The document is an introduction to an artificial intelligence course. It provides an overview of the course topics including agents, search, games, logic, learning, planning, and uncertainty. It also discusses the history of AI, including early milestones like the Dartmouth conference where the field was named. Examples of modern AI systems are given such as Deep Blue defeating Kasparov at chess and autonomous vehicles.
Artificial Intelligence and its applicationFELICIALILIANJ
1. No-code machine learning allows users to build machine learning applications and tools through a drag-and-drop interface without coding, making ML more accessible.
2. Tiny ML focuses on applying machine learning at the edge on small IoT devices to reduce latency, bandwidth usage, and ensure privacy while still enabling useful predictions from collected data.
3. Automated machine learning aims to simplify the entire machine learning process from data preprocessing to modeling to reduce costs and expertise needed, enabling more widespread use of analytical tools and technologies.
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 provides an overview of artificial intelligence, including its definition as "the science and engineering of making intelligent machines". It then lists the 7 course outcomes for the class, along with their associated weightages. The outcomes include summarizing AI environments, applying search algorithms and heuristics, implementing local search strategies, applying adversarial search techniques, utilizing knowledge representation, constructing plan graphs using planning techniques, and explaining expert systems.
The document provides information about an artificial intelligence course. It includes:
- 7 course outcomes focusing on search techniques, constraint satisfaction problems, adversarial search, knowledge representation, planning, and expert systems.
- A syllabus covering topics like search strategies, heuristic search, constraint satisfaction problems, games, knowledge representation, probabilistic reasoning, and planning.
- References for two textbooks and an online course on artificial intelligence.
This document provides an overview and syllabus for an Artificial Intelligence course. It introduces the instructor, Dr. Zulfiqar Ali, and provides contact information. It lists the primary textbook as Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig and notes other reference materials. The course aims to provide understanding of fundamental AI techniques like agents, search, knowledge representation, and planning under uncertainty. It outlines the topics to be covered in each section of the course.
This document provides an introduction to artificial intelligence (AI). It discusses definitions of intelligence and what AI aims to achieve, including acting humanly through techniques like the Turing Test. The document outlines key disciplines related to AI and provides a short history of the field from its origins in 1943 to modern successes. Challenges, conferences, courses and books relevant to AI are also listed. It concludes with questions and sources.
Artificial Intelligence and its applicationFELICIALILIANJ
1. No-code machine learning allows users to build machine learning applications and tools through a drag-and-drop interface without coding, making ML more accessible.
2. Tiny ML focuses on applying machine learning at the edge on small IoT devices to reduce latency, bandwidth usage, and ensure privacy while still enabling useful predictions from collected data.
3. Automated machine learning aims to simplify the entire machine learning process from data preprocessing to modeling to reduce costs and expertise needed, enabling more widespread use of analytical tools and technologies.
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 provides an overview of artificial intelligence, including its definition as "the science and engineering of making intelligent machines". It then lists the 7 course outcomes for the class, along with their associated weightages. The outcomes include summarizing AI environments, applying search algorithms and heuristics, implementing local search strategies, applying adversarial search techniques, utilizing knowledge representation, constructing plan graphs using planning techniques, and explaining expert systems.
The document provides information about an artificial intelligence course. It includes:
- 7 course outcomes focusing on search techniques, constraint satisfaction problems, adversarial search, knowledge representation, planning, and expert systems.
- A syllabus covering topics like search strategies, heuristic search, constraint satisfaction problems, games, knowledge representation, probabilistic reasoning, and planning.
- References for two textbooks and an online course on artificial intelligence.
This document provides an overview and syllabus for an Artificial Intelligence course. It introduces the instructor, Dr. Zulfiqar Ali, and provides contact information. It lists the primary textbook as Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig and notes other reference materials. The course aims to provide understanding of fundamental AI techniques like agents, search, knowledge representation, and planning under uncertainty. It outlines the topics to be covered in each section of the course.
This document provides an introduction to artificial intelligence (AI). It discusses definitions of intelligence and what AI aims to achieve, including acting humanly through techniques like the Turing Test. The document outlines key disciplines related to AI and provides a short history of the field from its origins in 1943 to modern successes. Challenges, conferences, courses and books relevant to AI are also listed. It concludes with questions and sources.
The document provides an overview of the history and development of artificial intelligence from its early beginnings in 1943 through modern applications. It discusses milestones like the Dartmouth conference that named AI in 1956 and the rise of neural networks and machine learning in the 1980s. Notable successes are outlined such as Deep Blue's chess victory in 1997 and AI systems used for logistics planning, robotics, and machine translation. The approaches of strong AI, weak AI, applied AI, and cognitive AI are also summarized.
The design and implementation of systems that possess, reason with, and acquire knowledge is arguably the ultimate intellectual challenge. So why then, when we open almost any
book on Artificial Intelligence, does it open with a painstaking, almost defensive, definition
of what AI is and what AI is not?
This is powerpoint on" ARTIFICIAL INTELLIGENCE".
AI is a Shining field of future technology.
Artificial intelligence,Machine Learning,and Robotics is a major advance technology of coming soon" ERA".
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
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.
This document provides an overview of artificial intelligence, including:
- The definition and history of AI, from its coining in 1956 to modern applications.
- The foundations and subareas of AI, including problem solving, machine learning, neural networks, and applications in business, engineering, and more.
- Approaches to building AI systems involving perception, reasoning, and action.
- Different perspectives on what constitutes intelligence and the goals of AI as developing systems that think rationally or like humans and act rationally or like humans.
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
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 an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
computer science engineering spe ialized in artificial IntelligenceKhanKhaja1
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system checked.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery problems, like the crankshaft position sensor
This document discusses the syllabus for the course CS6659 - Artificial Intelligence. It covers 5 units: (1) introduction to AI and production systems, (2) knowledge representation, (3) knowledge inference, (4) planning and machine learning, and (5) expert systems. It also provides definitions of AI, discusses the history and components of AI, and describes the differences between weak AI and strong AI. The document gives an overview of the key concepts and topics that will be covered in the AI course.
This document provides an introduction to an artificial intelligence lecture. It begins with basic information about the course including references, grading, and contact information. It then outlines the topics to be covered which include definitions of intelligence and AI, a brief history of AI, and the main subfields of AI. The document discusses several approaches to AI including thinking humanly by passing the Turing test, thinking rationally using logic, and acting rationally as an intelligent agent. It also reviews the foundations of AI from various contributing fields and provides examples of AI in everyday life.
This document provides an overview of artificial intelligence and its history. It discusses early concepts like the Turing Test and different views of what constitutes intelligence. Key developments are outlined chronologically, including early logical models in the 1950s, perceptrons and neural networks in the late 1950s, Shakey the robot in the 1960s-1970s, and the rise of expert systems and machine learning from the 1980s onward. The document also covers different models of intelligent agents and the rise of learning approaches over hand-coding functions.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
The document provides an overview of the history and development of artificial intelligence from its early beginnings in 1943 through modern applications. It discusses milestones like the Dartmouth conference that named AI in 1956 and the rise of neural networks and machine learning in the 1980s. Notable successes are outlined such as Deep Blue's chess victory in 1997 and AI systems used for logistics planning, robotics, and machine translation. The approaches of strong AI, weak AI, applied AI, and cognitive AI are also summarized.
The design and implementation of systems that possess, reason with, and acquire knowledge is arguably the ultimate intellectual challenge. So why then, when we open almost any
book on Artificial Intelligence, does it open with a painstaking, almost defensive, definition
of what AI is and what AI is not?
This is powerpoint on" ARTIFICIAL INTELLIGENCE".
AI is a Shining field of future technology.
Artificial intelligence,Machine Learning,and Robotics is a major advance technology of coming soon" ERA".
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
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.
This document provides an overview of artificial intelligence, including:
- The definition and history of AI, from its coining in 1956 to modern applications.
- The foundations and subareas of AI, including problem solving, machine learning, neural networks, and applications in business, engineering, and more.
- Approaches to building AI systems involving perception, reasoning, and action.
- Different perspectives on what constitutes intelligence and the goals of AI as developing systems that think rationally or like humans and act rationally or like humans.
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
This document provides an overview of the COMP3170 Artificial Intelligence and Machine Learning course at Hong Kong Baptist University. It includes information about the course lecturers and textbook, assessment details, a tentative schedule of topics and dates, and a brief description of the content to be covered each week.
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 an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
computer science engineering spe ialized in artificial IntelligenceKhanKhaja1
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Dr. C. Lee Giles is a professor at Penn State University who teaches a course on artificial intelligence and information sciences. The document provides an overview of artificial intelligence including definitions, theories, impact on information science, and topics covered in the course such as machine learning, information retrieval, text processing, and social networks. It also discusses the scientific method applied to developing theories in information sciences and contrasts weak and strong definitions of artificial intelligence.
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system inspected.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery issues, like the crankshaft position sensor
Here are the steps I would take to diagnose electrical problems with a car:
1. Check the spark plugs. Look for fouling, cracking, or gaps that are too wide or narrow. Replace as needed.
2. Check the ignition timing. Use a timing light to ensure it is properly set. Adjust if necessary.
3. Test the battery with a voltmeter. It should read over 12 volts. If lower, have the battery and charging system checked.
4. Inspect wires and connectors for cracks, corrosion or loose connections. Tighten or replace as needed.
5. Check for faulty sensors that could cause ignition or fuel delivery problems, like the crankshaft position sensor
This document discusses the syllabus for the course CS6659 - Artificial Intelligence. It covers 5 units: (1) introduction to AI and production systems, (2) knowledge representation, (3) knowledge inference, (4) planning and machine learning, and (5) expert systems. It also provides definitions of AI, discusses the history and components of AI, and describes the differences between weak AI and strong AI. The document gives an overview of the key concepts and topics that will be covered in the AI course.
This document provides an introduction to an artificial intelligence lecture. It begins with basic information about the course including references, grading, and contact information. It then outlines the topics to be covered which include definitions of intelligence and AI, a brief history of AI, and the main subfields of AI. The document discusses several approaches to AI including thinking humanly by passing the Turing test, thinking rationally using logic, and acting rationally as an intelligent agent. It also reviews the foundations of AI from various contributing fields and provides examples of AI in everyday life.
This document provides an overview of artificial intelligence and its history. It discusses early concepts like the Turing Test and different views of what constitutes intelligence. Key developments are outlined chronologically, including early logical models in the 1950s, perceptrons and neural networks in the late 1950s, Shakey the robot in the 1960s-1970s, and the rise of expert systems and machine learning from the 1980s onward. The document also covers different models of intelligent agents and the rise of learning approaches over hand-coding functions.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
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Robotic links
Robocup Video
Soccer Robocupf
Darpa Challenge
Darpa’s-challenge-video
http://www.darpa.mil/grandchallenge05/TechPapers/Stanford.pdf
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CS171
Course home page:
http://www.ics.uci.edu/~dechter/ics-171/fall-06/
schedule, lecture notes, tutorials, assignment, grading,
office hours, etc.
Textbook: S. Russell and P. Norvig Artificial
Intelligence: A Modern Approach Prentice Hall, 2003,
Second Edition
Grading: Homeworks and projects (30-40%)
Midterm and final (60-70%)
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Course overview
Introduction and Agents (chapters 1,2)
Search (chapters 3,4)
Games (chapter 5)
Constraints processing (chapter 6)
Representation and Reasoning with Logic
(chapters 7,8,9)
Learning (chapters 18,20)
Planning (chapter 11)
Uncertainty (chapters 13,14)
Natural Language Processing (chapter 22,23)
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Course Outline
Resources on the Internet
AI on the Web: A very comprehensive list of
Web resources about AI from the Russell and
Norvig textbook.
Essays and Papers
What is AI, John McCarthy
Computing Machinery and Intelligence, A.M.
Turing
Rethinking Artificial Intelligence, Patrick
H.Winston
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Today’s class
What is Artificial Intelligence?
A brief History
Intelligent agents
State of the art
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What is Artificial Intelligence
(John McCarthy , Basic Questions)
What is artificial intelligence?
It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using
computers to understand human intelligence, but AI does not have to confine
itself to methods that are biologically observable.
Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the
world. Varying kinds and degrees of intelligence occur in people, many
animals and some machines.
Isn't there a solid definition of intelligence that doesn't depend on
relating it to human intelligence?
Not yet. The problem is that we cannot yet characterize in general what kinds
of computational procedures we want to call intelligent. We understand some
of the mechanisms of intelligence and not others.
More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
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What is AI?
Views of AI fall into four categories:
Thinking humanly Thinking rationally
Acting humanly Acting rationally
The textbook advocates "acting rationally“
List of AI-topics
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What is Artificial
Intelligence?
Human-like (“How to simulate humans intellect and
behavior on by a machine.)
Mathematical problems (puzzles, games, theorems)
Common-sense reasoning (if there is parking-space,
probably illegal to park)
Expert knowledge: lawyers, medicine, diagnosis
Social behavior
Rational-like:
achieve goals, have performance measure
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What is Artificial
Intelligence
Thought processes
“The exciting new effort to make computers
think .. Machines with minds, in the full and
literal sense” (Haugeland, 1985)
Behavior
“The study of how to make computers do
things at which, at the moment, people are
better.” (Rich, and Knight, 1991)
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The Turing Test
(Can Machine think? A. M. Turing, 1950)
Requires
Natural language
Knowledge representation
Automated reasoning
Machine learning
(vision, robotics) for full test
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What is AI?
Turing test (1950)
Requires:
Natural language
Knowledge representation
automated reasoning
machine learning
(vision, robotics.) for full test
Thinking humanly:
Introspection, the general problem solver (Newell and
Simon 1961)
Cognitive sciences
Thinking rationally:
Logic
Problems: how to represent and reason in a domain
Acting rationally:
Agents: Perceive and act
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AI examples
Common sense reasoning
Tweety
Yale Shooting problem
Update vs revise knowledge
The OR gate example: A or B - C
Observe C=0, vs Do C=0
Chaining theories of actions
Looks-like(P) is(P)
Make-looks-like(P) Looks-like(P)
----------------------------------------
Makes-looks-like(P) ---is(P) ???
Garage-door example: garage door not included.
Planning benchmarks
8-puzzle, 8-queen, block world, grid-space world
Abduction: cambridge parking example
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History of AI
McCulloch and Pitts (1943)
Neural networks that learn
Minsky (1951)
Built a neural net computer
Darmouth conference (1956):
McCarthy, Minsky, Newell, Simon met,
Logic theorist (LT)- proves a theorem in Principia
Mathematica-Russel.
The name “Artficial Intelligence” was coined.
1952-1969
GPS- Newell and Simon
Geometry theorem prover - Gelernter (1959)
Samuel Checkers that learns (1952)
McCarthy - Lisp (1958), Advice Taker, Robinson’s
resolution
Microworlds: Integration, block-worlds.
1962- the perceptron convergence (Rosenblatt)
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The Birthplace of
“Artificial Intelligence”, 1956
Darmouth workshop, 1956: historical meeting of the precieved
founders of AI met: John McCarthy, Marvin Minsky, Alan
Newell, and Herbert Simon.
A Proposal for the Dartmouth Summer Research Project on
Artificial Intelligence. J. McCarthy, M. L. Minsky, N.
Rochester, and C.E. Shannon. August 31, 1955. "We propose
that a 2 month, 10 man study of artificial intelligence be
carried out during the summer of 1956 at Dartmouth College
in Hanover, New Hampshire. The study is to proceed on the
basis of the conjecture that every aspect of learning or any
other feature of intelligence can in principle be so precisely
described that a machine can be made to simulate it." And this
marks the debut of the term "artificial intelligence.“
50 anniversery of Darmouth workshop
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History, continued
1966-1974 a dose of reality
Problems with computation
1969-1979 Knowledge-based systems
Weak vs. strong methods
Expert systems:
• Dendral:Inferring molecular structures
• Mycin: diagnosing blood infections
• Prospector: recomending exploratory drilling (Duda).
Roger Shank: no syntax only semantics
1980-1988: AI becomes an industry
R1: Mcdermott, 1982, order configurations of computer
systems
1981: Fifth generation
1986-present: return to neural networks
Recent event:
AI becomes a science: HMMs, planning, belief network
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Abridged history of AI
1943 McCulloch & Pitts: Boolean circuit model of brain
1950 Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted
1952—69 Look, Ma, no hands!
1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
1965 Robinson's complete algorithm for logical reasoning
1966—73 AI discovers computational complexity
Neural network research almost disappears
1969—79 Early development of knowledge-based systems
1980-- AI becomes an industry
1986-- Neural networks return to popularity
1987-- AI becomes a science
1995-- The emergence of intelligent agents
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State of the art
Deep Blue defeated the reigning world chess
champion Garry Kasparov in 1997
Proved a mathematical conjecture (Robbins
conjecture) unsolved for decades
No hands across America (driving autonomously 98%
of the time from Pittsburgh to San Diego)
During the 1991 Gulf War, US forces deployed an AI
logistics planning and scheduling program that
involved up to 50,000 vehicles, cargo, and people
NASA's on-board autonomous planning program
controlled the scheduling of operations for a spacecraft
Proverb solves crossword puzzles better than most
humans
DARPA grand challenge 2003-2005, Robocup
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Robotic links
Robocup Video
Soccer Robocupf
Darpa Challenge
Darpa’s-challenge-video
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Agents
An agent is anything that can be viewed as
perceiving its environment through sensors
and acting upon that environment through
actuators
Human agent: eyes, ears, and other organs
for sensors; hands,
legs, mouth, and other body parts for
actuators
Robotic agent: cameras and infrared range
finders for sensors;
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Agents and environments
The agent function maps from percept
histories to actions:
[f: P* A]
The agent program runs on the physical
architecture to produce f
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Vacuum-cleaner world
Percepts: location and contents, e.g.,
[A,Dirty]
Actions: Left, Right, Suck, NoOp
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Rational agents
An agent should strive to "do the right
thing", based on what it can perceive and
the actions it can perform. The right action
is the one that will cause the agent to be
most successful
Performance measure: An objective
criterion for success of an agent's behavior
E.g., performance measure of a vacuum-
cleaner agent could be amount of dirt
cleaned up, amount of time taken, amount
of electricity consumed, amount of noise
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Rational agents
Rational Agent: For each possible
percept sequence, a rational agent
should select an action that is
expected to maximize its performance
measure, given the evidence provided
by the percept sequence and
whatever built-in knowledge the agent
has.
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What’s involved in Intelligence?
Intelligent agents
Ability to interact with the real world
to perceive, understand, and act
e.g., speech recognition and understanding and
synthesis
e.g., image understanding
e.g., ability to take actions, have an effect
Knowledge Representation, Reasoning and
Planning
modeling the external world, given input
solving new problems, planning and making decisions
ability to deal with unexpected problems, uncertainties
Learning and Adaptation
we are continuously learning and adapting
our internal models are always being “updated”
• e.g. a baby learning to categorize and recognize
animals
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Implementing agents
Table look-ups
Autonomy
All actions are completely specified
no need in sensing, no autonomy
example: Monkey and the banana
Structure of an agent
agent = architecture + program
Agent types
• medical diagnosis
• Satellite image analysis system
• part-picking robot
• Interactive English tutor
• cooking agent
• taxi driver
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Agent types
Example: Taxi driver
Simple reflex
If car-in-front-is-breaking then initiate-breaking
Agents that keep track of the world
If car-in-front-is-breaking and on fwy then initiate-
breaking
needs internal state
goal-based
If car-in-front-is-breaking and needs to get to hospital
then go to adjacent lane and plan
search and planning
utility-based
If car-in-front-is-breaking and on fwy and needs to
get to hospital alive then search of a way to get to the
hospital that will make your passengers happy.
Needs utility function that map a state to a real
function (am I happy?)
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Summary
What is Artificial Intelligence?
modeling humans thinking, acting, should think,
should act.
History of AI
Intelligent agents
We want to build agents that act rationally
Real-World Applications of AI
AI is alive and well in various “every day” applications
• many products, systems, have AI components
Assigned Reading
Chapters 1 and 2 in the text R&N