Knowledge Management Principles

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Questions and Answers

Which of the following are considered subcategories of Artificial Intelligence?

Neural networks (correct)

Deep learning (correct)

Robotics (correct)

Computer vision systems (correct)

Natural language processing (correct)

Expert systems (correct)

Genetic algorithms (correct)

Machine learning (correct)

Intelligent agents (correct)

What is the key difference between supervised and unsupervised learning in Machine learning?

Supervised learning requires labeled data, where the system is trained using examples of both desired inputs and outputs. Unsupervised learning, on the other hand, does not rely on labeled data and instead, the system is tasked with identifying patterns within the provided data.

In the context of knowledge management, what is taxonomy used for?

Taxonomy is a method of classifying information into meaningful categories according to a predetermined system. It helps organize information so that it can be easily accessed and utilized.

Which of the following are considered components of Knowledge Management?

Knowledge Acquisition: Signup and view all the answers

What are the key characteristics of knowledge workers?

Knowledge workers are individuals who create, manage, and utilize knowledge within an organization. They often possess advanced education, professional certifications, and expertise in specific domains. Signup and view all the answers

Which of these are considered key areas for applying Neural Networks?

Image recognition Signup and view all the answers

Define Tacit Knowledge.

Tacit knowledge is the kind of knowledge that is acquired and retained through experience and often difficult to articulate or document explicitly. Signup and view all the answers

What are the main advantages of using Expert systems?

Expert systems provide several benefits, including improved decision-making, reduced errors, lower costs, decreased training time, and enhanced quality and service. Signup and view all the answers

What is the significance of the 'inference engine' in Expert systems?

The inference engine is a crucial part of an Expert system as it acts as the reasoning engine that uses the knowledge base to deduce conclusions and make decisions. Signup and view all the answers

Machine learning algorithms are designed to identify patterns and relationships in massive datasets without the need for explicit programming, relying instead on statistical inference.

True Signup and view all the answers

Study Notes

Important Dimensions of Knowledge

Knowledge is distinct from data, information, and wisdom.
Data are events/transactions captured by organizational systems.
Information is transformed data.
Knowledge combines concepts, experience, and insights.
Wisdom applies knowledge to problem-solving.
Knowledge is a cognitive and physiological event within individuals and also collective within an organization.
Tacit knowledge is undocumented employee knowledge.
Explicit knowledge is documented knowledge.
Knowledge can reside in various formats (email, voicemail, graphics).
Knowledge is managed through organizational learning.
Organizations adjust their behavior based on learning, which is called organizational learning; this includes changes to business processes and managerial decision-making.

The Knowledge Management Value Chain

Knowledge management involves creating, storing, transferring, and applying knowledge within an organization.
Organizations acquire knowledge by identifying patterns from corporate data sources such as neural networks, genetic algorithms, natural language processing, and AI techniques.
Stored knowledge includes documents, patterns, and expert rules, using tools like expert systems, portals etc.
Knowledge dissemination involves sharing knowledge through email, instant messaging, wikis, and social media.
Knowledge application is crucial as knowledge not applied adds no business value.

Building Organizational and Management Capital

Managers create new roles (e.g., chief knowledge officer) and communities of practice (COPs) to improve knowledge acquisition.
COPs are informal networks of employees with shared work-related activities and interests.
COP activities include self-education, group learning, conferences, online newsletters, and day-to-day knowledge sharing.
COPs help reuse information efficiently.

Types of Knowledge Management Systems

Enterprise-wide knowledge management systems are used for general-purpose knowledge management across an entire organization.
Knowledge work systems aid specialized knowledge workers.

Artificial Intelligence (AI) and Machine Learning

AI encompasses computer-based systems designed to mimic human thought processes.
Machine Learning is a subset of AI where software detects patterns in large datasets with minimal specific programming.
AI is driven by Big Data, reduced cost of computer processing, and refined algorithms.
Types of AI include: expert systems, machine learning, neural networks, deep learning, genetic algorithms, natural language processing, computer vision, robotics, and intelligent agents.

Expert Systems

Capture expert knowledge through interviews and represent it as rules (IF-THEN).
Expert systems help with complex processes, decision-making, error reduction, cost reduction, and improved training.
Expert systems model human knowledge using a knowledge base and an inference engine to formulate conclusions.

Machine Learning

Machine learning identifies patterns in large datasets without specific programming, making statistical inferences.
Supervised learning trains systems using input-output examples, while unsupervised learning processes data without pre-defined examples.

Neural Networks

Neural networks detect patterns in data and learn through algorithms that strengthen connections between data points; they are used for pattern classification, optimization, and prediction.
Humans feed outcome examples to neural networks to help them learn, and this is done by dividing a data set.
The data set is split into a training data set and test data set.

Deep Learning

Deep learning uses multiple layers of neural networks to identify underlying patterns in complex data.
The system is expected to learn patterns without explicit programming.

Genetic Algorithms

Based on evolutionary biology, genetic algorithms search for optimal solutions.
They use populations of strings to represent possible solutions.
Optimization processes continue with mutation, selection, and crossover steps.
Solutions evolve over time leading to the best possible outcome.

Natural Language Processing (NLP)

NLP allows computers to interpret and understand human language.
NLP algorithms are often based on machine learning, including deep learning, to determine intent.

Computer Vision Systems

These systems imitate human vision to extract information from images.
Applications include autonomous vehicles and image understanding.

Robotics

Robotics involves building and using machines to perform tasks that substitute human labor.
Robots excel in dangerous or highly repetitive tasks.

Intelligent Agents

Intelligent agents are software programs that automate tasks for users, businesses, or software applications.
Examples include spam filtering, appointment scheduling, and online shopping tools.

Enterprise Content Management Systems (ECMs)

ECM systems manage both structured and unstructured knowledge assets for businesses.
They store, retrieve, distribute, and preserve knowledge while automating and helping with processes like knowledge capture.
ECM systems help preserve valuable knowledge and documents.
Businesses use these to improve business processes and are commonly found in large corporate settings.
Examples of the content maintained include documents, reports, presentations, best practices, and other information.

Learning Management Systems (LMSs)

Learning management systems help businesses manage, deliver, track, and assess employee learning.
They offer different delivery methods like downloadable videos and web-based classes.

Knowledge Work Systems Examples

Computer-aided design (CAD) systems automate design creation and revision.
3-D printing creates solid objects from digital files.
Virtual reality (VR) and augmented reality (AR) enhance visualization and interaction with information. These systems are typically used by engineers, scientists, and knowledge workers tasked with creating knowledge.

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Description

Explore the vital dimensions of knowledge and its management within organizations. This quiz covers the distinctions between data, information, and knowledge, as well as the processes involved in organizational learning. Test your understanding of how knowledge contributes to decision-making and problem-solving.