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
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.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.pptDaliaMagdy12
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
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.
Artificial and Human Intelligence in Business
Being a Webinar Paper Presented at the Institute of Chartered Accountants of Nigeria (ICAN)
Ikorodu & District Society on Saturday, 13th May 2023.
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
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.
EELU AI lecture 1- fall 2022-2023 - Chapter 01- Introduction.pptDaliaMagdy12
This document provides an overview of the ITF308-Artificial Intelligence course for 2022-2023. The course will cover foundations of symbolic intelligent systems including agents, search, problem solving, learning, knowledge representation, and reasoning. Programming experience in C++ or Java is required. The textbook is Artificial Intelligence: A Modern Approach by Russell and Norvig. Grading will be based on assignments, attendance, quizzes, a midterm, and a final exam. The course aims to understand intelligent behavior and build intelligent agents/systems through topics like search algorithms, knowledge representation, learning, and reasoning.
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.
Artificial and Human Intelligence in Business
Being a Webinar Paper Presented at the Institute of Chartered Accountants of Nigeria (ICAN)
Ikorodu & District Society on Saturday, 13th May 2023.
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
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 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.
1. The document discusses artificial intelligence and machine learning concepts including problem solving techniques in AI. It covers topics like knowledge representation, problem types, search algorithms, and formulating problems.
2. Key advances in AI are explained as increased computing power, availability of big data, and developments in deep learning algorithms. Applications of AI span many domains from medical to manufacturing.
3. Challenges in AI include dealing with large, complex, interdependent problems and developing practical solutions while addressing issues like costs and software development difficulties. Problem solving in AI involves defining problems, analyzing, planning, executing, and evaluating solutions.
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.
Based on Capabilities
B. Based on Functionality
1. Reactive Machines:
- Reactive machines are the simplest form of AI. They perceive their environment and
respond in a predetermined manner to achieve a specific goal.
- Examples include thermostats, industrial robots, and vacuum cleaners.
2. Limited Memory:
- These systems can remember past experiences and use that information to guide future
actions.
- Examples include chess playing programs and self-driving cars.
3. Theory of Mind:
- These systems can model other agents and take their beliefs, intentions, and desires into
account.
- Examples include personal assistants like Siri that understand context.
4. Self
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
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.
This document provides an introduction to artificial intelligence, including definitions of AI, key areas of AI like machine learning, and a brief history. It discusses what AI is, such as making computers think like humans through decision making and learning. The main topics in AI are described as search, knowledge representation, planning, learning, natural language processing, expert systems, and interacting with the environment. Applications of AI discussed include bioinformatics, text classification, video/image classification, music/art generation, and natural language processing.
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.
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.
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
- Sensors that allow the system to perceive its environment
- Actuators that allow the system to act on or change its environment
- An ability to reason and make rational decisions or plans of action based on its perceptions
The document discusses sensors and actuators in the context of intelligent agents - for humans, examples of sensors are eyes and ears, while actuators are hands, legs, and mouth. For robots, examples of sensors would be cameras and other input devices, while actuators would allow the robot to move or manipulate objects.
The document discusses artificial intelligence and provides details about:
- The goals of AI including deduction, reasoning, problem solving, knowledge representation, planning, natural language processing, motion and manipulation, perception, and social intelligence.
- The history and origins of AI research dating back to the 1950s.
- Popular AI programming languages like Lisp and how it is well suited for knowledge representation.
- Categories of AI approaches including conventional symbolic AI and computational intelligence methods.
- Applications of AI in fields like medicine, industry, games, speech recognition, natural language understanding, computer vision, and expert 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.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
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
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 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.
1. The document discusses artificial intelligence and machine learning concepts including problem solving techniques in AI. It covers topics like knowledge representation, problem types, search algorithms, and formulating problems.
2. Key advances in AI are explained as increased computing power, availability of big data, and developments in deep learning algorithms. Applications of AI span many domains from medical to manufacturing.
3. Challenges in AI include dealing with large, complex, interdependent problems and developing practical solutions while addressing issues like costs and software development difficulties. Problem solving in AI involves defining problems, analyzing, planning, executing, and evaluating solutions.
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.
Based on Capabilities
B. Based on Functionality
1. Reactive Machines:
- Reactive machines are the simplest form of AI. They perceive their environment and
respond in a predetermined manner to achieve a specific goal.
- Examples include thermostats, industrial robots, and vacuum cleaners.
2. Limited Memory:
- These systems can remember past experiences and use that information to guide future
actions.
- Examples include chess playing programs and self-driving cars.
3. Theory of Mind:
- These systems can model other agents and take their beliefs, intentions, and desires into
account.
- Examples include personal assistants like Siri that understand context.
4. Self
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
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.
This document provides an introduction to artificial intelligence, including definitions of AI, key areas of AI like machine learning, and a brief history. It discusses what AI is, such as making computers think like humans through decision making and learning. The main topics in AI are described as search, knowledge representation, planning, learning, natural language processing, expert systems, and interacting with the environment. Applications of AI discussed include bioinformatics, text classification, video/image classification, music/art generation, and natural language processing.
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.
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.
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
- Sensors that allow the system to perceive its environment
- Actuators that allow the system to act on or change its environment
- An ability to reason and make rational decisions or plans of action based on its perceptions
The document discusses sensors and actuators in the context of intelligent agents - for humans, examples of sensors are eyes and ears, while actuators are hands, legs, and mouth. For robots, examples of sensors would be cameras and other input devices, while actuators would allow the robot to move or manipulate objects.
The document discusses artificial intelligence and provides details about:
- The goals of AI including deduction, reasoning, problem solving, knowledge representation, planning, natural language processing, motion and manipulation, perception, and social intelligence.
- The history and origins of AI research dating back to the 1950s.
- Popular AI programming languages like Lisp and how it is well suited for knowledge representation.
- Categories of AI approaches including conventional symbolic AI and computational intelligence methods.
- Applications of AI in fields like medicine, industry, games, speech recognition, natural language understanding, computer vision, and expert 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.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
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.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
1. Pelatihan ABCD
Modul 1-1: Introduction to AI
Sekolah Teknik Elektro dan Informatika Institut Teknologi Bandung
Unviersitas Singaperbangsa Karawang
2. What is AI?
Artificial Intelligence vs Augmented Intelligence
} AI is an anything that makes machines act more intelligently.
} AI also like to think as augmented intelligence.
} AI shoud not attempt to replace human experts, but rather
extend human capabilities and accomplish tasks that neither
humans nor machines do on their own.
3. Augmented Intelligence
} The internet has given us access to more information
faster.
} Distributed computing and IoT have led to massive
amounts of data, and social networking has encouraged
most of that data to be unstructured.
} With Augmented Intelligence, we are putting
information that subject matter experts need at their
fingertips, and backing it with evidence so they can make
informed decisions.
} We want experts to scale their capabilities and let the
machines do the time-consuming work.
5. The Foundation of Artificial Intelligence
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Stuart Russell and Peter Norvig. Artificial Intelligence:
A Modern Approach.
Prentice Hall, fouth ed, 2020
http://aima.cs.berkeley.edu/contents.html
6. “The exciting new effort to make computers think …
machines with minds, in the full and literal sense”
(Haugeland, ‘85)
“[The automation of] activities that we associate with
human thinking, activities such as decision making,
problem solving, learning …” (Bellman, ‘78)
Thinking Humanly
“The study of mental faculties through the use
of computational models”
(Charniak and McDermott, ‘85)
“The study of the computations that make it
possible to perceive, reason, and act”
(Winston, ‘92)
Thinking Rationally
“The art of creating machines that perform functions
that require intelligence when performed by people”
(Kurzweil, ‘90)
“The study of how to make computers do things
which, at the moment, people do better” (Rich and
Knight, ‘91).
Acting Humanly
A field of study that seeks to explain and emulate
intelligent behavior in term of computational
processes” (Schalkoff, ‘90)
“The branch of computer science that is
concerned with the automation of intelligent
behavior” (Luger and Stubblefield, ‘93)
Acting Rationally
7. Acting Humanly: The Turing Test
} Alan Turing (1912-1954)
} “Computing Machinery and Intelligence” (1950)
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8. Acting Humanly: The Turing Test
} Predicted that by 2000, a machine might have a 30%
chance of fooling a lay person for 5 minutes.
} Anticipated all major arguments against AI in
following 50 years.
} Suggested major components of AI: knowledge,
reasoning, language, understanding, learning.
9. Thinking Humanly: Cognitive Modelling
} Not content to have a program correctly solving a
problem.
More concerned with comparing its reasoning steps
to traces of human solving the same problem.
} Requires testable theories of the workings of the
human mind: cognitive science.
10. Thinking Rationally: Laws of Thought
} Aristotle was one of the first to attempt to codify “right
thinking”, i.e., irrefutable reasoning processes.
} Formal logic provides a precise notation and rules for
representing and reasoning with all kinds of things in the
world.
} Obstacles:
- Informal knowledge representation.
- Computational complexity and resources.
11. Acting Rationally
} Acting so as to achieve one’s goals, given one’s beliefs.
} Does not necessarily involve thinking.
} Advantages:
- More general than the “laws of thought” approach.
- More amenable to scientific development than human-
based approaches.
12. AI classification
Based on strength, breadth and application, AI can be
describe:
} Weak AI or Narrow AI
} Strong AI or Generalized AI
} Super AI or Conscious AI
13. Weak AI - Narrow AI
} AI that is applied to a specific domain.
} For example: language translators, virtual assistants, self-
driving cars, AI-powered web searches, recommendation
engines, intelligent spam filters, etc.
} Applied AI can perform specific tasks, but not learn one
ones, making decisions based on programmed
algorithms, and training data
14. Strong AI - Generalized AI
} AI that can interact and operate a wide variety of
independent and unrelated tasks.
} It can learn new task to solve new problems, and it does
this by teaching itself new strategies.
} Generalized Intelligence is the combination of many AI
strategies that learn from experience and can perform at
a human level of intelligence.
15. Super AI - Conscious AI
} Ai with human-level consciousness, which would require
it to be self-aware.
} Because we are not yet able to adequately define what
consciousness is, it is unlikely that we will be able to
create a conscious AI in the near future.
17. AI is The Fusion of Many Fields of Study
} Computer science and electrical engineering determine
how AI is implemented in software and hardware.
} Mathematics and statistics determine viable models and
measure performance.
} Psychology and linguistics play an essential role in
understanding how AI might work.
} Philosophy provides guidance on intelligence and ethical
considerations.
19. The Foundations of Artificial Intelligence
} Philosophy (423 BC - present):
- Logic, methods of reasoning.
- Mind as a physical system.
- Foundations of learning, language, and rationality.
} Mathematics (c.800 - present):
- Formal representation and proof.
- Algorithms, computation, decidability, tractability.
- Probability.
20. The Foundations of Artificial Intelligence
} Psychology (1879 - present):
- Adaptation.
- Phenomena of perception and motor control.
- Experimental techniques.
} Linguistics (1957 - present):
- Knowledge representation.
- Grammar.
21. A Brief History of Artificial Intelligence
} The gestation of AI (1943 - 1956):
- 1943: McCulloch & Pitts: Boolean circuit model of brain.
- 1950: Turing’s “Computing Machinery and Intelligence”.
- 1956: McCarthy’s name “Artificial Intelligence” adopted.
} Early enthusiasm, great expectations (1952 - 1969):
- Early successful AI programs: Samuel’s checkers,
Newell & Simon’s Logic Theorist, Gelernter’s Geometry
Theorem Prover.
- Robinson’s complete algorithm for logical reasoning.
22. A Brief History of Artificial Intelligence
} A dose of reality (1966 - 1974):
- AI discovered computational complexity.
- Neural network research almost disappeared after
Minsky & Papert’s book in 1969.
} Knowledge-based systems (1969 - 1979):
- 1969: DENDRAL by Buchanan et al..
- 1976: MYCIN by Shortliffle.
- 1979: PROSPECTOR by Duda et al..
23. A Brief History of Artificial Intelligence
} AI becomes an industry (1980 - 1988):
- Expert systems industry booms.
- 1981: Japan’s 10-year Fifth Generation project.
} The return of NNs and novel AI (1986 - present):
- Mid 80’s: Back-propagation learning algorithm reinvented.
- Expert systems industry busts.
- 1988: Resurgence of probability.
- 1988: Novel AI (ALife, GAs, Soft Computing, …).
- 1995: Agents everywhere.
- 2003: Human-level AI back on the agenda.
24. Artificial Intelligence State of the Art
} Computer beats human in a chess game.
} Computer-human conversation using speech recognition.
} Expert system controls a spacecraft.
} Robot can walk on stairs and hold a cup of water.
} Language translation for webpages.
} Home appliances use fuzzy logic.
} Machine Learning
} Deep Learning
} ......
34. Image Understanding and Computer Vision
} 3 level of vision system – low, medium and high
} AI tools are required in high level vision system
34
35. Natural Interface
} Based on linguistic, psychology, computer science, etc
} Natural use of computer by humans
35
36. Speech and Natural Language Understanding
} Speech analysis, classifying words from their features.
} Language analysis – syntactic and semantic interpretation
} Eg. Robot and phonetic typewriter
36
37. Other Applications
In field of:
} Finance
} Medicine
} Heavy industry
} Telecommunication
} Gaming
} Transportation
37
38. AI Saat Ini
} Berkembang menjadi berbagai bidang ilmu yang fokus
pada area tertentu
} Global Optimization
} Expert Systems: symbolic & logic
} Evolutionary Computation: numeric
} Soft Computing: fuzzy logic, neural network, dsb.
} Machine Learning dan Deep Learning
} ...
41. Cognitive Computing
} From Conventional Computing to Cognitive Computing
} The forefront of a new era of computing
} It's a radically new kind of computing.
} Conventional computing solutions, based on the mathematical
principles that are programmed based on rules and logic intended to
derive mathematically precise answers, often following a rigid decision
tree approach.
} But with today's wealth of big data and the need for more complex
evidence-based decisions, such a rigid approach often breaks or fails to
keep up with available information.
} Cognitive Computing enables people to create a profoundly
new kind of value, finding answers and insights locked away in
volumes of data.
42. Cognitive Computing
} Cognitive Computing mirrors some of the key cognitive
elements of human expertise, systems that reason about
problems like a human does.
} Humans seek to understand something and to make a decision
through four key steps:
1. Observe visible phenomena and bodies of evidence.
2. Draw on what we know to interpret what we are seeing to generate
hypotheses about what it means.
3. Evaluate which hypotheses are right or wrong.
4. Decide, choosing the option that seems best and acting accordingly.
} cognitive systems use similar processes to reason about the
information they read, and they can do this at massive speed
and scale.
43. Cognitive Computing
} Understand unstructured data
} 80 percent of data today are unstructured.
} All of the information that is produced primarily by humans for other
humans to consume. This includes everything from literature, articles,
research reports to blogs, posts, and tweets.
} Rely on natural language
} Governed by rules of grammar, context, and culture.
} Implicit, ambiguous, complex, and a challenge to process
} While all human language is difficult to parse, certain idioms can be
particularly challenging.
} Understand context
} This is very different from simple speech recognition, which is how a
computer translates human speech into a set of words.
44. Cognitive Computing
} Cognitive systems try to understand the real intent of the
users language, and use that understanding to draw
inferences through a broad array of linguistic models and
algorithms.
} Cognitive systems learn, adapt, and keep getting smarter.
} They do this by learning from their interactions with us,
and from their own successes and failures, just like
humans do.
50. Machine learning is what enables machines to solve problems on their own
and make accurate predictions using the provided data
51.
52. • Deep learning algorithms can label and categorize information and identify patterns.
• It is what enables AI systems to continuously learn on the job, and improve the
quality and accuracy of results by determining whether decisions were correct.
53.
54. • A neural network in AI is a collection of small computing units called neurons
that take incoming data and learn to make decisions over time.
• Neural networks are often layered deep and are the reason deep learning
algorithms become more efficient as the datasets increase in volume,
• Machine learning algorithms may plateau as data increases.
55. there is one more differentiation that is important to understand,
that between artificial intelligence and data science.
56. • Data science makes possible to see patterns, find meaning from large volumes of
data, and use it to make decisions that drive business. Data science is a broad
term that encompasses the entire data processing methodology.
• AI includes everything that allows computers to learn how to solve problems and
make intelligent decisions
59. • ML uses computer algorithms to analyze data and make intelligent
decisions based on what it has learned.
• Instead of following rules-based algorithms, machine learning builds models
to classify and make predictions from data.
60. • Given data: Beats Per Minute, Body Mass Index, Age, Sex, and the
Result whether the heart has failed or not.
• With Machine Learning, using this dataset, we are able to learn and create a
model that given inputs will predict results.
61. • With traditional programming, we take data and rules, and use these to
develop an algorithm (based on mathematicl technique) that will give us an answer.
• We use the data to create an algorithm that will determine whether the heart will fail
or not (it would be an if-then-else statement).
• When we submit inputs, we get answers based on what the algorithm we
determined is, and this algorithm will not change.
62. • Machine Learning takes data and answers and creates the algorithm.
• What we get is a set of rules that determine what the machine learning model will be.
• What the model does is determine what the parameters are in a traditional algorithm.
• This model can be continuously trained and be used in the future to predict values.
66. This is what enables these systems to learn from unstructured
data such as photos, videos, and audio files.
67. • Deep Learning allows the natural language to work out the context and intent of
what is being conveyed.
• Deep learning algorithms rely on several layers of processing units. Each layer
passes its output to the next layer, which processes it and passes it to the next.
• The many layers is why it’s called deep learning.
68. • Deep learning algorithm train
the model by providing it with
lots of annotated examples.
• For instance, you give a deep
learning algorithm thousands
of images and labels that
correspond to the content of
each image.
69. • DL algorithm train the model
by providing it with lots of
annotated examples.
• For instance, you give a deep
learning algorithm thousands
of images and labels that
correspond to the content of
each image.
• The algorithm will run the those
examples through its layered
neural network, and adjust the
weights of the variables in each
layer of the neural network to
be able to detect the common
patterns that define the images
with similar labels.
70.
71. Deep Learning is also one of the main components of driverless cars.
73. • An artificial neural network is a collection of smaller units called
neurons, which are computing units modeled on the way the human brain
processes information.
74. • Artificial neural networks borrow some ideas from the biological neural
network of the brain, in order to approximate some of its processing
results.
• These units or neurons take incoming data like the biological neural
networks and learn to make decisions over time.
75.
76. • A collection of neurons is called a layer, and a layer takes in an input and
provides an output.
• Any neural network will have one input layer and one output layer.
• It will also have one or more hidden layers which simulate the types of
activity that goes on in the human brain.
77. • Hidden layers take in a set of weighted inputs and produce an output
through an activation function.
• A neural network having more than one hidden layer is referred to as a deep
neural network.
78.
79. • In a convolutional network, this process occurs over a series of layers, each of
which conducts a convolution on the output of the previous layer.
• CNNs are adept at building complex features from less complex ones.
80. • In a RNN, an input is processed through a number of layers and an output
is produced with an assumption that the two successive inputs are independent of
each other,
• RNNs can make use of information in long sequences, each layer of the network
representing the observation at a certain time.
81. Summary
} Machine Learning, a subset of AI, uses computer algorithms to analyze
data and make intelligent decisions based on what it has learned.
} Deep Learning, a specialized subset of Machine Learning, layers algorithms
to create a neural network enabling AI systems to learn from unstructured
data and continue learning on the job.
} Neural Networks, a collection of computing units modeled on biological
neurons, take incoming data and learn to make decisions over time. The
different types of neural networks include Perceptrons, Convolutional
Neural Networks or CNNs, and Recurrent Neural Networks or RNNs.
82. References
} Peter Norvig and Stuart J. Russell, Artificial
Intelligence: A Modern Approach, Pearson; 3rd
edition, 2009
} IBM, Introduction to AI, coursera.org
} Andrew Ng, AI For Everyone, deeplearning.ai