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BusBis > Chapter 11: Managing Knowledge and AI > Flashcards

Chapter 11: Managing Knowledge and AI Flashcards

1
Q

Knowledge Management System

A

Knowledge is useful and actionable when shared
- information economy: wealth and prosperity comes from distribution of info and knowledge
- knowledge projects can produce ROI

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1
Q

Knowledge is an Asset

A
  • data is useful for transaction
    needs processed into categories of understanding to be info
  • info needs expanded with additional research to discover patterns, rules, and contexts to turn into knowledge
  • Wisdom: applying knowledge to solve problems
  • knowledge based core competencies (2/3 things org does best) are key org assets
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2
Q

Data

A

simple observations
- easily captured and structure

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3
Q

Information

A

Data endowed with Relevance and purpose
- unit analysis, consensus meaning

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4
Q

Knowledge

A

valuable info from human mind
- hard to transfer or capture
(knowledge is knowing how to do something vs. info is what to do)

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5
Q

Traits of Knowledge

A
  • independent and collective attribute of firm
  • knowing how to do things efficiently in ways other cannot duplicate is prime source for profit and competitive advantage
  • stored variety of places (has location)
  • “sticky’ hard to move
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6
Q

Situational Knowledge

A

Conditional (when to apply)
Contextual (circumstances)

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7
Q

Tacit Knowledge

A

UNDOCUMENTED
- hard to formalize and communicate
- subjective
- not recorded
- experiences, beliefs, skills

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8
Q

Explicit Knowledge

A
  • easily transferrable, collected and organized
  • objective, logical, technical
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9
Q

Knowledge Attributes

A

total knowledge increases, half-life decreases
- can be in more than 1 place at a time
- permanent or time sensitive
- used without being consumed\
- selling doesn’t decrease supply
once disseminated, can’t recall

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10
Q

Organizational Learning

A

knowledge drive of new behavior
- process in which organizations learn
-gain experience through data collection, trial and error
- just behavior to reflect experiences

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11
Q

Knowledge Value

A
  • value is difficult to measure
  • value extracted when knowledge is used
  • sharing increases value of knowledge
  • value increases with abundance
  • buyer can’t judge in advance
  • can be added by filtering knowledge
  • value is not well related to acquisition cost
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12
Q

Knowledge Management Value Chain

A

business processes developed in org to create, store, transfer, and apply knowledge
Value Chain: activities that increase efficiency, add value
- each step adds value to info and raw data as they are transformed into knowledge (acquisition, storage, dissemination, application)

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13
Q

Knowledge Acquisition

A

process depends on type of knowledge
- discovering patterns via machine learning or
- business analytics processing data

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14
Q

Knowledge Dissemination

A
  • portals, email, IM, social, search engines
  • shared calendars, documentation storage
  • training programs, shared management experiences communicated through culture
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15
Q

Knowledge Applicaiton

A
  • knowledge that is not shared nor applied to solve problems add 0 business value
  • ROI: return on investment, drives decision making
  • knowledge must be applied or built in system
  • helps maintain internal business processes, relationships, stakeholders
  • management drives process by utilizing new knowledge to create new stuff
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16
Q

Collaboration, Communities of Practice (COPS) + office environments

A

COPS: informal social networks within and outside firm, similar work related activities and interest
- depend greatly on software environment that enable collaboration and communication
- easier to reuse knowledge: document repositories
- encourage discussion and contribution
- providing industry contacts - access to already established methods and tools
- “spawing around” for new ideas and techniques

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17
Q

Types of Knowledge Management Systems

A
  • Enterprise Wide KMS
  • Knowledge Worker system (KWS)
  • intelligent Techniques
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18
Q

Enterprise Wide KMS

A

general purpose firm-wide efforts to collect, store, distribute, and apply knowledge

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19
Q

Knowledge Worker system (KWS)

A

specialized systems built for enginers and scientists (those charged with discovering and creating new knowledge)

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20
Q

Intelligent Techniques

A

diverse techniquest used for various goals: discover knowledge, distilling it, and discovering optimal solutions

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21
Q

Intelligent Techniques (AI)

A

involves attempt to build comp sys. that think/act like humans
- take data input from environment, process inputs, generate outputs

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22
Q

Expert Systems inof

A
  • intelligent technology for capturing tact knowledge in a very specific, limited domain of human knowledge
  • capture knowledge of skilled employees in form of set of rules in software
    Knowledge Base: model human knowledge as set of rules to be followed
    Interference Engine: strategy used to search rules and form conclusions
23
Q

Expert Systems

A

represent knowledge of experts as set of rules that can be programmed so that it can assist human decision-makers

24
Q

Machine Learning

A

software that can identify patters in large databases with explicit programming (but human training)

25
Q

Neural Networks and Deep Learning

A

algorithms that can be trained to classify objects into known categories based on data inputs
- Deep Learning uses multiple layers of neural networks to reveal underlying patterns in data

26
Q

Genetic Algorithms

A

based loosley on evolutionary natural selection and mutation
- commonly used to generate high-quality solutions to optimize problems

27
Q

Natural Language Processing

A

algorithms that make it possible for computer to understand natural human language

28
Q

Computer Vision Systems

A

can view and extract data from real-world image

29
Q

Robotics

A

machines that can substitue for human movement and also have computer controls

30
Q

Intelligent Agents

A

software agents that use built in or learned knowledge to perform specific tasks

31
Q

Machine Learning Info

A
  • more than 75% AI today
    study of how computer programs can improve their performance WITHOUT explicit programming
    “training data”
  • can recognize patterns in data and change its behavior based on its recognitions of patterns, experience, or prior knowledge
32
Q

Supervised Learning

A

(majority of ML)
- system is trained by humans providing specific examples of inputs/outputs
(testing continues until algorithms produce accurate results)

33
Q

Unsupervised Learning

A

no human training.system asked to produce results of scanned database
- eventually teach itself by finding patterns

34
Q

Neural Networks Info

A
  • used for solving complex, poorly understood problems which have large amounts of unconnected data
  • detect patterns and relationships in massive amounts of data that would be too complicated and difficult for humans beings to analyze
    Diff from Experiment Systems: enterprise must be modeled with rules, physical machine emulates human brain + taught from experience
35
Q

Deep Learning

A
  • more complex with many layers of transformation of theinput data to produce output
    Nodes (layers –> collection of neurons
  • system is not told what to look for specifically, but simply discoers a pattern
    selt taught
36
Q

Limitations Neural Networks and Machine Learning

A
  • require very large data sets to identify patters
  • cannot see how system arrived at solution
  • best for binary solutions but most problems don’t have
  • no ethics
37
Q

Genetic Algorithms info

A
  • used in areas of optimization, product design, and monitoring industrial sys
  • min cost, masimize profits –> schedule use resources efficiently
  • most appropriate when probs are dynamic and complex and have lots of variable formulats
38
Q

Natural language processing info

A
  • uses advanced speech recognition tech, tries to analyze intent
  • used in customer contact centersC
39
Q

Computer Vision System

A

used to simulate human visual system to view and extract info from real world images
- incorporate image processing, pattern recognition, and image understanding

40
Q

Intelligent Agents

A

work without direct human intervention to carry out repetitive, predictable tasks
- use limited built-in or learned knowledge base
Chatbots
- Agent-based modeling applications
- model behavior of consumers, stock markets, supply chain

41
Q

Enterprise Content Management System

A

capabilities for knowledge capture, storage retrival, distribution, and presentation
- enable users to access external info
Taxonomy
- classification scheme to organize info into meaningful categories for ways access
Digital Asset Management System
- classify, store, distribute digital objects

42
Q

Learning Management Systems

A

tools for managment, delivery, trakcing, and assessmentof employee learning and training
- automates selection and administration of learning content
- measures learning effectiveness
- massive open online courses (MOOCs)
canvs

43
Q

Knowledge Work Systems

A

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

44
Q

Knowledge Workers

A

-researchers, designers, architects, scinetists
create knowledge for org
serve as internal consultants regarding area of expertise
change agents
keep org current in knowledge
EX: CAD, VR, AR

45
Q

Knowledge Work System Requirements

A
  • sufficient computing power for graphics, complex calculations
  • powerful graphics and analytics tools
  • communication and document management
    access to external databases
  • user-friendly interfaces
  • optimized for tasts to be performed
46
Q

Knowledge work systems require…

A

strong links to eternal knowledge bases in addition to specialize hard/software

47
Q

Computer-aided design (CAD)

A

creation of engineering or architectural designs (3D print)

48
Q

Virtual Reality systems

A

simulate real life environemnts

49
Q

Investment Workstations

A

streamline investment processes and consolidate internal, external data for brokers, traders, portfolio managers

50
Q

Intelligent Techniques

A

used to capture individual and collective knowledge to extend to knowledge base

51
Q

to capture tacit knowledge

A

expert systems, case based reasoning, fuzzy logic

52
Q

knowledege discovery

A

neural networks and data miningg

53
Q

generating solutions to complex probelms

A

genetic algorithms

54
Q

Artificial intelligence (AI) in tech

A

computer bases systems taht emulate human behavior

55
Q

Organizational Intelligence: Case abased reasoning

A

descriptions of past experiences of human specialists, stored in knowledge base
- system searches for cases with characteristics similar to new one and applies solutions of old case to new case
stores org intell

56
Q

Fuzzy Logic System

A
  • rule based tech that represents imprecision used in linguistic categories that represent range of values
  • describe a particular phenomenon or process linguistically and then represent the description in a small # of flexible rules
  • provides solutions to problems requiring expertise that is hard with if-=then statements

BusBis (4 decks)

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