The document is a lecture on artificial intelligence (AI) that covers the following key points:
1. It defines intelligence and discusses how AI aims to develop systems that exhibit intelligent behavior like humans.
2. It outlines the differences between intelligent computing in AI systems versus conventional rule-based computing.
3. It provides a brief history of AI, covering milestones from the 1940s to the present, and discusses fields that have contributed to AI's development.
What Artificial intelligence can Learn from Human EvolutionAbhimanyu Singh
The document discusses key aspects of human intelligence that could inform the development of artificial intelligence. It covers how human intelligence evolved over billions of years through natural selection to develop features like motivation, emotions, senses, language processing, and vision. These characteristics provide benefits like adaptability, decision making, and understanding the world. The document suggests artificial intelligence could replicate features like motivation through simulating greed and fear, developing emotions and social behaviors, and creating thought arenas to allow for object-based representation and reasoning. Progress is needed in natural language processing, computer vision, and developing a language the brain can use to think.
This document provides an overview of the history and development of artificial intelligence, beginning with early concepts of artificial beings and progressing through milestones like the Dartmouth Conference, development of expert systems in the 1970s-80s, advances in the 1990s with things like Deep Blue and robotics, and examples of modern applications like robotic vacuums and self-driving cars through challenges like DARPA's Grand Challenge.
1) Artificial intelligence is the science and engineering of making intelligent machines that can perceive and take actions to maximize their success.
2) Early AI programs included the Logic Theorist which solved math theorems, and programs for playing checkers that learned from experience.
3) Recent advances in data, computing power, and techniques like machine learning, deep learning and neural networks have greatly expanded what AI can accomplish, with applications including computer vision, speech recognition, translation and more.
4) While current AI is specialized or "weak," the goal is to develop "strong" or general human-level AI that can perform any intellectual task, but this poses risks that must be addressed to ensure such systems remain
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.
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 document is a lecture on artificial intelligence (AI) that covers the following key points:
1. It defines intelligence and discusses how AI aims to develop systems that exhibit intelligent behavior like humans.
2. It outlines the differences between intelligent computing in AI systems versus conventional rule-based computing.
3. It provides a brief history of AI, covering milestones from the 1940s to the present, and discusses fields that have contributed to AI's development.
What Artificial intelligence can Learn from Human EvolutionAbhimanyu Singh
The document discusses key aspects of human intelligence that could inform the development of artificial intelligence. It covers how human intelligence evolved over billions of years through natural selection to develop features like motivation, emotions, senses, language processing, and vision. These characteristics provide benefits like adaptability, decision making, and understanding the world. The document suggests artificial intelligence could replicate features like motivation through simulating greed and fear, developing emotions and social behaviors, and creating thought arenas to allow for object-based representation and reasoning. Progress is needed in natural language processing, computer vision, and developing a language the brain can use to think.
This document provides an overview of the history and development of artificial intelligence, beginning with early concepts of artificial beings and progressing through milestones like the Dartmouth Conference, development of expert systems in the 1970s-80s, advances in the 1990s with things like Deep Blue and robotics, and examples of modern applications like robotic vacuums and self-driving cars through challenges like DARPA's Grand Challenge.
1) Artificial intelligence is the science and engineering of making intelligent machines that can perceive and take actions to maximize their success.
2) Early AI programs included the Logic Theorist which solved math theorems, and programs for playing checkers that learned from experience.
3) Recent advances in data, computing power, and techniques like machine learning, deep learning and neural networks have greatly expanded what AI can accomplish, with applications including computer vision, speech recognition, translation and more.
4) While current AI is specialized or "weak," the goal is to develop "strong" or general human-level AI that can perform any intellectual task, but this poses risks that must be addressed to ensure such systems remain
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.
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.
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
This document discusses artificial intelligence (AI) and related concepts. It defines AI as making computers do things that require human intelligence. It explains that AI works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses machine learning methods, expert systems, applications of expert systems, the Turing test, and comparisons between human and artificial intelligence.
Artificial intelligence plays a major role in digital marketing. There are different types of AI:
Reactive machines simply react to input with output without learning. Limited memory types can store previous data and predictions to make better forecasts. Theory of mind AI is beginning to interact with human thoughts and emotions, as seen in self-driving cars interacting with other drivers. The final type is hypothetical self-aware AI that could achieve independent intelligence and potential negotiation with humans.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
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 and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Artificial intelligence(simulating the human mind)Dinesh More
This document provides an overview of cognitive science artificial intelligence (AI), which studies how to simulate human intelligence through AI to better understand the human mind and solve complex problems. Current research aims to develop human-level machine intelligence through projects that simulate human reasoning, decision-making, creativity, and goal management. Applications of this research could optimize design problems and advance cognitive science theories with implications for education and medicine. Future progress depends on improved understanding of human cognition and advanced computing technologies.
John McCarthy first coined the term "artificial intelligence" in 1956. The original concept of AI was for machines to simulate human learning and intelligence. There are two main types of AI - strong AI, which aims to simulate the human brain, and weak AI, which behaves intelligently without replicating the brain. Machine learning is a subset of AI that uses algorithms to improve performance over time by processing data. AI is now used widely in areas like digital assistants, social media, music/video streaming, navigation, business applications, drones, self-driving cars, and humanoid robots. While AI has benefits, there are also risks like limited abilities, unemployment, and autonomous weapons. The future of AI could include enhanced human abilities but
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
about the artificial intelligence how its work future expectations and real life examples , and also whats is machine learning. how is different with human intelligence.
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
This document provides an overview of artificial intelligence and its applications. It begins with an introduction defining AI as giving machines human-like thinking abilities. It then discusses how AI works through techniques like planning, pattern recognition, ontology, robotics, and more. Applications of AI discussed include medicine, the military, games, language processing, and expert systems. The document concludes with predictions for AI's future role in technologies like telephone translation, expanded use of expert systems, passing the Turing test, and research assistants.
Artificial Intelligence power point presentationDavid Raj Kanthi
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.
Artificial intelligence (AI) is the field of computer science that develops machines or software with human-like intelligence. There are two types of AI: narrow AI, which is limited to specific tasks, and strong AI, which would have general human-level intelligence. AI is being applied in many areas including healthcare, transportation, education, and more. Some key developments in AI history include the invention of the Turing test in 1950 to measure machine intelligence, IBM's Deep Blue beating the chess champion in 1997, and IBM Watson winning Jeopardy in 2011. Cognitive computing systems like Watson are aimed at simulating human thought processes.
This document discusses artificial intelligence, including its history, types, examples, and characteristics. It provides an overview of AI beginning with its definition as intelligence demonstrated by machines as proposed by John McCarthy. The document outlines the early pioneers of AI like Alan Turing and discusses weak and strong types of AI. Examples of AI applications are given like chess games and robotics competitions. Characteristics needed for human-level AI are described such as natural language processing, reasoning, and machine learning.
Artificial general intelligence research project at Keen Software House (3/2015)Marek Rosa
Keen Software House is an AI research company that aims to develop general human-level artificial intelligence within 10-50 years. Their team of over a dozen researchers uses an approach called Brain Simulator to develop AI that can learn from its environment like children do. Their short-term goals include developing AI that can learn to play a variety of games with complex environments and delayed rewards requiring long-term goal following. They also plan to commercialize their Brain Simulator platform and license their AI technologies to other companies. Their long-term goal is an artificial brain that can perceive, learn, adapt and maximize its rewards like humans through various cognitive functions and learning approaches.
This document presents an introduction to artificial intelligence. It begins with a definition of AI as using computer algorithms to solve complex problems like humans. The history of AI is then summarized, including early milestones from the 1940s to 2000s. Key reasons for AI are that computers can efficiently perform repetitive tasks that humans find monotonous. The document outlines applications of AI such as expert systems, natural language processing, speech recognition, computer vision, and robotics. Both advantages like medical applications and disadvantages like self-modifying systems are presented. The future of AI allowing command of personal robots or potential robot revolts is discussed before concluding with continued challenges in fully understanding intelligence.
This document discusses and debunks several myths about artificial intelligence (AI) and cognitive capabilities. Some key points made:
- Current AI progress is still limited and focused on narrow tasks, not general human-level intelligence. While inserting vast human knowledge may not be enough to create true intelligence on its own.
- With time and without unrealistic expectations, AI could develop some human-like cognitive abilities through a combination of experience, knowledge, and machine learning, but will not fully achieve human capabilities.
- Chatbots have advanced through different techniques like AIML, NLP/NLU, and machine learning, but truly human-like personality may require reinforcement learning and the ability to modify behavior through experience akin
This document provides an introduction to artificial intelligence. It discusses key concepts in AI including:
- The definition of AI as designing intelligence in artificial devices, with intelligence and artificial device being the two main ideas.
- What intelligence involves, including the ability to perceive, understand, reason, learn and adapt.
- Different approaches to defining AI such as thinking like a human through cognitive modeling or acting rationally as an ideal rational agent.
- Classic tests for AI like the Turing Test which evaluates if a machine can exhibit intelligent behavior equivalent to a human.
The document outlines an art exchange program that is student-centered and involves meaningful self-directed learning. Students will research specific artists and art movements, connect their projects to a realistic approach, and acquire meaningful learning experiences. Students will be grouped to research artists and consider how those artists would utilize modern ICT tools. Assessment involves students submitting an e-portfolio, completing self-reflection, and providing peer evaluation.
This document discusses definitions of artificial intelligence from different sources. It provides three definitions: 1) AI as the art of creating machines that perform functions requiring human intelligence; 2) AI as the study of how to make computers perform tasks that people currently do better; 3) AI as systems that act intelligently like humans in their interactions. The document explores various aspects and applications of AI.
Understanding artificial intelligence and it's future scopeChaitanya Shimpi
In the field of computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
This document discusses artificial intelligence (AI) and related concepts. It defines AI as making computers do things that require human intelligence. It explains that AI works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The document also discusses machine learning methods, expert systems, applications of expert systems, the Turing test, and comparisons between human and artificial intelligence.
Artificial intelligence plays a major role in digital marketing. There are different types of AI:
Reactive machines simply react to input with output without learning. Limited memory types can store previous data and predictions to make better forecasts. Theory of mind AI is beginning to interact with human thoughts and emotions, as seen in self-driving cars interacting with other drivers. The final type is hypothetical self-aware AI that could achieve independent intelligence and potential negotiation with humans.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
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 and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
Artificial intelligence(simulating the human mind)Dinesh More
This document provides an overview of cognitive science artificial intelligence (AI), which studies how to simulate human intelligence through AI to better understand the human mind and solve complex problems. Current research aims to develop human-level machine intelligence through projects that simulate human reasoning, decision-making, creativity, and goal management. Applications of this research could optimize design problems and advance cognitive science theories with implications for education and medicine. Future progress depends on improved understanding of human cognition and advanced computing technologies.
John McCarthy first coined the term "artificial intelligence" in 1956. The original concept of AI was for machines to simulate human learning and intelligence. There are two main types of AI - strong AI, which aims to simulate the human brain, and weak AI, which behaves intelligently without replicating the brain. Machine learning is a subset of AI that uses algorithms to improve performance over time by processing data. AI is now used widely in areas like digital assistants, social media, music/video streaming, navigation, business applications, drones, self-driving cars, and humanoid robots. While AI has benefits, there are also risks like limited abilities, unemployment, and autonomous weapons. The future of AI could include enhanced human abilities but
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
about the artificial intelligence how its work future expectations and real life examples , and also whats is machine learning. how is different with human intelligence.
1) The document defines AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies, communicate effectively with AI, and use AI as a tool.
2) It proposes 15 competencies across 5 themes - what AI is, what it can do, how it works, how it should be used, and how people perceive it.
3) The competencies focus on understanding intelligence, different types of AI, their strengths/weaknesses, how machine learning and data work, ethics, and interpreting AI systems.
This document provides an overview of artificial intelligence and its applications. It begins with an introduction defining AI as giving machines human-like thinking abilities. It then discusses how AI works through techniques like planning, pattern recognition, ontology, robotics, and more. Applications of AI discussed include medicine, the military, games, language processing, and expert systems. The document concludes with predictions for AI's future role in technologies like telephone translation, expanded use of expert systems, passing the Turing test, and research assistants.
Artificial Intelligence power point presentationDavid Raj Kanthi
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.
Artificial intelligence (AI) is the field of computer science that develops machines or software with human-like intelligence. There are two types of AI: narrow AI, which is limited to specific tasks, and strong AI, which would have general human-level intelligence. AI is being applied in many areas including healthcare, transportation, education, and more. Some key developments in AI history include the invention of the Turing test in 1950 to measure machine intelligence, IBM's Deep Blue beating the chess champion in 1997, and IBM Watson winning Jeopardy in 2011. Cognitive computing systems like Watson are aimed at simulating human thought processes.
This document discusses artificial intelligence, including its history, types, examples, and characteristics. It provides an overview of AI beginning with its definition as intelligence demonstrated by machines as proposed by John McCarthy. The document outlines the early pioneers of AI like Alan Turing and discusses weak and strong types of AI. Examples of AI applications are given like chess games and robotics competitions. Characteristics needed for human-level AI are described such as natural language processing, reasoning, and machine learning.
Artificial general intelligence research project at Keen Software House (3/2015)Marek Rosa
Keen Software House is an AI research company that aims to develop general human-level artificial intelligence within 10-50 years. Their team of over a dozen researchers uses an approach called Brain Simulator to develop AI that can learn from its environment like children do. Their short-term goals include developing AI that can learn to play a variety of games with complex environments and delayed rewards requiring long-term goal following. They also plan to commercialize their Brain Simulator platform and license their AI technologies to other companies. Their long-term goal is an artificial brain that can perceive, learn, adapt and maximize its rewards like humans through various cognitive functions and learning approaches.
This document presents an introduction to artificial intelligence. It begins with a definition of AI as using computer algorithms to solve complex problems like humans. The history of AI is then summarized, including early milestones from the 1940s to 2000s. Key reasons for AI are that computers can efficiently perform repetitive tasks that humans find monotonous. The document outlines applications of AI such as expert systems, natural language processing, speech recognition, computer vision, and robotics. Both advantages like medical applications and disadvantages like self-modifying systems are presented. The future of AI allowing command of personal robots or potential robot revolts is discussed before concluding with continued challenges in fully understanding intelligence.
This document discusses and debunks several myths about artificial intelligence (AI) and cognitive capabilities. Some key points made:
- Current AI progress is still limited and focused on narrow tasks, not general human-level intelligence. While inserting vast human knowledge may not be enough to create true intelligence on its own.
- With time and without unrealistic expectations, AI could develop some human-like cognitive abilities through a combination of experience, knowledge, and machine learning, but will not fully achieve human capabilities.
- Chatbots have advanced through different techniques like AIML, NLP/NLU, and machine learning, but truly human-like personality may require reinforcement learning and the ability to modify behavior through experience akin
This document provides an introduction to artificial intelligence. It discusses key concepts in AI including:
- The definition of AI as designing intelligence in artificial devices, with intelligence and artificial device being the two main ideas.
- What intelligence involves, including the ability to perceive, understand, reason, learn and adapt.
- Different approaches to defining AI such as thinking like a human through cognitive modeling or acting rationally as an ideal rational agent.
- Classic tests for AI like the Turing Test which evaluates if a machine can exhibit intelligent behavior equivalent to a human.
The document outlines an art exchange program that is student-centered and involves meaningful self-directed learning. Students will research specific artists and art movements, connect their projects to a realistic approach, and acquire meaningful learning experiences. Students will be grouped to research artists and consider how those artists would utilize modern ICT tools. Assessment involves students submitting an e-portfolio, completing self-reflection, and providing peer evaluation.
This document discusses definitions of artificial intelligence from different sources. It provides three definitions: 1) AI as the art of creating machines that perform functions requiring human intelligence; 2) AI as the study of how to make computers perform tasks that people currently do better; 3) AI as systems that act intelligently like humans in their interactions. The document explores various aspects and applications of AI.
The financial system works through financial markets. A financial market deals in money by borrowing from those with surplus and lending to those in need. Like supply and demand for goods, there is also demand and supply of money in financial markets.
Financial markets can be divided into money markets and capital markets based on their functions. Money markets deal in short-term debt instruments for up to one year while capital markets deal in long-term debt instruments of over one year.
Building a state of the art AI to play Magic: The GatheringMelvin Zhang
Building a state of the art AI to play Magic: The Gathering, Melvin Zhang compares different algorithms and iterates on implementations. Initial experiments show Monte Carlo tree search (MCTS) outperforms minimax when given more thinking time. Later work focuses on developing an honest, non-cheating MCTS for Magarena that does not require all cores for parallelization. Open problems remain in improving play when losing and reducing unfun games.
The document discusses a new approach to artificial intelligence called Artificial Intelligence 2.0 that is multi-agent, associative, abstracting, smart-contract based, and biologically-inspired. It argues that modern AI is outdated as it lacks multiplicity, enclosure, and the ability to evolve. The document proposes that a blockchain-based multi-agent AI that shares an "associative memory graph" can address these issues by allowing the agents to diverge, reflect on past work, and economically compete through smart contracts. This approach could give AI the same cognitive evolutionary abilities that written language provided for humans.
The Astonishing Resurrection of AI (A Primer on Artificial Intelligence)Matt Turck
The document discusses the recent resurgence of interest and funding in artificial intelligence due to advances in algorithms, computing power, and availability of large datasets. It notes several AI startups that are working on automating routine tasks through narrow AI applications. However, it also discusses concerns about the potential risks of developing superintelligent machines.
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 provides an overview of artificial intelligence, including its definition, history, approaches, tools for evaluation, applications, and predictions for the future. It discusses topics such as the traits of an intelligent system, methods like cybernetics and symbolic/statistical approaches, tools including search algorithms and neural networks, and applications in fields like medicine, robotics, and web search engines.
Artificial intelligence (AI) is the study and design of intelligent agents, with no single goal. It aims to put human-level intelligence into machines. The document traces the history of AI from its origins in 1941 to modern applications in areas like military, science, business, and entertainment. It discusses early developments like the Dartmouth conference that defined the field, and the creation of languages like Lisp and Prolog. Future developments may lead to more sophisticated AI in video games, self-governing robot societies, and abilities that surpass humans in games like chess, but this also raises ethical questions about controlling advanced AI.
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 is the study and design of intelligent agents, with no single goal. It aims to put the human mind into computers by developing machines that can achieve goals through computation. The origins of AI began in the 1940s with the development of electronic computers. Significant early developments included the first stored program computer in the 1950s, the Dartmouth Conference which coined the term "artificial intelligence" in the 1950s, and the development of the LISP programming language. In the following decades, AI research expanded and led to applications in fields like expert systems, games, and military systems. While progress has been made, the full extent of intelligence and the future of AI remains unknown.
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines different methods of AI such as symbolic AI, neural networks, and computational intelligence. It also discusses a wide range of applications of AI such as finance, medicine, gaming, robotics, and more. Finally, it discusses some achievements of AI and envisions continued growth and importance of AI in the future.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
Artificial intelligence (AI) is defined as the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving goals. The document discusses key areas of AI research like problem solving, knowledge representation, automated reasoning, machine learning, natural language processing, computer vision, and robotics. It also compares human and computer intelligence, noting that while humans are better at tasks like visual recognition, computers excel at tasks like searching large databases. The long-term goal of AI research is to create general or strong AI that can outperform humans at nearly any cognitive task.
CH-1 Introduction to Artificial Intelligence for class 9.pptxAadityaNanda
This document provides information about artificial intelligence and machine learning. It defines artificial intelligence as the field of computer science focused on developing intelligent machines. It discusses different types of AI like narrow AI, general AI, and artificial neural networks. Examples of applications of AI like IBM Watson and driverless cars are provided. Key components of AI like data, computer vision, and natural language processing are explained. The differences between machine learning and deep learning are summarized.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
AI Foundation, Challenges and Applications.pptxWaqas Ahmad
The document discusses an introduction to artificial intelligence lecture. It defines AI as the power of machines to mimic human behavior. It outlines the history, purposes, and foundations of AI, including logic, mathematics, psychology, computer science, linguistics. Examples of AI applications discussed include chatbots, self-driving cars, face recognition, and robotics. The types of AI are described as weak, strong, and super AI. The components of AI discussed are learning, reasoning, problem solving, perception, and language understanding. Challenges and issues with AI like costs, unemployment, and lack of emotions are also covered.
The document discusses various topics related to artificial intelligence including definitions of AI, goals of AI, whether machines can think, the Turing test, types of AI tasks including mundane, formal and expert tasks, technologies based on AI such as machine learning, natural language processing, computer vision, and applications of AI such as in healthcare, gaming, finance, data security, social media, travel and more.
Advancement in artificial intelligence: Should Humans be Worried?Raymond Owusu
This document provides an overview of artificial intelligence (AI) including its history, types, technologies, applications, and concerns about its advancement. It defines AI as making intelligent machines through computer programs. It discusses weak AI which is designed for specific tasks, and strong AI which would have general human-level abilities. Key AI technologies explained include machine learning, deep learning, natural language processing, computer vision, and robotics. The document outlines many applications of AI in fields such as healthcare, transportation, education, business, manufacturing and more. It notes concerns from experts like Hawking, Musk and Gates that achieving super intelligent AI without proper oversight could potentially threaten humanity.
The document provides an overview of artificial intelligence (AI), including definitions, components, types, applications, and levels. It defines AI as using computer science to create intelligent machines that can behave and think like humans. Intelligence involves reasoning, learning, problem-solving, perception, and language understanding. AI systems are composed of agents that perceive their environment and act on it. Examples of AI applications include autonomous vehicles, medical diagnosis, games, and online assistants. Machine learning is an advanced form of AI that allows machines to learn from experience rather than being explicitly programmed. The document also discusses the history of AI and describes six levels and two main types.
The document provides an overview of artificial intelligence (AI) including definitions of AI and machine learning. It discusses the history of AI from its origins in the 1940s and 50s to modern applications. The major branches of AI are described as well as common uses in areas like robotics, data mining, medical diagnosis, and video games. Both the advantages of AI such as efficiency and lack of errors as well as the disadvantages including cost and potential to decrease human labor are outlined. The document concludes by discussing the future of AI and some of the ethical issues that arise.
This document provides an overview of artificial intelligence (AI) presented by Dr. Jeyadeepa R. It begins with defining key terms like intelligence and AI. It then lists the objectives which include defining AI, listing its domains, outlining its history, differentiating human and artificial intelligence, explaining aspects of AI, classifying types of AI, and describing the need for and pros and cons of AI. The document provides details on the history and evolution of AI, achievements in the field, differences between human and artificial intelligence, central principles, types of AI classified by functionality and capabilities, and need and applications of AI. It concludes by thanking the audience.
The document discusses artificial intelligence and machine learning. It defines artificial intelligence as systems that perform tasks normally requiring human intelligence, such as visual perception and decision-making. The document outlines different types of AI based on capacity, including artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. It also discusses machine learning mechanisms like supervised, unsupervised, reinforcement, and deep learning. Finally, the document defines natural language processing as a field allowing machines to understand human language.
Artificial intelligence (AI) is defined as making computers do tasks that require human intelligence. AI works using artificial neurons that accept input signals and control contributions based on importance, and using scientific theorems like logic. Machine learning uses algorithms to mimic human intelligence. Some applications of AI include game playing, speech recognition, computer vision, and engineering. AI is needed to supplement human intelligence by doing what humans want, like with robots, and to reduce human labor and mistakes. The future of AI includes more autonomous robots and continued advances in areas like computer vision.
The Introductory part of 'Basics of Artificial Intelligence at Grade 10.' This presentation is composed of the types of intelligences, domains of AI, etc.
The document provides an introduction to artificial intelligence (AI), including definitions of AI, descriptions of the eras of AI development, types of AI approaches, and applications of AI. It discusses factors that have influenced recent advancement in AI and identifies areas of AI research focus. The summary is:
The document introduces artificial intelligence (AI), defining it as human-made thinking power. It describes the history and eras of AI development, different types and approaches of AI including weak AI, strong AI, and super AI. Furthermore, it discusses applications of AI and factors influencing recent advancement, and identifies areas of ongoing AI research focus.
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.
This document provides an introduction to artificial intelligence (AI) including definitions, goals, branches, and applications. It defines AI as computers with the ability to mimic human intelligence through learning from experience and handling complex problems. The main goals of AI are to better understand human intelligence by writing programs that emulate it and to create useful programs to do tasks normally requiring human experts. Branches of AI discussed include vision systems, learning systems, robotics, expert systems, and neural networks. The document also outlines some present and future aspects of AI as well as ethics and risks.
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These myths are a simple reflection of my own experience and experiences in the industry. Ai and cognitive are popular these days, but as engineers, data scientists and IT people in general we should make sure not to overate or misuse.
Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
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.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
3. INTRODUCTION
Common definition: AI is
field which attempts to
build intelligent machine
and tries to understand
intelligence entities.
Ques: What is intelligence?
Answer: Learning manipulating facts, but also
creativity, consciousness, emotion and intuition.
Ques: Can machine be intelligent ?
Answer: Up to the present day it is not sure whether
it is possible to build a machine that has all aspects
of intelligence.
This kind of research is central in the field of AI.
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4. HISTORY
McCarthy created the term "artificial
intelligence" and was a towering figure
in computer science at Stanford most of
his professional life. In his career, he
developed the programming language
LISP, played computer chess via
telegraph with opponents in Russia and
invented computer time-sharing.
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5. THE GOALS
1. Deduction, reasoning, Problem solving
2. Knowledge Representation
3. Planning
4. Natural Language Processing
5. Motion and Manipulation
6. Perception
7. Social Intelligence
8. General Intelligence
9. Creativity
The goals of artificial intelligence are :
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6. ADVANTAGES
1. With AI, the chances of
error are almost nil &
greater precision and
accuracy is achieved.
2. Artificial intelligence has made
daily life a lot easier like by use of
applications on phones or
computer that predict user actions
and also make recommendation
that user’s choice.
For eg: applications such as, GPS
and Maps applications etc
5. Fraud detection in smart
card-based systems is possible
with the use of AI. It is also
employed by financial
institution and banks to
organize and manage records
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7. 4. The greatest advantage of artificial
intelligence is that machines do not
requires sleep or breaks, and are able to
function without stopping. They can
continuously perform the task without
getting bored or tired.
5. Machine do not need to receive a
pay check every month. While they
are quite costly to maintain and
power, this cost is greatly less than
what an entire company full of
human employees would have to
paid. The costs are also minimized
and controlled.
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8. PROBLEMS FACED
Artificial intelligence is a gift, it is something extraordinary
but all glitters is not gold! It has its disadvantages as well.
• As you may have seen in many films
such as the matrix, robot or even kids
films such as WALL.E, if we rely on
machines to do almost everything for us
we have become so dependent, that if they
were to simple shut down.
• As they are machines they
obviously can’t provide you with
that ‘human touch and quality’,
the feeling of a togetherness and
emotional understanding, that
machines will lack the ability to
sympathies and empathize with
your situations
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9. • It wouldn’t be too smart on
our part not to have some sort
of backup plan to potential
issues that could arise, if the
machines ‘got real smart’.
• Human beings are emotional
intellectuals. They think and feel. Their
feelings guide their thoughts this is not
the case with machines. The intuitive
abilities that humans possess, the way
humans can judge based on previous
knowledge, the inherent abilities that
have, cannot be replicated by machine.
Also, machine lack common sense.
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10. Future of AI
AI is the best field for dreamers to play
around. It must be evolved from the thought
that making a human-machine is possible.
Though many conclude that this is not
possible, there is still a lot of
research
going on
in this field
to attain the
final objective.
There are inherent advantages of using
computers as they do not get tired or loosing
temper and are becoming faster and faster.
Only time will say what will be the future of
AI: will it attain human-level or above
human-level intelligence or not.
11. Why choose Artificial Intelligence?
• AI research is allowing us to understand our intelligence
behavior.
• Artificial intelligence aims to improve machine behavior
in tackling such complex tasks.
• For more complex problems, things get more difficult...
unlike humans, computers have trouble understanding
specific situations and adapting to new situations.
• Computers are fundamentally well suited to performing
mechanical computations, using fixed programmed rules.
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12. CONCLUSION
AIis at the centre of a new enterprise to build
computational models of intelligence. The main
assumption is that intelligence (human or otherwise) can
be represented in terms of symbol structures and
symbolic operations which can be programmed in a
digital computer. There is much debate as to whether such
an appropriately programmed computer would be a mind,
or would merely simulate one, but AI researchers need
not wait for the conclusion to that debate, nor for the
hypothetical computer that could model all of human
intelligence. Aspects of intelligent behavior, such as
solving problems, making inferences, learning, and
understanding language, have already been coded as
computer programs, and within very limited domains,
such as identifying diseases of soybean plants, AI
programs can outperform human experts. Now the great
challenge of AI is to find ways of representing the
commonsense knowledge and experience that enable
people to carry out everyday activities such as holding a
wide-ranging conversation, or finding their way along a
busy street. Conventional digital computers may be
capable of running such programs, or we may need to
develop new machines that can support the complexity of
human thought.
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