Artificial Intelligence
•Download as PPT, PDF•
1 like•376 views
This document provides an overview of key concepts in artificial intelligence. It discusses important application areas of AI like game playing, automated reasoning, expert systems, natural language processing, planning and robotics. It also outlines important features of AI, including using computers for reasoning and inference, focusing on problems without algorithmic solutions, dealing with incomplete information, reasoning about qualitative features, and using domain knowledge in expert systems. The document serves as an introduction to the major topics and approaches in the field of artificial intelligence.
Report
Share
Report
Share
Recommended
Artificial Intelligence
The document summarizes key points from George F Luger's 6th edition of Artificial Intelligence. It provides a revised definition of AI as the study of mechanisms underlying behavior through constructing and evaluating artifacts that attempt to enact those mechanisms. It discusses the physical symbol system hypothesis that intelligence requires the physical implementation of operations on symbol structures. It lists 7 critical open questions that remain for creating artificial intelligence, such as the representation problem and how embodiment, culture and interpretation relate to cognition.
AI chap1
This document provides an overview of key concepts in artificial intelligence including definitions of AI, questions in the field, what constitutes an intelligent entity, and important areas of AI research and application. It discusses game playing, automated reasoning, expert systems, natural language processing, modeling human performance, planning and robotics, programming languages for AI like LISP and Prolog, and machine learning. The document aims to introduce foundational topics in AI.
Artificial Intelligence
The document discusses different types of expert systems and problem solving techniques used in artificial intelligence. It provides examples of rule-based expert systems using if-then rules to diagnose automotive problems. It also describes model-based, case-based and hybrid systems, as well as planning techniques like using state space graphs and means-ends analysis to solve problems like rearranging blocks. The document contains diagrams illustrating concepts like the architecture of expert systems, production rule systems, and search techniques.
State Space Search in ai
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence
The document discusses production systems and the blackboard architecture for problem solving in artificial intelligence. It provides an overview of production systems, including how they separate knowledge and control, map onto state space search, use modular production rules and pattern-directed control. Examples of applying production systems to problems like the 8-puzzle and knight's tour are included. The blackboard architecture is also introduced as a way to organize knowledge sources to solve problems.
Artificial Intelligence
The document describes heuristic search techniques used in artificial intelligence and includes 38 figures illustrating concepts like:
- State space representations for games like tic-tac-toe.
- Heuristics applied to game states to evaluate positions.
- The process of heuristic search to find solutions in state spaces.
- Examples of heuristic search applied to problems like the 8-puzzle and nim.
- How techniques like alpha-beta pruning optimize minimax search.
Artificial Intelligence power point presentation document
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Computational Intelligence: concepts and applications using Athena
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
Recommended
Artificial Intelligence
The document summarizes key points from George F Luger's 6th edition of Artificial Intelligence. It provides a revised definition of AI as the study of mechanisms underlying behavior through constructing and evaluating artifacts that attempt to enact those mechanisms. It discusses the physical symbol system hypothesis that intelligence requires the physical implementation of operations on symbol structures. It lists 7 critical open questions that remain for creating artificial intelligence, such as the representation problem and how embodiment, culture and interpretation relate to cognition.
AI chap1
This document provides an overview of key concepts in artificial intelligence including definitions of AI, questions in the field, what constitutes an intelligent entity, and important areas of AI research and application. It discusses game playing, automated reasoning, expert systems, natural language processing, modeling human performance, planning and robotics, programming languages for AI like LISP and Prolog, and machine learning. The document aims to introduce foundational topics in AI.
Artificial Intelligence
The document discusses different types of expert systems and problem solving techniques used in artificial intelligence. It provides examples of rule-based expert systems using if-then rules to diagnose automotive problems. It also describes model-based, case-based and hybrid systems, as well as planning techniques like using state space graphs and means-ends analysis to solve problems like rearranging blocks. The document contains diagrams illustrating concepts like the architecture of expert systems, production rule systems, and search techniques.
State Space Search in ai
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence
The document discusses production systems and the blackboard architecture for problem solving in artificial intelligence. It provides an overview of production systems, including how they separate knowledge and control, map onto state space search, use modular production rules and pattern-directed control. Examples of applying production systems to problems like the 8-puzzle and knight's tour are included. The blackboard architecture is also introduced as a way to organize knowledge sources to solve problems.
Artificial Intelligence
The document describes heuristic search techniques used in artificial intelligence and includes 38 figures illustrating concepts like:
- State space representations for games like tic-tac-toe.
- Heuristics applied to game states to evaluate positions.
- The process of heuristic search to find solutions in state spaces.
- Examples of heuristic search applied to problems like the 8-puzzle and nim.
- How techniques like alpha-beta pruning optimize minimax search.
Artificial Intelligence power point presentation document
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
Computational Intelligence: concepts and applications using Athena
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
Computational Intelligence and Applications
Slides used at IEEE Computational Intelligence Society, Bangalore Chapter:
Winter School On Emerging Topics in Computational Intelligence -Theory and Applications
Introduction to Computational Intelligent
Introduction to Computational Intelligent
Motivation
Main umbrella: Natural Computing
Computational options: Levels of Abstraction
Definition: CI
Basic Properties of CI
CI Main Paradigms
Examples of Natural phenomenas
Computational Intelligence: Modeling Methodology
Applications of CI
Recommended References
Artificial Intelligence
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
AI: Introduction to artificial intelligence
This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and how to make computers capable of tasks that humans currently perform better. It outlines categories of systems that think like humans rationally, or act like humans rationally. It also describes the requirements for a system to exhibit intelligent behavior through natural language processing, knowledge representation, reasoning, and machine learning.
Case study on machine learning
This document provides an overview of machine learning through a case study submitted by computer science students. It discusses the history and evolution of machine learning from its early development in the 1940s-50s to major advances in the 21st century. The document also defines key machine learning terms, describes the typical machine learning process and steps involved, and lists different types of machine learning problems and algorithms. It aims to give readers a comprehensive introduction to the field of machine learning.
Chapter 1 (final)
This document provides an overview of artificial intelligence and discusses several key concepts:
1. It defines AI as making computers do things that people do better and discusses the goal of constructing a theory of intelligence.
2. It outlines several early AI problems and techniques like game playing, theorem proving, and expert systems.
3. It discusses challenges like natural language processing, computer vision, and commonsense reasoning that require extensive knowledge to solve.
4. It provides examples of AI techniques like symbolic representation, knowledge bases, and algorithms for solving problems like tic-tac-toe.
Case study on deep learning
This document provides an overview of deep learning including:
- A brief history of deep learning from 1943 to present day.
- An explanation of what deep learning is and how it works using neural networks similar to the human brain.
- Descriptions of common deep learning architectures like deep neural networks, deep belief networks, and recurrent neural networks.
- Examples of types of deep learning networks including feed forward neural networks and recurrent neural networks.
- Applications of deep learning in areas like computer vision, natural language processing, robotics, and more.
Ai introduction
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
Artificial Intelligence
This document summarizes chapter 14 from George F Luger's book "Artificial Intelligence 6th edition". The chapter introduces weak methods for automated reasoning and theorem proving, including the General Problem Solver which uses means-ends analysis and difference tables. It also describes resolution theorem proving which involves breaking statements into clauses and resolving them. Prolog logic programming is discussed as well as further issues in automated reasoning such as handling variables and extracting answers. Various examples and figures are provided to illustrate logical proofs and reasoning approaches.
Introduction to AI
This document provides an introduction to artificial intelligence (AI). It discusses definitions of intelligence and what AI aims to achieve, including acting humanly through techniques like the Turing Test. The document outlines key disciplines related to AI and provides a short history of the field from its origins in 1943 to modern successes. Challenges, conferences, courses and books relevant to AI are also listed. It concludes with questions and sources.
Artificial intelligence
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Ai notes
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Artificial Intelligence
This document discusses probabilistic machine learning models including hidden Markov models, dynamic Bayesian networks, and reinforcement learning. It provides definitions and examples of these concepts. Hidden Markov models are graphical models where the states are hidden and represented by observable outputs. Dynamic Bayesian networks are Bayesian networks that model systems changing over time. Reinforcement learning models sequential decision making problems where an agent takes actions in an environment to maximize rewards.
Artificial Intelligence power point presentation
A presentation about the basic idea about the present and future technologies which are dependent on the "ARTIFICIAL INTELLIGENCE".
AI is a branch of science which deals with the thinking, predicting, analyzing which are done by the computer itself.
The present presentation slides consists of the AI with machine learning and deep learning, goals of AI, Applications of AI and history of the Artificial intelligence etc.
Machine learning presentation
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
Towards which Intelligence? Cognition as Design Key for building Artificial I...
The document discusses approaches to building artificial intelligence systems based on human cognition. It argues that AI should focus on high-level cognitive functions like humans exhibit full intelligence. A cognitive AI approach models heuristics and bounded rationality used by humans. The document presents a case study of a common sense reasoning system that integrates heterogeneous conceptual representations like prototypes and exemplars, and uses a dual process of reasoning. The system is evaluated against human responses in categorization tasks with 84% accuracy, providing insights to refine the cognitive theory.
Machine Learning for dummies!
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
Adarsh gupta ppt
Neuro-Fuzzy and Soft Computing is a class that teaches techniques for creating intelligent systems that can handle real-world problems involving uncertainty and imprecision. The class will cover multiple soft computing techniques including fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning. It will present examples of industrial applications and discuss when each technique is applicable. Soft computing combines knowledge from these areas to develop systems that are human-like, adaptable, and able to explain their decisions. The techniques have already been successfully applied in areas like farming, manufacturing, and services.
Ai chapter1
This document provides an introduction to artificial intelligence. It outlines topics that will be covered, including problems and search, knowledge representation, and machine learning. It then discusses definitions of AI, the foundations of AI in fields like philosophy and psychology. A brief history of AI is presented, from its origins in the 1940s to current state-of-the-art capabilities like game playing and robotics. Different task domains for AI are listed. The document concludes with exercises analyzing definitions of artificial intelligence.
Artificial Intelligence Notes Unit 1
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
Artificial Intelligence
This document discusses various methods of knowledge representation in artificial intelligence, including semantic networks, conceptual graphs, frames, and scripts. It provides examples of each method through figures and descriptions. Different knowledge representation schemes are compared, along with their suitability for representing different types of knowledge.
Artificial Intelligence
This document contains excerpts from Chapter 12 of George F. Luger's textbook "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" 6th edition. The chapter discusses social and emergent models of machine learning, including genetic algorithms, classifier systems, artificial life, and cellular automata. It contains diagrams and tables illustrating these concepts, such as the genetic algorithm process, classifier systems interacting with the environment, and the game of life cellular automaton.
More Related Content
What's hot
Computational Intelligence and Applications
Slides used at IEEE Computational Intelligence Society, Bangalore Chapter:
Winter School On Emerging Topics in Computational Intelligence -Theory and Applications
Introduction to Computational Intelligent
Introduction to Computational Intelligent
Motivation
Main umbrella: Natural Computing
Computational options: Levels of Abstraction
Definition: CI
Basic Properties of CI
CI Main Paradigms
Examples of Natural phenomenas
Computational Intelligence: Modeling Methodology
Applications of CI
Recommended References
Artificial Intelligence
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
AI: Introduction to artificial intelligence
This document provides an introduction to artificial intelligence including definitions of AI, categories of AI systems, requirements for an artificially intelligent system, a brief history of AI, examples of AI in the real world, definitions of intelligent agents and different types of agent programs. It defines AI as the study of intelligent behavior in computational processes and how to make computers capable of tasks that humans currently perform better. It outlines categories of systems that think like humans rationally, or act like humans rationally. It also describes the requirements for a system to exhibit intelligent behavior through natural language processing, knowledge representation, reasoning, and machine learning.
Case study on machine learning
This document provides an overview of machine learning through a case study submitted by computer science students. It discusses the history and evolution of machine learning from its early development in the 1940s-50s to major advances in the 21st century. The document also defines key machine learning terms, describes the typical machine learning process and steps involved, and lists different types of machine learning problems and algorithms. It aims to give readers a comprehensive introduction to the field of machine learning.
Chapter 1 (final)
This document provides an overview of artificial intelligence and discusses several key concepts:
1. It defines AI as making computers do things that people do better and discusses the goal of constructing a theory of intelligence.
2. It outlines several early AI problems and techniques like game playing, theorem proving, and expert systems.
3. It discusses challenges like natural language processing, computer vision, and commonsense reasoning that require extensive knowledge to solve.
4. It provides examples of AI techniques like symbolic representation, knowledge bases, and algorithms for solving problems like tic-tac-toe.
Case study on deep learning
This document provides an overview of deep learning including:
- A brief history of deep learning from 1943 to present day.
- An explanation of what deep learning is and how it works using neural networks similar to the human brain.
- Descriptions of common deep learning architectures like deep neural networks, deep belief networks, and recurrent neural networks.
- Examples of types of deep learning networks including feed forward neural networks and recurrent neural networks.
- Applications of deep learning in areas like computer vision, natural language processing, robotics, and more.
Ai introduction
Artificial intelligence (AI) is intelligence exhibited by machines. It is the branch of computer science which deals with creating computers or machines that are as intelligent as humans. The document discusses the history and evolution of AI from its foundations in 1943 to modern applications. It also defines different types of AI such as narrow AI, artificial general intelligence, and artificial super intelligence. Popular AI techniques like machine learning, deep learning, computer vision and natural language processing are also summarized.
Artificial Intelligence
This document summarizes chapter 14 from George F Luger's book "Artificial Intelligence 6th edition". The chapter introduces weak methods for automated reasoning and theorem proving, including the General Problem Solver which uses means-ends analysis and difference tables. It also describes resolution theorem proving which involves breaking statements into clauses and resolving them. Prolog logic programming is discussed as well as further issues in automated reasoning such as handling variables and extracting answers. Various examples and figures are provided to illustrate logical proofs and reasoning approaches.
Introduction to AI
This document provides an introduction to artificial intelligence (AI). It discusses definitions of intelligence and what AI aims to achieve, including acting humanly through techniques like the Turing Test. The document outlines key disciplines related to AI and provides a short history of the field from its origins in 1943 to modern successes. Challenges, conferences, courses and books relevant to AI are also listed. It concludes with questions and sources.
Artificial intelligence
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
Ai notes
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Artificial Intelligence
This document discusses probabilistic machine learning models including hidden Markov models, dynamic Bayesian networks, and reinforcement learning. It provides definitions and examples of these concepts. Hidden Markov models are graphical models where the states are hidden and represented by observable outputs. Dynamic Bayesian networks are Bayesian networks that model systems changing over time. Reinforcement learning models sequential decision making problems where an agent takes actions in an environment to maximize rewards.
Artificial Intelligence power point presentation
A presentation about the basic idea about the present and future technologies which are dependent on the "ARTIFICIAL INTELLIGENCE".
AI is a branch of science which deals with the thinking, predicting, analyzing which are done by the computer itself.
The present presentation slides consists of the AI with machine learning and deep learning, goals of AI, Applications of AI and history of the Artificial intelligence etc.
Machine learning presentation
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
Towards which Intelligence? Cognition as Design Key for building Artificial I...
The document discusses approaches to building artificial intelligence systems based on human cognition. It argues that AI should focus on high-level cognitive functions like humans exhibit full intelligence. A cognitive AI approach models heuristics and bounded rationality used by humans. The document presents a case study of a common sense reasoning system that integrates heterogeneous conceptual representations like prototypes and exemplars, and uses a dual process of reasoning. The system is evaluated against human responses in categorization tasks with 84% accuracy, providing insights to refine the cognitive theory.
Machine Learning for dummies!
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
Adarsh gupta ppt
Neuro-Fuzzy and Soft Computing is a class that teaches techniques for creating intelligent systems that can handle real-world problems involving uncertainty and imprecision. The class will cover multiple soft computing techniques including fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning. It will present examples of industrial applications and discuss when each technique is applicable. Soft computing combines knowledge from these areas to develop systems that are human-like, adaptable, and able to explain their decisions. The techniques have already been successfully applied in areas like farming, manufacturing, and services.
Ai chapter1
This document provides an introduction to artificial intelligence. It outlines topics that will be covered, including problems and search, knowledge representation, and machine learning. It then discusses definitions of AI, the foundations of AI in fields like philosophy and psychology. A brief history of AI is presented, from its origins in the 1940s to current state-of-the-art capabilities like game playing and robotics. Different task domains for AI are listed. The document concludes with exercises analyzing definitions of artificial intelligence.
Artificial Intelligence Notes Unit 1
This document provides an overview of artificial intelligence (AI) including definitions of AI, different approaches to AI (strong/weak, applied, cognitive), goals of AI, the history of AI, and comparisons of human and artificial intelligence. Specifically:
1) AI is defined as the science and engineering of making intelligent machines, and involves building systems that think and act rationally.
2) The main approaches to AI are strong/weak, applied, and cognitive AI. Strong AI aims to build human-level intelligence while weak AI focuses on specific tasks.
3) The goals of AI include replicating human intelligence, solving complex problems, and enhancing human-computer interaction.
4) The history of AI
What's hot (20)
Towards which Intelligence? Cognition as Design Key for building Artificial I...
Towards which Intelligence? Cognition as Design Key for building Artificial I...
Viewers also liked
Artificial Intelligence
This document discusses various methods of knowledge representation in artificial intelligence, including semantic networks, conceptual graphs, frames, and scripts. It provides examples of each method through figures and descriptions. Different knowledge representation schemes are compared, along with their suitability for representing different types of knowledge.
Artificial Intelligence
This document contains excerpts from Chapter 12 of George F. Luger's textbook "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" 6th edition. The chapter discusses social and emergent models of machine learning, including genetic algorithms, classifier systems, artificial life, and cellular automata. It contains diagrams and tables illustrating these concepts, such as the genetic algorithm process, classifier systems interacting with the environment, and the game of life cellular automaton.
Artificial Intelligence
This document summarizes chapter 2 from George F Luger's book "Artificial Intelligence 6th edition" which discusses predicate logic and the predicate calculus. The chapter introduces predicates and functions, how to represent relationships between objects using predicates, and how to combine predicates using logical connectives and quantifiers. It also provides examples of using predicate logic to represent real-world scenarios like a blocks world and the unification process used in logical reasoning.
Artificial Intelligence
The document describes search strategies and structures used for complex problem solving in artificial intelligence, including graph theory, state space search, and predicate logic. It provides examples of different search algorithms like breadth-first search and depth-first search applied to problems like the 8-puzzle and traveling salesperson. It also shows how logic and grammars can be represented as graphs to solve problems using graph search techniques.
Artificial Intelligence
This document discusses various techniques for understanding natural language including:
1) Symbolic and statistical approaches to analyzing syntax and semantics including parsers using context free grammars, transition networks, and augmented transition networks.
2) The role of knowledge in mapping syntax to meaning using type hierarchies and case frames.
3) Techniques for querying databases and extracting information from natural language texts.
Greenfoot in Problem-solving and Artificial Intelligence
Looking at three examples of how greenfoot has been used in the teaching of problem solving (two examples) and in teaching artificial intelligence.
Artificial Intelligence
This document contains excerpts from George F Luger's book "Artificial Intelligence, 6th edition" which discusses reasoning under uncertainty. It includes sections on abductive inference, alternatives to logic-based approaches, stochastic approaches, and Bayesian networks including dynamic Bayesian networks and hidden Markov models. The document contains numerous figures illustrating different uncertainty reasoning concepts.
비아그라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 팝니다,비아그라 필름형,비아그라 정품판매,비아그라 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
Design solutions for reducing the energy needs
Nowadays the concept of Net ZEB is well-known
and widespread in the scientific community. The European
Union has set ambitious targets for 2020 and even more
ambitious for 2050. In order to reduce the domestic GHG
emissions by 80-95%, compared to 1990 levels- till 2050, the
building sector has to do its part and to pass through a deep
restructure. Therefore, it is grown the interest in design and
technical solutions for achieving a zero or nearly zero energy
building. This paper investigate several construction technologies
and system of energy production that can be adopted to build an
“enhanced saving” (parsimonious) building, which can strive for
the objective of NetZEB. Moreover the economic analysis of the
feasibility of the NZEB target has been developed.
레비트라 구입방법 카톡:DDF11 & DDF11.KR 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법, 레비트라 100mg구입방법...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
레비트라 100mg구입방법,레비트라 효과없음,레비트라 구입합니다,레비트라 녹색,레비트라 연예인,레비트라 제조사,레비트라 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법,
Vb.net presentation [pys]
This document outlines a computer shop management system created by 5 students - M.K.P.U. Maddumage, P.Y. Suriyaarachchi, C.S. Prabashwara, I.K.L. Madushanka, and C.D. Nagahawaththa - as evidenced by their student IDs listed.
레비트라 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg구매,레비트라 20mg정품판매처,레비트라 10mg구입처,레비트라 10mg구입방법,레비트라 10mg정품판매,
아드레닌 구입방법 카톡:DDF11 & DDF11.KR 아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg구매,아드레닌 20mg정품판매처,아드레닌 10mg구입처,아드레닌 10mg구입방법,아드레닌 10mg정품판매,
비아그라 구입방법 카톡:DDF11 & DDF11.KR 비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라 부작용,비아그라 제조법,비아그라 약효,
레비트라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 팝니다,레비트라 필름형,레비트라 정품판매,레비트라 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 1...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 100mg구매,씨알리스 20mg구매,씨알리스 10mg판매,씨알리스 10mg구매,씨알리스 10mg구입,
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비트라 복용법,레비트라 효능,
시알리스 구입방법 카톡:DDF11 & DDF11.KR 시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 ...
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
☆<< DDF11.KR >>☆
◆ 카톡:DDF11 ◆
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 판매가,시알리스 구매가격,시알리스 10mg정품판매처,시알리스 정품구해요,시알리스 정품지속시간,
Jermiah T
Jermiah T. Teodoro is a 26-year-old male born in Manila on October 10, 1988. He stands 5 feet 8 inches tall and weighs 177 pounds. He completed his elementary education at Dona Manuela Elementary School, high school at Las Pinas East National High school, and earned a B.S. in Nursing from Collegio De Sta. Monica from 2005 to 2009. His employment history includes positions as a data acquisition analyst, medical transcriptionist, customer service representative, and ride operator between 2010 and 2014.
Viewers also liked (20)
Greenfoot in Problem-solving and Artificial Intelligence
Greenfoot in Problem-solving and Artificial Intelligence
비아그라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
비아그라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,비아그라 정품파는곳,비아그라 정품구입처,비아그라 정품판매처,비아그라 정품구입방법,
레비트라 구입방법 카톡:DDF11 & DDF11.KR 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법, 레비트라 100mg구입방법...
레비트라 구입방법 카톡:DDF11 & DDF11.KR 후기,레비트라 복제약,레비트라 사는방법,레비트라 극복방법, 레비트라 100mg구입방법...
레비트라 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg...
레비트라 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구합니다,레비트라 50mg구입처,레비트라 20mg정품구입처,레비트라 50mg...
아드레닌 구입방법 카톡:DDF11 & DDF11.KR 아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg...
아드레닌 구입방법 카톡:DDF11 & DDF11.KR 아드레닌 구합니다,아드레닌 50mg구입처,아드레닌 20mg정품구입처,아드레닌 50mg...
비아그라 구입방법 카톡:DDF11 & DDF11.KR 비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라...
비아그라 구입방법 카톡:DDF11 & DDF11.KR 비아그라 판매,비아그라 구해요,비아그라 팔아요,비아그라 판매처,비아그라 추천,비아그라...
레비트라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
레비트라 구입방법 카톡:DDF11 & DDF11.KR 정품구입,레비트라 정품파는곳,레비트라 정품구입처,레비트라 정품판매처,레비트라 정품구입방법,
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 1...
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 씨알리스 50mg판매,씨알리스 100mg판매,씨알리스 100mg정품판매처,씨알리스 1...
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비...
씨알리스 구입방법 카톡:DDF11 & DDF11.KR 레비트라 구입,레비트라 직구,레비트라 사용후기,레비트라 복용후기,레비트라 사용법,레비...
시알리스 구입방법 카톡:DDF11 & DDF11.KR 시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 ...
시알리스 구입방법 카톡:DDF11 & DDF11.KR 시알리스 정품처방방법,시알리스 정품후불,시알리스 정품약효,시알리스 구입가격,시알리스 ...
Similar to Artificial Intelligence
AIML-M1 and M2-TIEpdf.pdf
The document discusses artificial intelligence (AI) and provides definitions, goals, techniques, branches, applications, and vocabulary related to AI. It defines AI as the study of how to make computers do things that people do better, such as problem solving, learning, and reasoning. The document outlines science and engineering based goals of AI and discusses techniques like knowledge representation, learning, planning, and inference. It also lists common branches of AI including logical AI, search, pattern recognition, and learning from experience. The document provides examples of AI applications and concludes with a discussion of knowledge representation techniques.
Artificial Intelligence
The document provides an overview of artificial intelligence and key developments in the field, including:
1. It discusses early definitions of intelligence and issues with defining AI, as well as tests like the Turing Test.
2. Early developments in AI focused on game playing to demonstrate problem solving abilities within limited domains.
3. Research then shifted to language processing with programs like ELIZA, which could hold basic conversations, and knowledge representation using semantic nets and logic programming.
271_AI Lect Notes.pdf
This document provides an overview of artificial intelligence techniques. It begins with definitions of AI and discusses branches of AI like logical AI, search, pattern recognition, knowledge representation, inference and more. It also discusses AI applications, problems in AI and the levels of modeling human intelligence. Several examples are then provided to illustrate increasingly sophisticated AI techniques for playing tic-tac-toe and answering questions to demonstrate moving towards knowledge representations that generalize information and are more extensible.
Unit I What is Artificial Intelligence.docx
The document discusses artificial intelligence (AI) and provides definitions, techniques, branches, and applications of AI. It defines AI as creating intelligent machines, especially computer programs, that can think like humans. It discusses representing knowledge to solve problems as an AI technique. Some branches of AI mentioned are logical AI, search, pattern recognition, representation, inference, common sense reasoning, learning from experience, planning, and applications in fields like robotics, natural language processing, and game playing.
Artificial-Intelligence--AI And ES Nowledge Base Systems
This document discusses teaching artificial intelligence concepts to students. It recommends using hands-on exercises and group work to effectively introduce topics. It also provides sample questions to test understanding of knowledge-based systems and expert systems, including their components, development, applications, benefits, and limitations.
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS KNOWLEDGE-BASED SYSTEMS TEACHING ...
This document discusses teaching artificial intelligence concepts to students. It recommends using hands-on exercises and group work to effectively introduce topics. It also provides sample questions to test understanding of knowledge-based systems and expert systems, including their components, development, applications, benefits, and limitations.
AI by Design
AI is a collection of quantitative approaches applied to a variety of data sources to generate artificially intelligent agents.
The goal is to substitute computers for human effort in narrow environments to perform semi-autonomous actions that are rational in the context of the human task and performance metrics.
The Ladder of Inference, described in this presentation helps practitioners, users, and stakeholders to envision and design Rational Agents and new business processes in which the agents will operate.
I have elaborated an example using the Ladder of Inference to design a rational agent for bodily injury claims arbitration.
I credit Stuart Russell and Peter Norvig for the background on AI and Rational Agents. The Ladder of Inference was first presented by Chris Argyris. https://lnkd.in/eNUevZm
Some of this material was presented during a webinar for members of the International Institue for Analytics @iianalytics @billfranksga
Machine learning
This seminar presentation discusses machine learning and its strategies and applications. It begins with introducing machine learning and some key concepts. Then it outlines different traditional and modern machine learning strategies such as rote learning, inductive learning, neural networks, and reinforcement learning. Several applications of machine learning are also presented, including computer vision, natural language processing, medical diagnosis, and more. Finally, it discusses challenges in machine learning like improving generalization ability and using data efficiently.
DOC-20221019-WA0003..pdf
The document provides an introduction to artificial intelligence, including:
1) Definitions of AI as the study of making computers intelligent like humans through techniques like problem solving, learning, reasoning, and perception.
2) Examples of different AI techniques for representing knowledge to solve problems like tic-tac-toe, with increasing complexity.
3) Branches and applications of AI like expert systems, machine learning, computer vision and natural language processing.
DOC-20221019-WA0003..pdf
The document provides an introduction to artificial intelligence including:
- Definitions of AI as the study of making computers intelligent like humans through techniques like problem solving and learning.
- Branches of AI including logical AI, search, pattern recognition, representation, inference, common sense reasoning and learning from experience.
- Applications of AI in areas like perception, robotics, natural language processing, planning, and machine learning.
- Techniques used in AI like knowledge representation and different approaches to problems like tic-tac-toe and question answering with increasing complexity.
Artificial intelligence
Artificial intelligence can be defined as the branch of computer science that is concerned with automation of computer system in an intelligent manner as like as humans.
Artificial Intelligence focuses on developing computer programs to solve complex problems and process.
What humans can do, now can be performed by machines too just, because of artificial intelligence.
AI is used because, it saves a whole lot of time and manpower.
estions for Discussion1. What is deep learning What can de.docx
estions for Discussion
1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/methods in AI.
3. What is representation learning, and how does it relate to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN activation functions.
5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.
6. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive comput-ing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
.
1010 chapter11
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
1010 chapter11
The document provides an overview of artificial intelligence and expert systems. It discusses the major branches of AI including perceptive systems, learning systems, natural language processing, neural networks, robotics, and expert systems. It also describes the components, development process, applications, and evolution of expert systems.
AI and Expert Systems
This document provides an overview of artificial intelligence and expert systems. It discusses key concepts in artificial intelligence including machine learning, problem solving, and visual processing. The major branches of AI are described as perceptive systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as storing knowledge and making inferences. The components, advantages, and applications of expert systems are also summarized.
GDG Community Day 2023 - Interpretable ML in production
Validating an ML model with train-test accuracy metrics offers an initial understanding of viability but generating consistent inferencing with contextual business goals requires understanding how the deployed model works in different nature and how they will behave in case of soft data drift.
In this talk, I will try to go through different explainability methods and how to employ them and how the choice of type of models affects or affects the interpretability in production inferencing.
420.doc
The document summarizes expectations and assessments for an Artificial Intelligence course regarding ethical and social implications. It is expected that instructors will spend an hour discussing these topics. Students are expected to research a topic on moral/ethical AI issues, summarize their findings, and write a paper presenting different positions on the issue and their own stance supported by arguments. Representative student essays of different quality levels will be kept for accreditation purposes. The assessment focuses on students' understanding of ethical considerations related to advances in intelligent systems.
1. What is deep learning What can deep learning do that tradition.docx
1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/ methods in AI.
3. What is representation learning, and how does it relate to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN activation functions.
5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.
6. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/ computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
.
1. What is deep learning What can deep learning do those tradit.docx
1. What is deep learning? What can deep learning do those traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/methods in AI.
3. What is representation learning, and how does it relate to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN activation functions.
5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.
6. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
.
1. What is deep learning What can deep learning do that traditional.docx
1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/ methods in AI.
3. What is representation learning, and how does it relate to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN activation functions.
5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.
6. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/ computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many realworld intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
.
Similar to Artificial Intelligence (20)
Artificial-Intelligence--AI And ES Nowledge Base Systems
Artificial-Intelligence--AI And ES Nowledge Base Systems
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS KNOWLEDGE-BASED SYSTEMS TEACHING ...
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS KNOWLEDGE-BASED SYSTEMS TEACHING ...
estions for Discussion1. What is deep learning What can de.docx
estions for Discussion1. What is deep learning What can de.docx
GDG Community Day 2023 - Interpretable ML in production
GDG Community Day 2023 - Interpretable ML in production
1. What is deep learning What can deep learning do that tradition.docx
1. What is deep learning What can deep learning do that tradition.docx
1. What is deep learning What can deep learning do those tradit.docx
1. What is deep learning What can deep learning do those tradit.docx
1. What is deep learning What can deep learning do that traditional.docx
1. What is deep learning What can deep learning do that traditional.docx
More from Muhammad Ahad
11. operating-systems-part-2
Windows is a popular operating system that runs on both PCs and servers. It provides a large collection of software solutions due to its popularity. While early versions of Windows were not true operating systems, modern versions like Windows Server provide stable and secure platforms for business applications and services. Failover clustering allows applications to remain highly available by failing over from one node to another in the case of hardware or software failures. The performance of an operating system depends on the underlying hardware, application load, and OS configuration.
11. operating-systems-part-1
The document provides an introduction to operating systems, covering their basic functions and components. It discusses how operating systems manage hardware resources and provide abstraction for applications. The key components described include the kernel, drivers, utilities, and applications/processes. It also covers process scheduling, file systems, APIs/system calls, memory management, and popular operating systems like IBM z/OS, IBM i, and OpenVMS.
10. compute-part-2
Midrange systems sit between mainframes and x86 servers, and are produced by IBM, HP, and Oracle. They use components from a single vendor and the vendor's operating system, making them stable, highly available, and secure. Shared memory architectures allow multiple CPUs to access all memory. UMA architectures have uniform memory access times, while NUMA architectures have non-uniform times depending on memory location. Virtualization provides logical partitions for isolation and high utilization. Compute availability features include hot-swappable components, parity and ECC memory, and lockstepping for redundancy. Virtualization provides failover clustering to automatically restart virtual machines if a host fails.
10. compute-part-1
The document provides an overview of compute infrastructure, including:
- Physical computers contain components like CPUs, memory, ports, and connectivity.
- Early computers used vacuum tubes and punch cards. Transistors and ICs reduced size and cost.
- Common processor types include Intel/AMD x86, ARM, IBM POWER, Oracle SPARC.
- Early computer memory included magnetic cores and RAM chips replaced it. RAM types are SRAM and DRAM.
- The BIOS controls a computer from power on until the operating system loads.
09. storage-part-1
This document provides an overview of storage technologies and concepts. It discusses the history of storage technologies from early drum memory to modern hard disks and solid state drives. Key concepts covered include RAID configurations, disk interfaces like SATA and SAS, tape storage technologies, storage controllers, and virtual tape libraries. The document concludes with a discussion of Kryder's law and projections for future disk capacity growth.
08. networking-part-2
This document provides an overview of key concepts in IT infrastructure architecture related to networking. It discusses the presentation and application layers, protocols like SSL/TLS, HTTP, and email protocols. It also covers infrastructure services like DHCP, DNS, NTP, and IPAM systems. Additionally, it summarizes network virtualization techniques like VLANs, VXLANs, virtual NICs, and virtual switches. Finally, it discusses software defined networking, network function virtualization, layered network topologies, spine-leaf architectures, network teaming, and the spanning tree protocol.
08. networking
This document provides an overview of IT infrastructure architecture and networking building blocks and concepts. It discusses the evolution from mainframe computers to local area networks and the internet. The key networking concepts covered include the OSI reference model, physical layer components like cables, patch panels and network interface cards, as well as datalink layer protocols like Ethernet and Wi-Fi.
07. datacenters
Datacenters house most IT infrastructure hardware in racks and provide power, cooling, fire protection and equipment space. Today's large datacenters contain thousands of servers in shipping container sized blocks. Datacenters are categorized based on size and tenant usage. Proper location, physical structure, layout, power supply, cooling, fire protection and availability tiers are important considerations for datacenter design and operation. Physical security restricts access to protect equipment.
06. security concept
This document outlines a security concept and risk management process. It discusses identifying risks and assets, assessing impact and probability, and determining appropriate risk responses such as acceptance, avoidance, mitigation, and transfer. It also describes common security controls around availability, confidentiality and integrity. Attack vectors like malware, denial of service attacks, social engineering and phishing are examined. Finally, it discusses security patterns for identity and access management, segregation of duties, layered security and cryptography.
05. performance-concepts-26-slides
The document discusses key concepts for designing IT infrastructure to ensure high performance. It covers perceived performance from a user perspective, benchmarking systems, profiling users to predict load, identifying and managing bottlenecks, scaling systems horizontally and vertically, load balancing, caching frequently used data, and designing systems based on their intended use to optimize performance. The overall goal is to design infrastructure that can meet performance requirements under all conditions, both currently and as load increases over time.
05. performance-concepts
This document discusses various concepts related to ensuring adequate performance of IT infrastructure. It covers perceived performance from an end user perspective and how to account for performance during infrastructure design. Methods discussed for evaluating performance during the design phase include benchmarking, leveraging vendor experience, prototyping, and user profiling. The document also addresses managing performance of running systems through techniques like performance testing, identifying and addressing bottlenecks, leveraging caching, and scaling infrastructure through vertical and horizontal expansion approaches like load balancing.
04. availability-concepts
This document discusses availability concepts for IT infrastructure architecture. Some key points include:
- Availability is calculated based on uptime percentage over time (usually annually or monthly), with 99.9% being a common service level agreement target.
- Downtime is influenced by factors like mean time between failures (MTBF) and mean time to repair (MTTR). Redundancy and failover help improve availability.
- Sources of downtime include human errors, software bugs, planned maintenance, hardware failures, environmental issues, and infrastructure complexity. Redundancy, failover, and fallback solutions can help address some causes of downtime.
03. non-functional-attributes-introduction-4-slides
Non-functional attributes describe qualitative behaviors of a system like security, performance, and availability rather than specific functions. While important for successful implementation and use, non-functional requirements often receive less attention than functional services but greatly impact a system's acceptance, as performance issues can deter users. An infrastructure architect must consider how infrastructure delivers non-functional attributes and present any conflicting requirements to stakeholders to make informed decisions on quality tradeoffs.
01. 03.-introduction-to-infrastructure
This document provides an introduction to IT infrastructure architecture, defining key concepts and building blocks. It discusses how infrastructures have become more complex with new applications and the need for agility. The definition of infrastructure is examined, noting it depends on perspective. Infrastructure comprises processes/information, applications, application platforms, and underlying hardware/network blocks. Non-functional requirements like availability, performance and security are crucial to infrastructure and often conflicting to balance. Architecture is needed to manage infrastructure design, use and changes.
01. 02. introduction (13 slides)
This document provides an introduction to IT infrastructure, defining its key components and concepts. It discusses how infrastructures have become more complex with new applications and specialized hardware. The infrastructure is comprised of building blocks including processes/information, applications, application platforms, and underlying infrastructure components like servers, storage, networking, and datacenters. Non-functional attributes like availability, performance, and security are also essential considerations in infrastructure architecture.
Chapter14
The document discusses the importance of documenting network designs, whether in response to a customer's request for proposal (RFP) or independently. It recommends that an RFP response follow the specified format and include topics like the design, implementation plan, pricing and qualifications. For independent documentation, it suggests including an executive summary, requirements, current/proposed designs, implementation plan and budget. Typical sections of a design document are executive summary, requirements, logical/physical designs, implementation plan and budget. Possible appendixes provide supplementary details.
Chapter13
This document discusses various methods for optimizing network design and performance, including using IP multicast to efficiently deliver multimedia streams, reducing serialization delay through link fragmentation and compressed RTP, and supporting quality of service through techniques like IP precedence, differentiated services, 802.1p prioritization, advanced switching and queuing methods, RED, CAR, and traffic shaping.
Chapter12
Testing a network design is important to:
1) Verify that key business and technical goals are met;
2) Validate technology and device selections; and
3) Identify any bottlenecks or connectivity problems.
Chapter11
This document discusses selecting technologies and devices for enterprise networks. It covers remote access technologies like PPP, ISDN, cable modems and DSL. For WANs it discusses leased lines, SONET, Frame Relay and ATM. Selection criteria for remote access devices, VPN concentrators, routers and WAN service providers are provided. Key factors include business needs, cost, performance, security, manageability, supported protocols and geographical coverage.
Chapter10
This document discusses selecting technologies and devices for campus network designs. It covers considerations for the physical cabling plant design including centralized vs distributed topologies. Common media types are discussed such as copper, optical fiber, and wireless. Specific technologies like Ethernet variants and their characteristics are outlined. Selection criteria for internetworking devices that will make up the campus network are presented, including processing power, port counts, latency, throughput and support/quality factors. The document provides guidance on evaluating options for building out the physical implementation of a previously designed logical campus network.
More from Muhammad Ahad (20)
03. non-functional-attributes-introduction-4-slides
03. non-functional-attributes-introduction-4-slides
Recently uploaded
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsGraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Recently uploaded (20)
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Artificial Intelligence
- 1. 1 George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving Artificial Intelligence: Its Roots and Scope Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice 1.2 Overview of AI Application Areas 1.3 Artificial Intelligence – A Summary 1.4 Epilogue and References 1.5 Exercises
- 2. 2 Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 Fig 1.1 The Turing test.
- 3. 3 Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 Important Research and Application Areas 1.2.1 Game Playing 1.2.2 Automated Reasoning and Theorem Proving 1.2.3 Expert Systems 1.2.4 Natural Language Understanding and Semantic Modeling 1.2.5 Modeling Human Performance 1.2.6 Planning and Robotics 1.2.7 Languages and Environments for AI 1.2.8 Machine Learning 1.2.9 Alternative Representations: Neural Nets and Genetic Algorithms 1.2.10 AI and Philosophy
- 4. 4 Important Features of Artificial Intelligence 1. The use of computers to do reasoning, pattern recognition, learning, or some other form of inference. 2. A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem-solving technique. 3. A concern with problem-solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems. 4. Reasoning about the significant qualitative features of a situation. 5. An attempt to deal with issues of semantic meaning as well as syntactic form. 6. Answers that are neither exact nor optimal, but are in some sense “sufficient”. This is a result of the essential reliance on heuristic problem-solving methods in situations where optimal or exact results are either too expensive or not possible. 7. The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems. 8. The use of meta-level knowledge to effect more sophisticated control of problem- solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential are of research. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009
Editor's Notes
- OWEN: Insert chapter number