This document provides an introduction to the CS321 Principles of Artificial Intelligence course. It defines intelligence and AI, discusses the history and foundations of AI, and outlines some common AI applications and risks. The document is divided into 34 slides covering topics such as the Turing test, rational agents, neural networks, deep learning, and how AI is used in areas like robotics, game playing, and medical diagnosis.
Ai introduction and production system and search patternsaadip5069118
This document provides an introduction and syllabus for an Artificial Intelligence course. The syllabus covers 5 units: introduction to AI and search techniques, optimization problems and search algorithms, knowledge representation structures and logic, uncertain knowledge and reasoning, and game playing techniques and applications to robotics. The course aims to develop an understanding of key concepts in AI like search, knowledge representation, reasoning, and applications. It will examine techniques such as production systems, logic programming, Bayesian networks, and game theory algorithms. Students will learn how to represent and reason with knowledge under uncertainty.
The document discusses artificial intelligence and its history. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It describes how AI works using approaches like machine learning, deep learning, and artificial neural networks. The document traces the origins and development of AI from its coining in 1956 to modern advances in algorithms, machine learning, and integrating statistical analysis. It discusses landmark concepts like the Turing Test and the development of expert systems using programming languages like LISP and PROLOG. The document also notes some limitations of current AI technologies like software interoperability, knowledge acquisition, and handling uncertainty.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
AI is the study of intelligent agents: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Major areas of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. Weak AI is limited to a specific task, while strong AI exhibits human-level intelligence across all cognitive tasks. The foundations of AI include philosophy, mathematics, psychology and linguistics. Notable AI milestones include Deep Blue defeating Kasparov at chess in 1997 and the development of expert systems, robotics, computer vision and natural language processing. Current trends include cognitive computing, which aims to develop systems that can perceive, learn, reason and assist
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.
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.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
Ai introduction and production system and search patternsaadip5069118
This document provides an introduction and syllabus for an Artificial Intelligence course. The syllabus covers 5 units: introduction to AI and search techniques, optimization problems and search algorithms, knowledge representation structures and logic, uncertain knowledge and reasoning, and game playing techniques and applications to robotics. The course aims to develop an understanding of key concepts in AI like search, knowledge representation, reasoning, and applications. It will examine techniques such as production systems, logic programming, Bayesian networks, and game theory algorithms. Students will learn how to represent and reason with knowledge under uncertainty.
The document discusses artificial intelligence and its history. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. It describes how AI works using approaches like machine learning, deep learning, and artificial neural networks. The document traces the origins and development of AI from its coining in 1956 to modern advances in algorithms, machine learning, and integrating statistical analysis. It discusses landmark concepts like the Turing Test and the development of expert systems using programming languages like LISP and PROLOG. The document also notes some limitations of current AI technologies like software interoperability, knowledge acquisition, and handling uncertainty.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
AI is the study of intelligent agents: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Major areas of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. Weak AI is limited to a specific task, while strong AI exhibits human-level intelligence across all cognitive tasks. The foundations of AI include philosophy, mathematics, psychology and linguistics. Notable AI milestones include Deep Blue defeating Kasparov at chess in 1997 and the development of expert systems, robotics, computer vision and natural language processing. Current trends include cognitive computing, which aims to develop systems that can perceive, learn, reason and assist
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.
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.
This document provides an introduction to artificial intelligence and cognitive science. It defines intelligence and AI, discusses different approaches to AI like thinking like humans, thinking rationally, acting like humans and acting rationally. It also summarizes the history of AI from early neural networks to modern applications. Key concepts covered include the Turing test, knowledge representation, rational agents, intelligent environments and knowledge-based systems.
This document provides an introduction to artificial intelligence including definitions, history, applications and branches of AI. It discusses different definitions of AI including systems that think like humans, think rationally, act like humans, and act rationally. The Turing test and rational agent approaches are explained. Applications mentioned include autonomous control, knowledge-based systems, game playing, data mining, problem solving, robotics, and AI agents. Branches of AI discussed are machine vision, speech synthesis/recognition, machine learning, robotics, natural language understanding, problem solving, and game playing. Discussion questions at the end address Turing's objections, current capabilities of AI systems, and whether reflex actions are rational/intelligent.
The document provides an overview of an artificial intelligence course. It includes recommended books, topics to be covered like problem solving, knowledge representation, machine learning, and applications. The goals of AI are discussed as engineering and scientific. Example applications are presented, including game playing, natural language processing, expert systems, robotics and more. An introduction to search problems, knowledge-based systems, neural networks, and artificial life is given.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
The IOT Academy Training for Artificial Intelligence ( AI)The IOT Academy
This document provides an overview of artificial intelligence (AI). It begins with definitions of AI as modeling human thinking and acting rationally. The history of AI is then summarized, including early developments in neural networks in the 1940s and the 1956 Dartmouth conference that coined the term "artificial intelligence." Real-world applications of AI are mentioned such as autonomous vehicles and IBM's Watson. The document concludes by outlining the objectives of an introductory AI course.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, from its prehistory in fields like philosophy, mathematics, and neuroscience to major developments like the Dartmouth conference and Deep Blue defeating Kasparov at chess.
3) An overview of different views of what constitutes intelligence and the approach taken in the textbook of designing rational agents that can perceive and act in their environment to achieve goals.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, from its prehistory in fields like philosophy, mathematics, and neuroscience to major developments like the Dartmouth conference and Deep Blue defeating Kasparov at chess.
3) An overview of different views of what constitutes intelligence and the approach taken in the textbook of designing rational agents that can perceive and act in their environment to achieve goals.
The document discusses artificial intelligence and provides information on various AI topics. It includes a list of 9 NPTEL video links on topics related to unit 1 of an AI course, learning outcomes of the course, definitions and descriptions of AI, areas and applications of AI, a brief history of AI, task domains and techniques in AI, and examples of search problems and search methods. Depth-first search is described as a method that exhaustively explores branches in a search tree to the maximum depth until a solution is found.
This document provides a general introduction to artificial intelligence (AI) including definitions of AI, different views on AI, a brief history of AI, core issues in AI, and applications of AI. It discusses what AI is, including strong AI which implies intelligent agents can become self-aware versus weak AI which implies agents can only simulate some human behaviors. It also summarizes different types of intelligent agents including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning and Problem solving - [Source: https://www.techopedia.com/definition/190/artificial-intelligence-ai]
This 3-sentence summary provides an overview of the document:
The document outlines the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2, including the course homepage, textbook, grading breakdown, and a tentative schedule covering topics like search, logic, planning, and learning. It also briefly discusses different views of what constitutes artificial intelligence and provides an abridged history of the field from its philosophical roots to recent successes in games, mathematics, logistics, and spacecraft planning.
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.
This document provides an overview of an introductory course on artificial intelligence. 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 maximize goal achievement given available information. A brief history of AI is also provided, from early work in philosophy, mathematics, and the sciences to landmark developments like Turing's 1950 paper posing the question "Can machines think?". The state of the art in AI is summarized with examples like Deep Blue defeating Kasparov at chess in 1997 and autonomous vehicles driving 98% of the time across the US.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of the CS3243 Foundations of Artificial Intelligence course from NUS for the 2003/2004 semester. It outlines the course details including the textbook, instructor, grading breakdown, and course topics. The course will cover introduction to AI concepts like agents, search, logic, planning, uncertainty, learning, and natural language processing. It also provides background on the history and state of the art in AI, including definitions of what AI is from different perspectives.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
This document provides an introduction to artificial intelligence including definitions, history, applications and branches of AI. It discusses different definitions of AI including systems that think like humans, think rationally, act like humans, and act rationally. The Turing test and rational agent approaches are explained. Applications mentioned include autonomous control, knowledge-based systems, game playing, data mining, problem solving, robotics, and AI agents. Branches of AI discussed are machine vision, speech synthesis/recognition, machine learning, robotics, natural language understanding, problem solving, and game playing. Discussion questions at the end address Turing's objections, current capabilities of AI systems, and whether reflex actions are rational/intelligent.
The document provides an overview of an artificial intelligence course. It includes recommended books, topics to be covered like problem solving, knowledge representation, machine learning, and applications. The goals of AI are discussed as engineering and scientific. Example applications are presented, including game playing, natural language processing, expert systems, robotics and more. An introduction to search problems, knowledge-based systems, neural networks, and artificial life is given.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
The IOT Academy Training for Artificial Intelligence ( AI)The IOT Academy
This document provides an overview of artificial intelligence (AI). It begins with definitions of AI as modeling human thinking and acting rationally. The history of AI is then summarized, including early developments in neural networks in the 1940s and the 1956 Dartmouth conference that coined the term "artificial intelligence." Real-world applications of AI are mentioned such as autonomous vehicles and IBM's Watson. The document concludes by outlining the objectives of an introductory AI course.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an overview of artificial intelligence. It defines intelligence as the ability to plan, solve problems, reason, learn, understand new situations, and apply knowledge. AI is described as building intelligent systems that can think and act like humans or rationally. The history of AI is discussed, from its origins in the 1950s to current applications. Key concepts to be learned in the semester include problem solving, machine learning, evolutionary computation, robotics, and intelligent agents. Python and NetLogo will be used as tools.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, from its prehistory in fields like philosophy, mathematics, and neuroscience to major developments like the Dartmouth conference and Deep Blue defeating Kasparov at chess.
3) An overview of different views of what constitutes intelligence and the approach taken in the textbook of designing rational agents that can perceive and act in their environment to achieve goals.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, from its prehistory in fields like philosophy, mathematics, and neuroscience to major developments like the Dartmouth conference and Deep Blue defeating Kasparov at chess.
3) An overview of different views of what constitutes intelligence and the approach taken in the textbook of designing rational agents that can perceive and act in their environment to achieve goals.
The document discusses artificial intelligence and provides information on various AI topics. It includes a list of 9 NPTEL video links on topics related to unit 1 of an AI course, learning outcomes of the course, definitions and descriptions of AI, areas and applications of AI, a brief history of AI, task domains and techniques in AI, and examples of search problems and search methods. Depth-first search is described as a method that exhaustively explores branches in a search tree to the maximum depth until a solution is found.
This document provides a general introduction to artificial intelligence (AI) including definitions of AI, different views on AI, a brief history of AI, core issues in AI, and applications of AI. It discusses what AI is, including strong AI which implies intelligent agents can become self-aware versus weak AI which implies agents can only simulate some human behaviors. It also summarizes different types of intelligent agents including simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning and Problem solving - [Source: https://www.techopedia.com/definition/190/artificial-intelligence-ai]
This 3-sentence summary provides an overview of the document:
The document outlines the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2, including the course homepage, textbook, grading breakdown, and a tentative schedule covering topics like search, logic, planning, and learning. It also briefly discusses different views of what constitutes artificial intelligence and provides an abridged history of the field from its philosophical roots to recent successes in games, mathematics, logistics, and spacecraft planning.
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.
This document provides an overview of an introductory course on artificial intelligence. 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 maximize goal achievement given available information. A brief history of AI is also provided, from early work in philosophy, mathematics, and the sciences to landmark developments like Turing's 1950 paper posing the question "Can machines think?". The state of the art in AI is summarized with examples like Deep Blue defeating Kasparov at chess in 1997 and autonomous vehicles driving 98% of the time across the US.
Artificial intelligence (AI) involves developing intelligent machines that can perform tasks normally requiring human intelligence. This document provides an overview of key concepts in AI including definitions of intelligence, AI, and artificial intelligence. It discusses different approaches to AI such as systems that act like humans by passing the Turing test, systems that think like humans through cognitive modeling, systems that think rationally using logical reasoning, and systems that act rationally as intelligent agents. The document also briefly outlines the history and foundations of the AI field.
This document provides an introduction to the course "Lecture 1: Introduction to Artificial Intelligence". The key points covered include:
- The course aims to provide knowledge and understanding of AI concepts like search, game playing, knowledge-based systems, planning and machine learning. Students will learn to use these concepts to solve AI tasks and critically evaluate solutions.
- The document discusses different definitions of artificial intelligence and what it means for a system to be intelligent based on its ability to be flexible, make sense of ambiguous messages, recognize importance, find similarities and differences, and learn from experience.
- The Turing test is introduced as a way to measure machine intelligence by having a human evaluator determine if they are interacting with a computer
This document provides an overview of the CS3243 Foundations of Artificial Intelligence course from NUS for the 2003/2004 semester. It outlines the course details including the textbook, instructor, grading breakdown, and course topics. The course will cover introduction to AI concepts like agents, search, logic, planning, uncertainty, learning, and natural language processing. It also provides background on the history and state of the art in AI, including definitions of what AI is from different perspectives.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
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Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
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Do you want Software for your Business? Visit Deuglo
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Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
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Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
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lec1_1.pdf
1. 2022 1/31
CS321: Principles of Artificial Intelligence
Introduction
Principles of Artificial
Intelligence
Course Code CS321
Faculty of Computing and Information Technology
Computer Science Department
Jan, 2022
These slides are based on lecture notes of the book’s author(Artificial
Intelligence: A Modern Approach,)
&
King Saud University course materials
&
Grokking Artificial Intelligence Algorithms
Lecturer: Wedad Al-Sorori
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2022
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Chapter Objectives
• At the end of this chapter, the student should be able to:
• Understand Artificial Intelligence (AI).
• Identify and describe AI foundations.
• Evaluate the various definitions of AI.
• Summarize the history of AI.
• Mention AI applications with examples.
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What is Intelligence
?
• What is intelligence?
• What is artificial intelligence?
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• Intelligence may be defined as:
1. The capacity to acquire and apply knowledge.
2. The faculty of thought and reason.
3. In general, things that are autonomous yet adaptive are considered to
be intelligent.
What is Intelligence?
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• Salvador Dali’ believed that ambition is an attribute of intelligence; he
said, “Intelligence without ambition is a bird without wings.”
• Albert Einstein believed that imagination is a big factor in intelligence;
he said, “The true sign of intelligence is not knowledge, but
imagination.”
• And Stephen Hawking said, “Intelligence is the ability to adapt,”
What is Intelligence?
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• “The art of creating machines that perform functions that require
intelligence when performed by people” (Kurzweil, 1990).
• “The branch of computer science that is concerned with the automation of
intelligent behavior.” (Luger and Stublefield, 1993)
• AI is concerned with real-world problems (difficult tasks), which require
complex and sophisticated reasoning processes and knowledge.
• Artificial intelligence concerned with not just understanding but also
building intelligent entities—machines that can compute how to act
effectively and safely in a wide variety of novel situations.
What is AI?
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• Grroking textbook defines AI as a synthetic system that exhibits
“intelligent” behavior.
• Douglas Hofstadter said, “AI is whatever hasn’t been done yet.”
• Russell textbook define AI as the study of agents that receive percepts from
the environment and perform actions. Each such agent implements a
function that maps percept sequences to actions, and we cover different
ways to represent these functions, such as reactive agents, real-time
planners, decision-theoretic systems, and deep learning systems.
What is AI?
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What is AI
• Some have defined intelligence in terms of fidelity to human
performance, while others prefer an abstract, formal definition
of intelligence called rationality—loosely speaking, doing the
“right thing.”
• Views of AI fall into four categories:
• Systems that think like humans
• Systems that act like humans
• Systems that think rationally
• Systems that act rationally
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In 1950 Turing proposed an operational definition of intelligence by using a Test composed of:
• An interrogator (a person who will ask questions)
• a computer (intelligent machine !!)
• A person who will answer to questions
• A curtain (separator)
• If the response of a computer to an unrestricted textual natural-
language conversation cannot be distinguished from that of a human
being then it can be said to be intelligent.
Acting humanly: The Turing Test
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• To give an answer, the computer would need to possess some capabilities:
• Natural language processing: To communicate successfully.
• Knowledge representation: To store what it knows or hears.
• Automated reasoning: to answer questions and draw conclusions using stored information.
• Machine learning: To adapt to new circumstances and to detect and extrapolate patterns.
• Computer vision: To perceive objects.
• Robotics to manipulate objects and move.
Acting humanly: The Turing Test
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• To say that a program thinks like a human, we must know how
humans think.
• Cognitive Science: Interdisciplinary field (AI, psychology, linguistics,
philosophy, anthropology) that tries to form computational theories
of human cognition.
• We can learn about human thought in three ways:
• introspection—trying to catch our own thoughts as they go by;
• psychological experiments—observing a person in action;
• brain imaging—observing the brain in action.
Thinking Humanly: Cognitive Modeling
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• Formalize “correct” reasoning using a mathematical model (e.g. of
deductive reasoning).
• We can learn about it through:
• Syllogisms
• Logicist
• Probability
• Logicist Program: Encode knowledge in formal logical statements and use
mathematical deduction to perform reasoning:
• Problems:
• Formalizing common sense knowledge is difficult.
• General deductive inference is computationally intractable.
Thinking Rationally: Laws of Thought
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• An agent is an entity that perceives its environment and is able to execute
actions to change it.
• Agents have inherent goals that they want to achieve (e.g. survive,
reproduce).
• A rational agent is one that acts so as to achieve the
• best outcome or, when there is uncertainty, the best expected outcome.
• True maximization of goals requires omniscience and unlimited
computational abilities.
• Limited rationality involves maximizing goals within the computational and
other resources available.
Acting Rationally: Rational Agents
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The Foundations of AI
• Philosophy: Philosophers (going back to 400 B.c.) made A1 conceivable by
considering the ideas that the mind is in some ways like a machine, that it operates
on knowledge encoded in some internal language, and that thought can be used to
choose what actions to take.
• Mathematics: Mathematician provided the tools to manipulate statements of
logical certainty as well as uncertain, probabilistic statements. They also set the
groundwork for understanding computation and reasoning about algorithms.
• Economics: Economists formalized the problem of making decisions that maximize
the expected outcome to the decision-maker.
• Neuroscience: physical substrate for mental activity
• Psychology: Psychologists adopted the idea that humans and animals can be
considered information processing machines. Linguists showed that language use
fits into this model.
• Computer engineering: Computer engineers provided the artifacts that make A1
applications possible. AI programs tend to be large, and they could not work
without the great advances in speed and memory that the computer industry has
provided.
• Control theory: It deals with designing devices that act optimally on the basis of
feedback from the environment. Initially, the mathematical tools of control theory
were quite different from AI, but the fields are coming closer together.
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Early AI: (The gestation of Artificial Intelligence)
1943 McCulloch & Pitts: Boolean circuit model of brain
1950 Turing's ``Computing Machinery and Intelligence''
1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
The birth of Artificial Intelligence (1956)
1956 McCarthy organizes Dartmouth meeting and includes
Minsky, Shannon, Newell, Samuel, Simon
Name ``Artificial Intelligence'' adopted
AI History
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Early enthusiasm, great expectations (1952-1969):
1957 General Problem Solver [Newell, Simon, Shaw @ CMU]
1958 Creation of the MIT AI Lab by Minsky and McCarthy
1958 LISP, [McCarthy], second high level language (MIT AI Memo 1)
1963 Creation of the Stanford AI Lab by McCarthy
1965 Robinson's complete algorithm for logical reasoning
A dose of reality (1966-1973):
1966-74 AI discovers computational complexity …
1966-72 Shakey, SRI’s Mobile Robot [Fikes, Nilson]
AI History
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Knowledge-based systems (1969-1979)
1969 Publication of “Perceptrons” [Minsky & Papert],
Neural network research almost disappears
1969-79 Early development of knowledge-based systems
• First expert system DENDRAL for interpreting mass spectrogram data to determine molecular structure by
Buchanan, Feigenbaum, and Lederberg (1969).
1970 SHRDLU, Winograd’s natural language system
1971 MACSYMA, an symbolic algebraic manipulation system
1975 MYCIN: diagnosis of bacterial infection
AI History
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AI becomes an Industry (1980 – present)
1980-88 Expert systems industry booms
1981 Japan: Fifth generation project to build intelligent
computers based on Prolog logic programming.
US: Microelectronics and Computer Technology Corp.
UK: Alvey (Natural Language Tools)
AI History
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The return of neural networks (1986 - present)
• New algorithms discovered for training more complex neural networks (1986).
• Cognitive modeling of many psychological processes using neural networks, e.g.
learning language.
1988-93 Expert systems industry busts: ``AI Winter''
1985-95 Neural networks return to popularity
AI History
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AI becomes a science (1987 – present)
1988 Resurgence of probabilistic and decision-theoretic methods
• General focus on learning and training methods to address knowledge-acquisition
bottleneck.
• Shift of focus from rule-based and logical methods to probabilistic and statistical
methods (e.g. Bayes nets, Hidden Markov Models).
• Increased interest in particular tasks and applications
• Data mining
• Intelligent agents and Internet applications(softbots, believable agents, intelligent information
access)
• Scheduling/configuration applications (Successful companies: I2, Red Pepper, Trilogy)
AI History
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Big data (2001–present)
• These data sets include trillions of words of text, billions of images, and billions of
hours of speech and video, as well as vast amounts of genomic data, vehicle
tracking data, clickstream data, social network data, and so on.
• the development of learning algorithms specially designed to take advantage of
very large data sets.
• The availability of big data and the shift towards machine learning helped AI recover
commercial attractiveness (Havenstein, 2005; Halevy et al., 2009)
AI History
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Deep learning (2011–present)
• deep learning refers to machine learning using multiple layers of simple,
adjustable computing elements.
• A remarkable successes have led to a resurgence of interest in AI among
students, companies, investors, governments, the media, and the general
public.
• Deep learning relies heavily on powerful hardware.
AI History
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• ROBOTIC VEHICLES:
• cars
• radio-controlled cars of the 1920s.
• 1980s without control
• driving on dirt roads in the 132-mile DARPA Grand Challenge in
2005
• driving on on streets with traffic in the 2007 Urban
Challenge
• In 2018, Waymo test vehicles passed the landmark of 10
million miles.
• commercial robotic taxi service.
• autonomous fixed-wing drones and Quadcopters.
• Legged locomotion BigDog, a quadruped robot by
Raibert et al. (2008).
• Atlas, a humanoid robot.
AI Applications
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• AUTONOMOUS PLANNING AND SCHEDULING
– NASA’s Remote Agent program
– The EUROPA planning toolkit and the SEXTANT system
• MACHINE TRANSLATION
• SPEECH RECOGNITION
– Alexa, Siri, Cortana, and Google offer assistants that can
answer questions and carry out tasks for the user
AI Applications
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• RECOMMENDATIONS:
– Companies such as Amazon, Facebook, Netflix, Spotify, YouTube,
Walmart, and others use ML to recommend what u like.
• IMAGE UNDERSTANDING
• MEDICINE
• Search Engines
AI Applications
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• Game Playing
●
Deep Blue defeated world chess
champion Garry Kasparov in 1997.
●
ALPHAGO surpassed all human Players on
Go.
●
ALPHAZERO, used no input from
humans (except for the rules of the
game), and was able to learn through
self-play alone to defeat all opponents,
human and machine, at Go, chess, and
shogi.
AI Applications
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• Expert Systems
Geology
• prospector expert system carries evaluation of mineral potential of geological site or region.
Diagnostic Systems
• Pathfinder, a medical diagnosis system (suggests tests and makes diagnosis) developed by Heckerman and other Microsoft
research.
• MYCIN system for diagnosing bacterial infections of the blood and suggesting treatments.
Financial Decision Making
• Credit card providers, banks, mortgage companies use AI systems to detect fraud and expedite financial transactions.
Configuring Hardware and Software
• AI systems configure custom computer, communications, and manufacturing systems, guaranteeing the purchaser
maximum efficiency and minimum setup time.
AI Applications
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Knowledge-based system
• Expert system (or knowledge-based system): a program which encapsulates knowledge from
some domain, normally obtained from a human expert in that domain
• components:
• Knowledge base (KB): repository of rules, facts (productions)
• working memory: (if forward chaining used)
• inference engine: the deduction system used to infer results from user input and KB
• user interface: interfaces with user
• external control + monitoring: access external databases, control,...
AI Applications
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• Why use expert systems:
• commercial viability: whereas there may be only a few experts whose time is expensive and rare, you can have many
expert systems
• expert systems can be used anywhere, anytime
• expert systems can explain their line of reasoning
• commercially beneficial: the first commercial product of AI
• Weaknesses:
• expert systems are as sound as their KB; errors in rules mean errors in diagnoses
• automatic error correction, learning is difficult (although machine learning research may change this)
• the extraction of knowledge from an expert, and encoding it into machine-inferrable form is the most difficult part of
expert system implementation.
AI Applications
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Risks and Benefits of AI
Our entire civilization is the product of our human intelligence. If we have
access to substantially greater machine intelligence, the ceiling on our
ambitions is raised substantially.
As Demis Hassabis, CEO of Google DeepMind, has suggested: “First solve AI,
then use AI to solve everything else.”
As AI systems find application in the real world, it has become necessary to
consider a wide range of risks and ethical consequences.
In the longer term, we face the difficult problem of controlling super intelligent
AI systems that may evolve in unpredictable ways.
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Risks and Benefits of AI
Risks
LETHAL AUTONOMOUS WEAPONS
SURVEILLANCE AND PERSUASION
BIASED DECISION MAKING
IMPACT ON EMPLOYMENT
SAFETY-CRITICAL APPLICATIONS
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Problem types and problem-solving paradigms
AI aims to solve:
Search problems: Find a path to a solution
Optimization problems: Find a good solution
Prediction and classification problems: Learn from patterns in data
Clustering problems: Identify patterns in data
Deterministic models: Same result each time it’s calculated
Stochastic/probabilistic models: Potentially different result each time it’s
calculated
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AI Concepts/fields
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• To conclude:
• AI is a very fascinating field. It can help us solve difficult, real-world
problems, creating new opportunities in business, engineering, and many
other application areas.
• AI has matured considerably compared to its early decades, both
theoretically and methodologically. As the problems that AI deals with
became more complex, the field moved from Boolean logic to probabilistic
reasoning, and from hand-crafted knowledge to machine learning from
data. This has led to improvements in the capabilities of real systems and
greater integration with other disciplines.
Summary