Here are the key steps in building a spoken dialogue system:
1. Speech recognition: Convert speech to text using acoustic and language models.
2. Natural language understanding: Parse text and extract meaning using NLP techniques like parsing.
3. Dialogue management: Maintain context of conversation and decide system responses using dialogue state tracking and policy models.
4. Natural language generation: Convert system responses to text using templates and/or data-to-text models.
5. Speech synthesis: Convert text to speech using text-to-speech models.
The goal is smooth turn-taking conversation where the system understands the user's intent and responds appropriately through speech. Challenges include speech recognition errors,
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
The document discusses artificial intelligence and defines it as the intelligence demonstrated by machines, in particular the ability to solve novel problems, act rationally, and act like humans. It covers the history of AI from its beginnings in 1943 to modern applications of machine learning and neural networks. While some problems like chess and math proofs have been solved, full human-level intelligence remains elusive and computers still cannot understand speech, plan optimally, or learn completely on their own without specific programming.
This document provides an introduction to an artificial intelligence lecture. It begins with basic information about the course including references, grading, and contact information. It then outlines the topics to be covered which include definitions of intelligence and AI, a brief history of AI, and the main subfields of AI. The document discusses several approaches to AI including thinking humanly by passing the Turing test, thinking rationally using logic, and acting rationally as an intelligent agent. It also reviews the foundations of AI from various contributing fields and provides examples of AI in everyday life.
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
The IOT Academy Training for Artificial Intelligence ( AI)The IOT Academy
This document provides an overview of artificial intelligence (AI). It begins with definitions of AI as modeling human thinking and acting rationally. The history of AI is then summarized, including early developments in neural networks in the 1940s and the 1956 Dartmouth conference that coined the term "artificial intelligence." Real-world applications of AI are mentioned such as autonomous vehicles and IBM's Watson. The document concludes by outlining the objectives of an introductory AI course.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
The document discusses artificial intelligence and defines it as the intelligence demonstrated by machines, in particular the ability to solve novel problems, act rationally, and act like humans. It covers the history of AI from its beginnings in 1943 to modern applications of machine learning and neural networks. While some problems like chess and math proofs have been solved, full human-level intelligence remains elusive and computers still cannot understand speech, plan optimally, or learn completely on their own without specific programming.
This document provides an introduction to an artificial intelligence lecture. It begins with basic information about the course including references, grading, and contact information. It then outlines the topics to be covered which include definitions of intelligence and AI, a brief history of AI, and the main subfields of AI. The document discusses several approaches to AI including thinking humanly by passing the Turing test, thinking rationally using logic, and acting rationally as an intelligent agent. It also reviews the foundations of AI from various contributing fields and provides examples of AI in everyday life.
This document provides an introduction to the CS 188: Artificial Intelligence course at UC Berkeley. It discusses key topics that will be covered in the course, including rational decision making, computational rationality, a brief history of AI, current capabilities in areas like natural language processing, computer vision, robotics, and game playing. The course will cover general techniques for designing rational agents and making decisions under uncertainty, with applications to domains like language, vision, games, and more. Students will learn how to apply existing AI techniques to new problem types.
Introduction to Artificial intelligence and MLbansalpra7
**Title: Understanding the Landscape of Artificial Intelligence: A Comprehensive Exploration**
**I. Introduction**
In recent decades, Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, influencing daily life, and pushing the boundaries of human capabilities. This comprehensive exploration delves into the multifaceted landscape of AI, encompassing its origins, key concepts, applications, ethical considerations, and future prospects.
**II. Historical Perspective**
AI's roots can be traced back to ancient history, where philosophers contemplated the nature of intelligence. However, it wasn't until the mid-20th century that AI as a field of study gained momentum. The influential Dartmouth Conference in 1956 marked the official birth of AI, with early pioneers like Alan Turing laying the theoretical groundwork.
**III. Foundations of AI**
Understanding AI requires grasping its foundational principles. Machine Learning (ML), a subset of AI, empowers machines to learn patterns and make decisions without explicit programming. Within ML, various approaches, such as supervised learning, unsupervised learning, and reinforcement learning, play crucial roles in shaping AI applications.
**IV. Types of Artificial Intelligence**
AI is not a monolithic entity; it spans a spectrum of capabilities. Narrow AI, also known as Weak AI, excels in specific tasks, like image recognition or language translation. In contrast, General AI, or Strong AI, would possess human-like intelligence across a wide range of tasks, a goal that remains a long-term aspiration.
**V. Applications of AI**
AI's impact is felt across diverse sectors. In healthcare, AI aids in diagnostics and personalized treatment plans. In finance, it enhances fraud detection and risk assessment. Self-driving cars exemplify AI in transportation, while virtual assistants like Siri and Alexa showcase its role in daily life. The convergence of AI with other technologies, such as the Internet of Things (IoT) and robotics, amplifies its transformative potential.
**VI. Machine Learning Algorithms**
The backbone of AI lies in its algorithms. Linear regression, decision trees, neural networks, and deep learning models are among the many tools in the ML toolkit. Exploring the mechanics of these algorithms reveals the intricacies of how AI processes information, learns from data, and makes predictions.
The IOT Academy Training for Artificial Intelligence ( AI)The IOT Academy
This document provides an overview of artificial intelligence (AI). It begins with definitions of AI as modeling human thinking and acting rationally. The history of AI is then summarized, including early developments in neural networks in the 1940s and the 1956 Dartmouth conference that coined the term "artificial intelligence." Real-world applications of AI are mentioned such as autonomous vehicles and IBM's Watson. The document concludes by outlining the objectives of an introductory AI course.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
This document provides an overview of artificial intelligence, including:
- The definition and history of AI, from its coining in 1956 to modern applications.
- The foundations and subareas of AI, including problem solving, machine learning, neural networks, and applications in business, engineering, and more.
- Approaches to building AI systems involving perception, reasoning, and action.
- Different perspectives on what constitutes intelligence and the goals of AI as developing systems that think rationally or like humans and act rationally or like humans.
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 a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
The document discusses various topics related to artificial intelligence including the Turing test, machine learning, and natural language processing. It provides definitions of AI, discusses its early history and development, comparisons to human intelligence, examples of applied AI, and challenges remaining for achieving human-level intelligence.
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.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
This document provides an overview of an artificial intelligence course, including:
- The course covers introduction to AI history and applications, knowledge representation, problem solving using search and reasoning, machine learning, robotics, and advanced AI topics.
- Required materials include an AI textbook, CLIPS programming guide, and reference books on AI structures and complex problem solving.
- The document then provides definitions and discussions of intelligence, artificial intelligence, applications of AI, and the current capabilities and limitations of AI systems.
Artificial intelligence is the area of computer science focused on creating intelligent machines. The document discusses the history and branches of AI. It provides examples of early successes in games like chess. It also discusses the knowledge needed to learn AI, such as mathematics and programming languages. Finally, it outlines several applications of AI in fields like medicine, transportation, and games.
The document provides an overview of artificial intelligence and robotics. It begins with an introduction from the CSE department of Mewar University and includes sections on definitions of AI, approaches of AI like strong AI and weak AI, techniques in AI like neural networks and genetic algorithms, famous AI systems such as Deep Blue and ALVINN, the history and foundations of AI, areas of AI like robotics and natural language processing, and recommended reference books. It discusses concepts like the Turing test, the Chinese room argument and architectures for general intelligence including LIDA and Sloman's architectures.
This document provides an overview and introduction to the topic of artificial intelligence from the textbook by Russell and Norvig. It defines AI as using computational methods to automate tasks that require human intelligence such as reasoning, problem-solving, and learning. The document discusses different definitions of AI and how its goal is to create computer systems that can perform intelligent tasks rationally rather than replicating human imperfections. It also outlines some of the major areas and achievements of AI as well as open questions regarding whether machines can truly exhibit human-like intelligence.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
The document discusses a presentation on artificial intelligence given by Biswajit Mondal, including a definition of AI as making computers able to mimic human brain functions, the various fields that contribute to AI like philosophy and computer engineering, and examples of applications like game playing and robotics.
Artificial Intelligence and its applicationFELICIALILIANJ
1. No-code machine learning allows users to build machine learning applications and tools through a drag-and-drop interface without coding, making ML more accessible.
2. Tiny ML focuses on applying machine learning at the edge on small IoT devices to reduce latency, bandwidth usage, and ensure privacy while still enabling useful predictions from collected data.
3. Automated machine learning aims to simplify the entire machine learning process from data preprocessing to modeling to reduce costs and expertise needed, enabling more widespread use of analytical tools and technologies.
Artificial Intelligence power point presentationDavid Raj Kanthi
A presentation about the basic idea about the present and future technologies which are dependent on the "ARTIFICIAL INTELLIGENCE".
AI is a branch of science which deals with the thinking, predicting, analyzing which are done by the computer itself.
The present presentation slides consists of the AI with machine learning and deep learning, goals of AI, Applications of AI and history of the Artificial intelligence etc.
The document discusses several topics related to artificial intelligence including definitions of intelligence and AI, approaches to AI like symbolic AI and biological AI, applications of AI such as expert systems and game playing, and techniques like the Turing test. It also provides a brief history of AI from 1943 to the present day and discusses components of AI programs like knowledge bases, control strategies, and inference mechanisms.
The document defines and discusses artificial intelligence from several perspectives: 1) focusing on intelligent behavior similar to humans, 2) how computers can perform tasks currently done by humans, 3) representing knowledge symbolically rather than numerically, and 4) pattern matching to describe objects and processes qualitatively. Major applications of AI discussed include expert systems, natural language processing, speech recognition, robotics, computer vision, and computer-aided instruction. The history and differences between artificial and natural intelligence are also summarized.
Sentiment analysis aims to identify the orientation of opinions in text, whether positive, negative, or neutral. It draws from fields like cognitive science, natural language processing, and machine learning. Challenges include domain dependence, sarcasm, and contrasting with standard text categorization where word presence indicates category. Approaches include subjectivity detection using graph algorithms, sentiment lexicons capturing word sentiment, and scoring adjective-adverb combinations. Applications include review analysis, question answering, and developing "hate mail" filters. Future work includes exploring the cognitive perspective on sentiment analysis.
The document describes the Naive Bayes classifier. It begins with background on probabilistic classification and generative models. It then covers probability basics and the probabilistic classification rule. It introduces Naive Bayes, making the independence assumption between attributes. The Naive Bayes algorithm is described for both the learning and testing phases. An example of classifying whether to play tennis is provided. Issues with Naive Bayes like violating independence and zero probabilities are discussed. Continuous attributes modeled with normal distributions are also covered. It concludes that Naive Bayes training is easy and fast while testing is straightforward, and it often performs competitively despite its independence assumption.
This document provides an overview of artificial intelligence, including:
- The definition and history of AI, from its coining in 1956 to modern applications.
- The foundations and subareas of AI, including problem solving, machine learning, neural networks, and applications in business, engineering, and more.
- Approaches to building AI systems involving perception, reasoning, and action.
- Different perspectives on what constitutes intelligence and the goals of AI as developing systems that think rationally or like humans and act rationally or like humans.
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 a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
The document discusses various topics related to artificial intelligence including the Turing test, machine learning, and natural language processing. It provides definitions of AI, discusses its early history and development, comparisons to human intelligence, examples of applied AI, and challenges remaining for achieving human-level intelligence.
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.
This document provides an overview of an Artificial Intelligence course, including:
- The course covers topics such as strong and weak AI, knowledge representation, problem solving using search techniques, machine learning, and more.
- The learning outcomes are to understand different approaches to AI and implications for cognitive science, expand knowledge of search and learning algorithms, and understand basic planning and reasoning methods.
- Required materials include an AI textbook and reference books, as well as a programming language for AI applications.
This document provides an overview of an artificial intelligence course, including:
- The course covers introduction to AI history and applications, knowledge representation, problem solving using search and reasoning, machine learning, robotics, and advanced AI topics.
- Required materials include an AI textbook, CLIPS programming guide, and reference books on AI structures and complex problem solving.
- The document then provides definitions and discussions of intelligence, artificial intelligence, applications of AI, and the current capabilities and limitations of AI systems.
Artificial intelligence is the area of computer science focused on creating intelligent machines. The document discusses the history and branches of AI. It provides examples of early successes in games like chess. It also discusses the knowledge needed to learn AI, such as mathematics and programming languages. Finally, it outlines several applications of AI in fields like medicine, transportation, and games.
The document provides an overview of artificial intelligence and robotics. It begins with an introduction from the CSE department of Mewar University and includes sections on definitions of AI, approaches of AI like strong AI and weak AI, techniques in AI like neural networks and genetic algorithms, famous AI systems such as Deep Blue and ALVINN, the history and foundations of AI, areas of AI like robotics and natural language processing, and recommended reference books. It discusses concepts like the Turing test, the Chinese room argument and architectures for general intelligence including LIDA and Sloman's architectures.
This document provides an overview and introduction to the topic of artificial intelligence from the textbook by Russell and Norvig. It defines AI as using computational methods to automate tasks that require human intelligence such as reasoning, problem-solving, and learning. The document discusses different definitions of AI and how its goal is to create computer systems that can perform intelligent tasks rationally rather than replicating human imperfections. It also outlines some of the major areas and achievements of AI as well as open questions regarding whether machines can truly exhibit human-like intelligence.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
The document provides an overview of artificial intelligence including definitions, types of AI tasks, foundations of AI, history of AI, current capabilities and limitations of AI systems, and techniques for problem solving and planning. It discusses machine learning, natural language processing, expert systems, neural networks, search problems, constraint satisfaction problems, linear and non-linear planning approaches. The key objectives of the course are introduced as understanding common AI concepts and having an idea of current and future capabilities of AI systems.
This document provides an introduction to artificial intelligence (AI) including its evolution, branches, applications, and conclusions. It discusses key concepts like the Turing test, definitions of AI, and intelligence. The history of AI is explored from early programs in the 1940s-50s to expert systems in the 1980s. Applications mentioned include expert systems, natural language processing, speech recognition, computer vision, and robotics. Both positive and negative potential futures of AI and robotics are considered. In conclusion, AI has increased understanding of intelligence while also revealing its complexity, providing ongoing challenges and opportunities.
The document discusses a presentation on artificial intelligence given by Biswajit Mondal, including a definition of AI as making computers able to mimic human brain functions, the various fields that contribute to AI like philosophy and computer engineering, and examples of applications like game playing and robotics.
Artificial Intelligence and its applicationFELICIALILIANJ
1. No-code machine learning allows users to build machine learning applications and tools through a drag-and-drop interface without coding, making ML more accessible.
2. Tiny ML focuses on applying machine learning at the edge on small IoT devices to reduce latency, bandwidth usage, and ensure privacy while still enabling useful predictions from collected data.
3. Automated machine learning aims to simplify the entire machine learning process from data preprocessing to modeling to reduce costs and expertise needed, enabling more widespread use of analytical tools and technologies.
Artificial Intelligence power point presentationDavid Raj Kanthi
A presentation about the basic idea about the present and future technologies which are dependent on the "ARTIFICIAL INTELLIGENCE".
AI is a branch of science which deals with the thinking, predicting, analyzing which are done by the computer itself.
The present presentation slides consists of the AI with machine learning and deep learning, goals of AI, Applications of AI and history of the Artificial intelligence etc.
The document discusses several topics related to artificial intelligence including definitions of intelligence and AI, approaches to AI like symbolic AI and biological AI, applications of AI such as expert systems and game playing, and techniques like the Turing test. It also provides a brief history of AI from 1943 to the present day and discusses components of AI programs like knowledge bases, control strategies, and inference mechanisms.
The document defines and discusses artificial intelligence from several perspectives: 1) focusing on intelligent behavior similar to humans, 2) how computers can perform tasks currently done by humans, 3) representing knowledge symbolically rather than numerically, and 4) pattern matching to describe objects and processes qualitatively. Major applications of AI discussed include expert systems, natural language processing, speech recognition, robotics, computer vision, and computer-aided instruction. The history and differences between artificial and natural intelligence are also summarized.
Sentiment analysis aims to identify the orientation of opinions in text, whether positive, negative, or neutral. It draws from fields like cognitive science, natural language processing, and machine learning. Challenges include domain dependence, sarcasm, and contrasting with standard text categorization where word presence indicates category. Approaches include subjectivity detection using graph algorithms, sentiment lexicons capturing word sentiment, and scoring adjective-adverb combinations. Applications include review analysis, question answering, and developing "hate mail" filters. Future work includes exploring the cognitive perspective on sentiment analysis.
The document describes the Naive Bayes classifier. It begins with background on probabilistic classification and generative models. It then covers probability basics and the probabilistic classification rule. It introduces Naive Bayes, making the independence assumption between attributes. The Naive Bayes algorithm is described for both the learning and testing phases. An example of classifying whether to play tennis is provided. Issues with Naive Bayes like violating independence and zero probabilities are discussed. Continuous attributes modeled with normal distributions are also covered. It concludes that Naive Bayes training is easy and fast while testing is straightforward, and it often performs competitively despite its independence assumption.
This document discusses decision support, data warehousing, and online analytical processing (OLAP). It defines these terms and compares online transaction processing (OLTP) to OLAP. It describes the evolution of decision support from batch reports to integrated data warehouses. The benefits of separating data warehouses from operational databases are outlined. Common architectures and the design/operational process are summarized.
The document discusses the ETL (Extract-Transform-Load) process used in data warehousing. The key steps of ETL are: 1) Capture/Extract which obtains data from source systems, 2) Scrub/Cleanse which cleans and formats the data, 3) Transform which converts the data to the structure of the data warehouse, and 4) Load/Index which loads the transformed data into the warehouse where it can be indexed. The ETL process results in data in the warehouse that is detailed, historical, normalized, comprehensive, timely, and quality controlled.
This lecture introduces a graduate-level course on machine learning for natural language processing (NLP). It covers the course overview, what is NLP, applications of NLP, and challenges in the field. Key NLP tasks like part-of-speech tagging, syntactic and dependency parsing, word sense disambiguation, and coreference resolution are discussed. Machine learning algorithms commonly used for NLP like classification, regression, and neural networks are also introduced. The lecture outlines the course content, assignments including paper summaries, presentations and a final project, and expectations for students.
The document summarizes a lecture on decision trees for classification. It introduces decision trees as a non-linear classification approach that learns directly from data representations. It then describes the basic ID3 algorithm for learning decision trees in a greedy top-down manner by choosing attributes that best split the data based on information gain at each step, recursively building the tree until reaching leaf nodes of single target classifications. The goal is to learn a compact tree representation of the training data.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
1. CES 510 Intelligent System Design
B. Ravikumar
Department of Engg Science
116 I Darwin Hall
664 3335
ravi93@gmail.com
2. Textbook
Chris Manning and Hinrich Shutze, Foundations of
Statistical Natural Language Processing, MIT Press,
1999.
Various supplementary readings.
Other Useful Books:
Jurafsky & Martin, SPEECH and LANGUAGE
PROCESSING: An Introduction to Natural Language
Processing, Computational Linguistics, and Speech
Recognition.
3. Overview of Artificial Intelligence
• major applications
• image processing and vision
• robotics
• game playing
• speech recognition
• natural language understanding
• etc.
4. What is Artificial Intelligence
(John McCarthy , Basic Questions)
What is artificial intelligence?
It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using
computers to understand human intelligence, but AI does not have to
confine itself to methods that are biologically observable.
Yes, but what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the
world. Varying kinds and degrees of intelligence occur in people, many
animals and some machines.
Isn't there a solid definition of intelligence that doesn't depend on relating it to
human intelligence?
Not yet. The problem is that we cannot yet characterize in general what
kinds of computational procedures we want to call intelligent. We
understand some of the mechanisms of intelligence and not others.
More in: http://www-formal.stanford.edu/jmc/whatisai/node1.html
5. What is Artificial Intelligence?
Human-like (“How to simulate humans intellect and behavior on
by a machine.)
• Mathematical problems (puzzles, games, theorems)
• Common-sense reasoning (if there is parking-space, probably illegal to
park)
• Expert knowledge: lawyers, medicine, diagnosis
• Social behavior
Rational-like:
• achieve goals, have performance measure
6. What is Artificial Intelligence
Thought processes
• “The exciting new effort to make computers think .. Machines with
minds, in the full and literal sense” (Haugeland, 1985)
Behavior
• “The study of how to make computers do things at which, at the
moment, people are better.” (Rich, and Knight, 1991)
7. The Turing Test
(Can Machine think? A. M. Turing, 1950)
Requires
• Natural language
• Knowledge representation
• Automated reasoning
• Machine learning
• (vision, robotics) for full test
8. What is AI?
Turing test (1950)
Requires:
• Natural language
• Knowledge representation
• automated reasoning
• machine learning
• (vision, robotics.) for full test
Thinking humanly:
• Introspection, the general problem solver (Newell and Simon 1961)
• Cognitive sciences
Thinking rationally:
• Logic
• Problems: how to represent and reason in a domain
Acting rationally:
• Agents: Perceive and act
9. History of AI
McCulloch and Pitts (1943)
• Neural networks that learn
Minsky (1951)
• Built a neural net computer
Darmouth conference (1956):
• McCarthy, Minsky, Newell, Simon met,
• Logic theorist (LT)- proves a theorem in Principia Mathematica-Russel.
• The name “Artficial Intelligence” was coined.
1952-1969
• GPS- Newell and Simon
• Geometry theorem prover - Gelernter (1959)
• Samuel Checkers that learns (1952)
• McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution
• Microworlds: Integration, block-worlds.
• 1962- the perceptron convergence (Rosenblatt)
10. The Birthplace of
“Artificial Intelligence”, 1956
Darmouth workshop, 1956: historical meeting of the perceived founders
of AI met: John McCarthy, Marvin Minsky, Alan Newell, and Herbert
Simon.
A Proposal for the Dartmouth Summer Research Project on Artificial
Intelligence. J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon.
August 31, 1955. "We propose that a 2 month, 10 man study of artificial
intelligence be carried out during the summer of 1956 at Dartmouth
College in Hanover, New Hampshire. The study is to proceed on the basis
of the conjecture that every aspect of learning or any other feature of
intelligence can in principle be so precisely described that a machine can
be made to simulate it." And this marks the debut of the term "artificial
intelligence.“
11. History, continued
1966-1974 a dose of reality
• Problems with computation
1969-1979 Knowledge-based system
• Expert systems:
—Dendral:Inferring molecular structures
—Mycin: diagnosing blood infections
—Prospector: recomending exploratory drilling (Duda).
1986-present: return to neural networks
Machine learning theory
Genetic algorithms, genetic programming
Statistical approaches and data mining
12. State of the art
Deep Blue defeated the reigning world chess champion Garry Kasparov
in 1997
Proved a mathematical conjecture (Robbins conjecture) unsolved for
decades
No hands across America (driving autonomously 98% of the time from
Pittsburgh to San Diego)
During the 1991 Gulf War, US forces deployed an AI logistics planning
and scheduling program that involved up to 50,000 vehicles, cargo, and
people
NASA's on-board autonomous planning program controlled the
scheduling of operations for a spacecraft
Proverb solves crossword puzzles better than most humans
DARPA grand challenge 2003-2005, Robocup
13. What’s involved in Intelligence?
Intelligent agents
Ability to interact with the real world
• to perceive, understand, and act
• e.g., speech recognition and understanding and synthesis
• e.g., image understanding
• e.g., ability to take actions, have an effect
Knowledge Representation, Reasoning and Planning
• modeling the external world, given input
• solving new problems, planning and making decisions
• ability to deal with unexpected problems, uncertainties
Learning and Adaptation
• we are continuously learning and adapting
• our internal models are always being “updated”
— e.g. a baby learning to categorize and recognize animals
14. Course overview
Intelligent systems are autonomous systems (hardware / software or a
combination) that behaves as if it exhibits some form of intelligence.
Concept goes back to Alan Turing who thought about machine
intelligence and devised Turing test to distinguish a machine from a
human through interaction.
Some major areas:
Symbolic information processing – deductive systems
Game playing – chess, backgammon etc.
natural language understanding – answering queries, translation, text
classification etc.
Machine learning - adaptive behavior through stimulus
Neural networks
Statistical modeling
Fuzzy logic, genetic programming etc.
15. Course overview
In this course we will introduce statistical
techniques for inferring structure from text. The
aim of the course is to introduce existing
techniques in statistical NLP and to stimulate
thought into bettering these.
Applications of NLP
Information Retrieval
Information Extraction
Natural language interface to database
Statistical Machine Translation
16. Tools
Probability Theory
Information Theory
Algorithms
Data Structures
Probabilistic AI
Grammars and automata
17. The Steps in NLP
Pragmatics
Syntax
Semantics
Pragmatics
Syntax
Semantics
Discourse
Morphology
18. The steps in NLP (Cont.)
Morphology: Concerns the way words are built up from
smaller meaning bearing units. (come(s),co(mes))
Syntax: concerns how words are put together to form
correct sentences and what structural role each word
has.
Semantics: concerns what words mean and how these
meanings combine in sentences to form sentence
meanings.
Pragmatics: concerns how sentences are used in
different situations and how use affects the
interpretation of the sentence.
Discourse: concerns how the immediately preceding
sentences affect the interpretation of the next
sentence.
19. Parsing (Syntactic Analysis)
Assigning a syntactic and logical form to an input sentence
uses knowledge about word and word meanings (lexicon)
uses a set of rules defining legal structures (grammar)
(S (NP (NAME Sam))
(VP (V ate)
(NP (ART the)
(N apple))))
I made her duck.
20. Word Sense Resolution
Many words have many meanings or senses.
We need to resolve which of the senses of an ambiguous
word is invoked in a particular use of the word.
I made her duck. (made her a bird for lunch or made her
move her head quickly downwards?)
21. Reference Resolution
Domain Knowledge (banking transaction)
Discourse Knowledge
World Knowledge
U: I would like to open a fixed deposit account.
S: For what amount?
U: Make it for 800 dollars.
S: For what duration?
U: What is the interest rate for 3 months?
S: Six percent.
U: Oh good then make it for that duration.
22. Why NLP is difficult?
Different ways of Parsing a sentence
Word category ambiguity
Word sense ambiguity
Words can mean more than their sum of parts (The Times of India)
Imparting world knowledge is difficult ("the blue pen ate the ice-
cream")
Fictitious worlds ("people on mars can fly")
Defining scope ("people like ice-cream," does this mean all people
like ice cream?)
Language is changing and evolving
Complex ways of interaction between the kinds of knowledge
exponential complexity at each point in using the knowledge
23. Inferring Knowledge from text
Words
word frequencies
collocations
word sense
n-grams (words appear in certain order)
Grammar
word categories
syntactic structure
Discourse
Sentence meanings
Applications
Information Retrieval
Information Extraction
Natural language interface
Statistical Machine Translation
24. Simple Applications
Word counters (wc in UNIX)
Spell Checkers, grammar checkers
Predictive Text on mobile handsets
25. More significant Applications
Intelligent computer systems
NLU interfaces to databases
Computer aided instruction, automatic graders
Information retrieval
Intelligent Web searching
Data mining
Machine translation
Speech recognition
Natural language generation
Question answering
27. Parts of the Spoken Dialogue System
Signal Processing:
Convert the audio wave into a sequence of feature vectors.
Speech Recognition:
Decode the sequence of feature vectors into a sequence of words.
Semantic Interpretation:
Determine the meaning of the words.
Discourse Interpretation:
Understand what the user intends by interpreting utterances in
context.
Dialogue Management:
Determine system goals in response to user utterances based on
user intention.
Speech Synthesis:
Generate synthetic speech as a response.
28. Levels of Sophistication in a Dialogue
System
Touch-tone replacement:
System Prompt: "For checking information, press or say one."
Caller Response: "One."
Directed dialogue:
System Prompt: "Would you like checking account information
or rate information?"
Caller Response: "Checking", or "checking account," or "rates."
Natural language:
System Prompt: "What transaction would you like to perform?"
Caller Response: "Transfer Rs. 500 from checking to savings."