The document outlines the objectives, outcomes, and learning outcomes of a course on artificial intelligence. The objectives include conceptualizing ideas and techniques for intelligent systems, understanding mechanisms of intelligent thought and action, and understanding advanced representation and search techniques. Outcomes include developing an understanding of AI building blocks, choosing appropriate problem solving methods, analyzing strengths and weaknesses of AI approaches, and designing models for reasoning with uncertainty. Learning outcomes include knowledge, intellectual skills, practical skills, and transferable skills in artificial intelligence.
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Artificial intelligence and knowledge representationSajan Sahu
The document discusses artificial intelligence and knowledge representation. It describes how computers can be made intelligent through speed of computation, filtering responses, using algorithms and neural networks. It also discusses knowledge representation techniques in AI like propositional logic, semantic networks, frames, predicate logic and nonmonotonic reasoning. The document provides examples and applications of AI like pattern recognition, robotics and natural language processing. It also discusses some fundamental problems of AI.
This presentation provides an overview of robotics and AI. It defines a robot as a machine that can sense its environment, think to follow instructions, and act. Current developments include robots that can perform surgery, explore hazardous areas, and recognize faces and objects. Industrial and manufacturing robots are widely used today. Issues include robots being unable to handle unexpected situations and potentially increasing unemployment, though future developments may focus on greater intelligence, learning ability, and human-friendly design.
The document discusses informed search techniques that use heuristic information to guide the search for a solution more efficiently. It describes how heuristic information about the problem domain can help constrain the search space. Hill climbing and best-first search are two informed search strategies discussed. Hill climbing iteratively moves to successor states with improved heuristic values until a local optimum is reached. Best-first search maintains an open list of promising nodes to explore and prioritizes expanding nodes with the best heuristic values to avoid getting stuck in local optima.
Power point presentation on basic of Artificial Intelligent Vishal Singh
The document provides an overview of artificial intelligence (AI). It defines AI as the study of computer systems that attempt to model human intelligence. The early history of AI is discussed, noting Alan Turing's 1950 paper that asked if machines can think. The current status of AI includes intelligent personal assistants on mobile devices, as well as applications in video games, navigation, robotics, and deep learning. Key challenges for AI are computing power, intuitive thinking, and judgment. The future of AI is seen in self-driving cars, improved healthcare, and new areas of exploration.
Artificial Intelligence Robotics (AI) PPT by Aamir Saleem AnsariTech
Artificial intelligence (AI) is the intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at an arbitrary goal.Colloquially, the term "artificial intelligence" is likely to be applied when a machine uses cutting-edge techniques to competently perform or mimic "cognitive" functions that we intuitively associate with human minds, such as "learning" and "problem solving".The colloquial connotation, especially among the public, associates artificial intelligence with machines that are "cutting-edge" (or even "mysterious"). This subjective borderline around what constitutes "artificial intelligence" tends to shrink over time; for example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" as it is nowadays a mundane routine technology.Modern examples of AI include computers that can beat professional players at Chess and Go, and self-driving cars that navigate crowded city streets.
AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
This document outlines presentations on computer vision, robotics, and an image analysis paper. It discusses what computer vision and robotics are, provides examples of applications and challenges. It also summarizes a paper on using image analysis to classify Ethiopian coffee varieties by region. Key topics include face recognition, types of robots and their purposes, and examples like Shakey and wall-climbing robots. The future directions discussed include developing universal robots and improving visual recognition and manipulation abilities.
This document discusses the application of robotics for path planning. It begins by defining robotics and describing some common applications of robots, such as jobs that are dirty, dull or dangerous. It then focuses on path planning, which allows robots to find optimal paths between two points using a map of the environment. Several path planning algorithms are described, including Dijkstra's algorithm, A*, D* and RRT. Map representations like occupancy grids and topological maps are also discussed.
This document provides an overview of swarm robotics. It begins with examples of decentralized control and self-organization in natural swarms like ants and bees. It then discusses how swarm robotics takes inspiration from these systems, using local control methods, local communication, and self-organization to complete collective tasks without centralized control. The rest of the document focuses on a proposed system for gesture recognition to allow human control of swarm robots. It describes hand detection, feature extraction, and hardware implementation using three foot-bot robots. It concludes with potential applications of swarm robotics and areas for future work.
Artificial intelligence and knowledge representationSajan Sahu
The document discusses artificial intelligence and knowledge representation. It describes how computers can be made intelligent through speed of computation, filtering responses, using algorithms and neural networks. It also discusses knowledge representation techniques in AI like propositional logic, semantic networks, frames, predicate logic and nonmonotonic reasoning. The document provides examples and applications of AI like pattern recognition, robotics and natural language processing. It also discusses some fundamental problems of AI.
This presentation provides an overview of robotics and AI. It defines a robot as a machine that can sense its environment, think to follow instructions, and act. Current developments include robots that can perform surgery, explore hazardous areas, and recognize faces and objects. Industrial and manufacturing robots are widely used today. Issues include robots being unable to handle unexpected situations and potentially increasing unemployment, though future developments may focus on greater intelligence, learning ability, and human-friendly design.
The document discusses informed search techniques that use heuristic information to guide the search for a solution more efficiently. It describes how heuristic information about the problem domain can help constrain the search space. Hill climbing and best-first search are two informed search strategies discussed. Hill climbing iteratively moves to successor states with improved heuristic values until a local optimum is reached. Best-first search maintains an open list of promising nodes to explore and prioritizes expanding nodes with the best heuristic values to avoid getting stuck in local optima.
Power point presentation on basic of Artificial Intelligent Vishal Singh
The document provides an overview of artificial intelligence (AI). It defines AI as the study of computer systems that attempt to model human intelligence. The early history of AI is discussed, noting Alan Turing's 1950 paper that asked if machines can think. The current status of AI includes intelligent personal assistants on mobile devices, as well as applications in video games, navigation, robotics, and deep learning. Key challenges for AI are computing power, intuitive thinking, and judgment. The future of AI is seen in self-driving cars, improved healthcare, and new areas of exploration.
Artificial Intelligence Robotics (AI) PPT by Aamir Saleem AnsariTech
Artificial intelligence (AI) is the intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at an arbitrary goal.Colloquially, the term "artificial intelligence" is likely to be applied when a machine uses cutting-edge techniques to competently perform or mimic "cognitive" functions that we intuitively associate with human minds, such as "learning" and "problem solving".The colloquial connotation, especially among the public, associates artificial intelligence with machines that are "cutting-edge" (or even "mysterious"). This subjective borderline around what constitutes "artificial intelligence" tends to shrink over time; for example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" as it is nowadays a mundane routine technology.Modern examples of AI include computers that can beat professional players at Chess and Go, and self-driving cars that navigate crowded city streets.
AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
This document outlines presentations on computer vision, robotics, and an image analysis paper. It discusses what computer vision and robotics are, provides examples of applications and challenges. It also summarizes a paper on using image analysis to classify Ethiopian coffee varieties by region. Key topics include face recognition, types of robots and their purposes, and examples like Shakey and wall-climbing robots. The future directions discussed include developing universal robots and improving visual recognition and manipulation abilities.
This document discusses the application of robotics for path planning. It begins by defining robotics and describing some common applications of robots, such as jobs that are dirty, dull or dangerous. It then focuses on path planning, which allows robots to find optimal paths between two points using a map of the environment. Several path planning algorithms are described, including Dijkstra's algorithm, A*, D* and RRT. Map representations like occupancy grids and topological maps are also discussed.
This document discusses artificial intelligence and robotics. It begins with definitions of AI from early researchers like John McCarthy and Alan Turing. It then discusses the history and development of AI, including important figures and programming languages. Applications of robotics are outlined in various fields like industrial uses, medical care, and entertainment. The document also explores how AI works with robots through natural language processing. It concludes by discussing the future of AI and warnings about potential issues that may arise from advanced AI systems.
This document provides an introduction and overview of robots. It discusses the history of robots from the first use of the term by Karel Capek to the building of the first robot called Unimate by George Devol and Joseph Engelberger in 1956. It then describes different types of robots including mobile robots, industrial robots, autonomous robots, remote-controlled robots, and virtual robots. The document concludes by discussing the future of robotics and advances being made through competitions like RoboCup.
What is Artificial Intelligence?
Beginning of AI ?
Advantages of Artificial Intelligence?
Future of Artificial Intelligence….
Application of AI…..
Risk of Artificial Intelligence….
This document provides an overview of machine learning, including what it is, how it compares to other concepts like learning and programming, known applications, and emerging trends. Machine learning uses algorithms and data to enable computers to learn without being explicitly programmed and can be used to make predictions that sometimes outperform humans in areas like customer feedback, recommendations, recognition, and fraud detection. It is becoming increasingly important for businesses for tasks like forecasting demand.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
The document provides an overview of artificial intelligence, including definitions, key concepts, and applications. It defines AI as the simulation of human intelligence in machines, and notes the differences between weak/narrow AI which focuses on specific problems, versus strong/general AI which aims to achieve human-level intelligence. The document also discusses how AI works by trying to think and act well, and by attempting to think and act like humans. It provides examples of AI application areas and practical tools used today.
Rule based systems are specialized software that encapsulate human intelligence and knowledge to make intelligent decisions quickly and repeatedly. They represent knowledge using if-then rules and work memory. There are two types of rules - forward chaining which is data-driven and deductive, and backward chaining which is goal-driven and inductive. While rule engines are the core of applications, fully utilizing them requires additional components for interfacing, data exchange, data storage, and version management. Examples of companies using rule engines include Dell, Cisco, Vodafone, and Blue Cross Blue Shield.
Robotics is the branch of science dealing with the design, construction, operation, and application of robots. Robots can take the place of humans in dangerous environments and resemble humans in appearance, behavior, and cognition. The word "robot" was introduced by Czech writer Karel Capek in 1920 and the term "robotics" was coined by Isaac Asimov in the 1940s. Asimov also proposed his three laws of robotics which govern a robot's behavior regarding humans. Robots are used for tasks that are dangerous, repetitive, impossible for humans, or require high precision. They have a variety of applications including space exploration, medical care, manufacturing, and assistance for disabled persons.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Stuart russell and peter norvig artificial intelligence - a modern approach...Lê Anh Đạt
This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.
Power Point Presentation on Artificial Intelligence and Cool Current Projects...PuneetGautam6
It is a Powerpoint Presentation on Artificial Intelligence and Current Projects of AI have also been covered on this powerpoint presentation. Some videos have also been added to it and you can mail to me for full ppt at me.punit.as@gmail.com
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents".
Robotics is the interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots,[1] as well as computer systems for their control, sensory feedback, and information processing.
Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences.
Intelligent mobile Robotics & Perception SystemsIntelligent mobile Robotics ...Gouasmia Zakaria
Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , Categories of AI, Types of AI, disadvantages , benefits , applications .
We hope it to be useful .
The document discusses the Blue Brain Project, which aims to recreate the human brain through detailed computer simulation. The project scans brain tissue to build biologically realistic models of neurons and networks. These simulations are run on IBM's Blue Gene supercomputer. The goal is to gain a complete understanding of the brain and enable better treatments for brain diseases. It is believed that within 30 years it will be possible to upload a person's brain contents onto a computer, allowing them to theoretically live on after death in virtual form. Both benefits and risks are discussed regarding the implications of creating virtual brains.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
The document discusses classical AI planning and different planning approaches. It introduces state-space planning which searches for a sequence of state transformations, and plan-space planning which searches for a plan satisfying certain conditions. It also discusses hierarchical planning which decomposes tasks into simpler subtasks, and universal classical planning which uses different refinement techniques including state-space and plan-space refinements. Classical planning makes simplifying assumptions but its principles can still be applied to games with some workarounds.
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.
The document provides an overview of artificial intelligence (AI), including its main areas of study, progress made, applications, and ongoing challenges. It discusses how AI involves automated perception, learning, reasoning and planning. While recognition and learning have advanced, planning and general reasoning remain challenging. The document outlines applications in industries like finance, medicine and transportation, but notes that many problems remain unsolved, making AI an active area of research.
This document discusses artificial intelligence and robotics. It begins with definitions of AI from early researchers like John McCarthy and Alan Turing. It then discusses the history and development of AI, including important figures and programming languages. Applications of robotics are outlined in various fields like industrial uses, medical care, and entertainment. The document also explores how AI works with robots through natural language processing. It concludes by discussing the future of AI and warnings about potential issues that may arise from advanced AI systems.
This document provides an introduction and overview of robots. It discusses the history of robots from the first use of the term by Karel Capek to the building of the first robot called Unimate by George Devol and Joseph Engelberger in 1956. It then describes different types of robots including mobile robots, industrial robots, autonomous robots, remote-controlled robots, and virtual robots. The document concludes by discussing the future of robotics and advances being made through competitions like RoboCup.
What is Artificial Intelligence?
Beginning of AI ?
Advantages of Artificial Intelligence?
Future of Artificial Intelligence….
Application of AI…..
Risk of Artificial Intelligence….
This document provides an overview of machine learning, including what it is, how it compares to other concepts like learning and programming, known applications, and emerging trends. Machine learning uses algorithms and data to enable computers to learn without being explicitly programmed and can be used to make predictions that sometimes outperform humans in areas like customer feedback, recommendations, recognition, and fraud detection. It is becoming increasingly important for businesses for tasks like forecasting demand.
Mika Kaukoranta presents what computer vision is and how it can be utilized in software testing by gaining high-level understanding from digital images or videos.
The document provides an overview of artificial intelligence, including definitions, key concepts, and applications. It defines AI as the simulation of human intelligence in machines, and notes the differences between weak/narrow AI which focuses on specific problems, versus strong/general AI which aims to achieve human-level intelligence. The document also discusses how AI works by trying to think and act well, and by attempting to think and act like humans. It provides examples of AI application areas and practical tools used today.
Rule based systems are specialized software that encapsulate human intelligence and knowledge to make intelligent decisions quickly and repeatedly. They represent knowledge using if-then rules and work memory. There are two types of rules - forward chaining which is data-driven and deductive, and backward chaining which is goal-driven and inductive. While rule engines are the core of applications, fully utilizing them requires additional components for interfacing, data exchange, data storage, and version management. Examples of companies using rule engines include Dell, Cisco, Vodafone, and Blue Cross Blue Shield.
Robotics is the branch of science dealing with the design, construction, operation, and application of robots. Robots can take the place of humans in dangerous environments and resemble humans in appearance, behavior, and cognition. The word "robot" was introduced by Czech writer Karel Capek in 1920 and the term "robotics" was coined by Isaac Asimov in the 1940s. Asimov also proposed his three laws of robotics which govern a robot's behavior regarding humans. Robots are used for tasks that are dangerous, repetitive, impossible for humans, or require high precision. They have a variety of applications including space exploration, medical care, manufacturing, and assistance for disabled persons.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
Useful Techniques in Artificial IntelligenceIla Group
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.
Stuart russell and peter norvig artificial intelligence - a modern approach...Lê Anh Đạt
This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.
Power Point Presentation on Artificial Intelligence and Cool Current Projects...PuneetGautam6
It is a Powerpoint Presentation on Artificial Intelligence and Current Projects of AI have also been covered on this powerpoint presentation. Some videos have also been added to it and you can mail to me for full ppt at me.punit.as@gmail.com
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents".
Robotics is the interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots,[1] as well as computer systems for their control, sensory feedback, and information processing.
Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences.
Intelligent mobile Robotics & Perception SystemsIntelligent mobile Robotics ...Gouasmia Zakaria
Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , Categories of AI, Types of AI, disadvantages , benefits , applications .
We hope it to be useful .
The document discusses the Blue Brain Project, which aims to recreate the human brain through detailed computer simulation. The project scans brain tissue to build biologically realistic models of neurons and networks. These simulations are run on IBM's Blue Gene supercomputer. The goal is to gain a complete understanding of the brain and enable better treatments for brain diseases. It is believed that within 30 years it will be possible to upload a person's brain contents onto a computer, allowing them to theoretically live on after death in virtual form. Both benefits and risks are discussed regarding the implications of creating virtual brains.
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
The document discusses classical AI planning and different planning approaches. It introduces state-space planning which searches for a sequence of state transformations, and plan-space planning which searches for a plan satisfying certain conditions. It also discusses hierarchical planning which decomposes tasks into simpler subtasks, and universal classical planning which uses different refinement techniques including state-space and plan-space refinements. Classical planning makes simplifying assumptions but its principles can still be applied to games with some workarounds.
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.
The document provides an overview of artificial intelligence (AI), including its main areas of study, progress made, applications, and ongoing challenges. It discusses how AI involves automated perception, learning, reasoning and planning. While recognition and learning have advanced, planning and general reasoning remain challenging. The document outlines applications in industries like finance, medicine and transportation, but notes that many problems remain unsolved, making AI an active area of research.
This document provides an overview of artificial intelligence and discusses key concepts in AI search. It begins by defining an intelligent agent and its interaction with the environment. It then discusses uninformed search strategies like breadth-first search and depth-first search. It also covers iterative deepening depth-first search, uniform-cost search, searching backwards from the goal, and bidirectional search. The document aims to introduce foundational AI concepts like state spaces, actions, search trees, and strategies for traversing the problem space in an attempt to find a solution.
- 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 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 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.
This document discusses computational thinking and provides examples of how it can be applied. It defines computational thinking as using logical reasoning and problem-solving skills to solve problems. It gives examples of computational thinking in everyday life, sciences, archaeology, journalism, and more. The document also discusses teaching computational thinking to others using block-based programming languages like Snap, Scratch, and Pictoblox. Key concepts covered include sequences, loops, conditionals, events, parallelism, operators, and data.
Here are three possible interpretations of the phrase "Time flies like an arrow":
1. The passage of time seems to go by very quickly, in the same way that an arrow flies through the air.
2. Certain types of insects that lay their eggs on decaying matter, known as flies, move through the air in a similar way to arrows.
3. The idiom is using "flies" to refer to time passing quickly in an abstract sense, similar to an arrow moving swiftly through space.
The key challenges with natural language understanding are ambiguity and context. Even a short phrase like this one could have multiple meanings without additional context clues. Determining the intended interpretation requires commonsense reasoning abilities that computers still lack
The document discusses various topics related to artificial intelligence including definitions of AI, goals of AI, whether machines can think, the Turing test, types of AI tasks including mundane, formal and expert tasks, technologies based on AI such as machine learning, natural language processing, computer vision, and applications of AI such as in healthcare, gaming, finance, data security, social media, travel and more.
This document provides an introduction and overview of an artificial intelligence course. It outlines the following key points:
- The objectives of the course are to cover many primary AI concepts and ideas but within 15 weeks not everything can be covered.
- Today's lecture will discuss what intelligence is, a brief history of AI including modern successes like Stanley the robot, and how much progress has been made in different aspects of AI.
- The course agenda includes fuzzy logic, propositional logic and expert systems, rough set theory, decision trees, k-nearest neighbors, naive Bayes, and neural networks.
Artificial intelligence (AI) is the ability of machines to think and act intelligently like humans. It involves creating machines that can think and act rationally. While AI does not occur naturally, it is created by humans to enable machines to think, reason, and understand instead of just performing tasks automatically. There are still many challenges to fully achieving human-level artificial general intelligence.
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 information about the COMPSCI 270: Artificial Intelligence course at Duke University. The course will be taught in the spring of 2019 by Professor Vincent Conitzer. It will cover topics such as search, constraint satisfaction, game playing, logic, knowledge representation, and planning. Assignments will count for 30% of the grade, midterms for 40%, and a final exam for 30%. The course assumes some programming experience and background in algorithms, probability, and discrete mathematics. It aims to cover general AI techniques applied to tasks like solving Rubik's cubes, scheduling meetings, and playing games like chess.
This document provides an introduction to artificial intelligence (AI) by discussing key concepts related to intelligence and different approaches to defining AI. It examines definitions of intelligence as the ability to reason, understand, learn from experience, and plan complex tasks. AI is defined as attempting to build intelligent computer systems that exhibit human-like intelligence. There are no widely agreed upon definitions of AI, but definitions generally fall into four categories: systems that think like humans, act like humans, think rationally, or act rationally. The document also discusses the Turing test, thinking rationally using logic, knowledge representation, and intelligent agents.
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.
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Artificial intelligence and machine learning are discussed. AI is defined as making computers intelligent like humans through understanding, reasoning, planning, communication and perception. Machine learning is a subset of AI that allows machines to learn from experience without being explicitly programmed. The document provides background on AI and ML, including definitions, history, and discussions of intelligence and applications.
This document provides an overview of a course on trends and research applications in natural language processing (NLP). It begins with introducing the goals of the course, which are to understand interesting NLP tasks and novel projects through a research-oriented webinar. The document then covers various NLP topics like question answering, machine translation, sentiment analysis, natural language generation applications, and challenges in NLP like grounded language and embodied language. It also provides tips for aspiring NLP researchers.
This document provides an introduction to artificial intelligence (AI) and related concepts. It defines key terms like data, information, knowledge and knowledge base. It discusses different views on defining AI, including thinking humanly, thinking rationally, acting humanly, and acting rationally. The document also covers the history of AI, applications of AI, knowledge representation, and the differences between human and artificial intelligence. It provides examples of expert systems like DENDRAL, MYCIN, PUFF and ELIZA to illustrate the concept.
The document provides an introduction to unsupervised learning and reinforcement learning. It then discusses eigen values and eigen vectors, showing how to calculate them from a matrix. It provides examples of covariance matrices and using Gaussian elimination to solve for eigen vectors. Finally, it discusses principal component analysis and different clustering algorithms like K-means clustering.
Cross validation is a technique for evaluating machine learning models by splitting the dataset into training and validation sets and training the model multiple times on different splits, to reduce variance. K-fold cross validation splits the data into k equally sized folds, where each fold is used once for validation while the remaining k-1 folds are used for training. Leave-one-out cross validation uses a single observation from the dataset as the validation set. Stratified k-fold cross validation ensures each fold has the same class proportions as the full dataset. Grid search evaluates all combinations of hyperparameters specified as a grid, while randomized search samples hyperparameters randomly within specified ranges. Learning curves show training and validation performance as a function of training set size and can diagnose underfitting
This document provides an overview of supervised machine learning algorithms for classification, including logistic regression, k-nearest neighbors (KNN), support vector machines (SVM), and decision trees. It discusses key concepts like evaluation metrics, performance measures, and use cases. For logistic regression, it covers the mathematics behind maximum likelihood estimation and gradient descent. For KNN, it explains the algorithm and discusses distance metrics and a numerical example. For SVM, it outlines the concept of finding the optimal hyperplane that maximizes the margin between classes.
The document provides information on solving the sum of subsets problem using backtracking. It discusses two formulations - one where solutions are represented by tuples indicating which numbers are included, and another where each position indicates if the corresponding number is included or not. It shows the state space tree that represents all possible solutions for each formulation. The tree is traversed depth-first to find all solutions where the sum of the included numbers equals the target sum. Pruning techniques are used to avoid exploring non-promising paths.
The document discusses the greedy method and its applications. It begins by defining the greedy approach for optimization problems, noting that greedy algorithms make locally optimal choices at each step in hopes of finding a global optimum. Some applications of the greedy method include the knapsack problem, minimum spanning trees using Kruskal's and Prim's algorithms, job sequencing with deadlines, and finding the shortest path using Dijkstra's algorithm. The document then focuses on explaining the fractional knapsack problem and providing a step-by-step example of solving it using a greedy approach. It also provides examples and explanations of Kruskal's algorithm for finding minimum spanning trees.
The document describes various divide and conquer algorithms including binary search, merge sort, quicksort, and finding maximum and minimum elements. It begins by explaining the general divide and conquer approach of dividing a problem into smaller subproblems, solving the subproblems independently, and combining the solutions. Several examples are then provided with pseudocode and analysis of their divide and conquer implementations. Key algorithms covered in the document include binary search (log n time), merge sort (n log n time), and quicksort (n log n time on average).
What is an Algorithm
Time Complexity
Space Complexity
Asymptotic Notations
Recursive Analysis
Selection Sort
Insertion Sort
Recurrences
Substitution Method
Master Tree Method
Recursion Tree Method
This document provides an outline for a machine learning syllabus. It includes 14 modules covering topics like machine learning terminology, supervised and unsupervised learning algorithms, optimization techniques, and projects. It lists software and hardware requirements for the course. It also discusses machine learning applications, issues, and the steps to build a machine learning model.
The document discusses problem-solving agents and their approach to solving problems. Problem-solving agents (1) formulate a goal based on the current situation, (2) formulate the problem by defining relevant states and actions, and (3) search for a solution by exploring sequences of actions that lead to the goal state. Several examples of problems are provided, including the 8-puzzle, robotic assembly, the 8 queens problem, and the missionaries and cannibals problem. For each problem, the relevant states, actions, goal tests, and path costs are defined.
The simplex method is a linear programming algorithm that can solve problems with more than two decision variables. It works by generating a series of solutions, called tableaus, where each tableau corresponds to a corner point of the feasible solution space. The algorithm starts at the initial tableau, which corresponds to the origin. It then shifts to adjacent corner points, moving in the direction that optimizes the objective function. This process of generating new tableaus continues until an optimal solution is found.
The document discusses functions and the pigeonhole principle. It defines what a function is, how functions can be represented graphically and with tables and ordered pairs. It covers one-to-one, onto, and bijective functions. It also discusses function composition, inverse functions, and the identity function. The pigeonhole principle states that if n objects are put into m containers where n > m, then at least one container must hold more than one object. Examples are given to illustrate how to apply the principle to problems involving months, socks, and selecting numbers.
The document discusses relations and their representations. It defines a binary relation as a subset of A×B where A and B are nonempty sets. Relations can be represented using arrow diagrams, directed graphs, and zero-one matrices. A directed graph represents the elements of A as vertices and draws an edge from vertex a to b if aRb. The zero-one matrix representation assigns 1 to the entry in row a and column b if (a,b) is in the relation, and 0 otherwise. The document also discusses indegrees, outdegrees, composite relations, and properties of relations like reflexivity.
This document discusses logic and propositional logic. It covers the following topics:
- The history and applications of logic.
- Different types of statements and their grammar.
- Propositional logic including symbols, connectives, truth tables, and semantics.
- Quantifiers, universal and existential quantification, and properties of quantifiers.
- Normal forms such as disjunctive normal form and conjunctive normal form.
- Inference rules and the principle of mathematical induction, illustrated with examples.
1. Set theory is an important mathematical concept and tool that is used in many areas including programming, real-world applications, and computer science problems.
2. The document introduces some basic concepts of set theory including sets, members, operations on sets like union and intersection, and relationships between sets like subsets and complements.
3. Infinite sets are discussed as well as different types of infinite sets including countably infinite and uncountably infinite sets. Special sets like the empty set and power sets are also covered.
The document discusses uncertainty and probabilistic reasoning. It describes sources of uncertainty like partial information, unreliable information, and conflicting information from multiple sources. It then discusses representing and reasoning with uncertainty using techniques like default logic, rules with probabilities, and probability theory. The key approaches covered are conditional probability, independence, conditional independence, and using Bayes' rule to update probabilities based on new evidence.
Planning involves representing an initial state, possible actions, and a goal state. A planning agent uses a knowledge base to select action sequences that transform the initial state into a goal state. STRIPS is a common planning representation that uses predicates to describe states and logical operators to represent actions and their effects. A STRIPS planning problem specifies the initial state, goal conditions, and set of operators. A solution is a sequence of ground operator instances that produces the goal state from the initial state.
Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
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Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
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Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
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Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
2. Objectives
1. To conceptualize the basic ideas and
techniques underlying the design of intelligent
systems.
2. To make students understand and explore the
mechanism of mind that enable intelligent
thought and action.
3. To make students understand advanced
representation formalism and search
techniques.
4. To make students understand how to deal with
uncertain and incomplete information.shiwani gupta 2
3. Outcomes
Learner will be able to
1. develop a basic understanding of AI building blocks
presented in intelligent agents.
2. choose an appropriate problem solving method and
knowledge representation technique.
3. analyze the strength and weaknesses of AI
approaches to knowledge – intensive problem
solving.
4. design models for reasoning with uncertainty as well
as the use of unreliable information.
5. design and develop the AI applications in real world
scenario.
shiwani gupta 3
4. Learning Outcomes
shiwani gupta 4
At the end of this course, learner should be able to:
• Knowledge and understanding
know and understand the basic concepts of Artificial
Intelligence including Search, Game Playing, KBS (including
Uncertainty), Planning and Machine Learning.
• Intellectual skills
use this knowledge and understanding of appropriate principles
and guidelines to synthesize solutions to tasks in AI and to
critically evaluate alternatives.
• Practical skills
use a well known declarative language (Prolog) and to
construct simple AI systems.
• Transferable Skills
solve problems and evaluate outcomes and alternatives.
5. List of AI Practical / Experiments
All the programs should be implemented in C/C++/Java/Prolog
under Windows or Linux environment. Experiments can also be
conducted using available open source tools.
1. One case study on NLP/Expert system based papers
published in IEEE/ACM/Springer or any prominent journal.
2. Program on uninformed and informed search methods.
3. Program on Local Search Algorithm.
4. Program on Optimization problem.
5. Program on adversarial search.
6. Program on Wumpus world.
7. Program on unification.
8. Program on Decision Tree.
Any other practical covering the syllabus topics and subtopics can
be conducted.shiwani gupta 5
6. REFERENCE BOOKS (Practicals)
1. Ivan Bratko "PROLOG Programming for Artificial Intelligence",
Pearson Education, Third Edition.
2. Elaine Rich and Kevin Knight "Artificial Intelligence "Third
Edition
3. Davis E.Goldberg, "Genetic Algorithms: Search, Optimization
and Machine Learning", Addison Wesley, N.Y., 1989.
4. Han Kamber, “Data Mining Concepts and Techniques”,
Morgann Kaufmann Publishers. Text Books:
shiwani gupta 6
7. TEXT BOOKS
1. Stuart J. Russell and Peter Norvig, "Artificial Intelligence A
Modern Approach “Second Edition" Pearson Education.
2. Saroj Kaushik “Artificial Intelligence” , Cengage Learning.
3. George F Luger “Artificial Intelligence” Low Price Edition ,
Pearson Education., Fourth edition.
shiwani gupta 7
8. REFERENCE BOOKS
1. Ivan Bratko “PROLOG Programming for Artificial Intelligence”,
Pearson Education, Third Edition.
2. Elaine Rich and Kevin Knight “Artificial Intelligence” Third
Edition
3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization
and Machine Learning”, Addison Wesley, N.Y., 1989.
4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE
Learning, India Edition.
5. Patrick Henry Winston , “Artificial Intelligence”, Addison-
Wesley, Third Edition.
6. Han Kamber, “Data Mining Concepts and Techniques”,
Morgann Kaufmann Publishers.
7. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”,
Oxford University Press.
shiwani gupta 8
10. • Introduction
• History of Artificial Intelligence
• Intelligent Systems: Categorization of Intelligent
System
• Components of AI Program
• Foundations of AI
• Sub-areas of AI
• Applications of AI
• Current trends in AI
shiwani gupta 10
12. shiwani gupta 12
Can Computers beat Humans at Chess?
Chess Playing is a classic AI problem
– well-defined problem
– very complex: difficult for humans to play well
Human World Champion
Deep Blue
Deep Thought
PointsRatings
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
1966 1971 1976 1981 1986 1991 1997
Ratings
13. shiwani gupta 13
Can Computers Talk?
• This is known as “speech synthesis”
– translate text to phonetic form
• e.g., “fictitious” -> fik-tish-es
– use pronunciation rules to map phonemes to actual sound
• e.g., “tish” -> sequence of basic audio sounds
• Difficulties
– sounds made by this “lookup” approach sound unnatural
– sounds are not independent
• e.g., “act” and “action”
• modern systems can handle this pretty well
– a harder problem is emphasis, emotion, etc
• humans understand what they are saying
• machines don’t; so they sound unnatural
• Conclusion:
– NO, for complete sentences
– YES, for individual words
14. shiwani gupta 14
Can Computers Recognize Speech?
• Speech Recognition:
– mapping sounds from a microphone into a list of words
eg. A deaf human
• Recognizing single words from a small vocabulary
• systems can do this with high accuracy (order of 99%)
• e.g., directory inquiries
– limited vocabulary (area codes, city names)
– computer tries to recognize you first, if unsuccessful hands
you over to a human operator
– saves millions of dollars a year for the telephone companies
15. shiwani gupta 15
• Recognizing normal speech is much more difficult
– speech is continuous: where are the boundaries between words?
• e.g., “John’s car has a flat tire”
– large vocabularies
• can be many thousands of possible words
• we can use context to help figure out what someone said
e.g., hypothesize and test
– try telling a waiter in a restaurant:
“I would like some sugar in my coffee”
– background noise, other speakers, accents, colds, etc
– on normal speech, modern systems are only about 60-70%
accurate
• Conclusion:
– NO, normal speech is too complex to accurately recognize
– YES, for restricted problems (small vocabulary, single speaker)
16. shiwani gupta 16
Can Computers Understand speech?
• Understanding is different to recognition:
– “Where is the water?”
• assume the computer can recognize all the words
• how many different interpretations are there?
– 1. in chemistry lab, it must be pure
– 2. when you are thirsty, it must be potable
– 3. dealing with a leaky roof, it can be filthy
but how could a computer figure this out?
– clearly humans use a lot of implicit commonsense
knowledge in communication
• Conclusion: NO, much of what we say is beyond the capabilities of a
computer to understand at present
17. shiwani gupta 17
Can Computers Learn and Adapt ?
• Learning and Adaptation
– consider a computer learning to drive on the freeway
– we could teach it lots of rules about what to do and what not to do
– or we could let it drive and steer it back on course when it heads for the
embankment
• systems like this are under development (e.g., Daimler Benz)
• e.g., RALPH at CMU
– in mid 90’s it drove 98% of the way from Pittsburgh to San Diego
without any human assistance
– machine learning allows computers to learn to do things without explicit
programming
– many successful applications require some “set-up”: does not mean your
PC can learn to forecast the stock market or become a brain surgeon
• Conclusion: YES, computers can learn and adapt, when presented with
information in the appropriate way
18. shiwani gupta 18
• Recognition vs Understanding (like Speech)
– Recognition and Understanding of Objects in a scene
• look around this room
• you can effortlessly recognize objects
• human brain can map 2d visual image to 3d “map”
• Why is visual recognition a hard problem?
Conclusion:
– mostly NO: computers can only “see” certain types of objects
under limited circumstances
– YES for certain constrained problems (e.g. face recognition)
Can Computers “see”?
19. shiwani gupta 19
Can computers plan and make optimal
decisions?• Intelligence
– involves solving problems and making decisions and plans
– e.g. you want to take a holiday in Brazil
• you need to decide on dates, flights
• you need to get to the airport, etc
• involves a sequence of decisions, plans, and actions
• What makes planning hard?
– the world is not predictable:
• your flight is canceled or there’s a backup
– there are a potentially huge number of details
• do you consider all flights? all dates?
– no: commonsense constrains your solutions
– AI systems are only successful in constrained planning problems
• Conclusion: NO, real-world planning and decision-making is still beyond the
capabilities of modern computers
exception: very well-defined, constrained problems
20. shiwani gupta 20
Summary of State of AI Systems in Practice
• Speech synthesis, recognition and understanding
– very useful for limited vocabulary applications
– unconstrained speech understanding is still too hard
• Computer vision
– works for constrained problems (hand-written zip-codes)
– understanding real-world, natural scenes is still too hard
• Learning
– adaptive systems are used in many applications: have their limits
• Planning and Reasoning
– only works for constrained problems: e.g., chess
– real-world is too complex for general systems
• Overall
– many components of intelligent systems are “doable”
– there are many interesting research problems remaining
22. shiwani gupta 22
Evolution / History of AI
• The gestation of artificial intelligence (1943-1956)
• Early enthusiasm, great expectations (1952-1969)
• A dose of reality (1966-1974)
• Knowledge-based systems: The key to power? (1969-
1979)
• AI becomes an industry (1980-1988)
• The return of neural networks (1986-present)
• Recent events (1987-present)
23. shiwani gupta 23
• 1943 Warren McCulloch & Walter Pitts: Boolean circuit model of
brain
• 1949 Donald Hebb discovered how to set connection strengths b/w
neurons
• 1950 Turing's "Computing Machinery and Intelligence“- Turing Test
(the imitation game)
• 1950 Marvin Minsky and Dean Edmonds built first neural net
computer (Snarc)
• 1950s Early AI programs, including Samuel's checkers program,
Newell & Simon's Logic Theorist, Gelernter's Geometry
Engine
• 1952 Arthur Samuel-checkers programs; learned how to improve,
quickly eclipsing Samuel himself
• 1952-69 Look, Ma, no hands! (first toy eg. with lots of enthusiasm)
• 1956 Dartmouth workshop: "Artificial Intelligence" adopted
• 1958 LISP from MIT by John McCarthy
• 1959 Hebert Gelernter (Geometry Theorem Prover)
• 1963 James Slagle-Saint solved basic integration problems.
Evolution / History of AI
24. shiwani gupta 24
• 1963 McCarthy founds AI lab at Stanford
• 1965 Robinson's complete algorithm for logical reasoning
• 1966-74 AI discovers computational complexity
1966-74 Neural network research almost disappears after Minsky and
Papert’s book in 1969
• 1967 Daniel Bobrow-Student solved algebra story problems
• 1969 DENDRAL by Buchanan et al..
• 1976 MYCIN by Edward Shortliffle in early 1970s.
• 1979 PROSPECTOR by Duda et al..
• 1980-88 Expert systems are a major industry
• 1981 Japan’s 10 year 5th generation project
• Mid 1980s Backpropogation learning algorithm reinvented
• 1985-95 Neural networks resurface connectionism turn to popularity
• 1988- Probability enters into general use
• 1988 Novel AI (ALife, Gas, soft computing,…)
• 1995- The emergence of intelligent agents as part of internet boom
• 2003- Human level AI back as topic of study
Evolution / History of AI
25. Defining AI
The branch of computer science concerned with
making computers behave like humans.
Study of agents that exist in an environment and
perceive and act.
AI strives to build intelligent entities and understand
them.
The term was coined in 1956 by John McCarthy at
the Massachusetts Institute of Technology.
26. shiwani gupta 26
"The automation of activities that we
associate with human thinking, activities such
as decision-making, problem solving, learning
..."(Bellman, 1978)
"The study of mental faculties through the
use of computational models“ (Charniak and
McDermott, 1985)
"The exciting new effort to make computers
think . . . machines with minds, in the full and
literal sense" (Haugeland, 1985)
"A field of study that seeks to explain and
emulate intelligent behavior in terms of
computational processes" (Schalkoff, 1990)
"The art of creating machines that perform
functions that require intelligence when
performed by people" (Kurzweil, 1990)
"The study of how to make computers do
things at which, at the moment, people are
better" (Rich and Knight, 1991)
"a collection of algorithms that are
computationally tractable, adequate
approximations of intractably specified
problems" (Partridge, 1991)
"the field of computer science that studies
how machines can be made to act
intelligently“ (Jackson, 1986)
"the getting of computers to do things that
seem to be intelligent" (Rowe, 1988).
"a very general investigation of the nature of
intelligence and the principles and
mechanisms required for understanding or
replicating it" (Sharpies et ai, 1989)
"a field of study that encompasses
computational techniques for performing
tasks that apparently require intelligence
when performed by humans" (Tanimoto,
1990)
"The study of the computations that make it
possible to perceive, reason, and act“
(Winston, 1992)
"The branch of computer science that is
concerned with the automation of intelligent
behavior" (Luger and Stubblefield, 1993)
"the enterprise of constructing a physical
symbol system that can reliably pass the
Turing Test" (Ginsberg, 1993)
27. shiwani gupta 27
Overview of AI
• Artificial intelligence: Computers with the ability to mimic or duplicate
the functions of the human brain
• Artificial intelligence systems: The people, procedures, hardware,
software, data, and knowledge needed to develop computer systems
and machines that demonstrate the characteristics of intelligence
• Intelligent behavior
– Learn from experience
– Apply knowledge acquired from experience
– Handle complex situations
– Solve problems when important information is missing
– Determine what is important
– React quickly and correctly to a new situation
– Understand visual images
– Process and manipulate symbols
– Be creative and imaginative
– Use heuristics
28. shiwani gupta 28
Major Branches of AI
Perceptive system
• A system that approximates the way a human sees, hears, and feels objects
Vision system
• Capture, store, and manipulate visual images and pictures
Robotics
• Mechanical and computer devices that perform tedious tasks with high
precision
Expert system
• Stores knowledge and makes inferences
Learning system
• Computer changes how it functions or reacts to situations based on feedback
Games playing
• Programming computers to play games such as chess and checkers
Natural language processing
• Computers understand and react to statements and commands made in a
“natural” language, such as English
Neural network
• Computer system that can act like or simulate the functioning of the human
brain
30. shiwani gupta 30
HAL: from the movie 2001
‘2001: A Space Odyssey” epic science fiction movie in
1968
part of the story centers around an intelligent computer
called HAL 9000
HAL is the “brains” of an intelligent spaceship
in the movie, HAL can
• speak easily with the crew
• see and understand the emotions of the crew
• navigate the ship automatically
• diagnose on-board problems
• make life-and-death decisions
• display emotions
In 1968 this was science fiction: is it still science fiction?
31. AI in Movies
• The Avengers: Age of Ultron (2015)
• Chappie (2015)
• Ex Machina (2015)
• Paul Blart: Mall Cop 2 (2015)
• Ash in Alien (1979)
• Bishop in Aliens (1986)
• Roy Batty in 'Blade Runner' (1982)
• WOPR in 'WarGames' (1983)
• Skynet in 'The Terminator' Series (1984 - 2015)
• Data in 'Star Trek: First Contact' (1996)
• Agent Smith in 'The Matrix' (1999)
• David in 'A.I.: Artificial Intelligence' (2001)
• Gerty in 'Moon' (2009)
• Samantha in 'Her' (2013)
shiwani gupta 31
32. shiwani gupta 32
Most common languages for AI
• LISP- HLL. Features of the language that are good for AI programming include:
garbage collection, dynamic typing, functions as data, uniform syntax, interactive
environment, and extensibility.
• PROLOG- This language wins 'cool idea' competition. It wasn't until the 70s that
people began to realize that a set of logical statements plus a general theorem
prover could make up a program. Prolog combines the high-level and traditional
advantages of Lisp with a built-in unifier, which is particularly useful in AI. Prolog
seems to be good for problems in which logic is intimately involved, or whose
solutions have a succinct logical characterization. Its major drawback is that it's hard
to learn.
• C/C++- The speed demon of the bunch, C/C++ is mostly used when the program is
simple, and execution speed is the most important. Statistical AI techniques such as
neural networks are common examples of this. Back propagation is only a couple of
pages of C/C++ code, and needs every ounce of speed that the programmer can
master.
• Java- The newcomer, Java uses several ideas from Lisp, most notably garbage
collection. Its portability makes it desirable for just about any application, and it has a
decent set of built in types. Java is still not as high-level as Lisp or Prolog, and not
as fast as C, making it best when portability is paramount.
• Python- This language does not have widespread acceptance yet, but several
people have suggested to me that it might end up passing Java soon. According to
Peter Norvig, "Python can be seen as either a practical (better libraries) version of
Scheme, or as a cleaned-up (no $@&%) version of Perl."
35. shiwani gupta 35
Views of AI
(understand and build intelligent entities)
The area of CS that focuses on creating machines that can
engage on behaviors that human consider intelligent
Thinking humanly Thinking rationally
Acting humanly Acting rationally
Modern AI focuses on acting rationally
36. Systems that think like humans:
cognitive modeling
• Humans as observed from ‘inside’
• How do we know how humans think?
– Introspection vs. psychological
experiments
• Cognitive Science
• “The exciting new effort to make computers
think … machines with minds in the full and
literal sense” (Haugeland)
• “[The automation of] activities that we
associate with human thinking, activities such
as decision-making, problem solving, learning
…” (Bellman)
37. shiwani gupta 37
Acting (doing) humanly:
“The Turing Test Approach”
• “The study of how to make computers do things at which, at the
moment, people are better.” (Rich and Knight)
• Alan Mathison Turing (1912-1954)
• A.M. Turing Award…..ACM's most prestigious technical award
is accompanied by a prize of $250,000. It is given to an
individual selected for contributions of a technical nature made
to the computing community. Financial support of the Turing
Award is provided by the Intel Corporation and Google Inc.
• Suggested major components of AI: Natural language
processing; Knowledge Representation; Automated reasoning;
Machine Learning
• Predicted that by 2000, a machine might have a 30% chance of
fooling a lay person for 5 minutes
38. shiwani gupta 38
The imitation game: “Computing machinery and intelligence”
devised by Alan Turing in 1950 defines intelligent behavior as the
ability to achieve human level performance in all cognitive tasks,
sufficient to fool an interrogator.
Test passes if human interrogator cannot distinguish AI system
from human when interrogated via a teletype (a computer
keyboard and screen) 70% of the time
Original Turing Test abstracts out physical interaction
Total Turing Test adds Computer Vision (perception) and Robotics
(manipulation)
39. shiwani gupta 39
Systems that act like humans
• natural language processing to enable it to
communicate successfully in English (or some
other human language)
• knowledge representation to store information
provided before or during the interrogation
• automated reasoning to use the stored
information to answer questions and to draw
new conclusions
• machine learning to adapt to new
circumstances and to detect and extrapolate
patterns.
40. shiwani gupta 40
ELIZA
(one of the first chatterbots in existence)
ELIZA was a computer program and an early example of primitive
natural language processing.
ELIZA operated by processing users' responses to scripts, the
most famous of which was DOCTOR.
In this mode, ELIZA mostly rephrased the user's statements as
questions and posed those to the 'patient.'
ELIZA was written by Joseph Weizenbaum between 1964 to
1966.
In DOCTOR mode, ELIZA might respond to "My head hurts" with
"Why do you say your head hurts?"
The response to "My mother hates me" would be "Who else in
your family hates you?"
ELIZA was implemented using simple pattern matching
techniques, but was taken seriously by several of its users,
even after Weizenbaum explained to them how it worked.
41. shiwani gupta 41
Reverse Turing Test
• STANDARD TURING TEST: judge is human
• REVERSE TURING TEST: judge is computer
The challenge would be for the computer to be able to determine
if it were interacting with a human or another computer.
• CAPTCHA is a form of reverse Turing test. Before being
allowed to perform some action on a website, the user is
presented with alphanumerical characters in a distorted graphic
image and asked to type them out. This is intended to prevent
automated systems from being used to abuse the site.
The rationale is that software sufficiently sophisticated to read and
reproduce the distorted image accurately does not exist (or is
not available to the average user), so any system able to do so
is likely to be a human.
42. shiwani gupta 42
Thinking rationally (ideally):
“The Laws of Thought Approach"
• Humans are not always ‘rational’
• Logic can’t express everything (e.g. uncertainty)
• Aristotle was one of the first to attempt to codify “right
thinking”, i.e., irrefutable reasoning processes.
– Given correct premises; his syllogisms gave correct
conclusions
– eg. Socrates is a man; all men are mortal. → Socrates is
mortal.
• Formal logic provides a precise notation and rules for
representing and reasoning with all kinds of things in the
world.
• What is the purpose of thinking? What thought should I have
and what thought could I have?
43. shiwani gupta 43
Laws of thought Approach emphasizes on correct inferences
Obstacles:
• it is not easy to take informal knowledge and state it in formal
terms, particularly when the knowledge is less than 100%
certain.
• there is a big difference between being able to solve a problem
"in principle" and doing so in practice. Even problems with just
a few dozen facts can exhaust the computational resources of
any computer unless it has some guidance as to which
reasoning steps to try first.
44. Systems that act rationally:
“Rational agent”
• Rational behavior: doing the right thing
• The right thing: that which is expected to
maximize goal achievement, given the available
information
• Giving answers to questions is ‘acting’.
45. Rational agents
An agent is an entity that perceives and acts
This course is about designing rational agents
For any given class of environments and
tasks, we seek the agent (or class of agents)
with the best performance
computational limitations make perfect
rationality unachievable
→ design best program for given machine
resources
46. From the above two definitions, we can see
that AI has two major roles:
– Study the intelligent part concerned with
humans.
– Represent those actions using computers.
47. shiwani gupta 47
Acting rationally (ideally):
The Rational Agent Approach
Acting so as to achieve one’s goals, given one’s beliefs (assuming
them to be correct). Does not necessarily involve thinking.
• Advantages
– More general than the “laws of thought” approach.
– More amenable to scientific development than human
behavior or human thought based approaches.
• Problems
– Always doing the right thing is not possible in complicated
environments
– Computational demands are just too high
A basic agent is just something that perceives and acts
48. shiwani gupta 48
• Cognitive skills required:
– Ability to represent knowledge and reason with it
– Ability to generate comprehensible sentences in natural
language
– Ability to perceive to get better idea of what an action might
achieve
• Rational agent: doing the right thing ( that which is expected to
achieve best expected outcome, given the available
information)
Doesn't necessarily involve thinking – e.g., blinking reflex –
but thinking should be in the service of rational action
Requires same skills as for Turing test and act even when
no provably correct way to act
Are broader in scope than previous ones.
Aristotle Every act and every enquiry, and similarly every
action and pursuit, is thought to aim at some good.
50. Prof Saroj Kaushik 50
Components of AI Program
• AI techniques must be independent of
the problem domain as far as possible.
• AI program should have
– knowledge base
– navigational capability
– inferencing
51. 51
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)
52. 52
Navigational Capability
• Navigational capability contains
various control strategies
• Control Strategy
– determines the rule to be applied
– some heuristics (thump rule) may be
applied
55. shiwani gupta 55
AI Foundation / Pre-History
o Philosophy(428 B.C.- present)
• Logic (no formal expression)
• Aristotle first to formulate a precise set of rules of rational
derivation
• methods of reasoning… A dog is an animal, all animals
have four legs → all dogs have four legs
• The emergence of intelligence in a physical brain
• Foundations of learning language and rationality
o Mathematics(c.800- present)
• Formal representation and proof
• Main areas: logic, computation and probability
• Logic: Mathematical Formulation
• Algorithms: First Euclid’s algorithm… calculate GCD
(analyze (un)decidability and (in)tractability)
• Probability theory: uncertainty in AI
56. shiwani gupta 56
AI Foundation / Pre-History
o Economics(1776- present)
• Formal theory for rational decision making
• The concept of utility
• Decision theory (expected utility)
• Game theory (distributed models)
• Markov Models (OR)
o Neuroscience(1861- present)
• Broca study of aphasia ca → functional areas in brain
• Models for memory
• Basic model for action generation
o Psychology(1879- present)
• Behaviorism- study only objective measures of percepts
• Cognitive psychology- cognitive science- adaptation
• Reasoning- action generation and derivation
57. shiwani gupta 57
AI Foundation / Pre-History
o Computer Engg.(1940- present)
• construction of efficient computers
• Languages for efficient implementation-
FORTRAN, LISP, PROLOG, BASIC, PASCAL,
C/C++, JAVA…
o Control theory and cybernetics(1948- present)
• Computer control of physical systems
• Basis for development of robotics, vision, language
processing
o Linguistics(1957- present)
• For understanding natural languages
• Formal languages
• Syntactic and semantic analysis
• Knowledge representation
59. Subareas of AI
shiwani gupta 59
Search
Vision
Planning
Machine
Learning
Knowledge
RepresentationLogic
Expert
SystemsRoboticsNLP
60. Prof Saroj Kaushik 60
Sub-areas of AI
– Knowledge representation
– Theorem proving
– Game playing
– Reasoning dealing with uncertainty and decision making
– Learning models, inference techniques, pattern recognition,
search and matching etc.
– Logic (fuzzy, temporal, modal) in AI
– Planning and scheduling
– Natural language understanding
– Computer vision
– Understanding spoken utterances
– Intelligent tutoring systems
– Robotics
– Machine translation systems
62. shiwani gupta 62
Applications of AI
• IBM supercomputer Deep Blue defeated the reigning world chess champion
Garry Kasparov (at grandmaster level) 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)
• ALVINN, grand challenge; cars can by now drive 200 km autonomously
• 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
• MEDICAL EXPERT SYSTEM in 1980 is the first expert level performance
diagnosis of blood infections, diabetes, muscle diseases
• CHESS examines 5 billion positions per second
• ROBOTIC races in desert and urban environments by fully autonomous
vehicles; succeeded
• 2006: face recognition software available in consumer cameras
63. AI Applications
• Autonomous Planning & Scheduling:
– Telescope scheduling
– Analysis of data
– Autonomous rovers
• Medicine:
– Image analysis and enhancement
– Image guided surgery
• Robotic toys:
• Games:
• Transportation:
– Autonomous vehicle control
69. shiwani gupta 69
AI APPLICATIONS: Machine Translation
• Language problems in international business
– e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no
common language
– or: you are shipping your software manuals to 127 countries
– solution; hire translators to translate
– would be much cheaper if a machine could do this
• How hard is automated translation
– very difficult! e.g., English to Russian
– “The spirit is willing but the flesh is weak” (English)
– “the vodka is good but the meat is rotten” (Russian)
– not only must the words be translated, but their meanings also!
– is this problem “AI-complete”?
• Nonetheless....
– commercial systems can do a lot of the work very well (e.g.,restricted
vocabularies in software documentation)
– algorithms which combine dictionaries, grammar models, etc.
– Recent progress using “black-box” machine learning techniques
71. Prof Saroj Kaushik 71
Latest Perception of AI
• Three typical components of AI Systems
THE WORLD
Perception Action
Reasoning
72. Prof Saroj Kaushik 72
Recent AI
• Heavy use of
– probability theory
– decision theory
– statistics
– logic (fuzzy, modal, temporal)
73. shiwani gupta 73
Intelligent Systems in Your Everyday Life
• Post Office
– automatic address recognition and sorting of mail
• Banks
– automatic check readers, signature verification systems
– automated loan application classification
• Customer Service
– automatic voice recognition
• The Web
– Identifying your age, gender, location, from your Web surfing
– Automated fraud detection
• Digital Cameras
– Automated face detection and focusing
• Computer Games
– Intelligent characters/agents
75. – more powerful and more useful computers
– new and improved interfaces
– solving new problems
– better handling of information
– relieves information overload
– conversion of information into knowledge
Some Advantages of Artificial
Intelligence
76. The Disadvantages
– increased costs
– difficulty with software development - slow
and expensive
– few experienced programmers
– few practical products have reached the
market as yet.
77. shiwani gupta 77
Question Bank
• Explain information, knowledge and intelligence
• What is AI? Explain components of AI with suitable eg. Or block
diagram.
• What do you mean by Intelligent agent? Explain various types.
State limitation of each and how is it overcome in other.
• Explain structure of intelligent agents that keep track of the
world.
• Describe environment simulator programs with performance
measure that can be used as test beds for agent programs.
• Consider vacuum cleaner problem and explain
➢ How is it rational? Which behavior will be irrational.
➢ Give success function. Explain performance measure.