dominating advisory services (see the example of Nordea Bank by Pohjanpalo, 2017).

For a list of the best robo advisors, see Eule (2017), O’Shea (2016), and investorjunkie.com/35919/roboadvisors. For comprehensive coverage of robo advi- sors in finance and investment, including the major companies in the advisory industry, see McClellan (2016).

An emerging commercial robo advisor is being developed at Cornell University under the name Gsphere. In addition, robo advisors appear in countries other than the United States (e.g., Marvelstone Capital in Singapore).

FINANCIAL INSTITUTIONS AND THEIR COMPETITION Several large financial institutions and banks have reacted to robo advisors by creating their own or partnering with them. It is difficult to assess the winners and losers in this competition because there are no suf- ficient long-term data. So far it seems that customers like robo advisors, basically because they cost as little as 10 percent of full-service human advisors. For a discussion and data, see Marino (2016). Note that some observers point to the danger of using robo advisors in a declining stock market due to their use of ETFs.

Managing Mutual Funds Using AI

Many institutions and some individual investors buy stocks using AI algorithms. Some people prefer to buy a mutual fund that picks its holding with AI. EquBot is such a fund (its symbol is AIEQ). Its 2017 performance was above average.

The AI algorithms used by EquBot can process 1 million pieces of data each day. They follow 6,000 companies. For details, see Ell (2018).

Other Professional Advisors

In addition to investment advisors, there are several other types of robo advisors ranging from travel to medicine to legal areas.

The following are examples of noninvestment advisors:

• Computer operations. To cut costs, major computer vendors (hardware and soft- ware) try to provide users with self-guides to solve encountered problems. If users cannot get help from the guides, they can contact live customer service agents. This service may not be available in real time, which can upset customers. Live agents are expensive, especially when provided 24/7. Therefore, companies are using in- teractive virtual advisors (or assistants).

As an example, Lenovo Computers use a generic bot called noHold’s AI to provide assistance to customers as a single point of help for conducting a search.

• Travel. Several companies provide advice on planning future national and inter- national trips.

For example, Utrip (utrip.com) helps plan European trips. Based on their stated objectives, travelers get recommendations for what to visit in certain destina- tions. The service is different from others in that it customizes trips.

• Medical and health advisors. A large number of health and medical care advi- sors operate in many countries. An example is Ad a Health of Germany. Founded in late 2017 as a chatbot, it assists people in activities such as deciphering their ail- ments and can connect patients to live physicians. This can be the future of health in adding bot-based patient-doctor collaboration.

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A list of the top useful chatbots as of 2017 is provided by TalKing (2017). It includes:

• Health Tap acts like a medical doctor by providing a solution to common symptoms provided by patients.

• YourMd is similar to Health Tap. • Florence is a personal nurse available on Facebook Messenger.

Other bots include OneStopHealth, HealthBot, GYANT, Buoy, Bouylon, and Mewhat.

• Bots are acting as companions (e.g., Endurance for dementia patients). In Japan, bots that look and feel like dogs are very popular companions for elderly peo- ple. Several bots are designed to increase patient engagement. For example, Lovett (2018) reports that a bot for patient engagement increased patients’ response rate to a flu shot campaign by 30 percent. Finally, the classic pioneering bot, ELIZA, acted as a very naïve psychologist.

• Shopping advisors (shopbots). Shopbots can act as shopping advisors. An example is Shop Advisor (see shopadvisor.com/our-platform. It is a comprehen- sive platform that includes three components to help companies attract customers. The platform is a self-learning system that improves its operation over time. Its components are:

1. Product intelligence, which processes complex and diverse product data. It includes a competitive analysis.

2. Context intelligence, which collects and catalogs contextual data points about marketing facilities and inventories in different locations.

3. Shopper intelligence, which studies consumers’ actions related to different mag- azines, mobile apps, and Web sites.

There are thousands of other shopping advisors. Sephora (opening vignette) has several of them. There are chatbots for Mercedes cars and for top department stores such as Nordstrom, Saks, and DFS. The use of shopping chatbots is increasing rapidly due to the use of mobile shopping and mobile chatting on social networks. Marketers, as we stated earlier, can collect customer data and deliver targeted ads and customer service to specific customers.

Another trend that facilitates online shopping with the assistance of bots is the increase in the number of virtual personal shopping assistants. Users only have to tell Alexa by voice, for example, to buy something for them. Better than that, they can use their smartphones from anywhere to tell Alexa to go shopping. Ordering via voice directly from vendors (e.g., delivery of pizzas) is becoming popular. In addition to chatbots that operate by sellers, there are bots for providing advice on what and where to buy.

Example: Smart Assistant Shopping Bots

Shopping bots ask a few questions to understand what a customer needs and prefers. Then they recommend the best match for the customer. This makes customers feel they are receiving personalized service. The assistance simplifies the customer’s decision- making process. Smart assistants also offer advice on issues of concern to customers via Q&A conversations. For a guided test, go to a demo at smartassistant.com/advicebots. Note that these bots are essentially recommendation systems and that users need to ask for advice whereas other recommendation systems (e.g., that of Amazon.com) provide advice even when users do not ask for it.

680 Part IV • Robotics, Social Networks, AI and IoT

A well-known global shopping assistant in the area of fashion is Alibaba’s Fashion AI. It helps customers who shop in stores. When shoppers enter a fitting room, the AI Fashion Consultant goes into action. For details of how this is done, see Sun (2017).

Another type of shopping advisor works as a virtual personal advisor to shoppers. This type was developed from traditional e-commerce intelligent agents, such as bizrate. com and pricegrabber.com.

IBM Watson

Probably the most knowledgeable virtual advisor is IBM Watson (see Chapter 6). Some examples of its use follow:

• Macy’s developed a service, Macy’s On Call, to help customers navigate its physi- cal stores while they shop. Using location-based software, the app knows where they are in the store. By using smartphones, customers can ask questions regarding products and services in the stores and then receive a customized response from the chatbot.

• Watson can help physicians make a diagnosis (or verify one) quickly and suggest the best treatment. Watson’s Medical Advisor can analyze images very fast and look for things that physicians may miss. Watson already is used extensively in India where there is a large shortage of doctors.

• Deep Thunder provides accurate weather-forecasting service. • Hilton Hotels are using Watson-based “Connie Robot” in their front desks. Connie

did a superb job in experiments, and its service is improving.

Clark (2016) reports that 1 billion people will use Watson by 2018. This is in part be- cause IBM Watson is coming to smartphones as an advisor. For more, see Noyes (2016).

u SECTION 12.6 REVIEW QUESTIONS

1. Define robo advisor. 2. Explain how robo advisors work for investments. 3. Discuss some of the shortcomings of robo advisors for investments. 4. Explain the people-machine collaboration in robo advising. 5. Describe IBM Watson as an advisor.

12.7 IMPLEMENTATION ISSUES

Several implementation issues are unique to chatbots and personal assistants. Examples of representative systems are described next.

Technology Issues

Many chatbots, including virtual personal assistants, have imperfect (but improving) voice recognition. There is no good feedback system yet for voice recognition systems to tell users, in real time, how well it understands them. In addition, voice recogni- tion systems may not know when to do a current task and need to ask for human intervention.

Chatbots that are internal to organizations need to be connected to an NLP system. This may be a problem, but a bigger one may exist when chatbots are connected to the Internet, due to security and connectivity difficulties.

Some chatbots need to be multilingual. Therefore, they need to be connected to a machine language translator.

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Disadvantages and Limitations of Bots

The following are points (which were observed at the time this book was written dur- ing 2017 and 2018) regarding bots’ disadvantages and limitations; some will disappear with time:

• Some bots provide inferior performance, at least during their initiation, making users frustrated.

• Some bots do not properly represent their brand. Poor design may result in poor representation.

• The quality of AI-based bots depends on the use of complex algorithms that are expensive to build and use.

• Some bots are not convenient to use. • Some bots operate in an inconsistent manner. • Enterprise chatbots pose great security and integration challenges.

For methods to eliminate some of the disadvantages and limitations, see Kaya 2017.

VIRTUAL ASSISTANTS UNDER ATTACK Cortana, Siri, Alexa, and Google Assistant are under attack by people who are enraged at machines in general, or just like to make fun of them. In some cases, the bots’ administrators try to compose a response to the attacks; in other cases, some machines provide senseless responses to the senseless attacks.

Quality of Chatbots

While the quality of most systems is not perfect, it is improving over time. However, the quality of those that retrieve information for users and are properly programmed can do a perfect job. Generally speaking, the more a company invests in acquiring or leasing a chatbot, the better its accuracy will be. In addition, bots that serve a large number of people, such as Alexa and Google Assistant, exhibit an increasing level of accuracy.

QUALITY OF ROBO ADVISORS Given the short time since the emergence of robo advi- sors for financial services, it is difficult to assess the quality of their advice. Backend Benchmarking publishes a quarterly report (theroboreport.com) regarding robo advisor companies. Some reports are free. According to this service, Schwab’s Intelligent Portfolio Robot was the top performer in 2017. However, note that portfolio performance needs to be measured for the long run (e.g., 5 to 10 years).

A major issue when engaging bots is the potential loss of human touch. It is needed to build trust and answer complex questions so customers can understand bots’ answers. Also, bots cannot bring empathy or a sense of friendship. According to Knight (2017b), there is a solution to this. First, bots should perform only tasks that they are suited to do. Second, they should provide a visible benefit to the customer. Finally, because the bots face customers, the interactions must be fully planned to make sure the customers are happy.

In addition, note that robo advisors provide personalized advice. For information as to which robo may be best for you based on your objectives, see Eule (2017), who also provides a scorecard for the leading companies in the field. Finally, Gilani (2016) provides a guide for robo advisors as well as their possible dangers.

MICROSOFT’S TAY Tay was a Twitter-based chatbot that failed and was discontinued by Microsoft. It collected information from the Internet, but Microsoft had not given the bot the knowledge of how to deal with some inappropriate material used on the Internet (e.g., trolls, fake news). Therefore, Tay’s output was useless and frequently offended its users. As a result, Microsoft discontinued the service of Tay.

682 Part IV • Robotics, Social Networks, AI and IoT

Setting Up Alexa’s Smart Home System

Alexa is useful in controlling smart homes. Crist (2017) proposed a six-step process for how to use Alexa in smart homes:

1. Get a speaker (e.g., Echo). 2. Think about the location of the speaker. 3. Set up the smart home devices. 4. Sync related gadgets with Alexa. 5. Set up group and scene. 6. Fine-tune during the process.

These steps are demonstrated at cnet.com/uk/how-to/how-to-get-started-with- an-alexa-smart-home/.

Constructing Bots

Earlier, we presented some companies that provide development platforms for chatbots. In addition, several companies can build bots for users, so they can also build a simple bot by themselves. A step-by-step guide with the tools used is provided by Ignat (2017). The bot was constructed on Facebook Messenger. Another guide for creating a Facebook Messenger bot is provided by Newlands (2017b), who suggested the following steps:

1. Give it a unique name. 2. Give customers guides on how to build a bot and how to converse with it. 3. Experiment in making a natural conversation flow. 4. Make the bot sound smart, but use simple terminology. 5. Do not deploy all features at the same time. 6. Optimize and maintain the bot to constantly improve its performance.

There are several free sources for building chatbots. Most of them include “how-to” instructions. Several messaging services (e.g., Facebook Messenger, Telegraph) provide both chatbot platforms as well as their own chatbots. For a 2017 list of enterprise chatbot platforms and their capabilities, see entrepreneur.com/article/296504.

USING MICROSOFT’S AZURE BOT SERVICE Azure is a comprehensive but not a very com- plex bot builder. Its Bot Service provides five templates for quick and easy creation of bots. According to docs.microsoft.com/en-us/bot-framework/azure-bot-service- overview/, any of the templates shown in Table 12.1 can be used.

For a detailed tutorial for creating bots, see “Create a Bot with Azure Bot Service” at docs.microsoft.com/en-us/bot-framework/azure-bot-service-overview/.

TABLE 12.1 Azure’s Templates

Template Description

Basic Creates a bot that uses dialogues to respond to user input.

Form Creates a bot that collects input from users via a guided conversation that is created using Form Flow.

Language understanding

Creates a bot that uses natural language models (LUIS) to understand user intent.

Proactive Creates a bot that uses Azure Functions to alert users of events.

Question & Answer Creates a bot that uses a knowledge base to answer users’ questions.

Note: Microsoft also provides a bot framework on which bots can be constructed (similar to that of Facebook Messenger). For Microsoft’s Bot and a tutorial, see Afaq (2017).

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 683

Chapter Highlights

• Chatbots can save organizations money, provide a 24/7 link with customers and/or business part- ners, and are consistent in what they say.

• An expert system was the first commercially ap- plied AI product.

• ES transfer knowledge from experts to machines so the machines can have the expertise needed for problem solving.

• Classical ES use business rules to represent knowledge and generate answers to users’ ques- tions from it.

• The major components of ES are knowledge acquisition, knowledge representation, knowl- edge base, user interface, and interface engine. Additional components may include an expla- nation subsystem and a knowledge-refining system.

• ES help retain scarce knowledge in organizations. • New types of knowledge systems are superior to

classical ES, making ES disappear. • We distinguish three major types of chatbots:

enterprise, virtual personal assistants, and robo advisors.

• A relatively new application of knowledge sys- tems is the virtual personal assistant. Major examples of such assistants are Amazon’s Alexa, Apple’s Siri, and Google’s Assistant.

• Knowledge for virtual personal assistants is cen- trally maintained in the “cloud” and it is usually disseminated via a Q&A dialog.

• Personal assistants can receive voice commands that they can execute.

• Personal assistants can provide personalized ad- vice to their owners.

• Special breeds of assistants are personal advisors, such as robo advisors, that provide personalized advice to investors.

• Recommenders today use several AI technologies to provide personalized recommendations about products and services.

• People can communicate with chatbots via writ- ten messages, voice, and images.

• Chatbots contain a knowledge base and a natural language interface.

• Chatbots are used primarily for information search, communication and collaboration, and rendering advice in limited, specific domains.

• Chatbots can facilitate online shopping by pro- viding information and customer service.

• Chatbots work very well with messaging systems (e.g., Facebook Messenger, WeChat).

• Enterprise chatbots serve customers of all types and can work with business partners. They can also serve organizational employees.

• Virtual personal assistants (VPAs) are designed to work with individuals and can be customized for them.

• VPAs are created as “native” products for the masses.

• A well-known VPA is Amazon’s Alexa that is ac- cessed via a smart speaker called Echo (or other smart speakers).

• VPAs are available from several vendors. Well known are Amazon’s Alexa, Apple’s Siri, and Google’s Assistant.

• VPAs can specialize in specific domains and work as investment advisors.

• Robo advisors provide personalized online in- vestment advice at a much lower cost than human advisors. So far, the quality seems to be comparable.

• Robo advisors can be combined with human ad- visors to handle special cases.

Key Terms

Alexa chatbot Echo

expert systems Google’s Assistant recommendation systems

robo advisors Siri virtual personal assistant (VPA)

684 Part IV • Robotics, Social Networks, AI and IoT

Questions for Discussion

1. Some people say that chatbots are inferior for chatting. Others disagree. Discuss.

2. Discuss the financial benefits of chatbots. 3. Discuss how IBM Watson will reach 1 billion people by

2018 and what the implications of that are. 4. Discuss the limitation of chatbots and how to overcome

them. 5. Discuss what made ES popular for almost 30 years

before their decline. 6. Summarize the difficulties in knowledge acquisition

from experts (also consult Chapter 2). 7. Compare the ES knowledge-refining system with knowl-

edge improvement in machine learning. 8. Discuss the difference of enterprises’ use of chatbots

internally and externally.

9. Some people say that without a virtual personal assis- tant, a home cannot be smart. Why?

10. Compare Facebook Messenger virtual assistant project M with that of competitors.

11. Examine Alexa’s skill in ordering drinks from Starbucks. 12. Discuss the advantages of robo advisors over human

advisors. What are the disadvantages? 13. Explain how marketers can reach more customers with

bots. 14. Are robo advisors the future of finance? Debate; start

with Demmissie (2017). 15. Research the potential impact of chatbots on work and

write a summary.

Exercises

1. Compare the chatbots of Facebook and WeChat. Which has more functionalities?

2. Enter nuance.com and find information about Dragon Medical Advisor. Describe its benefits. Write a report.

3. Enter shopadvisor.com/our-platform and review the platform’s components. Examine the product’s capabili- ties and compare them with those of two other shopping advisors.

4. Enter chatbots.org/ and join a forum of your interest. Also explore research issues of your interest. Write a report.

5. There is intense competition between all major tech com- panies regarding their virtual personal assistants. New innovations and capabilities appear daily. Research the status of these assistants for Amazon, Apple, Microsoft, Google, and Samsung. Write a report.

6. Some people believe that chatbots will change how peo- ple interact with the Internet and browse online. Prepare a report regarding this.

7. Explain why is Amazon’s Echo needed to work with Alexa? Read howtogeek.com/253719/do-i-need-an- amazon-echo-to-use-alexa/. Write a report.

8. Find out how Simon Property Group is using chatbots across over 200 shopping malls. Write about the benefits to different types of users and to the company.

9. Read recent information about enterprise bots. Write a report.

10. Enter gravityinvestments.com/digital-advice-platform- demo. Would you invest in this project? Research and write a report.

11. Enter visirule.co.uk and find all products it has for expert systems. List them and write a short report.

12. Research the role of chatbots in helping patients with dementia.

13. Find information on the Baidu’s Melody chatbot and how it works with Baidu Doctor.

14. Pose a question related to a chatbot on quora.com. Summarize the answers received in a report.

15. Nina is an intelligent chatbot from Nuance Communication Inc. that works for Alexa Internet of Things (IoT), smart homes, and more. Find information and write a report about Nina’s capabilities and benefits.

16. Microsoft partners with the government of Singapore to develop chatbots for e-services. Find out how this is done.

17. Study the Tommy Hilfiger Facebook Messenger bot. Find out how it is (and was) used in the company’s mar- keting campaigns.

18. Two comprehensive building tools for chatbots are Botsify and Personality Forge (personalityforge.com). Compare the tools. Write a report.

19. Find information about the Alibaba-backed robo advi- sor Youyu by Yunfeng’s Investment. What is unique about this service? Start by visiting http://www. international-adviser.com/news/1035281/alibaba- backed-retail-robo-adviser-youyu-launches-honk- kong/.

20. Enter exsys.com. Select three case studies and explain why they were successful.

21. It is time now to build your own bot. Consult with your instructor about which software to use. Have several bots constructed in your class and compare their capabilities. Use Microsoft’s Azure if you have some programming experience.

Chapter 12 • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants 685

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