Organizational Knowledge and Competencies

The Value of Knowledge to Organizations

• Knowledge-based core competencies are key organizational assets that provide a prime source of profit and competitive advantage.
• Having a unique build-to-order production system is an example of an organizational asset that cannot be duplicated.

Organizational Learning

• Organizational learning is a process in which organizations gain experience through collection of data, measurement, trial and error, and feedback.

The Knowledge Management Value Chain

• Knowledge management is the set of business processes developed in an organization to create, store, transfer, and apply knowledge.
• The knowledge management value chain consists of four stages:
  • Knowledge acquisition
  • Knowledge storage
  • Knowledge dissemination
  • Knowledge application
• Each stage adds value to raw data and information as they are transformed into usable knowledge.

Knowledge Acquisition

• Documenting tacit and explicit knowledge involves building corporate repositories of documents, reports, presentations, and best practices.

Machine Learning

• Machine learning is a type of AI that allows computer systems to learn from data without being explicitly programmed.
• ML algorithms use statistical models to identify patterns and relationships in data, and use these patterns to make predictions or decisions.
• ML algorithms are used for example in spam filtering, where they analyze the text of each email to identify whether the email is likely to be spam or not.

Neural Networks

• Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain.
• They are composed of layers of interconnected nodes (neurons) that process input data and make predictions or decisions based on that data.
• Neural networks are used to solve complex poorly understood problems for which large amounts of data have been collected.
• Face recognition is a classic example of how neural networks can be trained to recognize patterns in data.

Genetic Algorithms

• Genetic algorithms are a type of optimization algorithm inspired by the process of natural selection.
• They are used to solve complex problems by searching through a large space of potential solutions and gradually refining those solutions over time.
• Genetic algorithms are able to evaluate many alternatives quickly and are used in optimization problems such as minimization of costs, efficient scheduling, and optimal engine design.

Natural Language Processing

• Natural language processing is a branch of AI that focuses on the interaction between computers and human languages.
• NLP techniques are used to analyze, understand, and generate human language.
• Applications of NLP include language translation, sentiment analysis, and chatbots.

Computer Vision Systems

• Computer vision systems emulate the human visual system to view and extract information from real-world images.
• Examples of computer vision systems include Facebook’s DeepFace, which can identify friends in photos across their system and the entire web, and autonomous vehicles that can recognize signs, road markers, people, animals, and other vehicles.

Robotics

• Robotics combines AI, mechanical engineering, and electronics to create robots that can perform tasks autonomously.
• Robots are generally programmed to perform specific and detailed actions in limited domains, such as spray painting autos, assembling certain parts, welding, and heavy assembly movement.

Intelligent Agents

• Intelligent agents work without direct human intervention to carry out repetitive, predictable tasks like deleting junk e-mail or finding the cheapest airfare.
• Intelligent agents use a limited built-in or learned knowledge base and can be used in applications such as virtual assistants, chatbots, and language processing tools.
• Agent-based modeling applications include modeling behavior of consumers, stock markets, and supply chains, and predicting the spread of epidemics.