The document provides information about an Artificial Intelligence course including:
1. The course code and semester, instructor name, program and course outcomes related to developing an understanding of AI concepts and applications.
2. A brief introduction to AI in movies and real life, including success stories and application areas like search engines, science, and medicine.
3. Definitions of intelligence and AI, a discussion of what tasks have been easy and hard for AI to achieve, and a high-level overview of the history and growth of the AI field.
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.
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
AI in Manufacturing: Opportunities & ChallengesTathagat Varma
AI has significant potential to create value in manufacturing through operational performance improvements, workforce augmentation, and sustainability gains. However, manufacturers often struggle to realize this value due to challenges such as a mismatch between AI capabilities and operational needs, a lack of strategic leadership and communication, insufficient cross-functional skills, and issues with data availability and governance. Addressing these adoption challenges will be key to unlocking the full promise of AI in manufacturing.
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.
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
AI in Manufacturing: Opportunities & ChallengesTathagat Varma
AI has significant potential to create value in manufacturing through operational performance improvements, workforce augmentation, and sustainability gains. However, manufacturers often struggle to realize this value due to challenges such as a mismatch between AI capabilities and operational needs, a lack of strategic leadership and communication, insufficient cross-functional skills, and issues with data availability and governance. Addressing these adoption challenges will be key to unlocking the full promise of AI in manufacturing.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The document provides an overview of artificial intelligence (AI), including its history, goals, categories, fields of application, and future scope. It discusses how AI began in the 1950s and has since been applied in many domains including medicine, industry, games, speech recognition, and expert systems. The document also outlines the goals of simulating intelligence through traits like reasoning, knowledge representation, planning, and general intelligence. It describes the main categories of AI as conventional and computational intelligence approaches. Finally, it suggests that while narrow applications will continue improving, general artificial intelligence remains a challenge, but significant progress is expected in the coming decades.
The document provides an overview of artificial intelligence (AI), including its history, goals, categories, fields of application, and future scope. It discusses how AI originated in the 1950s and has since been applied in many domains, such as games, speech recognition, and healthcare. The document also outlines the goals of simulating intelligence through traits like reasoning, knowledge representation, and planning. It describes the two main categories of AI as conventional and computational intelligence. Finally, it proposes that while narrow applications will continue advancing, general artificial intelligence remains a long-term challenge.
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.
ppt for seminar formate artificial agencies.Artificial intelligence is defined as study of rational agents. A rational agent could be anything which makes decisions, like a person, firm, machine, or software. It carries out an action with the best outcome after considering past and current percepts(agent’s perceptual inputs at a given instance).
An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. An agent is anything that can be viewed as :
perceiving its environment through sensors and
acting upon that environment through actuators ,To understand the structure of Intelligent Agents, we should be familiar with Architecture and Agent Program. Architecture is the machinery that the agent executes on. It is a device with sensors and actuators, for example : a robotic car, a camera, a PC. Agent program is an implementation of an agent function. An agent function is a map from the percept sequence(history of all that an agent has perceived till date) to an action.
Radiology has historically been a leader in digital transformation in healthcare through the introduction of technologies like PACS and teleradiology. Radiology is now at another crossroads with new digital imaging technologies and there is potential for it to evolve into an integrated diagnostic service. Recent decades have seen the adoption of many new digital imaging modalities and pictures were initially printed but as technology improved, radiology has converted to a filmless digital environment. There is now significant interest in machine learning and artificial intelligence to help analyze medical images and aid radiologists.
Artificial intelligence (AI) is the simulation of human intelligence by machines. The document provides a history of AI, discussing its current status and applications. It describes goals of AI like problem solving, acting rationally, and acting like humans. The document also outlines advantages like reducing errors and performing repetitive jobs, as well as disadvantages such as high costs. The future scope of AI is discussed, such as improved speech and image recognition changing devices and personal assistants becoming more personalized.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Latest technologies in computer system AI(Artificial Intelligence) Knowledg...muhammad-Sulaiman
The document discusses various topics related to technology including artificial intelligence and knowledge management. It provides an overview of AI including definitions, the history of AI, applications such as game playing and robotics, and types of AI like expert systems and neural networks. Knowledge management is also summarized as capturing, developing, sharing, and using organizational knowledge effectively. Additionally, a wiki is defined as a server program that allows users to collaborate in forming website content.
Artificial intelligence is the science and engineering of creating intelligent machines that can perform tasks normally requiring human intelligence. The history of AI began in 1943 and has progressed through major developments in computational power and algorithms. Today, AI is applied in many areas including natural language processing, gaming, robotics, music, business, science, and the military. While AI has advantages like speed and ability to handle complex tasks, it also faces limitations and risks that researchers continue working to address.
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 discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation.
- While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
The document discusses the history of artificial intelligence from its origins in the 1940s to modern applications. It describes several key early developments, including the first artificial neuron model (1943), the proposal of the Turing Test (1950), and the coining of the term "artificial intelligence" at the Dartmouth Conference (1956). The document also notes periods of growth and funding declines ("AI winters") for the field throughout its development. Overall, the history shows steady progress in AI from its theoretical beginnings to impactful applications today.
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
This document provides an overview of the BCS-404 Artificial Intelligence course including:
- The 4 modules which cover formalized symbolic logic, probabilistic reasoning, matching techniques, and natural language processing.
- Recommended textbooks and reference books for the course.
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.
Artificial intelligence is the branch of computer science dealing with intelligent machines. There are two main methods of AI: symbolic AI which focuses on knowledge-based systems, and computational intelligence which includes neural networks, fuzzy systems, and evolutionary computing. Important early pioneers of AI included John McCarthy, Alan Turing, and researchers at Stanford. AI is being applied in many areas including cars, banking, phones, the internet, medicine, games, military, and more. While predicting the future is difficult, gradual advances will continue and AI may match human level intelligence in the coming decades, though jobs and work will continue to be impacted by intelligent technologies.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The document provides an overview of artificial intelligence (AI), including its history, goals, categories, fields of application, and future scope. It discusses how AI began in the 1950s and has since been applied in many domains including medicine, industry, games, speech recognition, and expert systems. The document also outlines the goals of simulating intelligence through traits like reasoning, knowledge representation, planning, and general intelligence. It describes the main categories of AI as conventional and computational intelligence approaches. Finally, it suggests that while narrow applications will continue improving, general artificial intelligence remains a challenge, but significant progress is expected in the coming decades.
The document provides an overview of artificial intelligence (AI), including its history, goals, categories, fields of application, and future scope. It discusses how AI originated in the 1950s and has since been applied in many domains, such as games, speech recognition, and healthcare. The document also outlines the goals of simulating intelligence through traits like reasoning, knowledge representation, and planning. It describes the two main categories of AI as conventional and computational intelligence. Finally, it proposes that while narrow applications will continue advancing, general artificial intelligence remains a long-term challenge.
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.
ppt for seminar formate artificial agencies.Artificial intelligence is defined as study of rational agents. A rational agent could be anything which makes decisions, like a person, firm, machine, or software. It carries out an action with the best outcome after considering past and current percepts(agent’s perceptual inputs at a given instance).
An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. An agent is anything that can be viewed as :
perceiving its environment through sensors and
acting upon that environment through actuators ,To understand the structure of Intelligent Agents, we should be familiar with Architecture and Agent Program. Architecture is the machinery that the agent executes on. It is a device with sensors and actuators, for example : a robotic car, a camera, a PC. Agent program is an implementation of an agent function. An agent function is a map from the percept sequence(history of all that an agent has perceived till date) to an action.
Radiology has historically been a leader in digital transformation in healthcare through the introduction of technologies like PACS and teleradiology. Radiology is now at another crossroads with new digital imaging technologies and there is potential for it to evolve into an integrated diagnostic service. Recent decades have seen the adoption of many new digital imaging modalities and pictures were initially printed but as technology improved, radiology has converted to a filmless digital environment. There is now significant interest in machine learning and artificial intelligence to help analyze medical images and aid radiologists.
Artificial intelligence (AI) is the simulation of human intelligence by machines. The document provides a history of AI, discussing its current status and applications. It describes goals of AI like problem solving, acting rationally, and acting like humans. The document also outlines advantages like reducing errors and performing repetitive jobs, as well as disadvantages such as high costs. The future scope of AI is discussed, such as improved speech and image recognition changing devices and personal assistants becoming more personalized.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Latest technologies in computer system AI(Artificial Intelligence) Knowledg...muhammad-Sulaiman
The document discusses various topics related to technology including artificial intelligence and knowledge management. It provides an overview of AI including definitions, the history of AI, applications such as game playing and robotics, and types of AI like expert systems and neural networks. Knowledge management is also summarized as capturing, developing, sharing, and using organizational knowledge effectively. Additionally, a wiki is defined as a server program that allows users to collaborate in forming website content.
Artificial intelligence is the science and engineering of creating intelligent machines that can perform tasks normally requiring human intelligence. The history of AI began in 1943 and has progressed through major developments in computational power and algorithms. Today, AI is applied in many areas including natural language processing, gaming, robotics, music, business, science, and the military. While AI has advantages like speed and ability to handle complex tasks, it also faces limitations and risks that researchers continue working to address.
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 discusses artificial intelligence, including its history, key areas such as knowledge representation and learning, and applications in areas like consumer marketing, identification technologies, predicting stock markets, and machine translation.
- While progress has been made in areas like recognition and learning, challenges remain in full natural language understanding, human-level planning and decision making. AI is being applied across many industries but remains an active area of research.
The document discusses the history of artificial intelligence from its origins in the 1940s to modern applications. It describes several key early developments, including the first artificial neuron model (1943), the proposal of the Turing Test (1950), and the coining of the term "artificial intelligence" at the Dartmouth Conference (1956). The document also notes periods of growth and funding declines ("AI winters") for the field throughout its development. Overall, the history shows steady progress in AI from its theoretical beginnings to impactful applications today.
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
This document provides an overview of the BCS-404 Artificial Intelligence course including:
- The 4 modules which cover formalized symbolic logic, probabilistic reasoning, matching techniques, and natural language processing.
- Recommended textbooks and reference books for the course.
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.
Artificial intelligence is the branch of computer science dealing with intelligent machines. There are two main methods of AI: symbolic AI which focuses on knowledge-based systems, and computational intelligence which includes neural networks, fuzzy systems, and evolutionary computing. Important early pioneers of AI included John McCarthy, Alan Turing, and researchers at Stanford. AI is being applied in many areas including cars, banking, phones, the internet, medicine, games, military, and more. While predicting the future is difficult, gradual advances will continue and AI may match human level intelligence in the coming decades, though jobs and work will continue to be impacted by intelligent technologies.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
3. COURSE OUTCOMES
CSC604.1 Ability to develop a basic understanding of AI building blocks
CSC604.2 Ability to identify the suitable intelligent agent
CSC604.3 Ability to choose an appropriate problem solving method and to analyze
the strength and weaknesses of AI approaches to knowledge– intensive
problem solving.
CSC604.4 Ability to choose appropriate knowledge representation technique and
to design models for reasoning with uncertainty as well as the use of
unreliable information.
CSC604.5 To understand the role of planning and learning in intelligent systems .
CSC604.6 Ability to design and develop AI applications in real world scenarios
6. ARTIFICIAL INTELLIGENCE IN REAL LIFE
Exciting and dynamic field, lots of uncharted territory left
Impressive success stories
“Intelligent” in specialized domains
Many application areas
7. WHY THE INTEREST IN AI?
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances What else?
8. WHAT IS ARTIFICIAL INTELLIGENCE?
• There is no clear consensus on the definition of AI
• John McCarthy coined the phrase AI in 1956
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 or other intelligence.
Intelligence
Intelligence is the computational part of the ability to achieve goals in the world.
It is the ability to learn, perceive, plan, problem solving, decision making
Varying kinds and degrees of intelligence occur in people, many animals and some
machines.
10. WHAT’S EASY AND WHAT’S HARD?
• It’s been easier to mechanize many of the high level cognitive tasks we
usually associate with “intelligence” in people
– e. g., symbolic integration, proving theorems, playing chess, some
aspect of medical diagnosis, etc.
• It’s been very hard to mechanize tasks that animals can do easily
– walking around without running into things
– catching prey and avoiding predators
– interpreting complex sensory information (visual, aural, …)
– modeling the internal states of other animals from their behavior
– working as a team (ants, bees)
• Is there a fundamental difference between the two categories?
• What are those things which are still difficult to achieve for AI
even today?
14. History of AI can be explained
mainly in six groups
1. Beginning: 1943–1952
2. 1952–1969: Early enthusiasm, high hopes – ELIZA chatbot
3. 1952–1969: Sobering up
4. 1970–1979: Knowledge-based systems
5. 1980–2010: (AI becomes an industry – era of Intelligent Agents,
Machine Learning, Robotics)
6. 2010–till date: (era of Deep Learning)
15. History of AI
• AI has roots in a number of scientific disciplines
– computer science and engineering (hardware and software)
– philosophy (rules of reasoning)
– mathematics (logic, algorithms, optimization)
– cognitive science and psychology (modeling high level
human/animal thinking)
– neural science (model low level human/animal brain activity)
– linguistics
• The birth of AI (1943 – 1956)
– McCulloch and Pitts (1943): simplified mathematical model of
neurons (resting/firing states) can realize all propositional logic
primitives (can compute all Turing computable functions)
– Alan Turing: Turing machine and Turing test (1950)
– Claude Shannon: information theory; possibility of chess playing
computers
– Boole, Aristotle, Euclid (logics, syllogisms)
16. • Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Alan Newell and Herbert Simon (GPS);
– Emphasis on intelligent general problem solving
GSP (means-ends analysis);
Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem
proving);
heuristic search (A*, AO*, game tree search)
• Emphasis on knowledge (1966 – 1974)
– domain specific knowledge is the key to overcome existing
difficulties
– knowledge representation (KR) paradigms
– declarative vs. procedural representation
17. • Knowledge-based systems (1969 – 1999)
– DENDRAL: the first knowledge intensive system (determining 3D
structures of complex chemical compounds)
– MYCIN: first rule-based expert system (containing 450 rules for
diagnosing blood infectious diseases)
EMYCIN: an ES shell
– PROSPECTOR: first knowledge-based system that made significant
profit (geological ES for mineral deposits)
• AI became an industry (1980 – 1989)
– wide applications in various domains
– commercially available tools
– AI winter
• Current trends (1990 – present)
– more realistic goals
– more practical (application oriented)
– distributed AI and intelligent software agents
– resurgence of natural computation - neural networks and emergence
of genetic algorithms – many applications
– dominance of machine learning (big apps)
24. AI PROGRAM SHOULD HAVE
knowledge base
navigational capability
inferencing
25. KNOWLEDGE BASE
AI programs should be learning in nature and
update its knowledge accordingly.
Knowledge base consists of facts and rules.
Characteristics of Knowledge:
It is voluminous in nature and requires proper
structuring
It may be incomplete and imprecise
It may keep on changing (dynamic)
32. Game playing
Mathematics
Autonomous control
Diagnosis
Logistics planning
Autonomous planning and
scheduling
Language understanding and
problem solving
Robotics
Natural Language Generation
Speech recognition
Virtual agents
Text analytics and NLP
Robotic process automation
Biometrics
Deep learning platforms
Decision management
AI-optimized hardware
Machine-learning platforms
APPLICATION AREAS OF AI
33. REAL LIFE EXAMPLES OF APPLICATIONS OF
AI
• AI in Marketing: Netflix
https://becominghuman.ai/how-netflix-uses-ai-and-machine-learning-a087614630fe
• AI in Banking:
HDFC Bank Chatbot EVA
https://v1.hdfcbank.com/htdocs/common/eva/index.html?query=hi
Mastercard
https://cio.economictimes.indiatimes.com/news/strategy-and-management/how-mastercard-is-using-
artificial-intelligence-to-stop-fraud-and-reduce-false-
declines/69929529#:~:text=We%20use%20AI%20for%20both%20internal%20and%20external%20business
%20operations%20of%20Mastercard.&text=We%20have%20an%20artificial%20intelligence,acquainted%2
0with%20machine%20learning%20quickly.
• AI in Finance:
Indian Stock Exchange
https://analyticsindiamag.com/why-d-street-is-tapping-into-ai-ml-to-improve-stock-market-
functioning/#:~:text=BSE%20is%20known%20to%20use%20emerging%20technologies%20like
%20AI%20and%20machine%20learning.&text=Further%2C%20machine%20learning%20and%
20deep,BSE%20site%20and%20Sentifi%20platform.
34. • AI In Agriculture: Berlin-based agricultural tech start-up called
PEAT, has developed an application called Plantix
https://plantix.net/en/
• AI in Healthcare: Cambio Health Care developed a clinical
decision support system for stroke prevention, Coala life
• https://www.newworldai.com/how-is-artificial-intelligence-empowering-
healthcare-today/
• AI in Gaming: Alpha Go , Alpha Go Zero
https://deepmind.com/blog/article/alphago-zero-starting-scratch
• AI in Space Exploration: NASA’s next rover mission to Mars,
the Mars 2020 Rover. The AEGIS, which is an AI-based Mars
rover is already on the red planet.
• https://mars.nasa.gov/news/8689/nasas-mars-rover-drivers-need-your-help/
35. • AI in autonomous Vehicles: Tesla’s self-driving car. Elon Musk
talks a ton about how AI is implemented in tesla’s self-driving cars
and autopilot features.
https://www.tesla.com/autopilot
• AI in Chatbots: Siri , Cortana, Amazon Echo
https://www.ometrics.com/blog/ai-and-amazon-alexa-echo/
• AI in Social Media: Facebook, Twitter’s AI, which is being used to
identify hate speech and terroristic language in tweets. The
company discovered and banned 300,000 terrorist-linked accounts,
95% of which were found by non-human, artificially intelligent
machines.
https://www.bernardmarr.com/default.asp?contentID=1373#:~:text=One%20of%
20the%20ways%20Twitter,tweets%20in%20reverse%20chronological%20order.
• AI in creativity: Musenet, wordsmith
https://openai.com/blog/musenet/
https://automatedinsights.com/wordsmith/
38. AI PROGRAMMING LANGUAGES
• AI could be a branch of engineering, which essentially aims for creating
computers which may think intelligently in a similar manner the intelligent
humans think.
• A number of programming languages exist that are used to build AI
systems. General programming languages, such as C++, R, Java, Python,
and LISP (List Processing) are frequently used, because these are the
languages with which most computer scientists have got experience.
• Here are some languages that are most typically used for creating the AI
projects:
Python
R
Java
C++
PROLOG
LISP
39. ARTIFICIAL INTELLIGENCE GROWTH
The number of AI startups since 2000 has increased 14 times.
72% of execs believe that AI will be the most significant business advantage of
the future.
AI will automate 16% of American jobs.
Machine learning is predicted to grow by 48% in the automotive industry
15% of enterprises are using AI, and 31% of them say that it is their agenda for
the next 12 months.
By 2021, customer insights-driven businesses will see $1.2 trillion more per year
than their less-informed peers.
Financial services are the future of AI
40. TOP 10 COMPANIES IN AI RESEARCH
1. Deepmind
2. Google
3. Facebook
4. OpenAI
5. Baidu
6. Microsoft
7. Apple
8. IBM
9. Amazon
10. NVIDIA