Artificial Intelligence

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Artificial Intelligence

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Koushik Mondal Artificial Intelligence ( A.I. ) Present by Mr.KOUSHIK MONDAL Student at SMU of B.C.A

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Koushik Mondal What is Artificial Intelligence Computerized Human-Being A.I

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In simple words, A.I. is the Science and Engineering Of making Intelligent Machines. The Term A.I. belongs to a Fifth Generation Computer System In which the System works in the same manner as human-being. In another sense, we can term it As the study and design of Intelligent Agents. Koushik Mondal Meaning Of A.I.

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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: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do" and Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved". In 1974, in response to the criticism of England's Sir James Light hill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. The next few years, when funding for projects was hard to find, would later be called an " AI winter ". HISTORY OF A.I Koushik Mondal

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ARTICLES OF A.I Automated Planning and Scheduling Knowledge Representation Commonsense Knowledge Natural Language Processing Reasoning, Problem solving Machine Learning Motion and Manipulation Social Intelligence Computational Creativity General Intelligence By Mr.Koushik Mondal

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Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices. In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be. However, if this is not true, it must periodically check if the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty. Automated Planning and Scheduling By Mr.Koushik Mondal

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The most difficult problems in knowledge representation are: Default Reasoning Qualification Problem Knowledge representation and knowledge engineering are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time ; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A complete representation of " what exists " is an ontology (borrowing a word from traditional philosophy), of which the most general are called upper ontologies. By Mr.Koushik Monda l Knowledge representatio

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By Mr.Koushik Mondal The breadth of commonsense knowledge The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g. Cyc ) require enormous amounts of laborious ontological engineering — they must be built, by hand, one complicated concept at a time. A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology. The subsymbolic form of Commonsense knowledge Much of what people know is not represented as "facts" or "statements" that they could actually say out loud. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and instantly realize that it is a fake. These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI or computational intelligence will provide ways to represent this kind of knowledge. Commonsense Knowledge

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Natural Language Processing By Mr.Koushik Mondal Many researchers hope that a sufficiently powerful natural language processing system would be able to acquire knowledge on its own, by reading the existing text available over the internet. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation . Natural language processing gives machines the ability to read and understand the languages that humans speak.

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Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles, play board games or make logical deductions. By the late 1980s and '90s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. For difficult problems, most of these algorithms can require enormous computational resources — most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem solving algorithms is a high priority for AI research. Reasoning, Problem solving By Mr.Koushik Mondal

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Machine Learning By Mr.Koushik Mondal Machine learning has been central to AI research from the beginning. Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression takes a set of numerical input/output examples and attempts to discover a continuous function that would generate the outputs from the inputs. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

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Motion and Manipulation The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation , with sub-problems of localization (knowing where you are), mapping ( learning what is around you ) and motion planning ( figuring out how to get there ) By Mr.Koushik Mondal

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Computational Creativity A sub-field of AI addresses creativity both theoretically (from a philosophical And psychological perspective) And practically (via specific implementations of systems that generate outputs that can be considered creative) TOPIO, a robot that can play table tennis, developed by TOSY. By Mr.Koushik Mondal

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General Intelligence By Mr.Koushik Mondal Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them. A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project. A related area of computational research is Artificial Intuition and Artificial Imagination. Many of the problems above are considered AI-complete: to solve one problem, you must solve them all. For example, even a straightforward, specific task like machine translation requires that the machine follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done as well as humans can do it

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List of Artificial Intelligence Projects 1 Brain simulation 2 Cognitive architectures 3 Games 4 Natural language processing 5 Speech recognition 6 Expert System 7 Handwriting Recognition By Mr.Koushik Mondal

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Brain Simulation By Mr.Koushik Mondal Artificial_Intelligence_System, a large scale distributed computing project involving over 10,000 computers HNeT (Holographic Neural Technology), a technology by AND Corporation (Artificial Neural Devices) based on non linear phase coherence/decoherence principles. Hierarchical Temporal Memory , a technology by Numenta to capture and replicate the properties of the neocortex.

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Cognitive architectures By Mr.Koushik Mondal CALO, a DARPA-funded , 25-institution effort to integrate numerous artificial intelligence approaches (natural language processing, speech recognition, machine vision, probabilistic logic, planning, reasoning, numerous forms of machine learning) into an AI assistant that learns to help manage your office environment. SHIAI (Semi Human Instinctive Artificial Intelligence) , an AI methodology based on the use of semi-human instincts, developed at Islamic Azad University in 2004. Virtual Woman , the oldest continuous form of virtual life — a chatterbot , virtual reality, artificial intelligence, video game, and virtual human.

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By Mr.Koushik Mondal Chinook , a computer program that plays English draughts; the first to win the world champion title in the competition against humans. Deep Blue , a chess-playing computer developed by IBM which beat Garry Kasparov in 1997. FreeHAL , a self-learning conversation simulator ( Chatterbot ) which uses semantic nets to organize its knowledge in order to imitate a very close human behavior within conversations. Poki , research into computer poker by the University of Alberta. TD-Gammon , a program that learned to play world-class backgammon partly by playing against itself (temporal difference learning with neural networks ) GAMES

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Natural Language Processing AIML, an XML dialect for creating natural language software agents. A.L.I.C.E., an award-winning natural language processing chatterbot. ELIZA, a famous 1966 computer program by Joseph Weizenbaum, which parodied person-centered therapy. InfoTame, a text analysis search engine originally developed by the KGB for sorting communications intercepts. Jabberwacky, a chatterbot by Rollo Carpenter, aiming to simulate a natural human chat. KAR-Talk, a chatterbot by I.-A.Industrie. PARRY, another early famous chatterbot, written in 1972 by Kenneth Colby, attempting to simulate a paranoid schizophrenic. Proverb, a system that can solve crossword puzzles better than most humans. SHRDLU, an early natural language processing computer program developed by Terry Winograd at MIT from 1968 to 1970. START, the world's first web-based question answering system, developed at the MIT CSAIL. SYSTRAN, a machine translation technology by a company of the same name, used by Yahoo!, AltaVista and Google, among others. Texai, an open source project to create artificial intelligence, starting with a bootstrap English dialog system that intelligently acquires knowledge and behaviors. Koushik Mondal

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Speech Recognition By Mr.Koushik Mondal Speech recognition (also known as automatic speech recognition or computer speech recognition ) converts spoken words to text. The term "voice recognition" is sometimes used to refer to speech recognition where the recognition system is trained to a particular speaker - as is the case for most desktop recognition software, hence there is an element of speaker recognition, which attempts to identify the person speaking, to better recognize what is being said. Speech recognition is a broad term which means it can recognize almost anybody's speech - such as a call-centre system designed to recognize many voices. Voice recognition is a system trained to a particular user, where it recognizes their speech based on their unique vocal sound. Speech recognition applications include voice dialing (e.g., "Call home"), call routing (e.g., "I would like to make a collect call"), domotic appliance control and content-based spoken audio search (e.g., find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g., a radiology report), speech-to-text processing (e.g., word processors or emails), and in aircraft cockpits (usually termed Direct Voice Input)

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An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain , and is a traditional application and/or subfield of artificial intelligence Expert systems were introduced by Edward Feigenbaum , the first truly successful form of AI software By Mr.Koushik Mondal Expert system

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User interface Interface Engine Knowledge Base Expert Acquisition tool Programmer Explanation Facility user Expert system By Mr.Koushik Mondal

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By Mr.Koushik Mondal Handwriting recognition . Handwriting recognition is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs , touch screens and other devices.

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By Mr.Koushik Mondal The elements of an on-line handwriting recognition interface typically include: a pen or stylus for the user to write with. a touch sensitive surface, which may be integrated with, or adjacent to, an output display. a software application which interprets the movements of the stylus across the writing surface, translating the resulting strokes into digital text. Elements of on line handwriting recognition

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By Mr.Koushik Mondal An Important Part Of AI ROBOT

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By Mr.Koushik Mondal It is a virtual or mechanical artificial agent. It is an electromechanical machine which is guided by computer or electro- programming and thus it is able to do tasks on its own. ROBOTS A BASIC DEFINITION

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By Mr.Koushik Mondal These robots can alter there physical form to suit a particular task. Example-T-1000 The researchers have only produced parts of this complex systems. These are also known as nano bits. These robots are inspired by the family of insects such as ants and bees. Example I-robot swarm. These robots have silicone bodies and flexible air muscles. TYPES OF ROBOTS

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They perform jobs more cheaply, with greater accuracy and with more reliability than humans. They are widely used in manufacturing, packing, transport and laboratory works. Positive Impacts Robots have their own entertainment value but their unsafe use can also cause danger. A heavy industrial robot can cause harm to humans. For example death of Sir Robert Williams was caused by a robot only. Negative Impacts By Mr.Koushik Mondal Positive & Negative impacts of robots

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By Mr.Koushik Mondal Some Basic Facts About Robots Roughly half of the Robots in this world are in ASIA of which 30% are in JAPAN. It is the ROBOTIC CAPITAL of the country. South Korea aims to put a Robot in every house by 2015-2020 so that every country is technologically equipped as JAPAN

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Thank You !!! For watching By Mr.Koushik Mondal

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