The document provides an overview of artificial intelligence and robotics. It begins with an introduction from the CSE department of Mewar University and includes sections on definitions of AI, approaches of AI like strong AI and weak AI, techniques in AI like neural networks and genetic algorithms, famous AI systems such as Deep Blue and ALVINN, the history and foundations of AI, areas of AI like robotics and natural language processing, and recommended reference books. It discusses concepts like the Turing test, the Chinese room argument and architectures for general intelligence including LIDA and Sloman's architectures.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
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?
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.
The document provides an introduction to artificial intelligence (AI), including:
1) Defining AI as designing intelligent systems and examining different views on what constitutes intelligence.
2) Describing typical AI problems like object recognition, language processing, and games, noting that expert tasks are now solvable by computers but common tasks remain challenging.
3) Discussing the practical impact of AI and different approaches like strong AI, weak AI, applied AI, and cognitive AI.
4) Noting the current limits of AI in areas requiring common sense knowledge or understanding unconstrained natural language.
This document provides an overview of artificial intelligence including:
- Defining intelligence and AI, discussing the history and types of AI such as neural networks.
- Explaining the Turing test and different types of intelligent agents and their environments.
- Comparing human and AI in terms of capabilities and limitations.
- Discussing applications of expert systems and how AI learns and resembles the human mind.
- Concluding that AI is an attempt to build computational models of intelligence.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks provide a way to represent knowledge that is inspired by the human brain - data is fed through a network of nodes that can strengthen or weaken connections to learn from examples. While narrow AI has achieved success in specialized tasks, the long term goal is to create general artificial intelligence that can match or exceed human abilities across a wide range of cognitive tasks.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks aim to mimic the human brain by using interconnected nodes that can learn from data. Machine learning algorithms like deep learning use neural networks to learn from large amounts of data without being explicitly programmed. [/SUMMARY]
This document provides an overview of artificial intelligence (AI) including definitions, approaches, foundations, capabilities, and comparisons to human intelligence. It defines AI as the study of intelligent behavior in machines, discusses the four main approaches of acting humanly, thinking humanly, thinking rationally, and acting rationally. The foundations of AI are explained including contributions from fields like philosophy, mathematics, psychology, neuroscience, and more. Both strong AI which aims to truly replicate human reasoning and weak AI which focuses on narrow domains are described. Current capabilities of AI systems in areas such as games, robotics, diagnosis, and planning are summarized. Finally, differences between human and machine intelligence are outlined.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
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?
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.
The document provides an introduction to artificial intelligence (AI), including:
1) Defining AI as designing intelligent systems and examining different views on what constitutes intelligence.
2) Describing typical AI problems like object recognition, language processing, and games, noting that expert tasks are now solvable by computers but common tasks remain challenging.
3) Discussing the practical impact of AI and different approaches like strong AI, weak AI, applied AI, and cognitive AI.
4) Noting the current limits of AI in areas requiring common sense knowledge or understanding unconstrained natural language.
This document provides an overview of artificial intelligence including:
- Defining intelligence and AI, discussing the history and types of AI such as neural networks.
- Explaining the Turing test and different types of intelligent agents and their environments.
- Comparing human and AI in terms of capabilities and limitations.
- Discussing applications of expert systems and how AI learns and resembles the human mind.
- Concluding that AI is an attempt to build computational models of intelligence.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks provide a way to represent knowledge that is inspired by the human brain - data is fed through a network of nodes that can strengthen or weaken connections to learn from examples. While narrow AI has achieved success in specialized tasks, the long term goal is to create general artificial intelligence that can match or exceed human abilities across a wide range of cognitive tasks.
Artificial intelligence (AI) is the study of intelligent agents that perceive their environment and take actions to maximize their chances of achieving their goals. There are several key areas of AI research including problem solving, machine learning, natural language processing, computer vision, and robotics. Neural networks aim to mimic the human brain by using interconnected nodes that can learn from data. Machine learning algorithms like deep learning use neural networks to learn from large amounts of data without being explicitly programmed. [/SUMMARY]
This document provides an overview of artificial intelligence (AI) including definitions, approaches, foundations, capabilities, and comparisons to human intelligence. It defines AI as the study of intelligent behavior in machines, discusses the four main approaches of acting humanly, thinking humanly, thinking rationally, and acting rationally. The foundations of AI are explained including contributions from fields like philosophy, mathematics, psychology, neuroscience, and more. Both strong AI which aims to truly replicate human reasoning and weak AI which focuses on narrow domains are described. Current capabilities of AI systems in areas such as games, robotics, diagnosis, and planning are summarized. Finally, differences between human and machine intelligence are outlined.
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.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons that mimic biological neurons. Neural networks are composed of interconnected artificial neurons. The Turing test tests a machine's ability to demonstrate intelligence comparable to a human. There are different types of AI like expert systems, machine learning, and intelligent agents. While AI can process large amounts of data fast without human limitations, it lacks common sense, intuition, and creativity that humans possess. Overall, AI aims to supplement natural human intelligence by performing tasks through machines to reduce human labor and mistakes.
The document provides an overview of artificial intelligence (AI) including definitions, techniques, and challenges. It discusses how AI aims to make computers intelligent like humans by giving them abilities such as perception, reasoning, learning, and problem solving. Some key techniques mentioned are search, knowledge representation, and abstraction. The document also discusses the Turing Test as a proposed method for determining if a machine can think like a human. It provides examples of problems AI aims to solve such as game playing, commonsense reasoning, and perception.
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.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.pptDaliaMagdy12
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or final conclusions) and self-correction. Particular applications of the AI include expert system speech recognition and artificial vision.
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
The document discusses several topics related to artificial intelligence including definitions of intelligence and AI, approaches to AI like symbolic AI and biological AI, applications of AI such as expert systems and game playing, and techniques like the Turing test. It also provides a brief history of AI from 1943 to the present day and discusses components of AI programs like knowledge bases, control strategies, and inference mechanisms.
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
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.
This document provides an introduction to an artificial intelligence course. The course aims to give students knowledge and understanding of core AI concepts like search, game playing, planning and machine learning. Students will learn how to apply these concepts to construct simple AI systems using a declarative language. The document outlines several core areas of AI including knowledge representation, reasoning, planning, learning, and interacting with the environment. It also discusses the history of AI and provides examples of modern AI applications.
The document discusses an artificial intelligence course, outlining 5 units that will cover topics like problem solving through search algorithms, propositional logic, knowledge representation, planning, and learning from uncertainty. The goals of the course are for students to understand concepts like state space representation, heuristic search techniques, and applying AI techniques to problems involving games, machine learning and more. The course will examine algorithms, knowledge representation methods, and applications of AI in areas such as games, theorem proving and machine learning.
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
The document discusses artificial intelligence (AI) and its key concepts. It begins by explaining how computers have grown more capable over time due to advances in AI. AI aims to create machine intelligence comparable to human intelligence. The document then discusses definitions of intelligence, the philosophy behind creating machine intelligence, goals and applications of AI like gaming, language processing and robotics. It also covers concepts important for AI like reasoning, learning, problem solving, perception and linguistic intelligence.
1. The document provides an introduction to the philosophy of artificial intelligence, discussing definitions of AI, the nature of human vs artificial intelligence, and different approaches to AI such as systems that think or act like humans and systems that think or act rationally.
2. Key aspects of AI discussed include machine learning, natural language processing, computer vision, robotics, and the differences between strong and weak AI.
3. The document also examines how AI aims to build intelligent machines that can perform tasks requiring human intelligence through techniques like problem solving, perception, reasoning, and learning.
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.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
This document outlines a Java project for a bus scheduling and booking system. The project aims to automate bus operator tasks like managing bus routes, staff details, and passenger information to reduce paperwork. Key aspects include a database structure with tables for supervisor info, bus info, schedules, routes, and bookings. The system is designed to be easy to use with a familiar interface and search/report capabilities. It will allow collecting management details efficiently in a secure centralized system.
This document discusses various types of logic gates. It begins with an introduction to logic gates and their basic components. It then explains the functionality and truth tables of common logic gates like AND, OR, NOT. It also covers universal gates such as NAND and NOR. Finally, it describes exclusive OR and exclusive NOR gates through their symbols and truth tables. The document serves to introduce the essential concepts of logic gates in digital circuits and electronics.
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.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons that mimic biological neurons. Neural networks are composed of interconnected artificial neurons. The Turing test tests a machine's ability to demonstrate intelligence comparable to a human. There are different types of AI like expert systems, machine learning, and intelligent agents. While AI can process large amounts of data fast without human limitations, it lacks common sense, intuition, and creativity that humans possess. Overall, AI aims to supplement natural human intelligence by performing tasks through machines to reduce human labor and mistakes.
The document provides an overview of artificial intelligence (AI) including definitions, techniques, and challenges. It discusses how AI aims to make computers intelligent like humans by giving them abilities such as perception, reasoning, learning, and problem solving. Some key techniques mentioned are search, knowledge representation, and abstraction. The document also discusses the Turing Test as a proposed method for determining if a machine can think like a human. It provides examples of problems AI aims to solve such as game playing, commonsense reasoning, and perception.
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.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.pptDaliaMagdy12
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or final conclusions) and self-correction. Particular applications of the AI include expert system speech recognition and artificial vision.
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
The document discusses several topics related to artificial intelligence including definitions of intelligence and AI, approaches to AI like symbolic AI and biological AI, applications of AI such as expert systems and game playing, and techniques like the Turing test. It also provides a brief history of AI from 1943 to the present day and discusses components of AI programs like knowledge bases, control strategies, and inference mechanisms.
Artificial intelligence (AI) is concerned with designing intelligent systems, with two key aspects - intelligence and an artificial device. There are different views on what constitutes intelligence - emulating human thought processes, passing the Turing test of indistinguishability from humans, focusing on logical reasoning abilities, or acting rationally to achieve goals. AI research involves problems like perception, reasoning, learning, language understanding, problem-solving and robotics. While today's AI can achieve limited success in tasks like computer vision, robotics, translation and games, it still cannot match human-level abilities in areas like language understanding, planning, learning or exhibiting true autonomy. The history of AI began in the 1950s and has progressed through periods focused on games
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
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.
This document provides an introduction to an artificial intelligence course. The course aims to give students knowledge and understanding of core AI concepts like search, game playing, planning and machine learning. Students will learn how to apply these concepts to construct simple AI systems using a declarative language. The document outlines several core areas of AI including knowledge representation, reasoning, planning, learning, and interacting with the environment. It also discusses the history of AI and provides examples of modern AI applications.
The document discusses an artificial intelligence course, outlining 5 units that will cover topics like problem solving through search algorithms, propositional logic, knowledge representation, planning, and learning from uncertainty. The goals of the course are for students to understand concepts like state space representation, heuristic search techniques, and applying AI techniques to problems involving games, machine learning and more. The course will examine algorithms, knowledge representation methods, and applications of AI in areas such as games, theorem proving and machine learning.
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
The document discusses artificial intelligence (AI) and its key concepts. It begins by explaining how computers have grown more capable over time due to advances in AI. AI aims to create machine intelligence comparable to human intelligence. The document then discusses definitions of intelligence, the philosophy behind creating machine intelligence, goals and applications of AI like gaming, language processing and robotics. It also covers concepts important for AI like reasoning, learning, problem solving, perception and linguistic intelligence.
1. The document provides an introduction to the philosophy of artificial intelligence, discussing definitions of AI, the nature of human vs artificial intelligence, and different approaches to AI such as systems that think or act like humans and systems that think or act rationally.
2. Key aspects of AI discussed include machine learning, natural language processing, computer vision, robotics, and the differences between strong and weak AI.
3. The document also examines how AI aims to build intelligent machines that can perform tasks requiring human intelligence through techniques like problem solving, perception, reasoning, and learning.
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.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
This document outlines a Java project for a bus scheduling and booking system. The project aims to automate bus operator tasks like managing bus routes, staff details, and passenger information to reduce paperwork. Key aspects include a database structure with tables for supervisor info, bus info, schedules, routes, and bookings. The system is designed to be easy to use with a familiar interface and search/report capabilities. It will allow collecting management details efficiently in a secure centralized system.
This document discusses various types of logic gates. It begins with an introduction to logic gates and their basic components. It then explains the functionality and truth tables of common logic gates like AND, OR, NOT. It also covers universal gates such as NAND and NOR. Finally, it describes exclusive OR and exclusive NOR gates through their symbols and truth tables. The document serves to introduce the essential concepts of logic gates in digital circuits and electronics.
Java is a programming language that is used widely on servers and mobile apps. It features include being platform independent and object-oriented. Java code is compiled to bytecode that runs on a Java Virtual Machine, allowing the same code to run on many systems.
Cryptography is the study of encryption principles and methods for transforming plaintext into ciphertext using an algorithm called a cipher and a key known only to the sender and receiver. Cryptanalysis is the study of deciphering ciphertext without knowing the key in order to recover the original plaintext. Cryptology encompasses both cryptography and cryptanalysis.
This document discusses intelligent software agents. It covers types of agents including intelligent agents, properties of agents, and classifications of intelligent agents using horizontal and vertical layering approaches. The document appears to be a student paper submitted to a professor for a B.Tech computer science course.
This document discusses symmetric encryption, also known as conventional or single-key encryption. Symmetric encryption uses a single key that is known to both the sender and receiver to encrypt plaintext into ciphertext and decrypt ciphertext back to plaintext. The document defines basic terminology related to symmetric encryption like plaintext, ciphertext, cipher, key, encryption, and decryption. It also discusses the principles of cryptography used in symmetric encryption like substitution and transposition ciphers. The document outlines advantages of symmetric encryption like speed but also disadvantages related to securely distributing the shared secret key between communicating parties.
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Decolonizing Universal Design for LearningFrederic Fovet
UDL has gained in popularity over the last decade both in the K-12 and the post-secondary sectors. The usefulness of UDL to create inclusive learning experiences for the full array of diverse learners has been well documented in the literature, and there is now increasing scholarship examining the process of integrating UDL strategically across organisations. One concern, however, remains under-reported and under-researched. Much of the scholarship on UDL ironically remains while and Eurocentric. Even if UDL, as a discourse, considers the decolonization of the curriculum, it is abundantly clear that the research and advocacy related to UDL originates almost exclusively from the Global North and from a Euro-Caucasian authorship. It is argued that it is high time for the way UDL has been monopolized by Global North scholars and practitioners to be challenged. Voices discussing and framing UDL, from the Global South and Indigenous communities, must be amplified and showcased in order to rectify this glaring imbalance and contradiction.
This session represents an opportunity for the author to reflect on a volume he has just finished editing entitled Decolonizing UDL and to highlight and share insights into the key innovations, promising practices, and calls for change, originating from the Global South and Indigenous Communities, that have woven the canvas of this book. The session seeks to create a space for critical dialogue, for the challenging of existing power dynamics within the UDL scholarship, and for the emergence of transformative voices from underrepresented communities. The workshop will use the UDL principles scrupulously to engage participants in diverse ways (challenging single story approaches to the narrative that surrounds UDL implementation) , as well as offer multiple means of action and expression for them to gain ownership over the key themes and concerns of the session (by encouraging a broad range of interventions, contributions, and stances).
Artificial Intelligence (AI) has revolutionized the creation of images and videos, enabling the generation of highly realistic and imaginative visual content. Utilizing advanced techniques like Generative Adversarial Networks (GANs) and neural style transfer, AI can transform simple sketches into detailed artwork or blend various styles into unique visual masterpieces. GANs, in particular, function by pitting two neural networks against each other, resulting in the production of remarkably lifelike images. AI's ability to analyze and learn from vast datasets allows it to create visuals that not only mimic human creativity but also push the boundaries of artistic expression, making it a powerful tool in digital media and entertainment industries.
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8+8+8 Rule Of Time Management For Better ProductivityRuchiRathor2
This is a great way to be more productive but a few things to
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- Some days may require more work or less sleep, demanding flexibility in your approach.
- The key is to be mindful of your time allocation and strive for a healthy balance across the three categories.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
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CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
LEC_2_AI_INTRODUCTION - Copy.pptx
1. ARTIFICIAL INTELLGENCE & ROBOTICS
AI INTRODUCTION
CSE DEPARTMENT OF MEWAR UNIVERSITY CHITTORGARGH, WELCOMES ALL OF YOU
NAME: SHIV KUMAR (CSE DEPARTMENT)
2. CONTENTS
INTRODUCTION TO AI
AGENT & ROBOTICS
ENVIRONMENT
LOGICS & REASONING- probability & mathematical logics
AI PROBLEM
PROBLEM SOLVING TECHNIQUES
SEARCH TECHNIQUES
PLANNING
RULE BASE SYSTEM
PRODUCTION SYSTEM
EXPERT SYSTEM
KR & REASONING
GA
NN
REINFORCEMENT LEARNING
NLP
PROLOG
LISP
PYTHON
REFERENCE BOOKS
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA
3. DEFINITION
What is AI ?
Artificial Intelligence is concerned with the design of intelligence in an artificial device.
The term was coined by McCarthy in 1956.
There are two ideas in the definition.
1. Intelligence
2. artificial device
What is intelligence?
– Is it that which characterize humans? Or is there an absolute standard of judgement?
– Accordingly there are two possibilities:
– A system with intelligence is expected to behave as intelligently as a human
– A system with intelligence is expected to behave in the best possible manner
– Secondly what type of behavior are we talking about?
– Are we looking at the thought process or reasoning ability of the system?
– Or are we only interested in the final manifestations of the system in terms of its actions?
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA
4. DEFINITION
What is involved in Intelligence
A) Ability to interact with the real world
- to perceive, understand, and act
- speech recognition, understanding, and synthesis
- image understanding (computer vision)
B) Reasoning and Planning
- modeling the external world
- problem solving, planning, and decision making
- ability to deal with unexpected problems, uncertainty
C) Learning and Adaptation
- we are continuously learning and adapting
- Also: we want systems that adapt to us!
- Major thrust of industry research.
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA
6. DEFINITION
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA
Rich and Knight: the study of how to make computers do things which, at the
moment, people do better.
Handbook of AI: the part of computer science concerned with designing
intelligent computer systems, that is, systems that exhibit the characteristics we
associate with intelligence in human behavior -
understanding language, learning, reasoning, solving problems, etc.
Dean, Allen and Aloimonos: the design and study of the computer
programs that behave intelligently.
Russell and Norvig: the study of [rational] agents that exist in an environment
and perceive and act. ******
9. APPROACHES OF AI
Strong AI aims to build machines that can truly reason and solve problems. These machines
should be self aware and their overall intellectual ability needs to be indistinguishable from
that of a human being. Excessive optimism in the 1950s and 1960s concerning strong AI has
given way to an appreciation of the extreme difficulty of the problem. Strong AI maintains
that suitably programmed machines are capable of cognitive mental states.
Weak AI: deals with the creation of some form of computer-based artificial intelligence that
cannot truly reason and solve problems, but can act as if it were intelligent. Weak AI holds
that suitably programmed machines can simulate human cognition.
Applied AI: aims to produce commercially viable "smart" systems such as, for example, a
security system that is able to recognise the faces of people who are permitted to enter a
particular building. Applied AI has already enjoyed considerable success.
Cognitive AI: computers are used to test theories about how the human mind works--for
example, theories about how we recognise faces and other objects, or about how we solve
abstract problems.
14. APPROACHES OF AI
Turing Test in AI
In 1950, Alan Turing introduced a test to check whether a machine can
think like a human or not, this test is known as the Turing Test. In this
test, Turing proposed that the computer can be said to be an intelligent
if it can mimic human response under specific conditions.
Turing Test was introduced by Turing in his 1950 paper, "Computing
Machinery and Intelligence," which considered the question, "Can
Machine think?"
15. APPROACHES OF AI
Turing Test in AI
Consider, Player A is a computer, Player B is human, and Player C is an
interrogator. Interrogator is aware that one of them is machine, but he
needs to identify this on the basis of questions and their responses.
The conversation between all players is via keyboard and screen so the
result would not depend on the machine's ability to convert words as
speech.
The test result does not depend on each correct answer, but only how
closely its responses like a human answer. The computer is permitted
to do everything possible to force a wrong identification by the
interrogator.
"In 1991, the New York businessman Hugh Loebner announces the
prize competition, offering a $100,000 prize for the first computer to
pass the Turing test. However, no AI program to till date, come close to
passing an undiluted Turing test".
16. APPROACHES OF AI
Turing Test in AI
Consider, Player A is a computer, Player B is human, and Player C is an
interrogator. Interrogator is aware that one of them is machine, but he
needs to identify this on the basis of questions and their responses.
The conversation between all players is via keyboard and screen so the
result would not depend on the machine's ability to convert words as
speech.
The test result does not depend on each correct answer, but only how
closely its responses like a human answer. The computer is permitted
to do everything possible to force a wrong identification by the
interrogator.
"In 1991, the New York businessman Hugh Loebner announces the
prize competition, offering a $100,000 prize for the first computer to
pass the Turing test. However, no AI program to till date, come close to
passing an undiluted Turing test".
17. APPROACHES OF AI
Chatbots to attempt the Turing test:
ELIZA: ELIZA was a Natural language processing computer program
created by Joseph Weizenbaum. It was created to demonstrate the
ability of communication between machine and humans. It was one of
the first chatterbots, which has attempted the Turing Test.
Parry: Parry was a chatterbot created by Kenneth Colby in 1972. Parry
was designed to simulate a person with Paranoid
schizophrenia(most common chronic mental disorder). Parry was
described as "ELIZA with attitude." Parry was tested using a variation
of the Turing Test in the early 1970s.
Eugene Goostman: Eugene Goostman was a chatbot developed in
Saint Petersburg in 2001. This bot has competed in the various number
of Turing Test. In June 2012, at an event, Goostman won the
competition promoted as largest-ever Turing test content, in which it
has convinced 29% of judges that it was a human.Goostman
resembled as a 13-year old virtual boy.
18. APPROACHES OF AI
The Chinese Room Argument:
There were many philosophers who really disagreed with the complete
concept of Artificial Intelligence. The most famous argument in this list
was "Chinese Room."
In the year 1980, John Searle presented "Chinese Room" thought
experiment, in his paper "Mind, Brains, and Program," which was
against the validity of Turing's Test. According to his argument,
"Programming a computer may make it to understand a
language, but it will not produce a real understanding of
language or consciousness in a computer."
He argued that Machine such as ELIZA and Parry could easily pass the
Turing test by manipulating keywords and symbol, but they had no real
understanding of language. So it cannot be described as "thinking"
capability of a machine such as a human.
19. APPROACHES OF AI
Features required for a machine to pass the Turing test:
•Natural language processing: NLP is required to communicate with
Interrogator in general human language like English.
•Knowledge representation: To store and retrieve information during
the test.
•Automated reasoning: To use the previously stored information for
answering the questions.
•Machine learning: To adapt new changes and can detect generalized
patterns.
•Vision (For total Turing test): To recognize the interrogator actions
and other objects during a test.
•Motor Control (For total Turing test): To act upon objects if
requested.
20. APPROACHES OF AI
I Building exact models of human cognition
• view from psychology and cognitive science
II The logical thought approach
• emphasis on ``correct'' inference
III Building rational ``agents''
• agent: something that perceives and acts
• emphasis on developing methods to match or exceed
human
performance [in certain domains]. Example: Deep Blue.
21. What can AI systems do?
What can AI systems do
Today’s AI systems have been able to achieve limited success in some of these tasks.
• In Computer vision, the systems are capable of face recognition
• In Robotics, we have been able to make vehicles that are mostly autonomous.
• In Natural language processing, we have systems that are capable of simple
machine translation.
• Today’s Expert systems can carry out medical diagnosis in a narrow domain
• Speech understanding systems are capable of recognizing several thousand words
continuous speech
• Planning and scheduling systems had been employed in scheduling experiments
with the Hubble Telescope.
• The Learning systems are capable of doing text categorization into about a 1000
topics
• In Games, AI systems can play at the Grand Master level in chess (world
champion), checkers, etc.
22. What can AI systems NOT do yet?
What can AI systems NOT do yet?
• Understand natural language robustly (e.g., read and understand articles in a
newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence
23. famous AI system
1. ALVINN:
Autonomous Land Vehicle In a Neural Network
In 1989, Dean Pomerleau at CMU created ALVINN. This is a system which learns
to control vehicles by watching a person drive. It contains a neural network whose
input is a 30x32 unit two dimensional camera
2. Deep Blue
In 1997, the Deep Blue chess program created by IBM, beat the current world chess
champion, Gary Kasparov.
3. Machine translation
A system capable of translations between people speaking different languages will
be a remarkable achievement of enormous economic and cultural benefit. Machine
translation is one of the important fields of endeavour in AI. While some translating
systems have been developed, there is a lot of scope for improvement in translation
quality.
24. famous AI system
4. Autonomous agents
In space exploration, robotic space probes autonomously monitor their surroundings,
make decisions and act to achieve their goals.
NASA's Mars rovers successfully completed their primary three-month missions in
April, 2004. The Spirit rover had been exploring a range of Martian hills that took
two months to reach. It is finding curiously eroded rocks that may be new pieces to
the puzzle of the region's past. Spirit's twin, Opportunity, had been examining
exposed rock layers inside a crater
5. Internet agents
The explosive growth of the internet has also led to growing interest in internet
agents to monitor users' tasks, seek needed information, and to learn which
information is most useful
26. AI COMMON TECHNIQUES
Representation- Knowledge needs to be represented somehow – perhaps as a
series of if-then rules, as a frame based system, as a semantic network, or in the
connection weights of an artificial neural network.
Learning- Automatically building up knowledge from the environment – such as
acquiring the rules for a rule based expert system, or determining the appropriate
connection weights in an artificial neural network.
Rules- These could be explicitly built into an expert system by a knowledge
engineer, or implicit in the connection weights learnt by a neural network.
Search- This can take many forms – perhaps searching for a sequence of states that
leads quickly to a problem solution, or searching for a good set of connection
weights for a neural network by minimizing a fitness function.
29. AI FOUNDATION / ROOTS
• Philosophy
• Mathematics
• Psychology/Cognitive Science
• ECONOMICS
• LINGUISTICS
• CONROL THEORY
• COMPUTER SCIENCE
30. SUB AREAS OF AI
• Neural Networks – e.g. brain modelling, time series prediction,
classification
• Evolutionary Computation – e.g. genetic algorithms, genetic
programming
• Vision – e.g. object recognition, image understanding
• Robotics – e.g. intelligent control, autonomous exploration
• Expert Systems – e.g. decision support systems, teaching systems
• Speech Processing– e.g. speech recognition and production
• Natural Language Processing – e.g. machine translation
• Planning – e.g. scheduling, game playing
• Machine Learning – e.g. decision tree learning, version space
learning
31. AI HISTORY
Prof. Peter Jackson (University of Edinburgh) classified the history of AI into three periods
as:
1. Classical Period:
It was started from 1950. In 1956, the concept of Artificial Intelligence came into
existance. During this period, the main research work carried out includes game plying,
theorem proving and concept of state space approach for solving a problem.
2. Romantic Period:
It was started from the mid 1960 and continues until the mid 1970. During this period
people were interested in making machine understand, that is usually mean the
understanding of natural language. During this period the knowledge representation
technique “semantic net” was developed.
3. Modern Period:
It was started from 1970 and continues to the present day. This period was developed to
solve more complex problems. This period includes the research on both theories and
practical aspects of Artificial Intelligence. This period includes the birth of concepts
like Expert system, Artificial Neurons, Pattern Recognition etc. The research of
the various advanced concepts of Pattern Recognition and Neural Network are
still going on.
32. AI ARCHITECTURE
for GENERAL INTELLIGENCE
The AGIRI website lists several features, describing machines
with human-level, and even superhuman, intelligence.
that generalize their knowledge across different domains.
that reflect on themselves.
and that create fundamental innovations and insights.
Figure . Attention and Action Selection
34. AI ARCHITECTURE- LIDA
Learning Intelligent Distribution Agent
• IDA denotes a conceptual and computational model of human cognition.
IDA, an acronym for Intelligent Distribution1 Agent, is an autonomous software
agent, which automates the tasks of the detailers[The US Navy has about
350,000 sailors. As each sailor comes to the end of a certain tour of duty, he or
she needs a new billet, a new job. The Navy employs some 300 detailers, as they
call them, personnel officers who assign these new billets. A detailer dialogs with
sailors, usually over the telephone, but sometime by email.]
Figure . Working Memory
36. REFERENCE BOOKS
• INTRODUCTION OF COMPUTER SCIENCE ‘C’ EDITION- ULMAN
• ‘C’ PROGRAMMING LANGUAGE- BRAIAN & RITCHIE
• PROGRAMMING IN ‘C’ –ASHOK N. KAMTHANE
• LET US ‘C’ – KANETKAR
• FUNDAMENTAL OF COMPUTER= RAJA RAMAN
International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering