AI – Terminology

Artificial Intelligence – Terminology ”; Previous Next Here is the list of frequently used terms in the domain of AI − Sr.No Term & Meaning 1 Agent Agents are systems or software programs capable of autonomous, purposeful and reasoning directed towards one or more goals. They are also called assistants, brokers, bots, droids, intelligent agents, and software agents. 2 Autonomous Robot Robot free from external control or influence and able to control itself independently. 3 Backward Chaining Strategy of working backward for Reason/Cause of a problem. 4 Blackboard It is the memory inside computer, which is used for communication between the cooperating expert systems. 5 Environment It is the part of real or computational world inhabited by the agent. 6 Forward Chaining Strategy of working forward for conclusion/solution of a problem. 7 Heuristics It is the knowledge based on Trial-and-error, evaluations, and experimentation. 8 Knowledge Engineering Acquiring knowledge from human experts and other resources. 9 Percepts It is the format in which the agent obtains information about the environment. 10 Pruning Overriding unnecessary and irrelevant considerations in AI systems. 11 Rule It is a format of representing knowledge base in Expert System. It is in the form of IF-THEN-ELSE. 12 Shell A shell is a software that helps in designing inference engine, knowledge base, and user interface of an expert system. 13 Task It is the goal the agent is tries to accomplish. 14 Turing Test A test developed by Allan Turing to test the intelligence of a machine as compared to human intelligence. Print Page Previous Next Advertisements ”;

AI – Intelligent Systems

Artificial Intelligence – Intelligent Systems ”; Previous Next While studying artificially intelligence, you need to know what intelligence is. This chapter covers Idea of intelligence, types, and components of intelligence. What is Intelligence? The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations. Types of Intelligence As described by Howard Gardner, an American developmental psychologist, the Intelligence comes in multifold − Intelligence Description Example Linguistic intelligence The ability to speak, recognize, and use mechanisms of phonology (speech sounds), syntax (grammar), and semantics (meaning). Narrators, Orators Musical intelligence The ability to create, communicate with, and understand meanings made of sound, understanding of pitch, rhythm. Musicians, Singers, Composers Logical-mathematical intelligence The ability of use and understand relationships in the absence of action or objects. Understanding complex and abstract ideas. Mathematicians, Scientists Spatial intelligence The ability to perceive visual or spatial information, change it, and re-create visual images without reference to the objects, construct 3D images, and to move and rotate them. Map readers, Astronauts, Physicists Bodily-Kinesthetic intelligence The ability to use complete or part of the body to solve problems or fashion products, control over fine and coarse motor skills, and manipulate the objects. Players, Dancers Intra-personal intelligence The ability to distinguish among one’s own feelings, intentions, and motivations. Gautam Buddhha Interpersonal intelligence The ability to recognize and make distinctions among other people’s feelings, beliefs, and intentions. Mass Communicators, Interviewers You can say a machine or a system is artificially intelligent when it is equipped with at least one and at most all intelligences in it. What is Intelligence Composed of? The intelligence is intangible. It is composed of − Reasoning Learning Problem Solving Perception Linguistic Intelligence Let us go through all the components briefly − Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types − Inductive Reasoning Deductive Reasoning It conducts specific observations to makes broad general statements. It starts with a general statement and examines the possibilities to reach a specific, logical conclusion. Even if all of the premises are true in a statement, inductive reasoning allows for the conclusion to be false. If something is true of a class of things in general, it is also true for all members of that class. Example − “Nita is a teacher. Nita is studious. Therefore, All teachers are studious.” Example − “All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother.” Learning − It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study. The ability of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as − Auditory Learning − It is learning by listening and hearing. For example, students listening to recorded audio lectures. Episodic Learning − To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly. Motor Learning − It is learning by precise movement of muscles. For example, picking objects, Writing, etc. Observational Learning − To learn by watching and imitating others. For example, child tries to learn by mimicking her parent. Perceptual Learning − It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations. Relational Learning − It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt. Spatial Learning − It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road. Stimulus-Response Learning − It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell. Problem Solving − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available. Perception − It is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner. Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication. Difference between Human and Machine Intelligence Humans perceive by patterns whereas the machines perceive by set of rules and data. Humans store and recall information by patterns, machines do it by searching algorithms. For example, the number 40404040 is easy to remember, store, and recall as its pattern is simple. Humans can figure out the complete object even if some part of it is missing or distorted; whereas the machines cannot do it correctly. Print Page Previous Next Advertisements ”;

AI – Research Areas

Artificial Intelligence – Research Areas ”; Previous Next The domain of artificial intelligence is huge in breadth and width. While proceeding, we consider the broadly common and prospering research areas in the domain of AI − Speech and Voice Recognition These both terms are common in robotics, expert systems and natural language processing. Though these terms are used interchangeably, their objectives are different. Speech Recognition Voice Recognition The speech recognition aims at understanding and comprehending WHAT was spoken. The objective of voice recognition is to recognize WHO is speaking. It is used in hand-free computing, map, or menu navigation. It is used to identify a person by analysing its tone, voice pitch, and accent, etc. Machine does not need training for Speech Recognition as it is not speaker dependent. This recognition system needs training as it is person oriented. Speaker independent Speech Recognition systems are difficult to develop. Speaker dependent Speech Recognition systems are comparatively easy to develop. Working of Speech and Voice Recognition Systems The user input spoken at a microphone goes to sound card of the system. The converter turns the analog signal into equivalent digital signal for the speech processing. The database is used to compare the sound patterns to recognize the words. Finally, a reverse feedback is given to the database. This source-language text becomes input to the Translation Engine, which converts it to the target language text. They are supported with interactive GUI, large database of vocabulary, etc. Real Life Applications of Research Areas There is a large array of applications where AI is serving common people in their day-to-day lives − Sr.No. Research Areas Real Life Application 1 Expert Systems Examples − Flight-tracking systems, Clinical systems. 2 Natural Language Processing Examples: Google Now feature, speech recognition, Automatic voice output. 3 Neural Networks Examples − Pattern recognition systems such as face recognition, character recognition, handwriting recognition. 4 Robotics Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc. 5 Fuzzy Logic Systems Examples − Consumer electronics, automobiles, etc. Task Classification of AI The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks. Task Domains of Artificial Intelligence Mundane (Ordinary) Tasks Formal Tasks Expert Tasks Perception Computer Vision Speech, Voice Mathematics Geometry Logic Integration and Differentiation Engineering Fault Finding Manufacturing Monitoring Natural Language Processing Understanding Language Generation Language Translation Games Go Chess (Deep Blue) Ckeckers Scientific Analysis Common Sense Verification Financial Analysis Reasoning Theorem Proving Medical Diagnosis Planing Creativity Robotics Locomotive Humans learn mundane (ordinary) tasks since their birth. They learn by perception, speaking, using language, and locomotives. They learn Formal Tasks and Expert Tasks later, in that order. For humans, the mundane tasks are easiest to learn. The same was considered true before trying to implement mundane tasks in machines. Earlier, all work of AI was concentrated in the mundane task domain. Later, it turned out that the machine requires more knowledge, complex knowledge representation, and complicated algorithms for handling mundane tasks. This is the reason why AI work is more prospering in the Expert Tasks domain now, as the expert task domain needs expert knowledge without common sense, which can be easier to represent and handle. Print Page Previous Next Advertisements ”;

AI – Overview

Artificial Intelligence – Overview ”; Previous Next Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time. A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings. What is Artificial Intelligence? According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. Philosophy of AI While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?” Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans. Goals of AI To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users. To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans. What Contributes to AI? Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving. Out of the following areas, one or multiple areas can contribute to build an intelligent system. Programming Without and With AI The programming without and with AI is different in following ways − Programming Without AI Programming With AI A computer program without AI can answer the specific questions it is meant to solve. A computer program with AI can answer the generic questions it is meant to solve. Modification in the program leads to change in its structure. AI programs can absorb new modifications by putting highly independent pieces of information together. Hence you can modify even a minute piece of information of program without affecting its structure. Modification is not quick and easy. It may lead to affecting the program adversely. Quick and Easy program modification. What is AI Technique? In the real world, the knowledge has some unwelcomed properties − Its volume is huge, next to unimaginable. It is not well-organized or well-formatted. It keeps changing constantly. AI Technique is a manner to organize and use the knowledge efficiently in such a way that − It should be perceivable by the people who provide it. It should be easily modifiable to correct errors. It should be useful in many situations though it is incomplete or inaccurate. AI techniques elevate the speed of execution of the complex program it is equipped with. Applications of AI AI has been dominant in various fields such as − Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge. Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans. Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users. Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example, A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas. Doctors use clinical expert system to diagnose the patient. Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist. Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc. Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text. Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment. History of AI Here is the history of AI during 20th century − Year Milestone / Innovation 1923 Karel Čapek play named “Rossum”s Universal Robots” (RUR) opens in London, first use of the word “robot” in English. 1943 Foundations for neural networks laid. 1945 Isaac Asimov, a Columbia University alumni, coined the term Robotics. 1950 Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search. 1956 John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University. 1958 John McCarthy invents LISP programming language for AI. 1964 Danny Bobrow”s dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly. 1965 Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English. 1969 Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving. 1973 The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of

Artificial Intelligence – Quick Guide

Artificial Intelligence – Quick Guide ”; Previous Next Artificial Intelligence – Overview Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time. A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings. What is Artificial Intelligence? According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. Philosophy of AI While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?” Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans. Goals of AI To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users. To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans. What Contributes to AI? Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving. Out of the following areas, one or multiple areas can contribute to build an intelligent system. Programming Without and With AI The programming without and with AI is different in following ways − Programming Without AI Programming With AI A computer program without AI can answer the specific questions it is meant to solve. A computer program with AI can answer the generic questions it is meant to solve. Modification in the program leads to change in its structure. AI programs can absorb new modifications by putting highly independent pieces of information together. Hence you can modify even a minute piece of information of program without affecting its structure. Modification is not quick and easy. It may lead to affecting the program adversely. Quick and Easy program modification. What is AI Technique? In the real world, the knowledge has some unwelcomed properties − Its volume is huge, next to unimaginable. It is not well-organized or well-formatted. It keeps changing constantly. AI Technique is a manner to organize and use the knowledge efficiently in such a way that − It should be perceivable by the people who provide it. It should be easily modifiable to correct errors. It should be useful in many situations though it is incomplete or inaccurate. AI techniques elevate the speed of execution of the complex program it is equipped with. Applications of AI AI has been dominant in various fields such as − Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge. Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans. Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users. Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example, A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas. Doctors use clinical expert system to diagnose the patient. Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist. Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc. Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text. Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment. History of AI Here is the history of AI during 20th century − Year Milestone / Innovation 1923 Karel Čapek play named “Rossum”s Universal Robots” (RUR) opens in London, first use of the word “robot” in English. 1943 Foundations for neural networks laid. 1945 Isaac Asimov, a Columbia University alumni, coined the term Robotics. 1950 Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search. 1956 John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University. 1958 John McCarthy invents LISP programming language for AI. 1964 Danny Bobrow”s dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly. 1965 Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English. 1969 Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving. 1973 The Assembly Robotics group at Edinburgh University built Freddy, the