artificial-intelligence
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The document provides an overview of artificial intelligence (AI). It defines AI as techniques for solving exponentially hard problems in polynomial time by exploiting domain knowledge. It then briefly discusses the history of AI, including its roots in logic developed by Aristotle and its modern conception introduced by Alan Turing and John McCarthy. The document also lists some common applications of AI like game playing, automated reasoning, expert systems, natural language processing, planning and robotics. It provides advantages of AI such as taking on complex work, completing tasks faster, and making fewer errors, but also disadvantages like lacking human qualities, potential job replacement, and possible misuse. Finally, it outlines four possible goals for AI systems: thinking like humans, thinking rationally, acting like
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Artificial Intelligence
The document discusses the topics of artificial intelligence including its areas, definitions, advantages, disadvantages and applications. It defines AI as the study of intelligent behavior in machines and the automation of activities requiring human intelligence. The main areas of AI discussed are search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Some advantages mentioned are more powerful computers, new interfaces and better handling of information. Disadvantages include increased costs and difficulty developing software. Applications discussed include autonomous planning, robotics, bioinformatics, text classification and natural language processing.
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Artificial intelligence
This document provides an overview of artificial intelligence (AI). It defines AI as the study of computer systems that attempt to model human intelligence. The document outlines the early history of AI beginning in 1950 with Alan Turing's paper on machine intelligence. It describes the current status of AI in applications such as mobile phones, video games, GPS, and robotics. Challenges for AI are discussed as well as the future potential in areas like self-driving cars and medical care. Both pros and cons of AI are presented before the document concludes with a definition of AI as the study and design of intelligent agents.
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The document provides an introduction to artificial intelligence, including definitions of AI, a brief history of the field, and the current state of the art. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that perceive and act to maximize goals. The document also outlines some of the key topics that will be covered in the course, including search, logic, planning, and learning.
artificial intelligence
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Artificial intelligence ppt
Artificial Intelligence (AI) is the intelligence of machine and the branch of computer science that aims to create it
Timo Honkela: An Introduction to Artificial Intelligence
A presentation given at the Modeling Meaning and Knowledge symposium on 1st of February 2016 at the University of Helsinki.
artificial intelligence
Artificial intelligence (AI) is defined as intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. AI is the study of ideas that enable computers to be intelligent by learning new concepts and tasks, reasoning and drawing useful conclusions, and understanding natural language. The evolution of AI has progressed slowly over time with advances in technology. There are different approaches to creating AI systems and various applications of AI in fields such as computer science, aviation, finance, and more.
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Artificial Intelligence
The document discusses the topics of artificial intelligence including its areas, definitions, advantages, disadvantages and applications. It defines AI as the study of intelligent behavior in machines and the automation of activities requiring human intelligence. The main areas of AI discussed are search, vision, planning, machine learning, knowledge representation, logic, expert systems and robotics. Some advantages mentioned are more powerful computers, new interfaces and better handling of information. Disadvantages include increased costs and difficulty developing software. Applications discussed include autonomous planning, robotics, bioinformatics, text classification and natural language processing.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...
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Artificial intelligence
This document provides an overview of artificial intelligence (AI). It defines AI as the study of computer systems that attempt to model human intelligence. The document outlines the early history of AI beginning in 1950 with Alan Turing's paper on machine intelligence. It describes the current status of AI in applications such as mobile phones, video games, GPS, and robotics. Challenges for AI are discussed as well as the future potential in areas like self-driving cars and medical care. Both pros and cons of AI are presented before the document concludes with a definition of AI as the study and design of intelligent agents.
M1 intro
The document provides an introduction to artificial intelligence, including definitions of AI, a brief history of the field, and the current state of the art. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that perceive and act to maximize goals. The document also outlines some of the key topics that will be covered in the course, including search, logic, planning, and learning.
artificial intelligence
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Artificial intelligence ppt
Artificial Intelligence (AI) is the intelligence of machine and the branch of computer science that aims to create it
Timo Honkela: An Introduction to Artificial Intelligence
A presentation given at the Modeling Meaning and Knowledge symposium on 1st of February 2016 at the University of Helsinki.
artificial intelligence
Artificial intelligence (AI) is defined as intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. AI is the study of ideas that enable computers to be intelligent by learning new concepts and tasks, reasoning and drawing useful conclusions, and understanding natural language. The evolution of AI has progressed slowly over time with advances in technology. There are different approaches to creating AI systems and various applications of AI in fields such as computer science, aviation, finance, and more.
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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.
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Artificial intelligence (AI) is an area of computer science that emphasizes creating intelligent machines that work like humans. AI involves programming computers for traits like perception, learning, planning, problem solving and knowledge. While initially developed in 1956, AI has grown in demand due to big data. AI can be categorized into weak AI, which is designed for specific tasks, and strong AI, which aims for general intelligence. AI has many applications in fields like law, education, business, finance, healthcare and manufacturing. It provides advantages such as increasing work efficiency and reducing costs and errors, but also faces disadvantages like potential misuse if not properly programmed. The future of AI is focused on developing smarter systems through advances in machine learning and deep learning.
Artificial intelligence research
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches, such as attempting to replicate the human brain through neural networks or developing intelligent programs through symbolic reasoning. AI is used today for applications like logistics, data mining, and medical diagnosis.
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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.
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3) The field studies both artificial and natural information processes through programming, design, and other computing arts rather than experimentation and testing of hypotheses as is typical in science.
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Prepared based on Cem Say ,Serdar Kuzuloğlu and Said Evren Şeker' s talking about artificial intelligence..
Artificial intelligence
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Artificial Intelligence
This presentation summarizes artificial intelligence and was presented by 5 students to a professor. It defines AI as developing computer systems that can perform tasks requiring human intelligence, such as visual perception and language translation. It notes that AI was coined in 1956 and discusses programming languages used for AI like Python and Java. It also summarizes the Turing test, different types of AI, applications like expert systems and robotics, and benefits like reducing human casualties. However, it also discusses potential disadvantages like super AI becoming a primary threat to humans in the future.
Artificial Intelligence(AI)
This Provides the Adequate knowledge on Artificial Intelligence (AI). It contains all the design issues to be kept in mind for a new designer of AI with different approaches. Also there are many more applications than specified.. Also for some typical terms pl go through note under the slide which helps in understanding the terms in the slide
Also this PPT is very useful for the students or professors who working on machine learning or AI... As both are go on parallel lines.. after referring to this ppt you may get a clear idea about what is AI and how it is Developed
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The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
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Introduction to artificial intelligence
Introduction to artificial intelligenceSchool of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.Introduction to Artificial Intelligence - Cybernetics Robo Academy
Class note by Prof. Khaled Hussain, CEO of Cybernetics Robo Limited, Bangladesh. www.cyberneticsrobo.com
Artificial Intelligence
This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Lecture 1
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
Contenu connexe
Tendances
Understanding artificial intelligence and it's future scope
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.
Jainish ms ai
Artificial intelligence (AI) is an area of computer science that emphasizes creating intelligent machines that work like humans. AI involves programming computers for traits like perception, learning, planning, problem solving and knowledge. While initially developed in 1956, AI has grown in demand due to big data. AI can be categorized into weak AI, which is designed for specific tasks, and strong AI, which aims for general intelligence. AI has many applications in fields like law, education, business, finance, healthcare and manufacturing. It provides advantages such as increasing work efficiency and reducing costs and errors, but also faces disadvantages like potential misuse if not properly programmed. The future of AI is focused on developing smarter systems through advances in machine learning and deep learning.
Artificial intelligence research
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches, such as attempting to replicate the human brain through neural networks or developing intelligent programs through symbolic reasoning. AI is used today for applications like logistics, data mining, and medical diagnosis.
[Seminar] 200731 Hyeonwook Lee
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.
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Artificial intelligence ppt project of electronics for bachelor of technology in computer science and engineering.
Book Summary: Calm Technology (Author : Amber Case)
The document summarizes the book "Calm Technology" by Amber Case. The book discusses how technology can overwhelm people with its lack of respect for their time and attention. It describes principles of "calm technology" that focuses on how humans interact with technology directly and peripherally. The book provides examples of technologies that fail to account for the human element and discusses how to design technologies that interact more effectively with people.
Transhumanism and brain
The document discusses transhumanism and compares human and artificial intelligence. It covers current technologies like mini antennas and potential future technologies like telepathy and neuroreality. It then compares how humans and artificial systems learn, with humans able to reach an understanding of essence while artificial intelligence relies on computational power. The document concludes that hard transhumanism, with robots becoming the dominant lifeform, clashes with philosophical views, while soft transhumanism involves more integration between humans and robots but maintains humans as the primary agents.
Is Computer Science Science?
1) The document argues that while early pioneers viewed computer science as a science, it is more accurately an art or technology rather than a true science.
2) Computer science combines principles of science, engineering, and mathematics but focuses more on practical applications and invention rather than fundamental explanations.
3) The field studies both artificial and natural information processes through programming, design, and other computing arts rather than experimentation and testing of hypotheses as is typical in science.
Past to Future Artifical Intellegent
Prepared based on Cem Say ,Serdar Kuzuloğlu and Said Evren Şeker' s talking about artificial intelligence..
Artificial intelligence
: AI is field which attempts to build intelligent machine and tries to understand intelligence entities.
Artificial Intelligence
This presentation summarizes artificial intelligence and was presented by 5 students to a professor. It defines AI as developing computer systems that can perform tasks requiring human intelligence, such as visual perception and language translation. It notes that AI was coined in 1956 and discusses programming languages used for AI like Python and Java. It also summarizes the Turing test, different types of AI, applications like expert systems and robotics, and benefits like reducing human casualties. However, it also discusses potential disadvantages like super AI becoming a primary threat to humans in the future.
Artificial Intelligence(AI)
This Provides the Adequate knowledge on Artificial Intelligence (AI). It contains all the design issues to be kept in mind for a new designer of AI with different approaches. Also there are many more applications than specified.. Also for some typical terms pl go through note under the slide which helps in understanding the terms in the slide
Also this PPT is very useful for the students or professors who working on machine learning or AI... As both are go on parallel lines.. after referring to this ppt you may get a clear idea about what is AI and how it is Developed
Domain-driven design - tactical patterns
The document discusses domain-driven design and tactical patterns. It emphasizes making implicit concepts explicit through proper language and code boundaries. These include defining contexts, modules, entities, services, value objects, and aggregates. Patterns like event sourcing, CQS, and CQRS are also mentioned. The document stresses that software development is a learning process and working code is a side effect of gaining understanding through explicitly defining domains and contexts.
The Future of AI is Generative not Discriminative 5/26/2021
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Artificial Intelligence: Expert Systems Components
This presentation is an introduction to artificial intelligence: expert systems components. Topics covered are the following: defining artificial intelligence; expert systems key terms; expert systems requirements; expert systems components; and selecting appropriate problems for expert systems.
Suzanne Gildert - Is it too Early for Robot Rights?
What does a future that includes robots as co-workers, friends, and perhaps even family look and feel like? How are we ensuring that this new “class” is integrated into our societies with respect and empathy? And what about those who worry about a robotic invasion or overlord, how can we ensure that humans feel safe to expand our view of humanity? Is this something we are ready to imagine? This talk focuses on the core issues of empathy, ethics, and rights as we move into a future reality that includes robots as an upgrade to our shared civilization.
Knowledge = Information + Context
The document discusses the importance of semantic contextualization in Europeana. It argues that Europeana is more than just an aggregation of digital objects and aims to enable knowledge generation about culture. It presents the DIKW/DIKT models to illustrate how data, information, and knowledge are related. Semantic contextualization in Europeana involves representing objects and their relationships using classes, properties, and embedding them in a linked open data architecture. This facilitates knowledge generation and even speculative thinking when interacting with Europeana.
Killer Robots, Evil Scientists and Other Tales of Woe
This document discusses how technophobia in popular culture can influence law and policy regarding new technologies. It notes that science fiction often depicts technologies like robots and scientific experiments leading to dystopian outcomes. This can fuel moral panics and calls for restrictive regulations on emerging technologies like the internet, stem cell research, GMOs, and cloning. However, overregulation from technophobic views can also stifle innovation, as seen through history's shifting stances on different technologies as understanding grows. The document argues that finding the right balance is important to support technological progress while addressing reasonable safety concerns.
Tendances (18)
Understanding artificial intelligence and it's future scope
Understanding artificial intelligence and it's future scope
Book Summary: Calm Technology (Author : Amber Case)
Book Summary: Calm Technology (Author : Amber Case)
The Future of AI is Generative not Discriminative 5/26/2021
The Future of AI is Generative not Discriminative 5/26/2021
Artificial Intelligence: Expert Systems Components
Artificial Intelligence: Expert Systems Components
Suzanne Gildert - Is it too Early for Robot Rights?
Suzanne Gildert - Is it too Early for Robot Rights?
Killer Robots, Evil Scientists and Other Tales of Woe
Killer Robots, Evil Scientists and Other Tales of Woe
Similaire à artificial-intelligence
Introduction to artificial intelligence
Introduction to artificial intelligenceSchool of Tourism and Hospitality Management Sant Ignasi, Ramon Llull University
This document provides an introduction to artificial intelligence (AI). It discusses the history and foundations of AI, including early philosophers who discussed the possibility of machine intelligence. It also defines key AI concepts like intelligence, rational thinking, and acting like humans. The document outlines different types of AI systems and why AI is powerful due to combining knowledge from many disciplines. It concludes with an overview of the history of AI from its beginnings in the 1940s to the growth of expert systems.Introduction to Artificial Intelligence - Cybernetics Robo Academy
Class note by Prof. Khaled Hussain, CEO of Cybernetics Robo Limited, Bangladesh. www.cyberneticsrobo.com
Artificial Intelligence
This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Lecture 1
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
1 Introduction to AI.pptx
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
Artificial inteligence
This document provides an introduction to artificial intelligence (AI), including how it works, its evolution and branches, applications, and conclusions. It defines AI as making computers do things that require human intelligence. AI works using artificial neurons and scientific theorems to mimic the human brain. The document outlines the history of AI from early programs in the 1950s to current applications in expert systems, natural language processing, computer vision, robotics, and more. It concludes that AI has increased understanding of intelligence while revealing the complexity of modeling human reasoning.
Artificial intelligence
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
History of AI, Current Trends, Prospective Trajectories
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Artificial intelligence.pptx
The document discusses the history and development of artificial intelligence. It defines intelligence as the ability to plan, solve problems, and reason. Early AI research focused on creating programs that could play games like chess at a human level. Major milestones included the development of LISP programming language in the 1950s and the first AI summer conference at Dartmouth College in 1956. While early work aimed to build human-like intelligence, later approaches focused on narrow applications and bottom-up modeling of neural networks and learning.
introduction to ai
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
AIML_Unit1.pptx
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.
1.INTRODUCTION AI.pdf
This document provides an introduction to artificial intelligence. It defines intelligence and discusses approaches to AI such as systems that act like humans through techniques like the Turing test. It also discusses systems that think rationally using logic and reasoning. The document outlines key concepts in AI like the Chinese room argument and differences between weak and strong AI. It examines using computational models to understand human thinking and formalizing rational thinking through logic.
Artificialintelignc 131019040643-phpapp01
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from its early development in the 1940s-50s to modern applications. It outlines key concepts in AI like the Turing Test and describes applications including expert systems, natural language processing, computer vision, speech recognition, and robotics. The conclusion states that AI has increased understanding of intelligence while also revealing its complexity, providing challenges for future research.
Artificial Intelligence
This document provides an overview of the key approaches to artificial intelligence, including neural networks, parallel computation, and top-down expert systems. It discusses how neural networks attempt to mimic the human brain by constructing electronic circuits that function like neurons. Pioneering work by McCulloch and Pitts in the 1940s linked neural processing to binary logic and laid the foundations for computer-simulated neural networks. Expert systems take a top-down approach, using stored information and rules to interpret data and solve problems in specific domains.
Cosc 208 lecture note-1
This document discusses different definitions and approaches to artificial intelligence (AI). It begins by defining AI as helping machines solve complex problems like humans by applying human-like algorithms. It then discusses AI's links to other fields and its history. The rest of the document explores definitions of AI and different goals or approaches in AI research, including systems that think or act like humans and systems that think or act rationally. It focuses on the Turing Test approach of acting humanly and the cognitive modeling approach of thinking humanly by modeling human cognition.
#1 Lecture .pptx
1. Artificial Intelligence aims to understand and build intelligent systems by studying human intelligence and behavior.
2. There are different approaches to defining AI such as thinking rationally, acting rationally, thinking humanly, and acting humanly.
3. The foundations of AI draw from various fields including philosophy, mathematics, economics, neuroscience, psychology, and computer engineering.
Advanced Artificial Intelligence
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?
Unit 1 ppt.pptx
- Sensors that allow the system to perceive its environment
- Actuators that allow the system to act on or change its environment
- An ability to reason and make rational decisions or plans of action based on its perceptions
The document discusses sensors and actuators in the context of intelligent agents - for humans, examples of sensors are eyes and ears, while actuators are hands, legs, and mouth. For robots, examples of sensors would be cameras and other input devices, while actuators would allow the robot to move or manipulate objects.
Lec-01.ppt
This document provides an overview of artificial intelligence (AI). It defines AI as the science of developing methods to solve problems usually associated with human intelligence. The document discusses different definitions and visions of AI, including thinking and acting like humans, thinking and acting rationally, and modeling human thinking through computational models. It also covers the history of AI from its origins in the 1940s to recent successes, as well as related fields and main areas of AI research like machine learning, robotics, and natural language processing.
Artificial Intelligence
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.
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Introduction to Artificial Intelligence - Cybernetics Robo Academy
Introduction to Artificial Intelligence - Cybernetics Robo Academy
History of AI, Current Trends, Prospective Trajectories
History of AI, Current Trends, Prospective Trajectories
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Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Columbus Data & Analytics Wednesdays - June 2024
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National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Project Management Semester Long Project - Acuity
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
artificial-intelligence
- 2. “AI is the study of techniques for solving exponentially hard problems in polynomial time by exploiting knowledge about the problem domain.“ - Elaine Rich
- 3. Although artificial intelligence as an independent field of study is relatively new, it has some roots in the past. We can say that it started 2,400 years ago when the Greek philosopher Aristotle invented the concept of logical reasoning. The effort to finalize the language of logic continued with Leibniz and Newton. George Boole developed Boolean algebra in the nineteenth century (Appendix E) that laid the foundation of computer circuits. However, the main idea of a thinking machine came from Alan Turing, who proposed the Turing test. The term “artificial intelligence” was first coined by John McCarthy in 1956. Hi st ory of art i f i ci al i nt el l i gence
- 4. ApplicatioApplicatio ns of AI :ns of AI : •Game playing •Automated Reasoning & Theorem Proving •Expert Systems •Natural Language Understanding & Semantic Modeling •Planning & Robotics
- 5. ARTIFICIALARTIFICIAL INTELLIGENCEINTELLIGENCE advantagesadvantages andand disadvantagesdisadvantages
- 6. ADVANTAGES OF ARTIFICIAL INTELLIGENCEADVANTAGES OF ARTIFICIAL INTELLIGENCE Can take on stressful and complex work that humans may struggle/can not do. Can complete task faster than a human can most likely To discover unexplored things. i.e. outer space Less errors and defects Function is infinite
- 7. DISADVANTAGES OF ARTIFICIAL INTELLIGENCEDISADVANTAGES OF ARTIFICIAL INTELLIGENCE Lacks the "human touch" Has the ability to replace human jobs Can malfunction and do the opposite of what they are programmed to do Can be misused leading to mass scale destruction May corrupt younger generation
- 8. We end up with four possible goals: Systems that think like humans (focus on reasoning and human framework) Systems that think rationally (focus on reasoning and a general concept of intelligence) Systems that act like humans (focus on behavior and human framework) Systems that act rationally (focus on behavior and a general concept of intelligence)
- 9. The robots are coming to steal our jobs
- 10. Thank You