Artificial intelligence
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Artificial Intelligence Course- Introduction
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.
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The document discusses artificial intelligence, including its fields, characteristics of intelligence, foundations in philosophy, mathematics, psychology, linguistics, and applications. It notes that AI aims to build intelligent agents, examines questions about computer and animal intelligence, and lists techniques used in AI like neural networks, genetic algorithms, and fuzzy logic.
Artificial intelligence in cyber defense
This document provides an overview of artificial intelligence and its applications in cyber defense. It discusses topics like what AI is, the Turing test, fields of AI like expert systems, neural networks and intelligent agents. It provides examples of expert systems and their architecture. It also discusses applications of AI like credit granting, information retrieval and virus detection. Neural networks are described as artificial representations of the human brain that try to simulate its learning process. Different types of neural networks like biological and artificial are also mentioned.
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The document defines and discusses artificial intelligence from several perspectives: 1) focusing on intelligent behavior similar to humans, 2) how computers can perform tasks currently done by humans, 3) representing knowledge symbolically rather than numerically, and 4) pattern matching to describe objects and processes qualitatively. Major applications of AI discussed include expert systems, natural language processing, speech recognition, robotics, computer vision, and computer-aided instruction. The history and differences between artificial and natural intelligence are also summarized.
BE-EEE-8th sem-Presentation Artificial intelligence in security managenent
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Knowledge based systems -- introduction
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.
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Artificial Intelligence Course- Introduction
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.
30928464 my-blue-brain-ppt
The document discusses the Blue Brain project which aims to create a virtual human brain through detailed computer simulation. The Blue Brain would function similarly to a natural human brain through receiving input, interpreting it, and generating output. Uploading a human brain would involve nanobots scanning brain structure and activity and inputting that data into a powerful supercomputer. The project seeks to advance understanding of human cognition and potentially treat conditions like Parkinson's.
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Lebanon SoftShore organized a seminar on Artificial Intelligence at USEK on March 28, 2014. This is the presentation of Dr Hayssam Serhan
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The document discusses artificial intelligence, including its fields, characteristics of intelligence, foundations in philosophy, mathematics, psychology, linguistics, and applications. It notes that AI aims to build intelligent agents, examines questions about computer and animal intelligence, and lists techniques used in AI like neural networks, genetic algorithms, and fuzzy logic.
Artificial intelligence in cyber defense
This document provides an overview of artificial intelligence and its applications in cyber defense. It discusses topics like what AI is, the Turing test, fields of AI like expert systems, neural networks and intelligent agents. It provides examples of expert systems and their architecture. It also discusses applications of AI like credit granting, information retrieval and virus detection. Neural networks are described as artificial representations of the human brain that try to simulate its learning process. Different types of neural networks like biological and artificial are also mentioned.
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The document defines and discusses artificial intelligence from several perspectives: 1) focusing on intelligent behavior similar to humans, 2) how computers can perform tasks currently done by humans, 3) representing knowledge symbolically rather than numerically, and 4) pattern matching to describe objects and processes qualitatively. Major applications of AI discussed include expert systems, natural language processing, speech recognition, robotics, computer vision, and computer-aided instruction. The history and differences between artificial and natural intelligence are also summarized.
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Knowledge based systems -- introduction
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.
Artificial intelligence
Artificial intelligence (AI) is defined as making computers intelligent like humans. It involves techniques like machine learning, neural networks, and computer vision. Specific characteristics of intelligent behavior include learning from experience, handling complex situations, solving problems with missing information, determining importance, and reacting quickly to new situations. The differences between natural and artificial intelligence are that humans are fallible with limited knowledge while AI may have high costs and raise ethical concerns. Major branches of AI are robotics, vision systems, natural language processing, learning systems, and neural networks. AI is used in gaming to produce intelligent behaviors in non-player characters.
Artificial Brain - Overview 2013
Artificial Intelligence is being supplanted by "Artificial Brain," i.e. neuromorphic technologies. Yet there still a whopping gap that neuromorphic systems need to close before they will become a match for successful AI applications.
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Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human 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).
(Ch#1) artificial intelligence
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.
Artificial Intelligence- An Introduction
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.“
What is Artificial Intelligence?
Artificial intelligence (AI) is defined as the scientific understanding of intelligent behavior and its implementation in machines. AI aims to create computer systems that can perform tasks requiring intelligence, such as understanding language, learning from experience, reasoning and problem solving. While modern AI has achieved impressive performance on specific tasks, it has not yet reached human-level intelligence in general or the ability to learn from experience like children. Researchers continue to study both computational and biological aspects of intelligence to develop more capable AI systems.
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This document discusses cognitive science and artificial intelligence (AI). It provides definitions and explanations of key topics including cognitive theory, cognitive science, and cognitive neuroscience. Cognitive science is defined as the interdisciplinary scientific study of the mind and its processes, focusing on how information is represented, processed, and transformed. The relationship between cognitive science and AI is explored, with AI being used both to augment human thinking and to understand human cognition. Applications of cognitive science and AI discussed include digital assistants, drones, wearable devices, and automated call center systems.
ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS
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
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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.
Mind reading computer
The team in the Computer Laboratory at the University of Cambridge has developed mind reading computers that implement a computational model of mind reading to infer mental states of people from their facial signals.
Using a digital video camera, the mind reading computer system analyzes a person’s underlying mental state, such as whether he/she is agreeing or disagreeing, interested or bored, thinking or confused.
PPT on mind reading computer
The document discusses the development of mind reading computers. It describes how these computers use techniques like facial expression analysis and functional near-infrared spectroscopy to infer a person's mental states. The technology has potential applications in helping paralyzed people communicate, assisting those in comas, and aiding the disabled. However, concerns exist around privacy breaches and the risk of the technology being misused if it could accurately predict human behavior.
Mind reading computer
This document provides an overview of mind reading computer technology. It discusses how mind reading works using functional near-infrared spectroscopy to measure blood oxygen levels in the brain. The technology could be used for applications like emergency braking in cars to detect driver drowsiness or distraction. While it offers advantages like helping paralyzed patients, there are also disadvantages like potential privacy breaches if sensitive thoughts or information are extracted. The document concludes that more research is still needed before computers can reliably predict human behavior based on brain activity readings.
From Narrow AI to Artificial General Intelligence (AGI)
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.
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This document discusses mind reading technology. It describes how mind reading machines use equipment like futuristic headbands with functional near-infrared spectroscopy to measure blood volume and oxygen levels in the brain. This data is analyzed using techniques like neural networks and active appearance models to detect facial expressions and classify emotions. The document outlines potential applications in emergencies, helping disabled people communicate, and gaming, but also notes serious disadvantages like threats to privacy and security if the technology is misused.
Cognitive Computing
Cognitive computing refers to the development of computer system modeled after the human brain.
This technology was introduced by IBM as 5 in 5.
In next five years IBM is planning to develop kind of Applications which will have capabilities of the right side of the human brain.
New technologies makes it possible for machines to mimic and augment the senses.
Mind Reading Device
This document discusses mind reading technology and its development. It begins with acknowledgements and then outlines the contents which include an introduction to mind reading, the technology used, instruments, developments, techniques for evaluation, advantages and uses, disadvantages and conclusions. It discusses how EEG, fNIRS and other tools are being used to decode brain activity and reconstruct images of what a person is viewing. While the technology offers advantages for applications like controlling wheelchairs with thought, it also raises issues regarding privacy and the risk of criminalizing innocent people. Further development is still needed to improve accuracy and address societal concerns before practical applications can be realized.
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Artificial intelligence
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Artificial_Intelligence.ppt
This document discusses artificial intelligence and its relationship to human intelligence. It defines intelligence as the ability to learn from and interact with one's environment. Artificial intelligence is defined as using computers to mimic human intelligence by performing tasks typically requiring human intelligence. AI works using artificial neurons and scientific theorems. Neural networks are composed of interconnected artificial neurons. Examples of AI applications include expert systems like PROSPECTOR for mineral exploration and PUFF for medical diagnosis. Machine learning uses algorithms to mimic human intelligence. While AI can process large amounts of data quickly, it currently lacks human abilities like intuition, creativity and common sense. The document compares human and artificial intelligence and their pros and cons.
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Artificial intelligence (AI) is defined as making computers intelligent like humans. It involves techniques like machine learning, neural networks, and computer vision. Specific characteristics of intelligent behavior include learning from experience, handling complex situations, solving problems with missing information, determining importance, and reacting quickly to new situations. The differences between natural and artificial intelligence are that humans are fallible with limited knowledge while AI may have high costs and raise ethical concerns. Major branches of AI are robotics, vision systems, natural language processing, learning systems, and neural networks. AI is used in gaming to produce intelligent behaviors in non-player characters.
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Artificial Intelligence is being supplanted by "Artificial Brain," i.e. neuromorphic technologies. Yet there still a whopping gap that neuromorphic systems need to close before they will become a match for successful AI applications.
Artificialintelligence 131226011156-phpapp02
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human 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).
(Ch#1) artificial intelligence
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.
Artificial Intelligence- An Introduction
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.“
What is Artificial Intelligence?
Artificial intelligence (AI) is defined as the scientific understanding of intelligent behavior and its implementation in machines. AI aims to create computer systems that can perform tasks requiring intelligence, such as understanding language, learning from experience, reasoning and problem solving. While modern AI has achieved impressive performance on specific tasks, it has not yet reached human-level intelligence in general or the ability to learn from experience like children. Researchers continue to study both computational and biological aspects of intelligence to develop more capable AI systems.
Cognitive Science and AI
This document discusses cognitive science and artificial intelligence (AI). It provides definitions and explanations of key topics including cognitive theory, cognitive science, and cognitive neuroscience. Cognitive science is defined as the interdisciplinary scientific study of the mind and its processes, focusing on how information is represented, processed, and transformed. The relationship between cognitive science and AI is explored, with AI being used both to augment human thinking and to understand human cognition. Applications of cognitive science and AI discussed include digital assistants, drones, wearable devices, and automated call center systems.
ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS
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
Mind reading computer
The document discusses mind reading computers that can summarize a person's mental state by analyzing facial expressions and head gestures using video cameras and machine learning. It can identify features like facial expressions that indicate emotions, thoughts, and mental workload. The technology works by tracking facial feature points and modeling the relationship between expressions and mental states over time. Potential applications include monitoring human interactions, detecting driver states, and developing assistive technologies like mind-controlled wheelchairs. Issues involve ensuring reliability and addressing ethical concerns around predicting future behaviors.
From anticipation to anticipatory intelligence
Carlos R. B. Azevedo introduces himself as a Brazilian researcher in AI and IoT technologies who is strongly inclined towards the humanities. He believes that AI systems that integrate with and anticipate situations involving human factors can help people live and work according to their values. He outlines anticipation and anticipatory systems, including how anticipation generates models of human actions to resolve conflicts. He discusses imaginative intelligence and flexible, collaborative decision making. Finally, he discusses the importance of ethics and human values in developing anticipatory intelligence.
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.
Mind reading computer
The team in the Computer Laboratory at the University of Cambridge has developed mind reading computers that implement a computational model of mind reading to infer mental states of people from their facial signals.
Using a digital video camera, the mind reading computer system analyzes a person’s underlying mental state, such as whether he/she is agreeing or disagreeing, interested or bored, thinking or confused.
PPT on mind reading computer
The document discusses the development of mind reading computers. It describes how these computers use techniques like facial expression analysis and functional near-infrared spectroscopy to infer a person's mental states. The technology has potential applications in helping paralyzed people communicate, assisting those in comas, and aiding the disabled. However, concerns exist around privacy breaches and the risk of the technology being misused if it could accurately predict human behavior.
Mind reading computer
This document provides an overview of mind reading computer technology. It discusses how mind reading works using functional near-infrared spectroscopy to measure blood oxygen levels in the brain. The technology could be used for applications like emergency braking in cars to detect driver drowsiness or distraction. While it offers advantages like helping paralyzed patients, there are also disadvantages like potential privacy breaches if sensitive thoughts or information are extracted. The document concludes that more research is still needed before computers can reliably predict human behavior based on brain activity readings.
From Narrow AI to Artificial General Intelligence (AGI)
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.
Mind reading
This document discusses mind reading technology. It describes how mind reading machines use equipment like futuristic headbands with functional near-infrared spectroscopy to measure blood volume and oxygen levels in the brain. This data is analyzed using techniques like neural networks and active appearance models to detect facial expressions and classify emotions. The document outlines potential applications in emergencies, helping disabled people communicate, and gaming, but also notes serious disadvantages like threats to privacy and security if the technology is misused.
Cognitive Computing
Cognitive computing refers to the development of computer system modeled after the human brain.
This technology was introduced by IBM as 5 in 5.
In next five years IBM is planning to develop kind of Applications which will have capabilities of the right side of the human brain.
New technologies makes it possible for machines to mimic and augment the senses.
Mind Reading Device
This document discusses mind reading technology and its development. It begins with acknowledgements and then outlines the contents which include an introduction to mind reading, the technology used, instruments, developments, techniques for evaluation, advantages and uses, disadvantages and conclusions. It discusses how EEG, fNIRS and other tools are being used to decode brain activity and reconstruct images of what a person is viewing. While the technology offers advantages for applications like controlling wheelchairs with thought, it also raises issues regarding privacy and the risk of criminalizing innocent people. Further development is still needed to improve accuracy and address societal concerns before practical applications can be realized.
Artificial Intelligence Techniques In Power Systems Paper Presentation
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.
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Artificial intelligence (AI) is defined as making computers do tasks that require intelligence when done by humans. There are two main types of AI: weak AI, where machines act intelligently to accomplish specific tasks, and strong AI, where machines have general human-level intelligence. AI works using artificial neurons and logic-based rules. It has many applications in areas like finance, medicine, manufacturing, customer service, and gaming. While AI provides benefits like speed and accuracy, it also faces limitations such as a lack of common sense and difficulty handling emergencies. The future of AI is uncertain but technology improvements may allow it to become more human-like over time.
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.
AI Mod1@AzDOCUMENTS.in.pdf
The document provides an overview of artificial intelligence (AI) including definitions, techniques, and challenges. It discusses how AI aims to make computers intelligent like humans by giving them abilities such as perception, reasoning, learning, and problem solving. Some key techniques mentioned are search, knowledge representation, and abstraction. The document also discusses the Turing Test as a proposed method for determining if a machine can think like a human. It provides examples of problems AI aims to solve such as game playing, commonsense reasoning, and perception.
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Introduction to objective, scope and outcome the subject
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Chapter 3
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Artificial intelligence
- 2. Here we will discuss about • What is Intelligence? • What Is Artificial Intelligence? • How Does AI Works?? • What is Neural AI Networking? • Examples Of Artificial Intelligence • Applications of Expert Systems and • Human Intelligence VS ArtificialIntelligence
- 3. What is Intelligence? Intelligence is the ability to learn about, to learn from, to understand about, and interact with one’s environment. Intelligence is the faculty of understanding Intelligence is not to make no mistakes but quickly to understand how to make them good
- 4. What Is Artificial Intelligence? Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI is the study of ideas that enable computers to be intelligent
- 5. How Does AI Works? Artificial intelligence works with the help of Artificial Neurons (Artificial Neural Network) And Scientific theorems(If-Then Statements, Logics)
- 6. What is Neural Networking? Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons).
- 8. Expert Systems! An expert system is a computer program that is designed to hold the accumulated knowledge of one or more domain experts It reasons with knowledge of some specialist subject with a view to solving problems or giving advice They are tested by being placed in the same real world problem solving situation
- 9. Some Applications of Artificial Intelligence 1. Knowledge reasoning. 2. Planning. 3. Machine learning. 4. Natural language processing. 5. Computer vision. 6. Robotics. 7. Artificial general intelligence.
- 10. Applications of Expert Systems PUFF: Medical system for diagnosis of respiratory conditions PROSPECTOR: Used by geologists to identify sites for drilling or mining
- 11. continue LITHIAN: Gives advice to archaeologists examining stone tools DENDRAL: Used to identify the structure of chemical compounds. First used in 1965
- 12. Human Intelligence VS Artificial Intelligence
- 13. Human Intelligence Artificial Intelligence Intuition, Common sense, judgment, Creativity, Beliefsetc Ability to simulate human behaviorand cognitive processes The ability to demonstrate their intelligence by communicatingeffectively Capture and preserve humanexpertise Plausible Reasoning and Criticalthinking Fast Response. The ability tocomprehend large amounts of dataquickly. Human IntelligenceVS ArtificialIntelligence difference
- 14. THE END