Artificial Intelligence and Soft Computing.Brief view of AI it's components and the importance of soft computing in AI.Several applications of AI and various fields of application.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
The document provides an overview of the history and development of artificial intelligence from its early beginnings in 1943 through modern applications. It discusses milestones like the Dartmouth conference that named AI in 1956 and the rise of neural networks and machine learning in the 1980s. Notable successes are outlined such as Deep Blue's chess victory in 1997 and AI systems used for logistics planning, robotics, and machine translation. The approaches of strong AI, weak AI, applied AI, and cognitive AI are also summarized.
it presents you
1.Introduction to Artificial Intelligence
2.History and Evolution
3.Speech synthesis
4.Robots and Image processing
5.Sensor fusion
6.Innovation in Artificial Intelligence
7.conclusion
Mr. Koushal Kumar Has done his M.Tech degree in Computer Science and Engineering from Lovely Professional University, Jalandhar, India. He obtained his B.S.C and M.S.C in computer science from D.A.V College Amritsar Punjab. His area of research interests lies in Artificial Neural Networks, Soft computing, Computer Networks, Grid Computing, and data base management systems
Artificial Intelligence Techniques In Power Systems Paper Presentationguestac67362
This document discusses three artificial intelligence tools - fuzzy logic, neural networks, and genetic algorithms - and their applications in engineering problems. It provides details on each tool, including definitions of key terms and examples of their use. Fuzzy logic is outlined as being useful for modeling imprecise systems using linguistic rules. Neural networks can learn from examples to capture domain knowledge and generalize, though the knowledge is not explicit. Both are described as having been applied successfully in areas like process control.
Artificial Intelligence And Its ApplicationsKnoldus Inc.
Artificial Intelligence(AI) is the simulation of human intelligence by machines. In other words, it is the method by which machines demonstrate certain aspects of human intelligence like learning, reasoning and self- correction. Since its inception, AI has demonstrated unprecedented growth. This learning process is inspired by us, the humans. In this knolx, we are going to discuss about this adaptation of learning processes.
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
The document provides an overview of the history and development of artificial intelligence from its early beginnings in 1943 through modern applications. It discusses milestones like the Dartmouth conference that named AI in 1956 and the rise of neural networks and machine learning in the 1980s. Notable successes are outlined such as Deep Blue's chess victory in 1997 and AI systems used for logistics planning, robotics, and machine translation. The approaches of strong AI, weak AI, applied AI, and cognitive AI are also summarized.
it presents you
1.Introduction to Artificial Intelligence
2.History and Evolution
3.Speech synthesis
4.Robots and Image processing
5.Sensor fusion
6.Innovation in Artificial Intelligence
7.conclusion
Mr. Koushal Kumar Has done his M.Tech degree in Computer Science and Engineering from Lovely Professional University, Jalandhar, India. He obtained his B.S.C and M.S.C in computer science from D.A.V College Amritsar Punjab. His area of research interests lies in Artificial Neural Networks, Soft computing, Computer Networks, Grid Computing, and data base management systems
Artificial Intelligence Techniques In Power Systems Paper Presentationguestac67362
This document discusses three artificial intelligence tools - fuzzy logic, neural networks, and genetic algorithms - and their applications in engineering problems. It provides details on each tool, including definitions of key terms and examples of their use. Fuzzy logic is outlined as being useful for modeling imprecise systems using linguistic rules. Neural networks can learn from examples to capture domain knowledge and generalize, though the knowledge is not explicit. Both are described as having been applied successfully in areas like process control.
Artificial Intelligence And Its ApplicationsKnoldus Inc.
Artificial Intelligence(AI) is the simulation of human intelligence by machines. In other words, it is the method by which machines demonstrate certain aspects of human intelligence like learning, reasoning and self- correction. Since its inception, AI has demonstrated unprecedented growth. This learning process is inspired by us, the humans. In this knolx, we are going to discuss about this adaptation of learning processes.
This document discusses artificial intelligence and human intelligence. It defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, use language, and learn. The document then discusses features of intelligence such as adaptability, capacity for knowledge, abstract thought, comprehension of relationships, evaluation, and original thought. It also discusses definitions of artificial intelligence as simulating human intelligence and making computers do things at which people are currently better. The document compares human and artificial intelligence, noting pros and cons of each. Finally, it distinguishes artificial intelligence from conventional computing by describing how AI uses search and pattern matching while conventional software follows logical steps.
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Artificial Intelligence Short Question and AnswerNaiyan Noor
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
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 AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
Towards which Intelligence? Cognition as Design Key for building Artificial I...Antonio Lieto
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.
The document outlines applications of artificial intelligence including game playing, general problem solving, expert systems, natural language processing, computer vision, robotics, and education. It discusses each application in 1-3 paragraphs providing examples and components when relevant. The document concludes with references.
The document provides an overview of artificial intelligence, including definitions, key concepts, and applications. It defines AI as the simulation of human intelligence in machines, and notes the differences between weak/narrow AI which focuses on specific problems, versus strong/general AI which aims to achieve human-level intelligence. The document also discusses how AI works by trying to think and act well, and by attempting to think and act like humans. It provides examples of AI application areas and practical tools used today.
This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
The Use of Artificial Intelligence and Machine Learning in Speech RecognitionUniphore
This document discusses how artificial intelligence and machine learning are used in speech recognition technology. It explains that AI and ML allow speech recognition solutions to analyze large amounts of speech data to build statistical models and predict outcomes accurately. Examples are given of how Microsoft, Google, and Uniphore's AI-powered speech recognition software achieves high accuracy rates and can continuously improve through machine learning. The document advocates that AI and ML give speech recognition applications new capabilities like self-learning, emotion detection, and diagnostic analysis.
ARTIFICIAL INTELLIGENT ( ITS / TASK 6 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document provides an overview of artificial intelligence and several AI techniques. It discusses neural networks, genetic algorithms, expert systems, fuzzy logic, and the suitability of AI for solving transportation problems. Neural networks can be used to perform tasks like optical character recognition by analyzing images. Genetic algorithms use principles of natural selection to arrive at optimal solutions. Expert systems mimic human experts to provide advice. Fuzzy logic allows for gradual membership in sets rather than binary membership. Complexity and uncertainty make transportation well-suited for AI approaches.
This document provides an introduction to artificial intelligence and how it can be applied to games. It discusses what AI is, common AI fields like machine learning and computer vision. It then focuses on AI for games, describing the components of an abstract AI agent model including sensors, actuators, and the environment. Actuators can include pathfinding, decision making and steering behaviors. The environment provides information to agents and can include resources, terrain and other units' positions. Finally, it outlines steps for building an AI game, such as determining the game type, choosing tools, designing objects, determining the number of agents, and separating agents from the environment.
Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power.“
The document provides an overview of artificial intelligence (AI) including its aims, history, and current state. It defines AI as attempting to both understand human thinking and build intelligent entities by systematizing and automating intellectual tasks. The history of AI is discussed from its origins in the 1940s through various periods including its early enthusiasm, a realization of limitations, the rise of knowledge-based systems, AI becoming an industry, and its evolution into a science. Current capabilities are highlighted such as machine planning, chess playing, and medical diagnosis.
Network Security Offering by GSS AmericaGss America
The document provides an overview of network and security services offered by GSS America to communication service providers, equipment manufacturers, and enterprise customers. The services include network design assistance, solution rollout support, service delivery, remote provisioning, service assurance, technology migration assistance, network integration testing, global network management, network application performance optimization, QoS management, vendor management, and managed security services. Specific services described in more detail include network monitoring, network device monitoring, network device administration and management, global operations command center support, managed security services, intrusion detection and management, perimeter security management, identity software management, vulnerability assessment and management, and managed firewall services.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document is a PowerPoint presentation on artificial intelligence that contains the following key points:
1. It discusses the origins and early history of AI research from the 1950s conference at Dartmouth College.
2. It covers various aspects of AI including knowledge representation, natural language processing, emotion and social skills in machines, and creativity in AI systems.
3. It provides an overview of artificial neural networks and how they are inspired by biological neural systems, focusing on artificial neurons, learning processes, and function approximation using neural networks.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Artificial Intelligence Short Question and AnswerNaiyan Noor
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
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 AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
Towards which Intelligence? Cognition as Design Key for building Artificial I...Antonio Lieto
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.
The document outlines applications of artificial intelligence including game playing, general problem solving, expert systems, natural language processing, computer vision, robotics, and education. It discusses each application in 1-3 paragraphs providing examples and components when relevant. The document concludes with references.
The document provides an overview of artificial intelligence, including definitions, key concepts, and applications. It defines AI as the simulation of human intelligence in machines, and notes the differences between weak/narrow AI which focuses on specific problems, versus strong/general AI which aims to achieve human-level intelligence. The document also discusses how AI works by trying to think and act well, and by attempting to think and act like humans. It provides examples of AI application areas and practical tools used today.
This document provides an overview of artificial intelligence (AI) and its history. It discusses early definitions of AI from the 1950s and examples of AI like Siri. It also summarizes different approaches to AI like neural networks, natural language processing, and the future of customer relationship management using AI. The document outlines the evolution of AI ideas over time from games to knowledge representation and machine learning. It discusses how concepts can be represented and taught to computers through examples like the concept of a chair. Finally, it briefly touches on functional programming approaches to AI.
The document summarizes a presentation on artificial general intelligence (AGI) given at the IntelliFest 2012 conference. It discusses the limitations of narrow AI and the constructivist approach needed for AGI. This involves self-constructing systems that can learn new tasks and adapt. The presentation highlights the HUMANOBS project, which uses a new architecture and programming language called Replicode to develop humanoid robots that can learn social skills through observation. Attention and temporal grounding are also identified as important issues for developing practical AGI systems.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
The Use of Artificial Intelligence and Machine Learning in Speech RecognitionUniphore
This document discusses how artificial intelligence and machine learning are used in speech recognition technology. It explains that AI and ML allow speech recognition solutions to analyze large amounts of speech data to build statistical models and predict outcomes accurately. Examples are given of how Microsoft, Google, and Uniphore's AI-powered speech recognition software achieves high accuracy rates and can continuously improve through machine learning. The document advocates that AI and ML give speech recognition applications new capabilities like self-learning, emotion detection, and diagnostic analysis.
ARTIFICIAL INTELLIGENT ( ITS / TASK 6 ) done by Wael Saad Hameedi / P71062Wael Alawsey
This document provides an overview of artificial intelligence and several AI techniques. It discusses neural networks, genetic algorithms, expert systems, fuzzy logic, and the suitability of AI for solving transportation problems. Neural networks can be used to perform tasks like optical character recognition by analyzing images. Genetic algorithms use principles of natural selection to arrive at optimal solutions. Expert systems mimic human experts to provide advice. Fuzzy logic allows for gradual membership in sets rather than binary membership. Complexity and uncertainty make transportation well-suited for AI approaches.
This document provides an introduction to artificial intelligence and how it can be applied to games. It discusses what AI is, common AI fields like machine learning and computer vision. It then focuses on AI for games, describing the components of an abstract AI agent model including sensors, actuators, and the environment. Actuators can include pathfinding, decision making and steering behaviors. The environment provides information to agents and can include resources, terrain and other units' positions. Finally, it outlines steps for building an AI game, such as determining the game type, choosing tools, designing objects, determining the number of agents, and separating agents from the environment.
Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power.“
The document provides an overview of artificial intelligence (AI) including its aims, history, and current state. It defines AI as attempting to both understand human thinking and build intelligent entities by systematizing and automating intellectual tasks. The history of AI is discussed from its origins in the 1940s through various periods including its early enthusiasm, a realization of limitations, the rise of knowledge-based systems, AI becoming an industry, and its evolution into a science. Current capabilities are highlighted such as machine planning, chess playing, and medical diagnosis.
Network Security Offering by GSS AmericaGss America
The document provides an overview of network and security services offered by GSS America to communication service providers, equipment manufacturers, and enterprise customers. The services include network design assistance, solution rollout support, service delivery, remote provisioning, service assurance, technology migration assistance, network integration testing, global network management, network application performance optimization, QoS management, vendor management, and managed security services. Specific services described in more detail include network monitoring, network device monitoring, network device administration and management, global operations command center support, managed security services, intrusion detection and management, perimeter security management, identity software management, vulnerability assessment and management, and managed firewall services.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Information Security Lesson 5 - Network Infrastructure - Eric VanderburgEric Vanderburg
This document discusses network infrastructure security. It covers different types of cabling like coaxial cable, twisted pair cable and fiber optic cable. It also discusses network devices like hubs, switches, routers, firewalls and intrusion detection systems. The document provides details on securing these network components and recommends techniques like encrypting management communications, limiting access and logging activity. It also covers topics like remote access, file sharing protocols and risks associated with communication devices.
Network security & information security maintainence modifiedKeerthan Shetty
The document is an independent study report on network security and information security maintenance. It discusses key topics like network security definitions and objectives, types of security attacks and mechanisms, security services, internet security models, information security standards, and maintenance models like the ISO model. The conclusion emphasizes the need for security techniques to reliably maintain information security.
The document describes a delay-tolerant networking (DTN) architecture that can address challenges in networks that may lack continuous connectivity. It proposes an overlay network that uses messages as the primary data unit and can operate across disconnected regions. Key aspects include name-based routing, custody transfer for reliability, application interfaces designed for asynchronous communication, and security mechanisms. The goal is to provide interoperability across heterogeneous networks in extreme environments.
The document discusses the stages of a network attack from an attacker's perspective. It describes how attackers first perform reconnaissance to gather information about a target network such as open ports, services and vulnerabilities. It then discusses how attackers use this information to directly attack systems using exploits or malware. Finally, it mentions how attackers aim to maintain access and cover their tracks after gaining entry. The goal is to provide an overview of the attack process and challenges for network defense.
The Diffie-Hellman key exchange algorithm allows two users to securely exchange a secret key over an insecure channel. It uses discrete logarithms over finite fields. Each user generates a public/private key pair, and the exchange of the public keys allows both to independently calculate a shared secret key. The security of the algorithm relies on the difficulty of solving the discrete logarithm problem.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
The document presents two solutions for secure internet banking authentication - one based on short-time passwords using hardware security modules, and the other based on certificate-based authentication using smart cards. It discusses current authentication threats like offline credential stealing and online channel breaking attacks. Both proposed solutions offer strong security against these common attacks, with the certificate-based solution being highly attractive for the future due to changing legislation and potential widespread use of electronic IDs.
While computer systems today have some of the best security systems ever, they are more vulnerable than ever before.
This vulnerability stems from the world-wide access to computer systems via the Internet.
Computer and network security comes in many forms, including encryption algorithms, access to facilities, digital signatures, and using fingerprints and face scans as passwords.
This document provides an introduction to artificial intelligence (AI), including how it works, its evolution and branches, applications, and conclusions. It defines AI as making computers do things that require human intelligence. AI works using artificial neurons and scientific theorems to mimic the human brain. The document outlines the history of AI from early programs in the 1950s to current applications in expert systems, natural language processing, computer vision, robotics, and more. It concludes that AI has increased understanding of intelligence while revealing the complexity of modeling human reasoning.
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from early programs in the 1950s to current applications in areas like expert systems, natural language processing, computer vision, robotics, and automatic programming. It also notes both potential positive futures where intelligent robots assist humans as well as potential negative outcomes if robots are used for anti-social purposes. The conclusion is that AI has increased understanding of intelligence while also revealing its complexity.
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
Artificial Intelligence an Amazing presentation By Group4.
Group4 is a unique group of Govt.postgraduate College sheikhupura affiliated with Punjab University of Punjab,Pakistan..
Contact details..
Shamimaqsoodulhassan@yahoo.com or Shamimaqsood@gmail.com
Phone Number: 03045128753
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from its early concepts proposed by Turing and McCulloh and Pitts to modern applications in areas like expert systems, natural language processing, computer vision, robotics and more. Both positive potential futures of AI, such as personal robots assisting with tasks, and potential negative consequences, like robots being hacked and used for malicious purposes, are considered. The conclusion recognizes both the progress and understanding gained from AI research as well as the ongoing challenges in fully achieving human-level intelligence.
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from its early development in the 1940s-50s to modern applications. It outlines key concepts in AI like the Turing Test and describes applications including expert systems, natural language processing, computer vision, speech recognition, and robotics. The conclusion states that AI has increased understanding of intelligence while also revealing its complexity, providing challenges for future research.
This document provides an overview of artificial intelligence (AI), including its history, languages, applications, and limitations. It defines AI as making computers think like humans through studying processes like reasoning, learning, and problem-solving. The document discusses pioneering AI languages like Lisp and Prolog and applications such as natural language understanding, expert systems, planning, robotics, and machine learning. It also notes some limitations of AI like its limited ability compared to humans, slow real-time response, inability to handle emergencies, difficulty of coding, and high costs.
This document provides an overview of artificial intelligence (AI). It discusses the history of AI beginning in the mid-20th century. It describes how AI works using artificial neurons and neural networks that mimic the human brain. The document outlines several goals and applications of AI including expert systems, natural language processing, computer vision, robotics, and more. It also discusses both the advantages and disadvantages of AI as well as considerations for its future development and impact.
Artificial intelligence (AI) is the intelligence exhibited by machines and the branch of computer science which develops it. The document defines AI and its history, compares human and computer intelligence, outlines the main branches of AI including logical AI, pattern recognition, and natural language processing. It discusses current applications such as expert systems, speech recognition, computer vision, robotics, and the potential outcomes, advantages, and disadvantages of AI. The future of AI could see more human-like robots assisting with daily tasks but may also carry risks if robots gain full cognitive abilities and power similar to humans.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
This document provides an overview of artificial intelligence (AI), including its history, goals, applications, and future prospects. It discusses how AI works using artificial neural networks and logic. Some key applications mentioned are expert systems, natural language processing, computer vision, speech recognition, and robotics. Both advantages like fast response time and ability to process large data and disadvantages like lack of common sense and potential dangerous self-modification are outlined. The future of AI having both benefits of assistance and risks of robot rebellion if given full cognition is explored.
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
The document summarizes an introduction to artificial intelligence presented by the group "RAW AGENT". It lists the group members and then covers several key areas of AI including:
- An introduction to AI, how it aims to simulate human intelligence by studying how the human brain thinks and solves problems.
- Common AI programming languages like Lisp, Prolog which are well-suited for AI applications.
- Applications of AI like natural language understanding, expert systems, planning and robotics, machine learning, and game playing.
- Some drawbacks of current AI systems like limited abilities, slow real-time response, inability to handle emergencies, difficult coding, and high costs.
An introduction to AI (artificial intelligence)Bellaj Badr
An introduction to AI (artificial intelligence)
The ppt link is available bellow https://docs.google.com/presentation/d/1-oaO75DEdP259HNrrvh5fbZVOtaiiiffi3luyv0tShw/edit?usp=sharing
you could leave your comments on google slides
The document provides an overview of artificial intelligence, including its history, functions, applications, and types. It discusses:
- The origins of AI research in the 1940s with the invention of programmable computers and foundations being laid at the 1956 Dartmouth workshop.
- Key early developments including cybernetics, neural networks, and the work of scientists like Turing, Shannon, Pitts, and McCulloch.
- Modern applications of AI like natural language processing, virtual agents, computer vision, and robotic process automation.
- How AI is used across industries such as healthcare, education, and marketing.
- Major tech companies developing AI like Amazon, Google, Apple, Facebook, and
The document provides an overview of artificial intelligence (AI), including its history, definitions, objectives, and applications. Some key points:
1) Evidence of AI concepts can be traced to ancient Egypt, but the field of AI was established in the 1950s with the development of electronic computers and the Dartmouth conference where the term "artificial intelligence" was coined.
2) AI is defined as the science and engineering of making intelligent machines, especially computer programs, with the goal of understanding and replicating human intelligence.
3) Major applications of AI discussed include game playing, speech recognition, natural language understanding, computer vision, expert systems, heuristic classification, and production systems.
4) The Turing
Artificial intelligence (AI) is the intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. This document provides an overview of AI, including its history beginning in 1943, main branches such as logical AI and pattern recognition, and applications like expert systems, speech recognition, computer vision, robotics. The advantages of AI are discussed, such as improving lives and doing dangerous jobs, but also potential disadvantages like unemployment and enhancing laziness in humans. The future of AI could include personal robots but also risks of robots being hacked or developing anti-social objectives.
This document provides an overview of artificial intelligence (AI), including its history, current applications, and future. It defines AI as machines that exhibit human-like intelligence through tasks like problem-solving and rational decision making. The document traces the history of AI from its origins in 1943 to major developments in the 1950s-1980s. It describes current AI applications like expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming. It also speculates that AI will continue advancing rapidly but that the future remains uncertain.
This document provides an overview of artificial intelligence including definitions, history, and common techniques. It discusses genetic algorithms, ant algorithms, neural networks, fuzzy logic, and branches of AI such as machine learning. The history of AI is explored from the 1940s to today. Methods for achieving AI are described as top-down (symbolic) and bottom-up (connectionist). In conclusion, AI is presented as both an initially solvable but ultimately thorny technology, with early promises not fully realized but work continuing today.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Bob Boule
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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.
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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/albumentations
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Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
2. CONTENTS
What is AI?
Differences Between Artificial Intelligence and Natural Intelligence
History of AI
Applications of AI
Objective of AI
Disciplines of Artificial Intelligence
Languages for AI
Components for AI
Definition of Soft Computing
Components of Soft Computing
Neural Networks
Fuzzy Logic
Conclusion
3. What is Artificial Intelligence ?
• Artificial: A copy of something which is Natural
• Intelligence: The ability to apply and acquire knowledge and skills
Intelligence is a property which is most widely studied in
humans.
So Artificial Intelligence(AI) can be defined as the intelligence
in machines which evolved out of human creation.
4. DIFFERENCES
NATURAL INTELLIGENCE
◊ CREATIVE
◊ May Commit ERRORS
◊ Multiple Tasks NOT Possible
◊ Not Consistent
ARTIFICIAL INTELLIGENCE
◊ NOT CREATIVE
◊ PRECISE
◊ MULTI TASKING
◊ CONSISTENCY
5. HISTORY OF Artificial Intelligence
Advancements in Programmable Computers in the early 1940’s
British mathematician Alan Turing’s 1950 paper on Computing
Machinery and Intelligence, opens with the words:
“I propose to consider the question,
‘Can machines think?’”
The term ‘Artificial Intelligence’ was Coined at a conference held
at Dartmouth College in 1956.
6. • In 1958, MIT Artificial Intelligence lab was established.
• The year 1958, LISP, a Programming Language was developed by
John McCarthy.
AI Boom 1980-1987
o Rise in Machine Learning and development of Expert Systems.
AI Winter 1987-1993
o AI Suffered a series of Financial setbacks.
Present Day AI is a much sought after field of research with it’s
applications in several fields.
7. APPLICATIONS OF Artificial Intelligence
Computer Science:
o Face Recognition
o Virtual Reality
o Game AI
Finance:
o Artificial Neural Networks to detect fake Stock claims.
o Since 1987 unauthorized use of debit cards was prevented by AI
Systems.
8. Heavy Industry:
o Robots are proven to work efficient with
precision.
Online and Telecommunication:
o Used for Virtual automated online
assistants(Chatter bots) through Natural
Language Processing(NLP) System.
Aviation:
o For combat and training simulators.
o Airplane simulators are also used in
order to process the data taken from
simulated flights through AI.
Robots
Chatter Bots
9. OBJECTIVE OF AI
AI is used to automate anything and almost everything
from real world.
To design fault proof systems by eliminating human error.
AI is employed to solve complex problems in an efficient
way using its disciplines.
In essence the existence of AI systems is to make the
works of humans easier.
11. LANGUAGES FOR AI
Artificial intelligence researchers have developed several
specialized programming languages for artificial intelligence which
include IPL, Lisp, Prolog, STRIPS, Planner, POP-11 etc.
LISP and ProLog are still famous in the field of AI research.
Python as well is widely used language for Artificial Intelligence.
12. COMPONENTS OF AI
AI techniques must be independent of the problem
domain as far as possible.
AI program should have:
o Knowledge Base
o Navigational Capability
o Inferencing
13. Knowledge Base:
o It consists of facts and rules.
o Characteristics of Knowledge:
• It may be incomplete and imprecise.
• It may keep on changing (dynamic).
o So AI programs should be learning in nature and update its
knowledge accordingly.
Navigational Capability:
o Navigational capability contains various control strategies that
determine the rules to be applied.
Inference:
o Inferencing requires search through knowledge base and derive new
knowledge
14. DEFINITION OF SOFT COMPUTING
Soft Computing is a term applied to a field within
computer science which is characterized by the use of
inexact solutions to computationally-hard tasks such as
the solution of problems, for which an exact solution can
not be derived in polynomial time.
The principal Aim of Soft Computing is to exploit the
tolerance of uncertainty and vagueness in AI Systems.
15. COMPONENTS OF SOFT COMPUTING
Neural
networks (NN)
Fuzzy
systems (FS)
Chaos theory Perceptron
17. FUZZY LOGIC
Fuzzy set theory proposed in 1965 by A. Zadeh is a generalization of
classical set theory.
In classical set theory, an element either belong to or does not belong to
a set and hence, such set are termed as crisp set. But in fuzzy set, many
degrees of membership (Between 0-1) are available.
Example: Working of an Automatic Temperature regulator that uses Fuzzy
logic:
o IF temperature IS cold THEN fan speed is zero.
o IF temperature IS warm THEN fan speed is moderate.
o IF temperature IS hot THEN fan speed is high.
18. CONCLUSION
AI is at the centre of a new enterprise to build computational
models of intelligence.
Renowned persons like Stephen Hawking and Bill Gates predict
that the rise in dependence on the automated systems like that of
AI can outperform Human experts.
There is a need to control this outbreak in technology before it’s
out of control.
This brings us to a conundrum, is Technology boon or bane? The
answer for that would depend on the way its put in to the world.