Artificial Intelligence

MASOOD AHMAD BHAT

S.ID:-S1400D9700032Batch Code:-B140045

NIITRESIDENCY ROAD SRINAGAR

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

CONTENTS

1) Intelligencea) Introduction

i) Knowledgeii) Learningiii) Understanding

2) Artificial Intelligencea) Introductionb) Major Branches of AI

i) Roboticsii) Vision Systemsiii) Natural Language Processingiv) Learning Systemsv) Neural Networksvi) Expert Systems

3) History of AIa) Great Achievements

i) Robocopii) Deep Blueiii) DARPA Grand Challenge

4) Today’s AI Applicationsa) Driver-Less Trainsb) Burglary Alarm Systemsc) Automatic Grading System in Education

INTELLIGENCE

Introduction

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Despite a long history of research and debate, there is still no standard definitionof intelligence. This has led some to believe that intelligence may be approximatelydescribed, but cannot be fully defined. Some definitions of Intelligence from different sources are:As many dictionaries source their definitions from other dictionaries, we have endeavored to always list theoriginal source.1.“The ability to use memory, knowledge, experience, understanding, reasoning, imagination and judgment in order to solve problems and adapt to new situations.” All Words Dictionary, 20062.“The capacity to acquire and apply knowledge.” The American Heritage Dictionary, fourth edition, 20003. “Individuals differ from one another in their ability to understand complexideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought.” American Psychological Association 4. “The ability to learn, understand and make judgments or have opinions thatare based on reason” Cambridge Advance Learner’s Dictionary, 20065. “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.” Common statement with 52 expert signatories6. “The ability to learn facts and skills and apply them, especially when this ability is highly developed.” Encarta World English Dictionary, 20067. “Ability to adapt effectively to the environment, either by making a changein oneself or by changing the environment or finding a new one intelligenceis not a single mental process, but rather a combination of many mental processes directed toward effective adaptation to the environment.” Encyclopedia Britannica, 20068. “the general mental ability involved in calculating, reasoning, perceiving relationships and analogies, learning quickly, storing

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and retrieving information, using language fluently, classifying, generalizing, and adjusting to new situations.” Columbia Encyclopedia, sixth edition, 20069. “Capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude in grasping truths, relationships, facts, meanings, etc.”Random House Unabridged Dictionary, 200610. “The ability to learn, understand, and think about things.” Longman Dictionary or Contemporary English, 200611. “ The ability to learn or understand or to deal with new or trying situations the skilled use of reason (2) : the ability to apply knowledge to manipulateone’s environment or to think abstractly as measured by objective criteria (astests)” Merriam-Webster Online Dictionary, 200612. “The ability to acquire and apply knowledge and skills.” Compact OxfordEnglish Dictionary, 200613. “. . . the ability to adapt to the environment.” World Book Encyclopedia, 2006 14. “Intelligence is a property of mind that encompasses many related mental abilities, such as the capacities to reason, plan, solve problems, think abstractly,comprehend ideas and language, and learn.” Wikipedia, 4 October, 200615. “Capacity of mind, especially to understand principles, truths, facts or meanings, acquire knowledge, and apply it to practice; the ability to learn andcomprehend.” Wiktionary, 4 October, 200616. “The ability to learn and understand or to deal with problems.” Word Central Student Dictionary, 200617. “The ability to comprehend; to understand and profit from experience.” Word net2.1, 200618. “The capacity to learn, reason, and understand.” Words myth Dictionary, 2006

Intelligence has been defined in many different ways including Knowledge, Learning, and Understanding.

1.Knowledge:Knowledge is a familiarity, awareness or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired

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through experience or education byperceiving, discovering, or learning.  Knowledge can refer to a theoretical or practical understanding of a subject. It can be implicit (as with practical skill or expertise) or explicit (as with the theoretical understanding of a subject); it can be more or less formal or systematic. In philosophy, the study of knowledge is called epistemology; the philosopher Plato famously defined knowledge as "justified true belief". However, no single agreed upon definition of knowledge exists, though there are numerous theories to explain it.

Knowledge acquisition involves complex cognitive processes: perception, communication, association and reasoning; while knowledge is also said to be related to the capacity of acknowledgment in human beings.

The definition of knowledge is a matter of ongoing debate among philosophers in the field of epistemology. The classical definition, described but not ultimately endorsed by Plato,specifies that a statement must meet three criteria in order to be considered knowledge: it must be justified, true, and believed. Some claim that these conditions are not sufficient, as Gettier case examples allegedly demonstrate. There are a number of alternatives proposed, includingRobert Nozick's arguments for a requirement that knowledge 'tracks the truth' and Simon Blackburn's additional requirement that we do not want to say that those who meet any of these conditions 'through a defect, flaw, or failure' have knowledge. Richard Kirkham suggests that our definition of knowledge requires that the evidence for the belief necessitates its truth.

2.Understanding:Understanding is a psychological process related to an abstract or physical object, such as a person, situation, or message whereby one is able to think about it and use concepts to deal adequately with that object.Understanding is a relation between the knower and an object of understanding. Understanding implies abilities and dispositions with respect to an object of knowledge sufficient to support intelligent behavior. An understanding is the limit of a conceptualization. To understand something is to have conceptualized it to a given measure.

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Examples

1. One understands the weather if one is able to predict and to give an explanation of some of its features, etc.

2. A psychiatrist understands another person's anxieties if he/she knows that person's anxieties, their causes, and can give useful advice on how to cope with the anxiety.

3. A person understands a command if he/she knows who gave it, what is expected by the issuer, and whether the command is legitimate, and whether one understands the speaker.

4. One understands a reasoning, an argument, or a language if one can consciously reproduce the information content conveyed by the message.

5. One understands a mathematical concept if one can solve problems using it, especially problems that are not similar to what one has seen before.

3.Learning:Learning is acquiring new, or modifying and reinforcing, existing knowledge, behaviors, skills, values, or preferencesand may involve synthesizing different types of information.The ability to learn is possessed by humans, animals and some machines. Progress over time tends to follow learning curves. Learning is not compulsory; it is contextual. It does not happen all at once, but builds upon and is shaped by what we already know. To that end, learning may be viewed as a process, rather than a collection of factual and procedural knowledge. Learning produces changes in the organism and the changes produced are relatively permanent.

Human learning may occur as part of education, personal development, schooling, or training. It may be goal-oriented and may be aided by motivation. The study of how learning occurs is part of neuropsychology, educational psychology, learning theory, and pedagogy. Learning may occur as a result of habituation or classical conditioning, seen in many animal species, or as a result of more complex activities such as play, seen only in relatively intelligent animals. Learning may occur consciously or without conscious awareness. Learning that an aversive event can't be avoided nor escaped is called learned helplessness. There is evidence for human behavioral learning prenatally, in

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which habituation has been observed as early as 32 weeks into gestation, indicating that the central nervous system is sufficiently developed and primed for learning and memory to occur very early on in development.

Play has been approached by several theorists as the first form of learning. Children experiment with the world, learn the rules, and learn to interact through play. Lev Vygotsky agrees that play is pivotal for children's development, since they make meaning of their environment through play. 85 percent of brain development occurs during the first five years of a child's life. The context of conversation based on moral reasoning offers some proper observations on the responsibilities of parents.

ARTIFICIAL INTELLIGENCE

Introduction

Artificial term is given by John McCarthy in 1950.He is known as the Father of Artificial intelligence.AI is both the intelligence of machines and the branch of computer sciencewhich aims to create it, through "the study and design of intelligent agents" or "rational agents", where an intelligent agent  is a system that perceives its environment and takes actions which maximize its chances of success. Among the traits that researchers hope machines will exhibitare reasoning, knowledge,planning, learning, communication andthe ability to move and manipulate objects. In the field of artificial intelligence there is no consensus on how closely the brain should be simulated.

Artificial intelligence (AI) is the intelligence exhibited by machines or software, and the branch of computer science that develops machines and software with human-like intelligence. Major AI researchers and 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".

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AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.

The central goals of AI research include reasoning, knowledge, planning, learning  natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still among the field's long term goals. 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 field was founded on the claim that a central property of humans, intelligence—the sapience ofHomo sapiens—can be sufficiently well described to the extent that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today it has become an essential part of the technology industry and defines many challenging problems at the forefront of research in computer science.

Major Branches

1.Robotics:Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots  as well as computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can take the place of humans in dangerous environments

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or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition. Many of today's robots are inspired by nature contributing to the field of bio-inspired robotics.

The concept of creating machines that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century. Throughout history, robotics has been often seen to mimic human behavior, and often manage tasks in a similar fashion. Today, robotics is a rapidly growing field, as technological advances continue, research, design, and building new robots serve various practical purposes, whether domestically, commercially, or militarily. Many robots do jobs that are hazardous to people such as defusing bombs, mines and exploring shipwrecks.

The word robotics was derived from the word robot, which was introduced to the public by Czech writer Karel Čapek in his play R.U.R. (Rossum's Universal Robots), which was published in 1920. The word robot comes from the Slavic word robota, which means labour. The play begins in a factory that makes artificial people called robots, creatures who can be mistaken for humans – similar to the modern ideas of androids. Karel Čapek himself did not coin the word. He wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he named his brother Josef Čapek as its actual originator.

History of Robotics

In 1927 the Maschinenmensch ("machine-human") gynoid humanoid robot (also called "Parody", "Futura", "Robotrix", or the "Maria impersonator") was the first depiction of a robot ever to appear on film was played by German actress Brigitte Helm in Fritz Lang's film Metropolis.

In 1942 the science fiction writer Isaac Asimov formulated his Three Laws of Robotics.

In 1948 Norbert Wiener formulated the principles of cybernetics, the basis of practical robotics.

Fully autonomous robots only appeared in the second half of the 20th century. The first digitally operated and programmable robot, the Unimate, was installed in 1961 to lift hot pieces of

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metal from a die casting machine and stack them. Commercial and industrial robots are widespread today and used to perform jobs more cheaply, or more accurately and reliably, than humans. They are also employed in jobs which are too dirty, dangerous, or dull to be suitable for humans. Robots are widely used in manufacturing, assembly, packing and packaging, transport, earth and space exploration, surgery, weaponry, laboratory research, safety, and the mass production of consumer and industrial goods.

2.Vision System:It branch of Artificial Intelligence concerned with computer processing of images from the real world.Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry. The scope of MV is broad. MV is related to, though distinct from, computer. The primary uses for machine vision are automatic inspection and industrial robot guidance.  Common machine vision applications include quality assurance, sorting, material handling, robot guidance, and optical gauging.

Machine vision methods are defined as both the process of defining and creating an MV solution, and as the technical process that occurs during the operation of the solution. Here the latter is addressed. As of 2006, there was little standardization in the interfacing and configurations used in MV. This includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.Nonetheless, the first step in the MV sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information. A common output from machine vision systems is pass/fail decisions. These decisions may in turn trigger mechanisms that reject failed items or sound an alarm. Other common outputs include object position and orientation information from robot guidance systems. Additionally, output types include numerical measurement data, data read from codes and characters, displays of the process or results, stored images, alarms from automated

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space monitoring MV systems, and process control signals.As recently as 2006, one industry consultant reported that MV represented a $1.5 billion market in North America. However, the editor-in-chief of an MV trade magazine asserted that "machine vision is not an industry per se" but rather "the integration of technologies and products that provide services or applications that benefit true industries such as automotive or consumer goods manufacturing, agriculture, and defense."

As of 2006, experts estimated that MV had been employed in less than 20% of the applications for which it is potentially useful.

3.Natural Language Processing:Natural language processing(NLP) is a field of artificial intelligence  concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation. Modern NLP algorithms are based on machine learning, especially statistical machine learning. The paradigm of machine learning is different from that of most prior attempts at language processing. Prior implementations of language-processing tasks typically involved the direct hand coding of large sets of rules. The machine-learning paradigm calls instead for using general learning algorithms — often, although not always, grounded in statistical inference — to automatically learn such rules through the analysis of large corpora of typical real-world examples. Corpus (plural, "corpora") is a set of documents (or sometimes, individual sentences) that have been hand-annotated with the correct values to be learned.

Many different classes of machine learning algorithms have been applied to NLP tasks. These algorithms take as input a large set of "features" that are generated from the input data. Some of the earliest-used algorithms, such as decision trees, produced systems of hard if-then rules similar to the systems of hand-written rules that were then common. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to each input feature. Such models have the

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advantage that they can express the relative certainty of many different possible answers rather than only one, producing more reliable results when such a model is included as a component of a larger system.

Systems based on machine-learning algorithms have many advantages over hand-produced rules:

The learning procedures used during machine learning automatically focus on the most common cases, whereas when writing rules by hand it is often not obvious at all where the effort should be directed.

Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to unfamiliar input (e.g. containing words or structures that have not been seen before) and to erroneous input (e.g. with misspelled words or words accidentally omitted). Generally, handling such input gracefully with hand-written rules — or more generally, creating systems of hand-written rules that make soft decisions — is extremely difficult, error-prone and time-consuming.

Systems based on automatically learning the rules can be made more accurate simply by supplying more input data. However, systems based on hand-written rules can only be made more accurate by increasing the complexity of the rules, which is a much more difficult task. In particular, there is a limit to the complexity of systems based on hand-crafted rules, beyond which the systems become more and more unmanageable. However, creating more data to input to machine-learning systems simply requires a corresponding increase in the number of man-hours worked, generally without significant increases in the complexity of the annotation process.

4.Learning Systems:Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning

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systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.

There are a wide variety of machine learning tasks and successful applications. Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic example of machine learning.

These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows:

Machine learning focuses on prediction, based on known properties learned from the training data.

Data mining focuses on the discovery of (previously) unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases.

The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. Human intuition cannot, however, be entirely eliminated, since the system's designer must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data.

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5.Neural Networks: A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain.An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), is a learning algorithm that is inspired by the structure and functional aspects of biological neural networks. Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation. Modern neural networks arenon-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

In computer science and related fields, artificial neural networks are computational models inspired by animals' central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network.

For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the network's designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read.

Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

6.Expert Systems:In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert.Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as IF-

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THEN rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of AI software.

An expert system is divided into two sub-systems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging capabilities.

Expert systems were introduced by the Stanford Heuristic Programming Project led by Edward Feigenbaum, who is sometimes referred to as the "father of expert systems". The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral).Dendral was a tool to study hypothesis formation in the identification of organic molecules. The general problem it solved—designing a solution given a set of constraints—was one of the most successful areas for early expert systems applied to business domains such as sales people configuring Dec Vax computers and mortgage loan application development.

SMH.PAL is an expert system for the assessment of students with multiple disabilities.

Mistral is an expert system for the monitoring of dam safety developed in the 90's by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam.

HISTORY OF AI

1950: Turing Test: In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines with true intelligence. He noted that "intelligence" is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then the machine could be called "intelligent." This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine" was at least plausible and the paper answered all the most common objections to the

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proposition. The Turing Test was the first serious proposal in the philosophy of artificial intelligence.

1956-1959:Golden Years:

The Dartmouth Conference of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it". The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research. At the conference Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field.[43] The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI. In 1958,John McCarthy (Massachusetts Institute of Technology or MIT) invented the Lisp programming language.In 1959,John McCarthy and Marvin Minsky founded the MIT AI Lab.

1965:ELIZA: ELIZA is a computer program and an early example of primitive natural language processing. ELIZA operated by processing users' responses to scripts, the most famous of which was DOCTOR, a simulation of a Rogerian psychotherapist. Using almost no information about human thought or emotion, DOCTOR sometimes provided a startlingly human-like interaction. ELIZA was written atMIT by Joseph Weizenbaum between 1964 and 1966.

When the "patient" exceeded the very small knowledge base, DOCTOR might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts?" A possible response to "My mother hates me" would be "Who else in your family hates you?" ELIZA was implemented using simple pattern matching techniques, but was taken seriously by several of its users, even after Weizenbaum explained to them how it worked. It was one of the first chatterbots in existence.

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1972:PROLOG Prologis a general purpose logic programming language associated withartificial intelligence and computational linguistics.

Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is declarative: the program logic is expressed in terms of relations, represented as facts and rules. A computation is initiated by running a query over these relations.

The language was first conceived by a group around Alain Colmerauer in Marseille, France, in the early 1970s and the first Prolog system was developed in 1972 by Colmerauer with Philippe Roussel.

Prolog was one of the first logic programming languages, and remains the most popular among such languages today, with many free and commercial implementations available. While initially aimed at natural language processing, the language has since then stretched far into other areas like theorem proving, expert systems, games, automated answering systems, ontologies and sophisticated control systems. Modern Prolog environments support creating graphical user interfaces, as well as administrative and networked applications.

1974:MYCIN.MYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases.

MYCIN was developed over five or six years in the early 1970s at Stanford University. It was written in Lisp as the doctoral dissertation of Edward Shortliffe under the direction of Bruce Buchanan,Stanley N. Cohen and others. It arose in the laboratory that had created the earlier Dendral expert system.

MYCIN was never actually used in practice but research indicated that it proposed an acceptable therapy in about 69% of cases, which was better than the performance of infectious disease experts who were judged using the same criteria.

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1988-93:AI Winter. In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several cycles of hype, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including:

1966: The failure of machine translation, 1970: The abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding

Research program at Carnegie Mellon University, 1973: The large decrease in AI research in the United

Kingdom in response to the Light hill report, 1973–74: DARPA's cutbacks to academic AI research in

general, 1987: The collapse of the Lisp machine market, 1988: The cancellation of new spending on AI by

the Strategic Computing Initiative, 1993: Expert systems slowly reaching the bottom, 1990s: The quiet disappearance of the fifth-generation

computer project's original goals,

The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank andMarvin Minsky—two leading AI researchers who had survived the "winter" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.

Hype cycles are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in

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2002 that "there's this stupid myth out there that AI has failed, but AI is around you every second of the day." Ray Kurzweil agrees: "Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry." He adds: "the AI winter is long since over."

Great Achievements

1.Robocup:RoboCup is an international robotics competition founded in 1997.The aim is to promote robotics and AI research, by offering a publicly appealing, but formidable challenge. The name Robocopis a contraction of the competition's full name, "Robot Soccer World Cup", but there are many other stages of the competition such as "RoboCupRescue", "RoboCup@Home" and "RoboCup Junior". In the U.S robocup is not very big, with the national competition being at New Jersey every year, but in other countries it is very popular. In 2013 the world's competition was in the Netherlands. In 2014 the world competition is in Brazil.

The official goal of the project:

"By the middle of the 21st century, a team of fully autonomous humanoid robot soccer players shall win a soccer game, complying with the official rules of FIFA, against the winner of the most recent World Cup. "

2.Deep Blue:Deep Blue was a chess-playing computer developed by IBM. On May 11, 1997, the machine, with human intervention between games, won the second six-game match against world champion Garry Kasparov by two wins to one with three draws.Kasparov accused IBM of cheating and demanded a rematch. IBM refused and retired Deep Blue. Kasparov had beaten a previous version of Deep Blue in 1996.

The project was started as ChipTest at Carnegie Mellon University by Feng-hsiung Hsu, followed by its successor, Deep Thought. After their graduation from Carnegie Mellon,

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Hsu, Thomas Anantharaman, and Murray Campbell from the Deep Thought team were hired by IBM Research to continue their quest to build a chess machine that could defeat the world champion. Hsu and Campbell joined IBM in autumn 1989, with Anantharaman following later. Anantharaman subsequently left IBM for Wall Street and Arthur Joseph Hoane joined the team to perform programming tasks. Jerry Brody, a long-time employee of IBM Research, was recruited for the team in 1990. The team was managed first by Randy Moulic, followed by Chung-Jen (C J) Tan.

After Deep Thought's 1989 match against Kasparov, IBM held a contest to rename the chess machine and it became "Deep Blue", a play on IBM's nickname, "Big Blue". After a scaled down version of Deep Blue, Deep Blue Jr., played Grandmaster Joel Benjamin, Hsu and Campbell decided that Benjamin was the expert they were looking for to develop Deep Blue's opening book, and Benjamin was signed by IBM Research to assist with the preparations for Deep Blue's matches against Garry Kasparov.

In 1995 "Deep Blue prototype" (actually Deep Thought II, renamed for PR reasons) played in the 8th World Computer Chess Championship. Deep Blue prototype played the computer programWchess to a draw while Wchess was running on a personal computer. In round 5 Deep Blue prototype had the white pieces and lost to the computer program Fritz 3 in 39 moves while Fritz was running on an Intel Pentium 90Mhz personal computer. In the end of the championship Deep Blue prototype was tied for second place with the computer program Junior while Junior was running on a personal computer.

3.DARPA Grand challenge:The DARPA Grand Challenge is a prize competition for American autonomous vehicles, funded by the Defense Advanced Research Projects Agency,the most prominent research organization of the United States Department of Defense. Congress has authorized DARPA to award cash prizes to further DARPA's mission to sponsor revolutionary, high-payoff research that bridges the gap between fundamental discoveries and military use. The initial DARPA Grand Challenge was created to spur the development of technologies needed to create the first

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fully autonomous ground vehicles capable of completing a substantial off-road course within a limited time. The third event, the DARPA Urban Challenge extended the initial Challenge to autonomous operation in a mock urban environment. The most recent Challenge, the 2012 DARPA Robotics Challenge, focused on autonomous emergency-maintenance robots. The most recent Challenge, the 2012 DARPA Robotics Challenge, focused on autonomous emergency-maintenance robots.

Fully autonomous vehicles have been an international pursuit for many years, from endeavors in Japan (starting in 1977), Germany (Ernst Dickmanns and VaMP), Italy (the ARGO Project), the European Union (EUREKA Prometheus Project), the United States of America, and other countries.

The Grand Challenge was the first long distance competition for driverless cars in the world; other research efforts in the field of Driverless cars take a more traditional commercial or academic approach. The U.S. Congress authorized DARPA to offer prize money ($1 million) for the first Grand Challenge to facilitate robotic development, with the ultimate goal of making one-third of ground military forces autonomous by 2015. Following the 2004 event, Dr. Tony Tether, the director of DARPA, announced that the prize money had been increased to $2 million for the next event, which was claimed on October 9, 2005. The first, second and third places in the 2007 Urban Challenge received $2 million, $1 million, and $500,000, respectively.

The competition was open to teams and organizations from around the world, as long as there were at least one U.S. citizen on the roster. Teams have participated from high schools, universities, businesses and other organizations. More than 100 teams registered in the first year, bringing a wide variety of technological skills to the race. In the second year, 195 teams from 36 U.S. statesand 4 foreign countries entered the race.

TODAY’S AI APPLICATIONS

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1.Driver-Less trains and Metros: Driverless metro lines are currently operational in the variouscities, such as, London,Barcelona,Dubai etc.Advantages of driverless metros:

Lower expenditure for staff (staff swallows a significant part of the costs of running a transport system). However, service and security personnel is common in automated systems.

Trains can be shorter and instead run more frequently without increasing expenditure for staff.

Service frequency can easily be adjusted to meet sudden unexpected demands.

Despite common psychological concerns, driverless metros are safer than traditional ones. None of them ever had a serious accident.

Intruder detection systems can be more effective than humans in stopping trains if someone is on the tracks.

Financial savings in both energy and wear-and-tear costs because trains are driven to an optimum specification.

Train turnover time at terminals can be extremely short (train goes into the holding track and returns immediately), reducing the number of train sets needed for operation.

2.Burglary Alarm System: A Burglary alarm is a system designed to detect intrusion – unauthorized entry – into a building or area. Security alarms are used in residential, commercial, industrial, and military properties for protection against burglary (theft) or property damage, as well as personal protection against intruders.Car alarms likewise protect vehicles and their contents. Prisons also use security systems for control of inmates.

Some alarm systems serve a single purpose of burglary protection; combination systems provide both fire and intrusion protection. Intrusion alarm systems may also be combined with closed-circuit television surveillance systems to automatically record the activities of intruders, and may interface to access control systems for electrically locked doors. Systems range from small, self-contained noisemakers, to complicated, multi-area systems with computer monitoring and control.

3.Automatic Essay Scoring in Education:Automated essay scoring (AES) is the use of specialized computer programs to

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assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification.

Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost.

The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test).

Submitted to :- NIIT Residency Road Srinagar

Submitted by:-Masood Ahmad Bhat

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Student ID:-S1400D9700032

Batch Code:-B140045

Sig. Of HOC NIIT Sig. Of Concerned Faculty.

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