Chapter 11: Managing Knowledge and Artificial Intelligence

Question 1

Managing AI: Grand vision vs Realistic vision

Answer 1

• grand vision: computer hardware & software that are as “smart” as humans, so far this is not the case - at least holistically speaking
• realistic vision: systems take data inputs, process them & produce outputs, but on a complexity level that is far superior to human capabilities

Question 2

Major types of AI

Answer 2

• expert systems
• machine learning
• neural networks & deep learning networks
• genetic algorithms
• natural language processing (LLMs)
• computer vision
• robotics

Question 3

Expert systems

Answer 3

• capturing tacit knowledge in very specific & limited domain of human expertise, knowledge as set of rules
• performing limited tasks- discrete, highly structured DM
• knowledge base: set of hundreds or thousands of rules
• inference engine: Strategy used to search knowledge base, using forward chaining (data-driven technique towards a goal) or backward chaining (goal-driven technique from a goal towards origin to extract facts)

Question 4

Machine learning

Answer 4

• Software that can identify patterns & relationships in very large data sets without programming, but significant human training
• pattern recognition
• requires experience through prior learnings (database)
• supervised learning turns into unsupervised learning
  –> google searches, recommender systems
  –> sustainability issue

Question 5

Neural networks

Answer 5

• definition: algorithms loosely based on the processing patterns of the biological brain that can be trained to classify objects into known categories based on data inputs
• purpose: inding patterns and relationships in massive amounts of data
• functioning: “learn” patterns by searching for relationships, building models & correcting repeatedly
• data input: humans “train” network by feeding it data inputs for which outputs are known
• input layer –> hidden layer –> output layer

Question 6

Genetic algorithms

Answer 6

• problem-solving methods that promote the evolution of solutions to specified problems using the model of living organism adapting to their environment (evolution)
• useful for finding optimal solution for specific problem by examining very large number of possible solutions (used in optimization problems)

Question 7

Natural language processing

Answer 7

• understand & speak in natural language, read natural language & translate
• typically today based on machine learning, aided by very large databases of common phrases & sentences in a given language
• Google Translate, spam filtering systems, customer call center interactions, assistances (Siri, Alexa, Cortana, Google Assistant)

Question 8

Computer vision systems

Answer 8

• digital image systems that create a digital map of an image (like a face, or a street sign)
• Facebook’s DeepFace, passport control, identifying people in crowds, autonomous vehicles, industrial machine (robot) vision

Question 9

Robotics

Answer 9

• design, construction, and operation of machines that can substitute for humans in many factory, office & home applications (home vacuums)
• generally programmed to perform specific and detailed actions in limited domains
• Used in dangerous situations like bomb disposal
• Surgical robots are expanding their capabilities

Question 10

Intelligent agents

Answer 10

• work without direct human intervention to carry out repetitive, predictable tasks (limited built-in or learned knowledge base) –> chatbots, agent-based modeling applications

Question 11

What is hybrid intelligence & what is it used for? (NOT RELEVANT)

Answer 11

„ability to achieve complex goals by combining human and artificial intelligence, thereby reaching superior results to those each of them could have accomplished separately, and continuously improve by learning from each other“
- collectivity, superior results, continuous learning
- used when the result of joint work is better than the separate work
- most probable paradigm for division of human & machine labor, as it highlights complementary strenghts (contrary to collective intelligence)

Question 12

Different types of intelligence (NOT RELEVANT)

Answer 12

• intelligence: „the ability to accomplish complex goals, learn, reason, and adaptively perform effective actions within an environment” –> acquire and apply knowledge
• collective intelligence: „groups of individuals acting collectively in ways that seem intelligent’‘
• artificial intelligence: ‘‘[…] systems that perform activities that we associate with human thinking, activities such as decision-making, problem solving, learning“

Question 13

What is the Moravec paradox? (NOT RELEVANT)

Answer 13

It is comparatively easy to make coputers exhibit adult level performance on intelligence tests etc., but the skills of a 1-year old (perception, mobility, common sense) are difficult to emulate

Question 14

What is the “human in the loop”? (NOT RELEVANT)

Answer 14

integration of a human workforce in the AI pipeline in order to train and validate models in a continuous way

Question 15

Relevance of knowledge management systems in business

Answer 15

• fastest growing areas of software investment
• 37% U.S. labor force: knowledge and information workers
• 55% U.S. GDP from knowledge and information sectors
• Substantial part of a firm’s stock market value is related to intangible assets: knowledge, brands, reputations, and unique business processes

Question 16

Dimensions of knowledge

Answer 16

• (raw) data, knowledge & wisdom (where, when & how to apply knowledge)
• tacit knowledge: the knowledge that is implicit in a person’s actions without them being able to describe their skills verbally
• explicit knowledge: knowledge that can be readily articulated, conceptualized, codified, formalized, stored and accessed
• knowledge is a firm asset, has different forms, has a location & is situational
• Knowing how to do things effectively and efficiently in ways others cannot duplicate is a prime source of profit and competitive advantage (example: Having a unique build-to-order production system)

Question 17

Define knowledge management

Answer 17

Set of business processes developed in an organization to create, store, transfer & apply knowledge

Question 18

Define organizational learning

Answer 18

Process in which organizations gain experience through collection of data, measurement, trial and error & feedback

Question 19

What is the knowledge management value chain?

Answer 19

Knowledge management today involves both information systems activities and a host of enabling management and organizational activities. 4 stages:
1. knowledge acquisition
- documenting tacit & explicit knowledge, creating knowledge, tracking data from TPS & external sources
2. knowledge storage
- databases, document management systems, role of management
3. knowledge dissemination
- portals, wikis, e-mail, instant messaging, search engines, collaboration tools, tools to focus attention on important information
4. knowledge application
- new business practices, new products & services, new markets

Question 20

What measures can beimplemented to build org & management capital?

Answer 20

• Developing new organizational roles and responsibilities for the acquisition of knowledge
• Chief knowledge officer executives
• Dedicated staff / knowledge managers
• Communities of practice (COPs): Informal social networks of professionals and employees, Activities including education, online newsletters, sharing knowledge, reducing learning curves of new employees

Question 21

Types of knowledge management systems

Answer 21

• Enterprise-wide knowledge management systems: General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge
• Knowledge work systems (KWS): Specialized systems built for engineers, scientists, other knowledge workers charged with discovering & creating new knowledge
• Intelligent techniques: Diverse group of techniques such as data mining used for various goals: discovering knowledge, distilling knowledge, discovering optimal solutions

Question 22

Types of knowledge in an enterprise

Answer 22

• structured documents: reports, presentations, formal rules (only 20% in a business)
• semistructured documents: e-mails, videos
• unstructured, tacit knowledge

Question 23

Use cases of AI

Answer 23

• Recognize millions of faces in seconds
• Interpret millions of CT scans in minutes
• Analyze millions of financial records
• Detect patterns in very large Big Data databases
• Improve their performance over time (“learn”)
• Navigate a car in certain limited conditions
• Respond to questions from humans (natural language), speech activated assistants like Siri, Alexa, and Cortana

Question 24

What does an enterprise content management system do?

Answer 24

• capabilities for classifying, organizing & managing structured and semistructured knowledge and making it available throughout the enterprise
• bring in external resources, such as news feeds & research
• tools for communication & collaboration
• key problem: developing taxonomy
• solution: digital asset management (DAM) systems

Question 25

What is a DAM system?

Answer 25

• used by companies to store, organize, locate and share digital files.
• granting employees as well as internal & external stakeholders access to the content of a brand’s digital media library, i.e. its images, videos, presentations, documents and other digital assets

Question 26

Locating & sharing expertise through knowledge management systems

Answer 26

• Provide online directory of corporate experts in well-defined knowledge domains
• Search tools enable employees to find appropriate expert in a company –> Problem: access rights
• Social networking and social business tools for finding knowledge outside the firm (Saving, Tagging, Sharing web pages)

Question 27

What does a learning management system (LMS) do?

Answer 27

• Provide tools for management, delivery, tracking & assessment of employee learning and training
• Support multiple modes of learning (CD-ROM, web-based classes, online forums etc.)
• Automates selection & administration of courses
• Assembles & delivers learning content
• Measures learning effectiveness
• Massively open online courses (MOOCs) (many participants)
• Goal for the future: Personalized LMS or even individualized learning journey

Question 28

What are knowledge workers & what are their roles?

Answer 28

Researchers, designers, architects, scientists, engineers who create knowledge for the
organization, 3 key roles:
1. Keeping organization current in knowledge
2. Serving as internal consultants regarding their areas of expertise
3. Acting as change agents, evaluating, initiating, and promoting change projects

Question 29

Define knowledge work systems

Answer 29

Specialized systems for knowledge workers to help create new knowledge and integrate that knowledge into
business

Question 30

Name the requirements of knowledge work systems (6)

Answer 30

• Sufficient computing power for graphics, complex calculations
• Powerful graphics and analytical tools
• Communications & document management
• Access to external databases
• User-friendly interfaces
• Optimized for tasks to be performed (design engineering, financial analysis)

Question 31

Examples of knowledge work systems

Answer 31

• CAD (computer-aided design): Creation of engineering or architectural designs, 3D printing
• Virtual reality systems: Simulate real-life environments, 3D medical modeling for surgeons, Augmented reality (AR) systems, VRML

Question 32

Name intelligent techniques to capture individual & collective knowledge to extend knowledge base

Answer 32

• capturing tacit knowledge: Expert systems, case-based reasoning, fuzzy logic
• Knowledge discovery: Neural networks & data mining
• Generating solutions to complex problems: Genetic algorithms
• Automating tasks: Intelligent agents