1. Alan Turing was a pioneering computer scientist who made fundamental contributions to artificial intelligence, cryptography and more.
2. During WWII, Turing worked at Bletchley Park where he helped crack German codes and is credited with shortening the war by at least two years.
3. Turing introduced the concept of a Turing machine, which formed the basis for modern computers and demonstrated that a single machine can simulate any other machine. He also proposed the Turing test for machine intelligence.
The Turing test, developed by Alan Turing in 1950, is a test to determine if a machine can exhibit intelligent behavior equivalent to a human. It involves a questioner interrogating both a human and computer respondent without seeing them. If the questioner cannot reliably tell which is human and which is computer, the computer is said to have passed the Turing test. Alan Turing, a mathematician, computer scientist and cryptanalyst, invented the test to explore whether a computer could convincingly converse like a human.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
The document discusses artificial intelligence (AI) and defines it as the science and engineering of making intelligent machines, especially intelligent computer programs that have the abilities to learn, reason, perceive and understand language. It outlines several key AI technologies like machine learning, computer vision, natural language processing and speech recognition. It provides examples of applications in areas such as game playing, robotics, education, medical diagnosis and more. The document also gives a brief history of AI and discusses some programming languages commonly used in AI like Lisp.
This document discusses the Turing Test, which aims to determine if a machine can exhibit intelligent behavior that is indistinguishable from a human. It explores the outcomes of passing or failing the test, and whether those outcomes are justified. While the Turing Test has its limitations and does not encompass all types of intelligence, it has still inspired significant research in artificial intelligence and assessing machine behavior. The document concludes that the Turing Test, though not perfect, still provides a useful framework for categorizing machines and furthering the field of AI.
This document discusses artificial intelligence and its components. It begins with definitions of artificial intelligence as making computers behave like humans and as the intelligence exhibited by machines. The field was founded in 1956 at a conference where leaders like John McCarthy established AI research. The components discussed include playing games like chess, developing expert systems, natural language processing, and robotics. It provides examples of computers defeating humans at chess and the use of robots in manufacturing.
Presentation on artificial intelligenceKawsar Ahmed
This presentation provides an overview of artificial intelligence (AI) and how it works. It defines intelligence as the ability to learn from and interact with one's environment. Artificial intelligence is defined as making computers do intelligent tasks like humans. AI works using artificial neurons in artificial neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. Examples of AI applications include expert systems like PROSPECTOR for geology and PUFF for medicine diagnosis. Machine learning allows AI to mimic human intelligence by learning from failure, being told, or exploration. While human intelligence has intuition and creativity, AI can simulate human behavior, comprehend large data quickly, and preserve human expertise to achieve more than is known. AI is needed to
The document discusses Alan Turing and the Turing Test. It provides details on:
- Alan Turing created the Turing Test in the 1950s to determine if a computer can exhibit intelligent behavior equivalent to a human.
- The Turing Test involves an interrogator asking questions to both a human and computer to determine which is which based on their responses.
- In 2014, a computer program passed the Turing Test by convincing 33% of judges that it was a human during conversations, marking the first successful passing of the test.
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.
The Turing test, developed by Alan Turing in 1950, is a test to determine if a machine can exhibit intelligent behavior equivalent to a human. It involves a questioner interrogating both a human and computer respondent without seeing them. If the questioner cannot reliably tell which is human and which is computer, the computer is said to have passed the Turing test. Alan Turing, a mathematician, computer scientist and cryptanalyst, invented the test to explore whether a computer could convincingly converse like a human.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
This document provides an introduction to artificial intelligence. It discusses four views of AI: thinking like humans through cognitive modeling; acting like humans by passing the Turing test; thinking rationally through logical reasoning; and acting rationally by maximizing goals. The document also summarizes the history of AI from its foundations in philosophy, mathematics, and other fields to modern achievements like Deep Blue beating Kasparov at chess. It concludes with examples of state-of-the-art AI systems being used for logistics planning, spacecraft scheduling, and solving crossword puzzles.
The document discusses artificial intelligence (AI) and defines it as the science and engineering of making intelligent machines, especially intelligent computer programs that have the abilities to learn, reason, perceive and understand language. It outlines several key AI technologies like machine learning, computer vision, natural language processing and speech recognition. It provides examples of applications in areas such as game playing, robotics, education, medical diagnosis and more. The document also gives a brief history of AI and discusses some programming languages commonly used in AI like Lisp.
This document discusses the Turing Test, which aims to determine if a machine can exhibit intelligent behavior that is indistinguishable from a human. It explores the outcomes of passing or failing the test, and whether those outcomes are justified. While the Turing Test has its limitations and does not encompass all types of intelligence, it has still inspired significant research in artificial intelligence and assessing machine behavior. The document concludes that the Turing Test, though not perfect, still provides a useful framework for categorizing machines and furthering the field of AI.
This document discusses artificial intelligence and its components. It begins with definitions of artificial intelligence as making computers behave like humans and as the intelligence exhibited by machines. The field was founded in 1956 at a conference where leaders like John McCarthy established AI research. The components discussed include playing games like chess, developing expert systems, natural language processing, and robotics. It provides examples of computers defeating humans at chess and the use of robots in manufacturing.
Presentation on artificial intelligenceKawsar Ahmed
This presentation provides an overview of artificial intelligence (AI) and how it works. It defines intelligence as the ability to learn from and interact with one's environment. Artificial intelligence is defined as making computers do intelligent tasks like humans. AI works using artificial neurons in artificial neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. Examples of AI applications include expert systems like PROSPECTOR for geology and PUFF for medicine diagnosis. Machine learning allows AI to mimic human intelligence by learning from failure, being told, or exploration. While human intelligence has intuition and creativity, AI can simulate human behavior, comprehend large data quickly, and preserve human expertise to achieve more than is known. AI is needed to
The document discusses Alan Turing and the Turing Test. It provides details on:
- Alan Turing created the Turing Test in the 1950s to determine if a computer can exhibit intelligent behavior equivalent to a human.
- The Turing Test involves an interrogator asking questions to both a human and computer to determine which is which based on their responses.
- In 2014, a computer program passed the Turing Test by convincing 33% of judges that it was a human during conversations, marking the first successful passing of the test.
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.
This document provides an introduction to an Artificial Intelligence course. It outlines practical details like the course homepage and textbook. It then gives an overview of course topics including what AI is, problem solving, planning, learning, and communicating. It also provides a brief history of AI, discussing early work in neural networks and logic programming. It notes differences between Lisp and Scheme programming languages.
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
Artificial intelligence (AI) is the study and creation of intelligent machines and software. The document discusses the history and goals of AI, including how it was founded in the 1950s and experienced periods of increased and decreased funding. It also covers what intelligence is, definitions of artificial intelligence, tools and applications of AI in various industries, as well as the pros and cons of AI technology.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
First-order logic allows for more expressive power than propositional logic by representing objects, relations, and functions in the world. It includes constants like names, predicates that relate objects, functions, variables, logical connectives, equality, and quantifiers. Relations can represent properties of single objects or facts about multiple objects. Models represent interpretations of first-order logic statements graphically. Terms refer to objects as constants or functions. Atomic sentences make statements about objects using predicates. Complex sentences combine atomic sentences with connectives. Universal quantification asserts something is true for all objects, while existential quantification asserts something is true for at least one object.
The document discusses artificial intelligence and how it works. It defines artificial intelligence as making computers do intelligent tasks like humans. It discusses neural networks which are composed of artificial neurons that mimic biological neurons. The document also discusses machine learning approaches like failure driven learning, learning by being told, and learning by exploration. Examples of applications of AI are given, like expert systems used in geology and medicine. The key differences between human and artificial intelligence are noted.
Applications of artificial intelligence assiginment2Pal Neeraj
This document discusses various applications of artificial intelligence. It summarizes that AI is applied in games like chess, medical diagnosis, autonomous vehicles, scheduling, expert systems, robotics, language processing, translation, computer vision, e-commerce, and classification. Specific examples provided include Deep Blue defeating Kasparov at chess, medical diagnosis systems, Alvinn steering a vehicle autonomously, and AI assistants being used in e-commerce for tasks like recommendations and fraud detection.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] 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.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
The document provides a history of artificial intelligence, key figures in AI development, and examples of modern AI technologies. It discusses how the idea of AI originated in ancient Greece and how Alan Turing introduced the Turing test in 1937. Examples of modern AI include Sophia, a humanoid robot created by Hanson Robotics, and Rashmi, an Indian humanoid robot that can speak three languages. The document outlines advances in AI and its applications in fields such as military technology, space exploration, healthcare, and more.
The document discusses artificial intelligence, including how it works using artificial neurons and algorithms, its evolution and applications. It compares human and artificial intelligence, noting humans' advantages in intuition and creativity vs machines' abilities to process large data quickly and retain expertise. Major branches of AI are described like computer vision, robotics, and natural language processing. Examples of robots like ASIMO and AIBO are provided. The future of AI is discussed as achieving more than we understand.
Artificial intelligence plays a major role in digital marketing. There are different types of AI:
Reactive machines simply react to input with output without learning. Limited memory types can store previous data and predictions to make better forecasts. Theory of mind AI is beginning to interact with human thoughts and emotions, as seen in self-driving cars interacting with other drivers. The final type is hypothetical self-aware AI that could achieve independent intelligence and potential negotiation with humans.
Artificial intelligence is the field of computer science that deals with creating intelligent machines. It involves giving computers abilities like human intelligence such as understanding language, learning, reasoning and problem solving. The goals of AI are to build systems that exhibit intelligent behavior and to understand intelligence in order to model it. Some applications of AI discussed include smart cars that can recognize speech, provide navigation assistance and warn of hazards, as well as military robots that can operate in combat zones with little human supervision. Advantages of AI include machines being able to do complex, stressful and repetitive work faster than humans while disadvantages are that AI currently lacks human qualities and could potentially replace some human jobs.
Emerging trends in computer science and related technologiesSidraAfreen
This document discusses emerging trends in computer science and technology, including artificial intelligence, robotics, big data, cloud computing, cyber security, blockchain, bioinformatics, flying cars, and autonomous vehicles. It provides examples of each trend, such as how AI can be used for automated transportation and solving climate change. Robotics integrates computing, sensors, materials and AI to perform complex tasks. Big data deals with storing, processing, and analyzing massive amounts of data. Ensuring cyber security requires coordinating security efforts across information systems. Blockchain creates a distributed digital ledger to securely record transactions. Autonomous vehicles use sensors like radar and computer vision to navigate without human input.
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.
Conceptual Dependency (CD) is a theory that uses a set of primitive symbols to represent complicated knowledge and solve problems by graphically presenting high-level concepts. Various primitives used in CD include actions like transferring objects or information, moving body parts, grasping objects, ingesting objects, and speaking. An example CD representation shows "John ate the egg" using symbols like INGEST to represent the action of eating.
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.
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
The document discusses the Turing Test, a test proposed by Alan Turing in 1950 to determine if a machine can demonstrate intelligent behavior that is indistinguishable from a human. It describes the original imitation game format involving a judge communicating via written notes with a man and woman, one of which is actually a machine. Various variants of the Turing Test are presented, including the standard Turing Test, original imitation game, and reverse Turing Test where the machine acts as the judge.
This document provides an introduction to an Artificial Intelligence course. It outlines practical details like the course homepage and textbook. It then gives an overview of course topics including what AI is, problem solving, planning, learning, and communicating. It also provides a brief history of AI, discussing early work in neural networks and logic programming. It notes differences between Lisp and Scheme programming languages.
This document provides an introduction and overview of artificial intelligence (AI). It discusses the history of AI, including early programs in the 1950s-1960s and advances such as neural networks and deep learning. It defines AI and describes its goals such as reasoning, knowledge representation, planning, natural language processing, perception, and social intelligence. The document outlines two main categories of AI: conventional AI which uses symbolic and statistical methods, and computational intelligence which uses machine learning techniques like neural networks. It gives examples of applications such as pattern recognition, robotics, and game playing. Finally, it discusses related fields where AI is used such as automation, cybernetics, and intelligent control systems.
This presentation will give you a brief about the Artificial intelligence concept with the below-mentioned contents
- What is AI?
- Need for AI
- Languages used for AI development
- History of AI
- Types of AI
- Agents in AI
- How AI works
- Technologies of AI
- Application of AI
Artificial intelligence (AI) is the study and creation of intelligent machines and software. The document discusses the history and goals of AI, including how it was founded in the 1950s and experienced periods of increased and decreased funding. It also covers what intelligence is, definitions of artificial intelligence, tools and applications of AI in various industries, as well as the pros and cons of AI technology.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
First-order logic allows for more expressive power than propositional logic by representing objects, relations, and functions in the world. It includes constants like names, predicates that relate objects, functions, variables, logical connectives, equality, and quantifiers. Relations can represent properties of single objects or facts about multiple objects. Models represent interpretations of first-order logic statements graphically. Terms refer to objects as constants or functions. Atomic sentences make statements about objects using predicates. Complex sentences combine atomic sentences with connectives. Universal quantification asserts something is true for all objects, while existential quantification asserts something is true for at least one object.
The document discusses artificial intelligence and how it works. It defines artificial intelligence as making computers do intelligent tasks like humans. It discusses neural networks which are composed of artificial neurons that mimic biological neurons. The document also discusses machine learning approaches like failure driven learning, learning by being told, and learning by exploration. Examples of applications of AI are given, like expert systems used in geology and medicine. The key differences between human and artificial intelligence are noted.
Applications of artificial intelligence assiginment2Pal Neeraj
This document discusses various applications of artificial intelligence. It summarizes that AI is applied in games like chess, medical diagnosis, autonomous vehicles, scheduling, expert systems, robotics, language processing, translation, computer vision, e-commerce, and classification. Specific examples provided include Deep Blue defeating Kasparov at chess, medical diagnosis systems, Alvinn steering a vehicle autonomously, and AI assistants being used in e-commerce for tasks like recommendations and fraud detection.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] 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.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
The document provides a history of artificial intelligence, key figures in AI development, and examples of modern AI technologies. It discusses how the idea of AI originated in ancient Greece and how Alan Turing introduced the Turing test in 1937. Examples of modern AI include Sophia, a humanoid robot created by Hanson Robotics, and Rashmi, an Indian humanoid robot that can speak three languages. The document outlines advances in AI and its applications in fields such as military technology, space exploration, healthcare, and more.
The document discusses artificial intelligence, including how it works using artificial neurons and algorithms, its evolution and applications. It compares human and artificial intelligence, noting humans' advantages in intuition and creativity vs machines' abilities to process large data quickly and retain expertise. Major branches of AI are described like computer vision, robotics, and natural language processing. Examples of robots like ASIMO and AIBO are provided. The future of AI is discussed as achieving more than we understand.
Artificial intelligence plays a major role in digital marketing. There are different types of AI:
Reactive machines simply react to input with output without learning. Limited memory types can store previous data and predictions to make better forecasts. Theory of mind AI is beginning to interact with human thoughts and emotions, as seen in self-driving cars interacting with other drivers. The final type is hypothetical self-aware AI that could achieve independent intelligence and potential negotiation with humans.
Artificial intelligence is the field of computer science that deals with creating intelligent machines. It involves giving computers abilities like human intelligence such as understanding language, learning, reasoning and problem solving. The goals of AI are to build systems that exhibit intelligent behavior and to understand intelligence in order to model it. Some applications of AI discussed include smart cars that can recognize speech, provide navigation assistance and warn of hazards, as well as military robots that can operate in combat zones with little human supervision. Advantages of AI include machines being able to do complex, stressful and repetitive work faster than humans while disadvantages are that AI currently lacks human qualities and could potentially replace some human jobs.
Emerging trends in computer science and related technologiesSidraAfreen
This document discusses emerging trends in computer science and technology, including artificial intelligence, robotics, big data, cloud computing, cyber security, blockchain, bioinformatics, flying cars, and autonomous vehicles. It provides examples of each trend, such as how AI can be used for automated transportation and solving climate change. Robotics integrates computing, sensors, materials and AI to perform complex tasks. Big data deals with storing, processing, and analyzing massive amounts of data. Ensuring cyber security requires coordinating security efforts across information systems. Blockchain creates a distributed digital ledger to securely record transactions. Autonomous vehicles use sensors like radar and computer vision to navigate without human input.
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.
Conceptual Dependency (CD) is a theory that uses a set of primitive symbols to represent complicated knowledge and solve problems by graphically presenting high-level concepts. Various primitives used in CD include actions like transferring objects or information, moving body parts, grasping objects, ingesting objects, and speaking. An example CD representation shows "John ate the egg" using symbols like INGEST to represent the action of eating.
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.
Artificial Intelligence power point presentation documentDavid Raj Kanthi
This document provides a certificate for a seminar report on the topic of artificial intelligence. It was completed by a student in partial fulfillment of an M.C.A. degree program in 2016-2017. The document includes an acknowledgment, declaration, abstract, and index sections that provide information about the student, guide, and overall content covered in the seminar report on artificial intelligence.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
The document discusses the Turing Test, a test proposed by Alan Turing in 1950 to determine if a machine can demonstrate intelligent behavior that is indistinguishable from a human. It describes the original imitation game format involving a judge communicating via written notes with a man and woman, one of which is actually a machine. Various variants of the Turing Test are presented, including the standard Turing Test, original imitation game, and reverse Turing Test where the machine acts as the judge.
This paper describes BABAR, a knowledge extraction and representation system, completely implemented in CLOS, that is primarily geared towards organizing and reasoning about knowledge extracted from the Wikipedia Website. The system combines natural language processing techniques, knowledge representation paradigms and machine learning algorithms. BABAR is currently an ongoing independent research project that when sufficiently mature, may provide various commercial opportunities.
BABAR uses natural language processing to parse both page name and page contents. It automatically generates Wikipedia topic taxonomies thus providing a model for organizing the approximately 4,000,000 existing Wikipedia pages. It uses similarity metrics to establish concept relevancy and clustering algorithms to group topics based on semantic relevancy. Novel algorithms are presented that combine approaches from the areas of machine learning and recommender systems. The system also generates a knowledge hypergraph which will ultimately be used in conjunction with an automated reasoner to answer questions about particular topics.
This document discusses key concepts related to knowledge management, including ontology, epistemology, explicit vs tacit knowledge, and knowing-that vs knowing-how.
It explains that ontology is the study of what exists, while epistemology is the study of how knowledge is acquired and what can be known. There are two main epistemological perspectives - logical positivism which sees knowledge as objectively reflecting reality, and constructivism which sees knowledge as personally constructed.
The document also distinguishes between explicit knowledge which can be readily articulated and shared, tacit knowledge which is harder to articulate but provides context, knowing-that which is factual knowledge and knowing-how which is practical skill or procedural knowledge.
This document discusses the social media analysis solution space. It describes who the solution providers are (researchers, software, services), what they provide (social media analysis and analytics-infused advisory services), who they serve (business users), and how (through various technologies). The document also outlines some key business questions that social media analysis can help answer, and the different approaches taken by industry to work backwards from goals and insights to determine appropriate data, methods, and presentations.
This document discusses theories of knowledge representation in the mind. It describes how knowledge can be represented through mental images, words, or abstract propositions. The dual-coding theory proposes that knowledge uses both visual/pictorial and linguistic/verbal representations. Propositional theory suggests knowledge is represented through abstract propositions rather than images or words. The document also discusses mental imagery and ambiguous figures, which can challenge propositional representations and be open to multiple interpretations through reference frame manipulation.
7. knowledge acquisition, representation and organization 8. semantic network...AhL'Dn Daliva
This document discusses knowledge acquisition, representation, and organization. It describes the two types of knowledge - declarative and procedural - and five guidelines for knowledge acquisition. It also discusses theories of knowledge representation including rule-based production models, distributed networks, and propositional models. A key point is that semantic networks can be used to represent knowledge as a system of interconnected concepts. The document also discusses long-term memory and its two types - episodic and semantic memory. It describes cognitive semantic networks and models by Collins and Quillian as well as schema theory. Concept maps are discussed as a way to visualize relationships between concepts.
The document discusses various models of how knowledge is represented and organized in semantic memory. It describes semantic network models including feature comparison models, Collins and Quillian's network model of a hierarchical semantic structure, and spreading activation theory. It also discusses propositional models such as HAM and ACT-R that represent knowledge as propositions connected in a network.
Yuval Shahar, M.D., Ph.D.
Medical Informatics Research Center
Department of Information Systems Engineering
Ben-Gurion University
Beer Sheva, Israel
(16/10/08, Plenary session 3)
Artificial Intelligence is branch of computer science concerned with the study and creation of computer system that exhibits some form of intelligence.
Knowledge representation and Predicate logicAmey Kerkar
1. The document discusses knowledge representation and predicate logic.
2. It explains that knowledge representation involves representing facts through internal representations that can then be manipulated to derive new knowledge. Predicate logic allows representing objects and relationships between them using predicates, quantifiers, and logical connectives.
3. Several examples are provided to demonstrate representing simple facts about individuals as predicates and using quantifiers like "forall" and "there exists" to represent generalized statements.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Alan Turing was a pioneering British mathematician and logician in the early 20th century. He made fundamental contributions to mathematics, cryptanalysis, logic, philosophy, and computer science. He invented the concept of a universal machine and laid the foundations for modern computing by conceptualizing programmable, general-purpose computers. During World War II, Turing worked at Bletchley Park where he played a pivotal role in cracking the German Enigma codes, which accelerated the Allied victory. Despite his achievements, Turing was prosecuted for homosexuality and died in 1954 at the age of 41. He is now widely considered one of the most influential scientists in history.
Alan Turing was a pioneering computer scientist and mathematician in the early 20th century. He played a pivotal role in cracking the German Enigma code during World War II, which helped the Allies win the war. Turing is considered the father of computer science and his conceptualization of a universal machine laid the foundations for modern computers. However, he was prosecuted for his homosexuality in 1952 and died by suicide at age 42. His contributions helped enable future technologies like the iPhone and iPad.
The document discusses the Alan Turing Quiz held to celebrate Alan Turing's centenary. It provides context on Turing's work developing the Turing Test and other contributions to computer science such as LU decomposition. It asks multiple choice questions testing knowledge of Turing's life and accomplishments which participants in the quiz would have to answer.
Alan Turing and the Programmable Universe (lite version)piero scaruffi
Alan Turing, the cultural context of his world, and what would Turing say of today's high-tech world. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
This document summarizes the history of computers from the 1940s to the 1960s. It describes several important figures who contributed to early computer development including Alan Turing, John Vincent Atanasoff, Clifford Berry, Konrad Zuse, John von Neumann, Howard Aiken, Grace Hopper, Claude Shannon, and Douglas Engelbart. It provides details on some of the earliest computers such as the Z3, the Atanasoff–Berry Computer, the Harvard Mark I, Colossus, and ENIAC. It also discusses the development of programming languages, compilers, transistors, and human-computer interaction innovations like the computer mouse.
1) Alan Turing was a pioneering computer scientist and mathematician born in 1912 in London. He made fundamental contributions to computer science, including conceiving of the Turing machine and the concept of a universal machine.
2) During World War II, Turing worked at Bletchley Park where he played a pivotal role in cracking the German Enigma code, making major breakthroughs including developing the bombe machine.
3) After the war, Turing envisioned the idea of a general-purpose computer and the concept of stored programs. He worked on early computing projects but saw little of his ambitious ideas realized.
AI Artificial Intelligence1Reading responsePeter .docxoreo10
AI: Artificial Intelligence
1
Reading response
Peter Dormer, “Craft and the Turing Test for Practical Thinking,” in The Challenge of Technology.
What is personal know-how? What is distributed knowledge?
How do they relate to the Turing test?
Give one example of your own how these concepts matter today to artists and makers, or better yet, in your own experience?
Journal homework
Keep a record (text and drawings) of events in daily life where human and machine intersect and interact. Fill at least two pages with your observations.
Mary Shelley, Frankenstein, or The Modern Prometheus, 1818
Boris Karloff in Frankenstein in 1931 directed by James Whale
Mary Shelley first published Frankenstein, or the Modern Prometheus 1818. the novel allegorizes the Romantic obsession with discovering the power or principle of life. Ideas about a life power were consistent with the scientific understanding of the day. Darwin himself spoke of an organizing “spirit of animation” in his Zoonomia; or, The Laws of Organic Life, in which he stated “the world itself might have been generated, rather than created.”
Dr. Frankenstein picked all the parts for his monster based on their beauty, but when it comes to life, the monster is unbearably ugly. “I had worked hard for nearly two years, for the sole purpose of infusing life into an inanimate body…the beauty of the dream vanished, and breathless horror and disgust filled my heart. Unable to endure the aspect of the being I had created, I rushed out of the room”.
4
Two definitions of AI:
“The use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular.
--Margaret Boden
“The science of making machines do things that would require intelligence if done by humans.”
-Marvin Minsky
BOTH OF THESE STATEMENTS ORIGINATE IN ALAN TURING’S FIRST COMPUTER SCIENCE ARTICLE
Working assumption: all cognition is computable
Question:
Is what’s not yet known to be computable actually computable?
if so, then what?
if not, why not, and what does that tell us about cognition?
7
Who was Alan Turing?
B. 1912 London, attended King’s College, Cambridge and Princeton University. He studied mathematics and logic (he hadn’t invented computer science yet)
At 23, he invented the “Turing machine” and published “On Computable Numbers in 1936, the first and most important paper in comp. sci.
During WWII, solved the German Enigma code by use of electromechanical devices—a precursor to the computer
Laid the foundation for major subfields of comp sci: theory of computation, design of hardware and software, and the study of artificial intelligence
“The Imitation Game,”
aka
“The Turing Test”
In 1950, Turing posited a way to test machine intelligence: a person in a room before a screen. S/he would correspond with two agents and based on their responses, decide which was a machine and which was human. If the machine can pass fo.
SOME INSIGHTS FROM ALAN TURING’S ARTIFICIAL COGNITION RESEARCHIJCI JOURNAL
- Alan Turing is known for his work on computability and cryptography during WWII. In later years, he became interested in artificial intelligence and modeling biological processes.
- In 1948, he wrote an unpublished article called "Intelligent Machinery" where he outlined challenges for machine intelligence and proposed a new type of Turing machine (TM48) that could learn from experience and sensory input.
- He regularly met with British cyberneticists through the Ratio Club to discuss topics like self-organization. This influenced his work toward understanding cognition through biological and organic forms rather than just functional abstraction.
- His 1952 article on morphogenesis used mathematical models to explain how complex biological patterns and forms can emerge from simple local interactions,
SOME INSIGHTS FROM ALAN TURING’S ARTIFICIAL COGNITION RESEARCHIJCI JOURNAL
- Alan Turing's early work focused on mathematical logic and computability, while his later work examined the chemical basis of morphogenesis. This article argues that Turing's desire to develop "artificial cognition" and his involvement with the cybernetics-focused Ratio Club connected his two periods of research.
- After WWII, Turing worked to develop machines capable of human-like reasoning. His 1948 article "Intelligent Machinery" introduced concepts like organized vs. unorganized machinery and open vs. closed problems to address limitations of early computing machines.
- Turing's interactions with the Ratio Club exposed him to biological conceptions of self-organization and pattern formation, shifting his focus to understanding natural cognition through modeling
Alan turing - Life History & how he broke enigma code?Hariharan Ganesan
Alan Turing was a British mathematician who made pioneering contributions to computer science. During World War II, he worked at Bletchley Park cracking German codes transmitted by the Enigma machine. To help solve this problem faster than could be done by hand, Turing conceived of an electromechanical machine called the Bombe that could methodically search for potential code settings. Over 200 Bombes were built, allowing the Allies to decrypt thousands of messages per day and gain valuable intelligence. Turing's work is credited with shortening the war by as much as two years. He later did foundational work in computer science and artificial intelligence.
1. The document discusses the Turing test, which proposes determining if a machine can exhibit intelligent behavior indistinguishable from a human by having an interrogator question both the machine and human without seeing them.
2. It describes John Searle's Chinese room argument against the idea that running a computer program is sufficient for a machine to have a mind or understanding.
3. There is debate around whether strong AI, which claims computers could match or exceed human intelligence through algorithms, is possible or if intelligence requires aspects like consciousness that computers may lack.
Below you will find an outline for an informative speech.pdfstudy help
Alan Turing made important contributions to both computer science and World War II. He developed the theoretical model of computation known as the Turing machine, laying the foundations of computer science. During World War II, Turing worked to crack the German Enigma code. He built machines called bombes to search for patterns in encrypted messages and help decrypt them. Turing's work in breaking the Enigma code had a major impact and may have shortened the war by up to two years.
This document provides an overview of artificial intelligence, including definitions of AI, the history and development of AI, applications of AI such as robotics and neural networks, and methods used in AI like the Turing test, brute force searching, and heuristics. Key topics covered include Alan Turing's proposal of the Turing test to determine machine intelligence, the use of neural networks to simulate human brain functions, and applications of AI in areas like fingerprint identification, credit card fraud detection, and robotics.
The document discusses the organization and work of people at Bletchley Park during World War 2. Bletchley Park was a mansion outside London that was converted into a codebreaking facility. It was home to around 1000 people at the beginning of the war and around 7000 by the end. People at Bletchley Park included codebreakers like Alan Turing as well as administrative staff and women who operated machines. The site worked to decrypt encoded German messages sent using the Enigma machine.
This document summarizes Alan Turing's seminal 1950 paper "Computing Machinery and Intelligence" which proposed what is now known as the Turing Test. The Turing Test involves an interrogator determining which of two entities, a human or computer, they are communicating with via teletyped responses. Turing argued that if a computer could successfully pass as human, it should be considered thinking. The document outlines Turing's description of the "Imitation Game" protocol and responses to philosophical counterarguments against the possibility of machine thought. It concludes by noting the impact of Turing's work on artificial intelligence and philosophy of computing.
This document provides an overview of the history and current state of artificial intelligence. It discusses key events like the Dartmouth workshop in 1956 which is seen as the official birth of AI. The document also explores different applications of AI like in movies, news, and real world tasks. It discusses challenges for the future like ensuring AI is beneficial to humanity and aligned with human values and preferences.
Carl Gauss was a German mathematician born in 1777 who published foundational work in number theory and algebra that modern computing relies on. John von Neumann designed the architecture for modern computers in the 1930s and 1940s. Alan Turing developed the concept of a Turing machine and the Turing test for artificial intelligence. Benoit Mandelbrot discovered fractal geometry which is used in computer graphics and chip design. These mathematicians made discoveries that unlocked the modern world and underlie technologies we use today.
The document provides an overview of the history of artificial intelligence from its origins in the 1940s through modern applications. It discusses early pioneers like Alan Turing and his Turing test, as well as important early programs like the Logic Theorist. It then outlines the growth of the field through research centers in the 1950s and 1960s, the development of languages like LISP, and applications of AI in gaming. The summary concludes with a brief discussion of modern uses of AI in areas like robotics, the military, self-driving cars, and video games.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
2. Salient Points of the Presentation
A Brief History of Turing’s Life
Turing’s Contributions to Computer Science
Turing Machine
Turing Test
Turing Completeness(Programming Language)
ACE (Automated Computing Engine)
AI (Artificial Intelligence)
Artificial Life
Halting Problem
3. After the War he On his return He met his
During WWII, he demise on:
Alan Mathias worked at The
reformulated from the U.S,he
Turing: Born 23 Government Code Hilbert’s decision joined the 7 June
June 1912,Maida & Cipher School at problem and gave National 1954 (aged 4
Vale, London, Bletchley Park as a his own hypothesis Physical 1)
England, United Cryptanalyst. which is now know Laboratory, in Wilmslow
Kingdom. But was Where he invented as the Halting Cheshire,
Manchester,
conceived in the bombe to England,
Problem where he United
Chatrapur Odisha, crack the German
his father worked codes. He was There after,he developed the Kingdom,
for the Indian Civil placed in Hut – 8, worked under Automated
Cause of
Sevices.He the division Alonso Church at Computing death
attended responsible for Princeton and gave Engine(ACE),wh according to
Sherbone School Intercepting and the Church-Turing ich is a the reports is
in
Dorset,thereafter
breaking the hypothesis which primitive type cyanide
German gives a formal of Computer. poisoning.
enrolled in Kings codes.According to Thereafter, he
College and where some estimates his definition of This marks
“Turing Machine” became the end of an
he passed with a work reduced the
First Class honours war time in the interested in a era and the
in Mathematics Atlantic by at least subject called beginning of
“Artificial Life” a revolution.
two years.
4. A wartime hero in all respects, as he was instrumental in the defeat of the Germans in the
Atlantic.
Popularly known as Father of Computer Science, indeed true in all respects.
His turing machine formed the basis of the Modern Computer.
His reformulation of the Halting Problem,which was first introduced by Hilbert as the
“Entscheidungsproblem", later led to a set of problems known as NP complete.
The Turing Test served as the benchmark for recognizing an intelligent machine, this was
published in his 1936 paper, where he rigorously debated on the topic-”Can Machines
think”.
This paper set the ball rolling for the development of Artificial Intelligence and Machine
Learning.
Turing Complete- A term associated with programming languages provides the measure of
computing power of any given language.
He himself envisioned Computers one day beating Grandmasters at Chess.(which is today a
reality).
His contributions to Speech Cryptography is lesser known albeit he developed “Delilah” at
Bell Labs.
In his last years he was deeply interested in mathematical biology, especially finding patterns
in Chemical Morphogenesis.
Hence to sum it all up, his contribution to the development of Computer Science was akin to
that of the fire and wheel in the progress and survival of the human race.
5.
6. The Turing Machine which I call the “simple wonder”,
consists of a tape with symbols etched on them, and a head
which is restricted to move only forward and backward,
reading the symbols and performing necessary actions
according to a table of rules.
The most simple Turing machine can be approximated to
a DFA(Deterministic Finite Automata).The following example
will illustrate the same. 1/0
0
1
Start State Final State
7. A turing machine consists of a tape which is divided into cells,each cell has
an alphabet etched on it or it might be left blank, the alphabet can belong to
any “Formal Language” or a Regular Expression.”
A head which is capable of movement in the left and right directions and it
reads the symbols which are written on the tape.
A State Register which stores the state of the of the machine.
Then there is an action table, which stores the actions to be performed on
encountering a specific symbol or state.
Thus a complete turing machine can be thought of as a tuple of the
following:
{ Q , ∑ , δ , q0 , F }
Where:
Q : Finite Set of States
∑ : Finite Set of Input Symbols
δ : Transition State a function of f(Q,sigma)
q0 : The initial state or the start state
F : Final State or the Accept State.
8. The Universal Turing Machine: A
turing Machine which can mimic
any other turing machine is
known as the Universal Turing
Machine. In simple words it can
solve any problem which is posed
to it.
The Turing Oracle: It is a turing
Machine, which has an infinite
amount of tape length. Its is used
to solve decision problems,
though it fails in the halting
problem.
9. Can Machines Think???....
That was the same question that Turing asked in his 1950 paper titled
“Computing Machinery and Intelligence”. Rather than going into the details and
definitions of various terms in the question . He instead decided to reframe the
question as “Can machines Imitate humans”.
This was the most pertinent question which early researchers in the field of
Artificial Intelligence have asked and in this field Turing was the torch-bearer. His
paper countered all the objections raised against a machine thinking as intelligently
as humans.
This was when he presented the Turing Test which has now become the Litmus
test of Machine Intelligence. It was based on the ‘Imitation Game’ , infact there are
several versions of this the most important among them are ‘Imitation Game’ and
the ‘Standard Turing Test’.
Several machines have passed the turing test till date,first among them are
Joseph Weizenbaum’s “ELIZA” and Kenneth Colby’s “PARRY”.
Most recently there are several chatterbots existing.
IBM’s Watson is the most powerful and intelligent machine, till date which has
Natural Language Processing capabilities.It has beaten the all-time champions of
popular show Jeopardy,and is on it’s way to commercialization.
10. The Imitation Game: This is also known as
the party game,where there are two people
on man and a woman and a third person
who is the interrogator. The Interrogator can
only interact with the two people through
text , that is letters. The man tries to trick
the interrogator into believing that he is a
woman and the woman tries to befool the
interrogator that she is a man. The
interrogator must recognize the truth
behind the messages. In turing’s version of
the game either the man or the woman is
replaced by a digital computer.
The Standard Turing Test: In this test
the differences between the sexes is
dissolved , there is a computer and a
person, and an interrogator. The
Computer tricks the
Interrogator into believing that it is a
human . If it passes then it is Intelligent.
11. Reverse Turing Test: This is where a computer decides for itself where it interacts with a
human or not. CAPTCHA’s are examples of this.Though breaking the CAPTCHA code is quite easy
with artificial Neural Networks.
The Loebner Prize : Its is an annual competition held at Center for Behavioral Studies in
Cambridge , Massachusetts (U.S). This is where various intelligent machines try to deceive the
judges and since its inception in the year 1991 it has received , overwhelming participation from
various corners of the world. Though there is enough scepticism regarding the rules and the
procedure of testing , most consider this prestigious award as the recognition of their machine’s
intelligence.
Drawbacks: The most significant one raised by John Searle which states that the Turing Test
doesnot take into account the Mental State and the previous knowledge of the interrogator
,that is whether the interrogator knows he is testing a Computer or not. Then there are
quintessential human emotions like greed & avarice, rancour & jealously. Tenacity, and most
importantly Imagination the brainchild of human survival and the harbinger of innovation which
machines lack.
12.
13. 1. In Computability theory a system of data-manupulation rules
which can simulate any single taped turing machine is known as
Turing Complete.
2. In practice, Turing completeness means that rules followed in
sequence on arbitrary data can produce the result of any
calculation. In imperative languages, this can be satisfied by
having, at a minimum, conditional branching (e.g., an "if" and
"goto" statement) and the ability to change
arbitrary memory locations (e.g., having variables). To show that
something is Turing complete, it is enough to show that it can be
used to simulate the most primitive computer, since even the
simplest computer can be used to simulate the most complicated
one.
3. Thus , Turing Completeness is a measure of the computational
power of any given programming language.
15. 1. The Automatic Computing Engine (ACE) was an
early electronic stored-program computer design produced
by Alan Turing at the invitation of John R. Womersley,
superintendent of the Mathematics Division of the National
Physical Laboratory (NPL). It was based on turing’s work at
Bletchley Park , where he designed the Colossus Computers for
breaking the German Codes.
2. Turing’s draft on the construction of the Engine stated that “It
should use stored program in its memory”, he had laid out the
detailed logic diagrams and the circuits for construction of the
Computer.
3. The Pilot Model of the ACE was developed and soon after John
Von Nuemann proposed his design for the EDVAC which was
based on turing work and this is the same architecture that is
used in the Modern Computers.
16.
17. Navigating through the Intelligence…
I. Artificial intelligence (AI) is the intelligence of machines and the
branch of computer science that aims to create it. AI textbooks
define the field as "the study and design of intelligent
agents" where an intelligent agent is a system that perceives its
environment and takes actions that maximize its chances of
success. John McCarthy, who coined the term in 1955, defines it as
"the science and engineering of making intelligent machines.“
II. The field of AI research was founded at a conference on the
campus of Dartmouth College in the summer of 1956.The
attendees, including John McCarthy, Marvin Minsky, Allen
Newell and Herbert Simon, became the leaders of AI research for
many decades. They and their students wrote programs that were,
to most people, simply astonishing.
18. Continued…
The field of AI has seen its ups and downs, it has seen the oft termed
“AI winter” in the 70’s and the early 90’s.
But today it is soaring high and has brought almost all the
Computational fields under its ambit.
Popular applications of AI include : “Data Mining”, “Search
Optimization”, “Social Intelligence”, “Neural Networks”, “Logic”.
Google’s Search Engine uses AI to optimize your search queries.
Robotics which is soon going to a significant game-changer has AI as
its cornerstone.
Examples of AI based systems include:
IBM’s chess playing Deep Blue, IBM’s Watson,
DARPA Grand Challenge.
HOAP 2
20. Turing’s Contribution…
Turing worked from 1952 until his death in 1954
on mathematical biology, specifically morphogenesis. He
published one paper on the subject called The Chemical Basis
of Morphogenesis in 1952, putting forth the Turing
hypothesis of pattern formation. His central interest in the
field was understanding Fibonacci phyllotaxis the existence of
Fibonacci numbers in plant structures. He used reaction–
diffusion equations which are central to the field of pattern
formation. Later papers went unpublished until 1992 when
Collected Works of A.M. Turing was published. His
contribution is considered a seminal piece of work in this
field.
21.
22. The Problem Statement is : Can there ever be a program which
can predict whether another program will halt on a specific input.
DOES-HALT(program,input) // halts if the program goes into an infinte
loop else does-not halt.
SELF-HALT(program) {
if (DOES-HALT(program,program)) {
infinite loop
} else {
halt
}
}
Now we give the input to the function self-halt as follows:
SELF-HALT(SELF-HALT)
What happens….????
23. A A A
Given above is a buffer gate which consists of two NOT gates .
As is evident since it undergoes a double inversion we get the output same as
the input. A turing Machine can be roughly compared to a NOT gate for the
sake of simplicity and understanding and not for any real implications. In
reality the turing machine is a DFA(Deterministic Finite Automata), that is its
states are fixed and predetermined and can transition to only one state on a
single input. Here we are trying to create a parallelism between the Buffer
gate and the Halting Problem for a better and much easier understanding of
the latter. Similar to the Buffer Gate the Output of one turing machine when
fed as the Input of another equivalent turing machine should yield the original
input but in reality it doesnot.This is the paradox and using this we can prove
that such a machine cannot exist which can predict whether a program will
stop or not