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This document discusses Bronfenbrenner's ecological systems theory and how it can be applied to interpreters and their work environments. It notes that an ecological systems perspective recognizes that individuals exist within nested environmental systems and that all parts of a system are interdependent. Applying this lens, an interpreter is influenced by and influences their microsystem of immediate relationships, the mesosystem of connections between microsystems, and the larger exosystem of social settings that do not directly involve the interpreter but still impact them. Understanding systems theory is important for interpreters to effectively navigate interpersonal relationships, support organizational goals, and make professionally appropriate decisions.
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Overview
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Determine repeating patterns, or models, in systems — according to the behavior of the system over time
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Overview
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Application of Systems Methods
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Interactive Planning
Soft Systems Methodology
Critical Systems Heuristic
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The document discusses expert systems, which are intelligent computer programs that use knowledge and inference procedures to solve problems that require significant human expertise. It describes how expert systems consist of a knowledge base containing knowledge and an inference engine that draws conclusions. It provides examples of applications like medical diagnosis and mining site selection. It outlines the key components of an expert system and discusses their benefits in replacing human experts as well as limitations like limited domains and high development costs.
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5 ai lecture-05 expert system
1. An expert system is a computer program that aims to mimic the problem-solving abilities of a human expert. It contains a knowledge base of domain-specific knowledge and uses an inference engine to draw conclusions.
2. The major components of an expert system are the knowledge base, which contains domain knowledge; working memory, which stores facts about the current problem; and the inference engine, which matches facts to rules to draw new inferences.
3. Expert systems can replace or assist human experts. They are always available, location-independent, durable, and generally faster than humans. However, they have limited learning abilities compared to humans.
Ai lecture 05 expert system
1. An expert system is a computer program that aims to mimic the problem-solving abilities of a human expert. It contains a knowledge base of domain-specific knowledge and uses an inference engine to draw conclusions.
2. The major components of an expert system are the knowledge base, which contains domain knowledge; working memory, which stores facts about the current problem; and the inference engine, which matches facts to rules to draw new inferences.
3. Expert systems can replace or assist human experts. They are always available, cost-effective, and provide explanations for their conclusions. Key people involved in developing an expert system are domain experts, knowledge engineers, and end users.
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The document provides an overview of information systems, defining a system, characteristics of systems, and types of information systems. It can be summarized as follows:
1) A system is a group of interrelated components working together toward a common goal by accepting inputs and producing outputs through organized processes.
2) There are four levels of information systems - operational, knowledge, management, and strategic - that support different levels in an organization from daily transactions to strategic planning.
3) Characteristics of systems include organization, interaction, interdependence, environment, purpose, boundary, constraints, inputs, and outputs.
Expert System
The slide covers the explanation of expert system as application of Artificial intelligence along with its architecture and design procedure.
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The system analyst is responsible for converting customer needs and requirements into computerized systems. They must have strong technical skills to work with programmers, developers, and administrators, as well as good communication and presentation skills to understand customers and present systems. As the role requires interacting with many different departments, system analysts must be good problem solvers, creative thinkers, and able to work with a variety of people.
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System analysis identifies what a system should do and its current functions, while system design establishes the overall framework and physical implementation of components. A system analyst bridges technical staff and administrators by observing client requirements, helping the technical team work on the project, and communicating any changed needs. Their main challenge is finding the best solution among alternatives.
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This document discusses different types of information systems used at various levels of management:
1. Transaction processing systems are used at the operational level to record daily transactions like sales and orders. Management information systems are used at the middle level and provide periodic summary reports of transactions.
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Systems thinking training course covers the hypothesis, sane, apparatuses, and procedures related with systems thinking. Systems thinking enables you to comprehend the relationship and associations between a framework's parts, rather than taking a gander at the individual segments one by one. Systems thinking training covers the hypothesis, reasonable, apparatuses, and procedures related with systems thinking.
Added Value of Systems Thinking Training:
Risk reduction
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Crucial tools for competitive strategy
Tools for scenario building in a non-linear world
Effective assessment of the already existing reality and SWOT
Tools for productive involvement with diverse stakeholders
Enhanced determination of most efficient actions to drive greater “bang for the buck”
Audience:
Systems thinking training is a 2-day course designed for:
Business leaders
Executives
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What Will You Learn?
Below are the major topics are covered in this course:
Overview
Systems Methods and Techniques
Application of Systems Methods
Creativity and Systems
Hard Systems Thinking
System Dynamics: The Fifth Discipline
Complexity Theory
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Interactive Planning
Soft Systems Methodology
Critical Systems Heuristic
Team Syntegrity
Postmodern Systems Thinking
Total Systems Intervention
Critical Systems Practice
Hands-On Activities
TONEX Systems Thinking Training Sample Workshop
Training Objectives:
Determine repeating patterns, or models, in systems — according to the behavior of the system over time
Plot the components in a system and the streams between those components
Determine causes and effects within a system
Involve stakeholders in cooperative action to impact the results of a system
Explain systems thinking
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Derive their abilities to model and examine business postulations, problems and decisions. More...
Course Outline:
Overview
Systems Methods and Techniques
Application of Systems Methods
Creativity and Systems
Hard Systems Thinking
System Dynamics: The Fifth Discipline
Complexity Theory
Strategic Assumption Surfacing and Testing
Interactive Planning
Soft Systems Methodology
Critical Systems Heuristic
Team Syntegrity
Postmodern Systems Thinking
Total Systems Intervention
Critical Systems Practice
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ai-presention.pptx
This document provides an overview of expert systems, including their components and characteristics. It introduces the presenters and states that the topic is the components of expert systems. It then defines an expert system, lists some key characteristics, and describes two common inference strategies: backward chaining and forward chaining. The main body explains each of the core components of an expert system: the knowledge base, inference engine, explanation facility, knowledge acquisition facility, and user interface. It concludes by discussing advantages and limitations of expert systems.
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The document discusses expert systems, which are intelligent computer programs that use knowledge and inference procedures to solve problems that require significant human expertise. It describes how expert systems consist of a knowledge base containing knowledge and an inference engine that draws conclusions. It provides examples of applications like medical diagnosis and mining site selection. It outlines the key components of an expert system and discusses their benefits in replacing human experts as well as limitations like limited domains and high development costs.
Expert system (mis)
An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that typically require significant human expertise. It consists of a knowledge base containing expertise and an inference engine that draws conclusions. Expert systems can assist humans when experts are unavailable, replace human decision making, and are often used in applications like accounting, geology, and medical diagnosis. However, expert systems also have limitations like being limited to narrow problems and not learning on their own.
5 ai lecture-05 expert system
1. An expert system is a computer program that aims to mimic the problem-solving abilities of a human expert. It contains a knowledge base of domain-specific knowledge and uses an inference engine to draw conclusions.
2. The major components of an expert system are the knowledge base, which contains domain knowledge; working memory, which stores facts about the current problem; and the inference engine, which matches facts to rules to draw new inferences.
3. Expert systems can replace or assist human experts. They are always available, location-independent, durable, and generally faster than humans. However, they have limited learning abilities compared to humans.
Ai lecture 05 expert system
1. An expert system is a computer program that aims to mimic the problem-solving abilities of a human expert. It contains a knowledge base of domain-specific knowledge and uses an inference engine to draw conclusions.
2. The major components of an expert system are the knowledge base, which contains domain knowledge; working memory, which stores facts about the current problem; and the inference engine, which matches facts to rules to draw new inferences.
3. Expert systems can replace or assist human experts. They are always available, cost-effective, and provide explanations for their conclusions. Key people involved in developing an expert system are domain experts, knowledge engineers, and end users.
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The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
Artificial Intelligence Notes Unit 5
This document provides an overview of expert systems and AI languages. It discusses the need and justification for expert systems, as well as common expert system architectures including rule-based systems and non-production systems. It also covers knowledge acquisition and case studies of expert systems. For AI languages, it mentions Prolog syntax and programming as well as Lisp syntax and programming, including backtracking in Prolog. The document includes sample questions for 2 marks and 7 marks.
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The document discusses people platform patterns for organizations implementing microservices at scale. It introduces patterns for grouping decision makers, coordinating work, and distributing decisions, including menu of choices, results-oriented teams, and gated autonomy. These patterns aim to balance autonomy and standardization. The document also discusses elements of culture and how culture shapes decisions in organizations.
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The document discusses people platform patterns for organizations implementing microservices at scale. It introduces patterns for grouping decision makers, coordinating work, and distributing decisions, including menu of choices, results-oriented teams, and gated autonomy. These patterns aim to balance autonomy and standardization. The document also discusses elements of culture and how culture shapes decisions in organizations.
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Introduction and architecture of expert system
An expert system is an interactive computer program that uses knowledge acquired from experts to solve complex problems in a specific domain. It consists of an inference engine that applies rules and logic to the facts contained within a knowledge base in order to provide recommendations or advice to users. The first expert system was called DENDRAL and was developed in the 1970s at Stanford University to identify unknown organic molecules. Expert systems are used in applications like diagnosis, financial planning, configuration, and more to perform tasks previously requiring human expertise. They have benefits like increased productivity and quality, reduced costs and errors, and the ability to capture scarce human knowledge. However, they also have limitations such as difficulty acquiring and representing human expertise and an inability to operate outside their
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This document discusses the skills needed to be a successful systems analyst, including analytical skills, technical skills, management skills, and interpersonal skills. It focuses on analytical skills, describing key concepts like systems thinking, organizational knowledge, problem identification, and problem analysis and solving. Systems thinking involves understanding systems as interconnected components with inputs, outputs, and boundaries. Analytical skills also include understanding how organizations work and being able to identify problems by comparing the current situation to the desired outcome. The document provides examples to illustrate these concepts.
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The document provides an overview of information systems, defining a system, characteristics of systems, and types of information systems. It can be summarized as follows:
1) A system is a group of interrelated components working together toward a common goal by accepting inputs and producing outputs through organized processes.
2) There are four levels of information systems - operational, knowledge, management, and strategic - that support different levels in an organization from daily transactions to strategic planning.
3) Characteristics of systems include organization, interaction, interdependence, environment, purpose, boundary, constraints, inputs, and outputs.
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The slide covers the explanation of expert system as application of Artificial intelligence along with its architecture and design procedure.
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Foundational lenses
- 2. Premises • System has a purpose • All parts are necessary – All must be present and working • Each part has a purpose – Each part must function according to its purpose • Systems are somewhat dynamic – They respond to feedback
- 5. Interpreters – system view employment interpreter employee
- 6. Interpreters – interpreter view Interpreter Microsystem Mesosystem Exosystem
- 7. Why the systems view is critical • Effective interrelationships become possible – Overcoming systems concerns about us – Supporting system goals • “conduct appropriate to the situation” – Decision latitude – Professional autonomy
- 9. Re-visioning the Code of Professional Conduct Respect colleagues, interns, students and consumers Conduct appropriate to the situation Interpersonal skills Knowledge Engage in professional development Professional (technical) skills Possess skills and knowledge required for the situation Business skills Ethical business practices confidentiality
- 10. Systemic guides to professional decision making • System policies and procedures • System rules • Laws
- 11. Interpreter Knowledge Interpersonal skills Technical skills Business skills system Knowledge Interpersonal skills Technical skills Business skills