Open In Colab

Download content from wikipedia

First wikipedia library must be installed

In [1]:
!pip install wikipedia
Collecting wikipedia
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Building wheels for collected packages: wikipedia
  Building wheel for wikipedia ( ... done
  Created wheel for wikipedia: filename=wikipedia-1.4.0-cp36-none-any.whl size=11686 sha256=4d0687269d3ce3c40f5894cbc04c417674fea0bb2edf07a0c3661a0638a1ea81
  Stored in directory: /root/.cache/pip/wheels/87/2a/18/4e471fd96d12114d16fe4a446d00c3b38fb9efcb744bd31f4a
Successfully built wikipedia
Installing collected packages: wikipedia
Successfully installed wikipedia-1.4.0

Download content from Wikipedia on

  • Data Science
  • Artificial intelligence
  • Machine Learning
  • European Central Bank
  • Bank
  • Financial technology
  • International Monetary Fund
  • Basketball
  • Swimming
  • Tennis
In [2]:
import pandas as pd
import wikipedia
articles=['Data Science','Artificial intelligence','Machine Learning',
          'European Central Bank','Bank','Financial technology','International Monetary Fund',
for article in articles:
    print("loading content: ",article)
print("examine content")
loading content:  Data Science
loading content:  Artificial intelligence
loading content:  Machine Learning
loading content:  European Central Bank
loading content:  Bank
loading content:  Financial technology
loading content:  International Monetary Fund
loading content:  Basketball
loading content:  Swimming
loading content:  Tennis
examine content
['Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.In 2012, when Harvard Business Review called it "The Sexiest Job of the 21st Century", the term "data science" became a buzzword.  It is now often used interchangeably with earlier concepts like business analytics, business intelligence, predictive modeling, and statistics.  Even the suggestion that data science is sexy was paraphrasing Hans Rosling, featured in a 2011 BBC documentary with the quote, "Statistics is now the sexiest subject around." Nate Silver referred to data science as a sexed up term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as "data science" to be more attractive, which can cause the term to become "dilute[d] beyond usefulness." While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To the discredit of the discipline, however, many data-science and big-data projects fail to deliver useful results, often as a result of poor management and utilization of resources.\n\n\n== History ==\nThe term "data science" has appeared in various contexts over the past thirty years but did not become an established term until recently. In an early usage, it was used as a substitute for computer science by Peter Naur in 1960. Naur later introduced the term "datalogy". In 1974, Naur published Concise Survey of Computer Methods, which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.\nThe modern definition of "data science" was first sketched during the second Japanese-French statistics symposium organized at the University of Montpellier II (France) in 1992. The attendees acknowledged the emergence of a new discipline with a specific focus on data from various origins, dimensions, types and structures. They shaped the contour of this new science based on established concepts and principles of statistics and data analysis with the extensive use of the increasing power of computer tools.\nIn 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"), after the term was introduced in a roundtable discussion by Chikio Hayashi.In November 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics = Data Science?" for his appointment to the H. C. Carver Professorship at the University of Michigan.\nIn this lecture, he characterized statistical work as a trilogy of data collection, data modeling and analysis, and decision making. In his conclusion,\nhe initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.\nLater, he presented his lecture entitled "Statistics = Data Science?" as the first of his 1998 P.C. Mahalanobis Memorial Lectures. These lectures honor Prasanta Chandra Mahalanobis, an Indian scientist and statistician and founder of the Indian Statistical Institute.\nIn 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique. In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.\nIn April 2002, the International Council for Science (ICSU): Committee on Data for Science and Technology (CODATA) started the Data Science Journal, a publication focused on issues such as the description of data systems, their publication on the internet, applications and legal issues. Shortly thereafter, in January 2003, Columbia University began publishing The Journal of Data Science, which provided a platform for all data workers to present their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published "Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century" defining data scientists as "the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection" whose primary activity is to "conduct creative inquiry and analysis."Around 2007, Turing award winner Jim Gray envisioned "data-driven science" as a "fourth paradigm" of science that uses the computational analysis of large data as primary scientific method and "to have a world in which all of the science literature is online, all of the science data is online, and they interoperate with each other."In the 2012 Harvard Business Review article "Data Scientist: The Sexiest Job of the 21st Century", DJ Patil claims to have coined this term in 2008 with Jeff Hammerbacher to define their jobs at LinkedIn and Facebook, respectively. He asserts that a data scientist is "a new breed", and that a "shortage of data scientists is becoming a serious constraint in some sectors", but describes a much more business-oriented role.\nIn 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched. In 2013, the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg, establishing the European Association for Data Science (EuADS). The first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, General Assembly launched student-paid bootcamp and The Data Incubator launched a competitive free data science fellowship.  In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science".  In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics.  In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.\n\n\n== Relationship to statistics ==\n"Data science" has recently become a popular term among business executives. However, many critical academics and journalists see no distinction between data science and statistics, whereas others consider it largely a popular term for "data mining" and "big data". Writing in Forbes, Gil Press argues that data science is a buzzword without a clear definition and has simply replaced "business analytics" in contexts such as graduate degree programs. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, "I think data-scientist is a sexed up term for a statistician....Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician."On the other hand, responses to criticism are as numerous. In a 2014 Wall Street Journal article, Irving Wladawsky-Berger compares the data science enthusiasm with the dawn of computer science. He argues data science, like any other interdisciplinary field, employs methodologies and practices from across the academia and industry, but then it will morph them into a new discipline. He brings to attention the sharp criticisms of computer science, now a well respected academic discipline, had to once face. Likewise, NYU Stern\'s Vasant Dhar, as do many other academic proponents of data science, argues more specifically in December 2013 that data science is different from the existing practice of data analysis across all disciplines, which focuses only on explaining data sets. Data science seeks actionable and consistent pattern for predictive uses.  This practical engineering goal takes data science beyond traditional analytics. Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models.In an effort similar to Dhar\'s, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms. First, for Donoho, data science does not equate to big data, in that the size of the data set is not a criterion to distinguish data science and statistics. Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines. Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. As a statistician, Donoho, following many in his field, champions the broadening of learning scope in the form of data science, like John Chambers who urges statisticians to adopt an inclusive concept of learning from data, or like William Cleveland who urges to prioritize extracting from data applicable predictive tools over explanatory theories. Together, these statisticians envision an increasingly inclusive applied field that grows out of traditional statistics and beyond.\nFor the future of data science, Donoho projects an ever-growing environment for open science where data sets used for academic publications are accessible to all researchers. US National Institute of Health has already announced plans to enhance reproducibility and transparency of research data. Other big journals are likewise following suit. This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms. As Donoho concludes, "the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available."\n\n\n== References ==',
 'In computer science,  artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler\'s Theorem says "AI is whatever hasn\'t been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.\nArtificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field\'s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.\nThe field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.\n\n\n== History ==\n\nThought-capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley\'s Frankenstein or Karel Čapek\'s R.U.R. (Rossum\'s Universal Robots). These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to  Alan Turing\'s theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour". The first work that is now generally recognized as AI was McCullouch and Pitts\' 1943 formal design for Turing-complete "artificial neurons".The field of AI research was born at a workshop at Dartmouth College in 1956, where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener. Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies (c. 1954) (and by 1959 were reportedly playing better than the average human), solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) 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 optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation ... the problem of creating \'artificial intelligence\' will substantially be solved".They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter", a period when obtaining funding for AI projects was difficult.\nIn the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan\'s fifth generation computer project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. The success was due to increasing computational power (see Moore\'s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.In 2011, a Jeopardy! quiz show exhibition match, IBM\'s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. The Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research as do intelligent personal assistants in smartphones. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. In the 2017 Future of Go Summit,  AlphaGo  won a three-game match with Ke Jie, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.\nAccording to Bloomberg\'s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks. He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft\'s development of a Skype system that can automatically translate from one language to another and Facebook\'s system that can describe images to blind people. In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes". Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower". However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.\n\n\n== Definitions ==\nComputer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”\n\n\n== Basics ==\nA typical AI analyzes its environment and takes actions that maximize its chance of success. An AI\'s intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Do mathematically similar actions to the ones succeeded in the past"). Goals can be explicitly defined, or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others. Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute. A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:\nIf someone has a "threat" (that is, two in a row), take the remaining square. Otherwise,\nif a move "forks" to create two threats at once, play that move. Otherwise,\ntake the center square if it is free. Otherwise,\nif your opponent has played in a corner, take the opposite corner. Otherwise,\ntake an empty corner if one exists. Otherwise,\ntake any empty square.Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial. For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain\'s machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam\'s razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam\'s razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.\n\nSettling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don\'t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.\n\nCompared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "naïve physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.) This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.\n\n\n== Challenges of AI ==\nThe cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.\n\n\n=== Reasoning, problem solving ===\nEarly researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.  In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.\n\n\n=== Knowledge representation ===\n\nKnowledge representation and knowledge engineering are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain 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 representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining "interesting" and actionable inferences from large databases), and other areas.Among the most difficult problems in knowledge representation are:\n\nDefault reasoning and the qualification problem\nMany of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.\nThe breadth of commonsense knowledge\nThe number of atomic facts that the average person knows is very large. 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.\nThe subsymbolic form of some commonsense knowledge\nMuch of what people know is not represented as "facts" or "statements" that they could express verbally. 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 realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. 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, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.\n\n\n=== Planning ===\n\nIntelligent agents must be able to set goals and achieve them. They need a way to visualize the future—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 available choices.In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.\n\n\n=== Learning ===\n\nMachine learning (ML), a fundamental concept of AI research since the field\'s inception, is the study of computer algorithms that improve automatically through experience.Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization. In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.\n\n\n=== Natural language processing ===\n\nNatural language processing (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering and machine translation. Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.\n\n\n=== Perception ===\n\nMachine perception is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.\n\n\n=== Motion and manipulation ===\n\nAI is heavily used in robotics. Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage. A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient\'s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object. Moravec\'s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility". This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.\n\n\n=== Social intelligence ===\n\nMoravec\'s paradox can be extended to many forms of social intelligence. Distributed multi-agent coordination of autonomous vehicles remains a difficult problem. Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction. Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naïve users an unrealistic conception of how intelligent existing computer agents actually are.\n\n\n=== General intelligence ===\n\nHistorically, projects such as the Cyc knowledge base (1984–) and the massive Japanese Fifth Generation Computer Systems initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation). Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas. Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. Besides transfer learning, hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author\'s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author\'s original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.\n\n\n== Approaches ==\nThere is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?\nCan intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?\n\n\n=== Cybernetics and brain simulation ===\n\nIn the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter\'s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.\n\n\n=== Symbolic ===\n\nWhen access to digital computers became possible in the mid 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI". During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.\nResearchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.\n\n\n==== Cognitive simulation ====\nEconomist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.\n\n\n==== Logic-based ====\nUnlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.\n\n\n==== Anti-logic or scruffy ====\nResearchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat\'s Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.\n\n\n==== Knowledge-based ====\nWhen computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.\n\n\n=== Sub-symbolic ===\nBy the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems. Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.\n\n\n==== Embodied intelligence ====\nThis includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.\nWithin developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).\n\n\n==== Computational intelligence and soft computing ====\nInterest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s. Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.\n\n\n=== Statistical learning ===\nMuch of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible. Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.\n\n\n=== Integrating the approaches ===\nIntelligent agent paradigm\nAn intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic artificial neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.Agent architectures and cognitive architectures\nResearchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. Some cognitive architectures are custom-built to solve a narrow problem; others, such as Soar, are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are hybrid intelligent systems that include both symbolic and sub-symbolic components.\n\n\n== Tools ==\nAI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.\n\n\n=== Search and optimization ===\n\nMany problems in AI can be solved in theory by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and grasping objects use local searches in configuration space. Many learning algorithms use search algorithms based on optimization.\nSimple exhaustive searches are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size.A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.\n\nEvolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).\n\n\n=== Logic ===\n\nLogic is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning.Several different forms of logic are used in AI research. Propositional logic involves truth functions such as "or" and "not". First-order logic adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train\'s brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.Default logics, non-monotonic logics and circumscription are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics; situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus; and modal logics.Overall, qualitative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.\n\n\n=== Probabilistic methods for uncertain reasoning ===\n\nMany problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.Bayesian networks are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters). Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis, and information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.\n\n\n=== Classifiers and statistical learning methods ===\n\nThe simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,k-nearest neighbor algorithm,kernel methods such as the support vector machine (SVM),Gaussian mixture model, and the extremely popular naive Bayes classifier. Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.\n\n\n=== Artificial neural networks ===\n\nNeural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), cast a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks\' early successes included predicting the stock market and (in 1995) a mostly self-driving car. In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.The study of non-learning artificial neural networks began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.\nThe main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning.Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa, and was introduced to neural networks by Paul Werbos.Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".\n\n\n==== Deep feedforward neural networks ====\n\nDeep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a "credit assignment path" (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length. Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.According to one overview, the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986 and gained traction after\nIgor Aizenberg and colleagues introduced it to artificial neural networks in 2000. The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965. These networks are trained one layer at a time. Ivakhnenko\'s 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980. In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.\nSince 2011, fast implementations of CNNs on GPUs have\nwon many visual pattern recognition competitions.CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind\'s "AlphaGo Lee", the program that beat a top Go champion in 2016.\n\n\n==== Deep recurrent neural networks ====\n\nEarly on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs) which are in theory Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning. RNNs can be trained by gradient descent but suffer from the vanishing gradient problem. In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC). At Google, Microsoft and Baidu this approach has revolutionised speech recognition. For example, in 2015, Google\'s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users. Google also used LSTM to improve machine translation, Language Modeling and Multilingual Language Processing. LSTM combined with CNNs also improved automatic image captioning and a plethora of other applications.\n\n\n=== Evaluating progress ===\n\nAI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI." Moravec\'s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory. E-sports such as StarCraft continue to provide additional public benchmarks. There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.\n\n\n== Applications ==\n\nAI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays, prediction of judicial decisions, targeting online advertisements,  and energy storageWith social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.\n\n\n=== Healthcare ===\n\nAI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high risk patients for population health. The breadth of applications is rapidly increasing.\nAs an example, AI is being applied to the high cost problem of dosage issues—where findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients. \nArtificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover". Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.According to CNN, a recent study by surgeons at the Children\'s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig\'s bowel during open surgery, and doing so better than a human surgeon, the team claimed. IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels).  Watson has struggled to achieve success and adoption in healthcare.\n\n\n=== Automotive ===\n\nAdvancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016, there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren\'t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger.  To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians.  The car\'s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car.  But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programming of the car in these situations is crucial to a successful driver-less automobile.\n\n\n=== Finance and economics ===\nFinancial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.\nBanks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.AI is also being used by corporations. Whereas AI CEO\'s are still 30 years away, robotic process automation (RPA) is already being used today in corporate finance. RPA uses artificial intelligence to train and teach software robots to process transactions, monitor compliance and audit processes automatically.The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.. In August 2019, the AICPA introduced AI training course for accounting professionals.\n\n\n=== Government ===\n\nArtificial intelligence paired with facial recognition systems may be used for mass surveillance. This is already the case in some parts of China. An artificial intelligence has also competed in the Tama City mayoral elections in 2018.\nIn 2019, the tech city of Bengaluru in India is set to deploy AI managed traffic signal systems across the 387 traffic signals in the city. This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.\n\n\n=== Video games ===\n\nIn video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a "solved problem" for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).\n\n\n=== Military ===\n\nThe main military applications of Artificial Intelligence and Machine Learning are to enhance C2, Communications, Sensors, Integration and Interoperability.\nArtificial Intelligence technologies enables coordination  of  sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also  inside  Manned  and  Unmanned Teams (MUM-T).Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015. Military drones capable of autonomous action are widely considered a useful asset.  Many artificial intelligence researchers seek to distance themselves from military applications of AI.\n\n\n=== Audit ===\nFor financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.\n\n\n=== Advertising ===\nIt is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically. A documented case reports that online gambling companies were using AI to improve customer targeting.Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.\n\n\n=== Art ===\n\nArtificial Intelligence has inspired numerous creative applications including its usage to produce visual art. The exhibition "Thinking Machines: Art and Design in the Computer Age, 1959–1989" at MoMA provides a good overview of the historical applications of AI for art, architecture, and design. Recent exhibitions showcasing the usage of AI to produce art include the Google-sponsored benefit and auction at the Gray Area Foundation in San Francisco, where artists experimented with the deepdream algorithm and the exhibition "Unhuman: Art in the Age of AI," which took place in Los Angeles and Frankfurt in the fall of 2017. In the spring of 2018, the Association of Computing Machinery dedicated a special magazine issue to the subject of computers and art highlighting the role of machine learning in the arts. The Austrian Ars Electronica and Museum of Applied Arts, Vienna opened exhibitions on AI in 2019. The Ars Electronica\'s 2019 festival "Out of the box" extensively thematized the role of arts for a sustainable societal transformation with AI.\n\n\n== Philosophy and ethics ==\n\nThere are three philosophical questions related to AI:\n\nIs artificial general intelligence possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish?\nAre intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?\nCan a machine have a mind, consciousness and mental states in exactly the same sense that human beings do? Can a machine be sentient, and thus deserve certain rights? Can a machine intentionally cause harm?\n\n\n=== The limits of artificial general intelligence ===\n\nCan a machine be intelligent? Can it "think"?\n\nAlan Turing\'s "polite convention"\nWe need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test.The Dartmouth proposal\n"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956.Newell and Simon\'s physical symbol system hypothesis\n"A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus\' critique of AI.)Gödelian arguments\nGödel himself, John Lucas (in 1961) and Roger Penrose (in a more detailed argument from 1989 onwards) made highly technical arguments that human mathematicians can consistently see the truth of their own "Gödel statements" and therefore have computational abilities beyond that of mechanical Turing machines. However, some people do not agree with the "Gödelian arguments".The artificial brain argument\nThe brain can be simulated by machines and because brains are intelligent, simulated brains must also be intelligent; thus machines can be intelligent. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software and that such a simulation will be essentially identical to the original.The AI effect\nMachines are already intelligent, but observers have failed to recognize it. When Deep Blue beat Garry Kasparov in chess, the machine was acting intelligently. However, onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not "real" intelligence after all; thus "real" intelligence is whatever intelligent behavior people can do that machines still cannot. This is known as the AI Effect: "AI is whatever hasn\'t been done yet."\n\n\n=== Potential harm ===\nWidespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.The potential negative effects of AI and automation are a major issue for Andrew Yang\'s presidential campaign.\n\n\n==== Existential risk ====\n\nPhysicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race".\n\nThe development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn\'t compete and would be superseded.\n\nIn his book Superintelligence, Nick Bostrom provides an argument that artificial intelligence will pose a threat to humankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down. If this AI\'s goals do not reflect humanity\'s—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.\nConcern over risk from artificial intelligence has led to some high-profile donations and investments. A group of prominent tech titans including Peter Thiel, Amazon Web Services and Musk have committed $1billion to OpenAI, a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. Other technology industry leaders believe that artificial intelligence is helpful in its current form and will continue to assist humans. Oracle CEO Mark Hurd has stated that AI "will actually create more jobs, not less jobs" as humans will be needed to manage AI systems. Facebook CEO Mark Zuckerberg believes AI will "unlock a huge amount of positive things," such as curing disease and increasing the safety of autonomous cars.In January 2015, Elon Musk donated ten million dollars to the Future of Life Institute to fund research on understanding AI decision making. The goal of the institute is to "grow wisdom with which we manage" the growing power of technology. Musk also funds companies developing artificial intelligence such as Google DeepMind and Vicarious to "just keep an eye on what\'s going on with artificial intelligence. I think there is potentially a dangerous outcome there."For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence.\n\n\n==== Devaluation of humanity ====\n\nJoseph Weizenbaum wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life.\n\n\n==== Social justice ====\nOne concern is that AI programs may be programmed to be biased against certain groups, such as women and minorities, because most of the developers are wealthy Caucasian men. Support for artificial intelligence is higher among men (with 47% approving) than women (35% approving).\nAlgorithms have a host of applications in today\'s legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants. COMPAS (an acronym for Correctional Offender Management Profiling for Alternative Sanctions) counts among the most widely utilized commercially available solutions. It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.\n\n\n==== Decrease in demand for human labor ====\n\nThe relationship between automation and employment is complicated. While automation eliminates old jobs, it also creates new jobs through micro-economic and macro-economic effects. Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that "the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution" is "worth taking seriously". Subjective estimates of the risk vary widely; for example, Michael Osborne and Carl Benedikt Frey estimate 47% of U.S. jobs are at "high risk" of potential automation, while an OECD report classifies only 9% of U.S. jobs as "high risk". Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy. Author Martin Ford and others go further and argue that many jobs are routine, repetitive and (to an AI) predictable; Ford warns that these jobs may be automated in the next couple of decades, and that many of the new jobs may not be "accessible to people with average capability", even with retraining. Economists point out that in the past technology has tended to increase rather than reduce total employment, but acknowledge that "we\'re in uncharted territory" with AI.\n\n\n==== Autonomous weapons ====\n\nCurrently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers and drones.\n\n\n=== Ethical machines ===\nMachines with intelligence have the potential to use their intelligence to prevent harm and minimize the risks; they may have the ability to use ethical reasoning to better choose their actions in the world. Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks.\n\n\n==== Artificial moral agents ====\nWendell Wallach introduced the concept of artificial moral agents (AMA) in his book Moral Machines For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as "Does Humanity Want Computers Making Moral Decisions" and "Can (Ro)bots Really Be Moral". For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs.\n\n\n==== Machine ethics ====\n\nThe field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making. The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: "Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this. In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics." Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. A variety of perspectives of this nascent field can be found in the collected edition "Machine Ethics" that stems from the AAAI Fall 2005 Symposium on Machine Ethics.\n\n\n==== Malevolent and friendly AI ====\n\nPolitical scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be benevolent. He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.\nOne proposal to deal with this is to ensure that the first generally intelligent AI is \'Friendly AI\' and will be able to control subsequently developed AIs. Some question whether this kind of check could actually remain in place.\nLeading AI researcher Rodney Brooks writes, "I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence."\n\n\n=== Machine consciousness, sentience and mind ===\n\nIf an AI system replicates all key aspects of human intelligence, will that system also be sentient—will it have a mind which has conscious experiences? This question is closely related to the philosophical problem as to the nature of human consciousness, generally referred to as the hard problem of consciousness.\n\n\n==== Consciousness ====\n\nDavid Chalmers identified two problems in understanding the mind, which he named the "hard" and "easy" problems of consciousness. The easy problem is understanding how the brain processes signals, makes plans and controls behavior. The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain.\nFor example, consider what happens when a person is shown a color swatch and identifies it, saying "it\'s red". The easy problem only requires understanding the machinery in the brain that makes it possible for a person to know that the color swatch is red. The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.) Everyone knows subjective experience exists, because they do it every day (e.g., all sighted people know what red looks like). The hard problem is explaining how the brain creates it, why it exists, and how it is different from knowledge and other aspects of the brain.\n\n\n==== Computationalism and functionalism ====\n\nComputationalism is the position in the philosophy of mind that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing. Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the mind-body problem. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers Jerry Fodor and Hilary Putnam.\n\n\n==== Strong AI hypothesis ====\n\nThe philosophical position that John Searle has named "strong AI" states: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.\n\n\n==== Robot rights ====\n\nIf a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? This issue, now known as "robot rights", is currently being considered by, for example, California\'s Institute for the Future, although many critics believe that the discussion is premature. Some critics of transhumanism argue that any hypothetical robot rights would lie on a spectrum with animal rights and human rights.  The subject is profoundly discussed in the 2010 documentary film Plug & Pray, and many sci fi media such as Star Trek Next Generation, with the character of Commander Data, who fought being disassembled for research, and wanted to "become human", and the robotic holograms in Voyager.\n\n\n=== Superintelligence ===\n\nAre there limits to how intelligent machines—or human-machine hybrids—can be? A superintelligence, hyperintelligence, or superhuman intelligence is a hypothetical agent that would possess intelligence far surpassing that of the brightest and most gifted human mind. Superintelligence may also refer to the form or degree of intelligence possessed by such an agent.\n\n\n==== Technological singularity ====\n\nIf research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity". Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.Ray Kurzweil has used Moore\'s law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.\n\n\n==== Transhumanism ====\n\nRobot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger.\nEdward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler\'s "Darwin among the Machines" as far back as 1863, and expanded upon by George Dyson in his book of the same name in 1998.\n\n\n== Economics ==\nThe long-term economic effects of AI are uncertain. A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed.\n\n\n== In fiction ==\n\nThought-capable artificial beings appeared as storytelling devices since antiquity,\nand have been a persistent theme in science fiction.\nA common trope in these works began with Mary Shelley\'s Frankenstein, where a human creation becomes a threat to its masters. This includes such works as Arthur C. Clarke\'s and Stanley Kubrick\'s 2001: A Space Odyssey (both 1968), with HAL 9000, the murderous computer in charge of the Discovery One spaceship, as well as The Terminator (1984) and The Matrix (1999). In contrast, the rare loyal robots such as Gort from The Day the Earth Stood Still (1951) and Bishop from Aliens (1986) are less prominent in popular culture.Isaac Asimov introduced the Three Laws of Robotics in many books and stories, most notably the "Multivac" series about a super-intelligent computer of the same name. Asimov\'s laws are often brought up during lay discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov\'s laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity.Transhumanism (the merging of humans and machines) is explored in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s, artist Hajime Sorayama\'s Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.\nSeveral works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer. This appears in Karel Čapek\'s R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.\n\n\n== See also ==\n\n\n== Explanatory notes ==\n\n\n== References ==\n\n\n=== AI textbooks ===\n\n\n=== History of AI ===\n\n\n=== Other sources ===\n\n\n== Further reading ==\n\n\n== External links ==\n\n"Artificial Intelligence". Internet Encyclopedia of Philosophy.\nThomason, Richmond. "Logic and Artificial Intelligence".  In Zalta, Edward N. (ed.). Stanford Encyclopedia of Philosophy.\nAITopics – A large directory of links and other resources maintained by the Association for the Advancement of Artificial Intelligence, the leading organization of academic AI researchers.\nArtificial Intelligence, BBC Radio 4 discussion with John Agar, Alison Adam & Igor Aleksander (In Our Time, Dec. 8, 2005)',
 'Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.\nMachine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.\n\n\n== Overview ==\nThe name machine learning was coined in 1959 by Arthur Samuel. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing\'s proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". In Turing\'s proposal the various characteristics that could be possessed by a thinking machine and the various implications in constructing one are exposed.\n\n\n=== Machine learning tasks ===\n\nMachine learning tasks are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object. In special cases, the input may be only partially available, or restricted to special feedback. Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn\'t have labels.\nClassification algorithms and regression algorithms are types of supervised learning. Classification algorithms are used when the outputs are restricted to a limited set of values. For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. For an algorithm that identifies spam emails, the output would be the prediction of either "spam" or "not spam", represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.\nIn unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.\nActive learning algorithms access the desired outputs (training labels) for a limited set of inputs based on a budget, and optimize the choice of inputs for which it will acquire training labels. When used interactively, these can be presented to a human user for labeling. Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment, and are used in autonomous vehicles or in learning to play a game against a human opponent. Other specialized algorithms in machine learning include topic modeling, where the computer program is given a set of natural language documents and finds other documents that cover similar topics. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Meta learning algorithms learn their own inductive bias based on previous experience. In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies, and imitation.\n\n\n== History and relationships to other fields ==\n\nArthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM. A representative book of the machine learning research during 1960s was the Nilsson\'s book on Learning Machines, dealing mostly with machine learning for pattern classification. The interest of machine learning related to pattern recognition continued during 1970s, as described in the book of Duda and Hart in 1973.  In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.  \nAs a scientific endeavor, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. It also benefited from the increasing availability of digitized information, and the ability to distribute it via the Internet.\n\n\n=== Relation to data mining ===\nMachine learning and data mining often employ the same methods and overlap significantly, but while 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). Data mining uses many machine learning methods, but with different goals; 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 other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.\n\n\n=== Relation to optimization ===\nMachine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.\n\n\n=== Relation to statistics ===\nMachine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.\nSome statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.\n\n\n== Theory ==\n\nA core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.\nThe computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.\nFor the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.\n\n\n== Approaches ==\n\n\n=== Types of learning algorithms ===\nThe types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.\n\n\n==== Supervised learning ====\n\nSupervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and a desired output, also known as a supervisory signal.  In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.Supervised learning algorithms include classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.\nIn the case of semi-supervised learning algorithms, some of the training examples are missing training labels, but they can nevertheless be used to improve the quality of a model. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.\n\n\n==== Unsupervised learning ====\n\nUnsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, though unsupervised learning encompasses other domains involving summarizing and explaining data features.\nCluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.\n\n\n==== Reinforcement learning ====\n\nReinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov Decision Process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.\n\n\n==== Self learning ====\nSelf learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning  named Crossbar Adaptive Array (CAA).  It is a learning with no external rewards and no external teacher advices. The CAA self learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. \nThe self learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning  routine: \n\n In situation s perform action a;\n Receive consequence situation s’;\n Compute emotion of being in consequence situation v(s’);\n Update crossbar memory  w’(a,s) = w(a,s) + v(s’).\n\nIt is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, where from it initially and only once receives initial emotions about situations to be encountered in the  behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking  behavior, in an environment which contains both desirable and undesirable situations. \n\n\n==== Feature learning ====\n\nSeveral learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.\nFeature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.  Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.\n\n\n==== Sparse dictionary learning ====\n\nSparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.\n\n\n==== Anomaly detection ====\n\nIn data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model.\n\n\n==== Association rules ====\n\nAssociation rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.\nBased on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule \n  \n    \n      \n        {\n        \n          o\n          n\n          i\n          o\n          n\n          s\n          ,\n          p\n          o\n          t\n          a\n          t\n          o\n          e\n          s\n        \n        }\n        ⇒\n        {\n        \n          b\n          u\n          r\n          g\n          e\n          r\n        \n        }\n      \n    \n    {\\displaystyle \\{\\mathrm {onions,potatoes} \\}\\Rightarrow \\{\\mathrm {burger} \\}}\n   found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.\nLearning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.\nInductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.\n\n\n=== Models ===\nPerforming machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.\n\n\n==== Artificial neural networks ====\n\nArtificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.\nAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.\nThe original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.\nDeep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.\n\n\n==== Decision trees ====\n\nDecision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item\'s target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.\n\n\n==== Support vector machines ====\n\nSupport vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.  An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.\n\n\n==== Regression analysis ====\n\nRegression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is oftentimes extended by regularization (mathematics) methods to mitigate overfitting and high bias, as can be seen in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (e.g. used for trendline fitting in Microsoft Excel ), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space. \n\n\n==== Bayesian networks ====\n\nA Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.\n\n\n==== Genetic algorithms ====\n\nA genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.\n\n\n=== Training models ===\nUsually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model.\n\n\n==== Federated learning ====\n\nFederated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users\' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users\' mobile phones without having to send individual searches back to Google.\n\n\n== Applications ==\nThere are many applications for machine learning, including:\n\nIn 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.  A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers\' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors\' jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings, and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning.\n\n\n== Limitations ==\nAlthough machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.\n\n\n=== Bias ===\n\nMachine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”\n\n\n== Model assessments ==\nClassification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model\'s diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver Operating Characteristic (ROC) and ROC\'s associated Area Under the Curve (AUC).\n\n\n== Ethics ==\nMachine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.\nBecause human languages contain biases, machines trained on language corpora will necessarily also learn these biases.Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public\'s interest, but as income generating machines. This is especially true in the United States where there is a perpetual ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm\'s proprietary owners hold stakes in. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.\n\n\n== Software ==\nSoftware suites containing a variety of machine learning algorithms include the following:\n\n\n=== Free and open-source software ===\n\n\n=== Proprietary software with free and open-source editions ===\n\n\n=== Proprietary software ===\n\n\n== Journals ==\nJournal of Machine Learning Research\nMachine Learning\nNature Machine Intelligence\nNeural Computation\n\n\n== Conferences ==\nConference on Neural Information Processing Systems\nInternational Conference on Machine Learning\n\n\n== See also ==\n\n\n== References ==\n\n\n== Further reading ==\n\n\n== External links ==\nInternational Machine Learning Society\nmloss is an academic database of open-source machine learning software.\nMachine Learning Crash Course by Google. This is a free course on machine learning through the use of TensorFlow.',
 'The European Central Bank (ECB) is the central bank for the euro and administers monetary policy within the Eurozone, which comprises 19 member states of the European Union and is one of the largest monetary areas in the world. Established by the Treaty of Amsterdam, the ECB is one of the world\'s most important central banks and serves as one of seven institutions of the European Union, being enshrined in the Treaty on European Union (TEU). The bank\'s capital stock is owned by all 28 central banks of each EU member state. The current President of the ECB is Christine Lagarde. Headquartered in Frankfurt, Germany, the bank formerly occupied the Eurotower prior to the construction of its new seat.\nThe primary objective of the ECB, mandated in Article 2 of the Statute of the ECB, is to maintain price stability within the Eurozone. Its basic tasks, set out in Article 3 of the Statute, are to set and implement the monetary policy for the Eurozone, to conduct foreign exchange operations, to take care of the foreign reserves of the European System of Central Banks and operation of the financial market infrastructure under the TARGET2 payments system and the technical platform (currently being developed) for settlement of securities in Europe (TARGET2 Securities). The ECB has, under Article 16 of its Statute, the exclusive right to authorise the issuance of euro banknotes. Member states can issue euro coins, but the amount must be authorised by the ECB beforehand.\nThe ECB is governed by European law directly, but its set-up resembles that of a corporation in the sense that the ECB has shareholders and stock capital. Its capital is €11 billion held by the national central banks of the member states as shareholders. The initial capital allocation key was determined in 1998 on the basis of the states\' population and GDP, but the capital key has been adjusted. Shares in the ECB are not transferable and cannot be used as collateral.\n\n\n== History ==\n\nThe European Central Bank is the de facto successor of the European Monetary Institute (EMI). The EMI was established at the start of the second stage of the EU\'s Economic and Monetary Union (EMU) to handle the transitional issues of states adopting the euro and prepare for the creation of the ECB and European System of Central Banks (ESCB).  The EMI itself took over from the earlier European Monetary Co-operation Fund (EMCF).The ECB formally replaced the EMI on 1 June 1998 by virtue of the Treaty on European Union (TEU, Treaty of Maastricht), however it did not exercise its full powers until the introduction of the euro on 1 January 1999, signalling the third stage of EMU. The bank was the final institution needed for EMU, as outlined by the EMU reports of Pierre Werner and President Jacques Delors. It was established on 1 June 1998.The first President of the Bank was Wim Duisenberg, the former president of the Dutch central bank and the European Monetary Institute. While Duisenberg had been the head of the EMI (taking over from Alexandre Lamfalussy of Belgium) just before the ECB came into existence, the French government wanted Jean-Claude Trichet, former head of the French central bank, to be the ECB\'s first president. The French argued that since the ECB was to be located in Germany, its president should be French. This was opposed by the German, Dutch and Belgian governments who saw Duisenberg as a guarantor of a strong euro. Tensions were abated by a gentleman\'s agreement in which Duisenberg would stand down before the end of his mandate, to be replaced by Trichet.Trichet replaced Duisenberg as President in November 2003.\n\nThere had also been tension over the ECB\'s Executive Board, with the United Kingdom demanding a seat even though it had not joined the Single Currency. Under pressure from France, three seats were assigned to the largest members, France, Germany, and Italy; Spain also demanded and obtained a seat. Despite such a system of appointment the board asserted its independence early on in resisting calls for interest rates and future candidates to it.When the ECB was created, it covered a Eurozone of eleven members. Since then, Greece joined in January 2001, Slovenia in January 2007, Cyprus and Malta in January 2008, Slovakia in January 2009, Estonia in January 2011, Latvia in January 2014 and Lithuania in January 2015, enlarging the bank\'s scope and the membership of its Governing Council.On 1 December 2009, the Treaty of Lisbon entered into force, ECB according to the article 13 of TEU, gained official status of an EU institution.\nIn September 2011, when German appointee to the Governing Council and Executive board, Jürgen Stark, resigned in protest of the ECB\'s "Securities Market Programme" which involved the purchase of sovereign bonds by the ECB, a move that was up until then considered as prohibited by the EU Treaty. The Financial Times Deutschland referred to this episode as "the end of the ECB as we know it", referring to its hitherto perceived "hawkish" stance on inflation and its historical Deutsche Bundesbank influence.On 1 November 2011, Mario Draghi replaced Jean-Claude Trichet as President of the ECB.\nIn April 2011, the ECB raised interest rates for the first time since 2008 from 1% to 1.25%, with a further increase to 1.50% in July 2011. However, in 2012–2013 the ECB sharply lowered interest rates to encourage economic growth, reaching the historically low 0.25% in November 2013. Soon after the rates were cut to 0.15%, then on 4 September 2014 the central bank reduced the rates by two thirds from 0.15% to 0.05%. Recently, the interest rates were further reduced reaching 0.00%, the lowest rates on record.On 1 November 2019, Christine Lagarde, former Managing Director of the International Monetary Fund, replaced Mario Draghi as President.In November 2014, the bank moved into its new premises.\n\n\n== Mandate and tasks ==\n\n\n=== Mandate and inflation target ===\n\nThe primary objective of the European Central Bank, set out in Article 127(1) of the Treaty on the Functioning of the European Union, is to maintain price stability within the Eurozone. The Governing Council in October 1998 defined price stability as inflation of under 2%, “a year-on-year increase in the Harmonised Index of Consumer Prices (HICP) for the euro area of below 2%” and added that price stability ”was to be maintained over the medium term”. (Harmonised Index of Consumer Prices) Unlike for example the United States Federal Reserve System, the ECB has only one primary objective—but this objective has never been defined in statutory law, and the HICP target can be termed ad hoc.\nThe Governing Council confirmed this definition in May 2003 following a thorough evaluation of the ECB\'s monetary policy strategy. On that occasion, the Governing Council clarified that “in the pursuit of price stability, it aims to maintain inflation rates below, but close to, 2% over the medium term”. All lending to credit institutions must be collateralised as required by Article 18 of the Statute of the ESCB. The Governing Council clarification has little force in law.Without prejudice to the objective of price stability, the Treaty also states that "the ESCB shall support the general economic policies in the Union with a view to contributing to the achievement of the objectives of the Union".\n\n\n=== Tasks ===\nTo carry out its main mission, the ECB\'s tasks include:\n\nDefining and implementing monetary policy\nManaging foreign exchange operations\nMaintaining the payment system to promote smooth operation of the financial market infrastructure under the TARGET2 payments system and being currently developed technical platform for settlement of securities in Europe (TARGET2 Securities).\nConsultative role: by law, the ECB\'s opinion is required on any national or EU legislation that falls within the ECB\'s competence.\nCollection and establishment of statistics\nInternational cooperation\nIssuing banknotes: the ECB holds the exclusive right to authorise the issuance of euro banknotes. Member states can issue euro coins, but the amount must be authorised by the ECB beforehand (upon the introduction of the euro, the ECB also had exclusive right to issue coins).\nFinancial stability and prudential policy\nBanking supervision: since 2013, the ECB has been put in charge of supervising systemically relevant banks.\n\n\n== Organization ==\nThe ECB has four decision-making bodies, that take all the decisions with the objective of fulfilling the ECB\'s mandate:\n\nthe Executive Board,\nthe Governing Council,\nthe General Council, and\nthe Supervisory Board.\n\n\n=== Decision-making bodies of the ECB ===\n\n\n==== The Executive Board ====\n\nThe Executive Board is responsible for the implementation of monetary policy (defined by the Governing Council) and the day-to-day running of the bank. It can issue decisions to national central banks and may also exercise powers delegated to it by the Governing Council. Executive Board members are assigned a portfolio of responsibilities by the President of the ECB. The Executive Board normally meets every Tuesday.\nIt is composed of the President of the Bank (currently Christine Lagarde), the Vice-President (currently Luis de Guindos) and four other members. They are all appointed for non-renewable terms of eight years. Member of the Executive Board of the ECB are appointed "from among persons of recognised standing and professional experience in monetary or banking matters by common accord of the governments of the Member States at the level of Heads of State or Government, on a recommendation from the Council, after it has consulted the European Parliament and the Governing Council of the ECB".José Manuel González-Páramo, a Spanish member of the Executive Board since June 2004, was due to leave the board in early June 2012, but no replacement had been named as of late May. The Spanish had nominated Barcelona-born Antonio Sáinz de Vicuña – an ECB veteran who heads its legal department – as González-Páramo\'s replacement as early as January 2012, but alternatives from Luxembourg, Finland, and Slovenia were put forward and no decision made by May. After a long political battle and delays due to the European Parliament\'s protest over the lack of gender balance at the ECB, Luxembourg\'s Yves Mersch was appointed as González-Páramo\'s replacement.\n\n\n==== The Governing Council ====\nThe Governing Council is the main decision-making body of the Eurosystem. It comprises the members of the Executive Board (six in total) and the governors of the National Central Banks of the euro area countries (19 as of 2015).\nSince January 2015, the ECB has published on its website a summary of the Governing Council deliberations ("accounts"). These publications came as a partial response to recurring criticism against the ECB\'s opacity. However, in contrast to other central banks, the ECB still does not disclose individual voting records of the governors seating in its Council.\n\n\n==== The General Council ====\nThe General Council is a body dealing with transitional issues of euro adoption, for example, fixing the exchange rates of currencies being replaced by the euro (continuing the tasks of the former EMI). It will continue to exist until all EU member states adopt the euro, at which point it will be dissolved. It is composed of the President and vice-president together with the governors of all of the EU\'s national central banks.\n\n\n==== The Supervisory Board ====\nThe Supervisory Board meets twice a month to discuss, plan and carry out the ECB\'s supervisory tasks. It proposes draft decisions to the Governing Council under the non-objection procedure. It is composed of Chair (appointed for a non-renewable term of five years), Vice-Chair (chosen from among the members of the ECB\'s Executive Board) four ECB representatives and representatives of national supervisors. If the national supervisory authority designated by a Member State is not a national central bank (NCB), the representative of the competent authority can be accompanied by a representative from their NCB. In such cases, the representatives are together considered as one member for the purposes of the voting procedure.It also includes the Steering Committee, which supports the activities of the Supervisory Board and prepares the Board’s meetings. It is composed by the Chair of the Supervisory Board, Vice-Chair of the Supervisory Board, one ECB representative and five representatives of national supervisors. The five representatives of national supervisors are appointed by the Supervisory Board for one year based on a rotation system that ensures a fair representation of countries.\n\n\n=== Capital subscription ===\nThe ECB is governed by European law directly, but its set-up resembles that of a corporation in the sense that the ECB has shareholders and stock capital. Its initial capital was supposed to be €5 billion and the initial capital allocation key was determined in 1998 on the basis of the member states\' populations and GDP, but the key is adjustable. The euro area NCBs were required to pay their respective subscriptions to the ECB\'s capital in full. The NCBs of the non-participating countries have had to pay 7% of their respective subscriptions to the ECB\'s capital as a contribution to the operational costs of the ECB. As a result, the ECB was endowed with an initial capital of just under €4 billion. The capital is held by the national central banks of the member states as shareholders. Shares in the ECB are not transferable and cannot be used as collateral. The NCBs are the sole subscribers to and holders of the capital of the ECB.\nToday, ECB capital is about €11 billion, which is held by the national central banks of the member states as shareholders. The NCBs’ shares in this capital are calculated using a capital key which reflects the respective member’s share in the total population and gross domestic product of the EU. The ECB adjusts the shares every five years and whenever a new country joins the EU. The adjustment is made on the basis of data provided by the European Commission.\nAll national central banks (NCBs) that own a share of the ECB capital stock as of 1 January 2015 are listed below. Non-Euro area NCBs are required to pay up only a very small percentage of their subscribed capital, which accounts for the different magnitudes of Euro area and Non-Euro area total paid-up capital.\n\n\n=== Reserves ===\nIn addition to capital subscriptions, the NCBs of the member states participating in the euro area provided the ECB with foreign reserve assets equivalent to around €40 billion. The contributions of each NCB is in proportion to its share in the ECB\'s subscribed capital, while in return each NCB is credited by the ECB with a claim in euro equivalent to its contribution. 15% of the contributions was made in gold, and the remaining 85% in US dollars and UK pound Sterlings.\n\n\n=== Languages ===\nThe internal working language of the ECB is generally English, and press conferences are usually held in English. External communications are handled flexibly: English is preferred (though not exclusively) for communication within the ESCB (i.e. with other central banks) and with financial markets; communication with other national bodies and with EU citizens is normally in their respective language, but the ECB website is predominantly English; official documents such as the Annual Report are in the official languages of the EU.\n\n\n== Independence framework ==\nThe European Central Bank (and by extension, the Eurosystem) is often considered as the "most independent central bank in the world". In general terms, this means that the Eurosystem tasks and policies can be discussed, designed, decided and implemented in full autonomy, without pressure or need for instructions from any external body. The main justification for the ECB\'s independence is that such an institutional setup assists the maintenence of price stability.\n\n\n=== Independence ===\nIn practice, the ECB\'s independence is pinned by four key principles:\nPolitical independence: The Community institutions and bodies and the governments of the member states may not seek to influence the members of the decision-making bodies of the ECB or of the NCBs in the performance of their tasks. Symmetrically, EU institutions and national governments are bound by the treaties to respect the ECB\'s independence.\nOperational and legal independence: the ECB has all required competences to achieve its price stability mandate and thereby can steer monetary policy in full autonomy and by means of high level of discretion. The ECB\'s governing council deliberates with a high degree of secrecy, since individual voting records are not disclosed to the public (leading to suspicions that Governing Council members are voting along national lines.) In addition to monetary policy decisions, the ECB has the right to issue legally binding regulations, within its competence and if the conditions laid down in Union law are fulfilled, it can sanction non-compliant actors if they violate legal requirements laid down in directly applicable Union regulations. The ECB\'s own legal personality also allows the ECB to enter into international legal agreements independently from other EU institutions, and be party of legal proceedings. Finally, the ECB can organise its internal structure as it sees fit.\nPersonal independence: the mandate of ECB board members is purposefully very long (8 years) and Governors of national central banks have a minimum renewable term of office of five years. In addition, ECB board members and are vastly immune from judicial proceedings. Indeed, removals from office can only be decided by the Court of Justice of the European Union (CJEU), under the request of the ECB\'s Governing Council or the Executive Board (i.e. the ECB itself). Such decision is only possible in the event of incapacity or serious misconduct. National governors of the Eurosystem\' national central banks can be dismissed under national law (with possibility to appeal) in case they can no longer fulfil their functions or are guilty of serious misconduct.\nFinancial independence: the ECB is the only body within the EU whose statute guarantees budgetary independence through its own resources and income. The ECB uses its own profits generated by its monetary policy operations and cannot be technically insolvent. The ECB\'s financial independence reinforces its political independence. Because the ECB does not require external financing and symmetrically is prohibited from direct financing to public institutions, this shields it from potential pressure from public authorities.\n\n\n=== Transparency ===\nIn addition to its independence, the ECB is subject to limited transparency obligations in contrast to EU Institutions standards and other major central banks. Indeed, as pointed out by Transparency International, "The Treaties establish transparency and openness as principles of the EU and its institutions. They do, however, grant the ECB a partial exemption from these principles. According to Art. 15(3) TFEU, the ECB is bound by the EU’s transparency principles “only when exercising [its] administrative tasks” (the exemption – which leaves the term “administrative tasks” undefined – equally applies to the Court of Justice of the European Union and to the European Investment Bank)."In practice, there are several concrete examples where the ECB is less transparent than other institutions:\n\nVoting secrecy: while other central banks publish the voting record of its decision makers, the ECB\'s Governing Council decisions are made in full discretion. Since 2014, the ECB has published "accounts" of its monetary policy meetings, but those remain rather vague and do not include individual votes.\nAccess to documents: The obligation for EU bodies to make documents freely accessible after a 30-year embargo applies to the ECB. However, under the ECB\'s Rules of Procedure the Governing Council may decide to keep individual documents classified beyond the 30-year period.\nDisclosure of securities: The ECB is less transparent than the Fed when it comes to disclosing the list of securities being held in its balance sheet under monetary policy operations such as QE.\n\n\n=== Democratic accountability ===\nIn return to its high degree of independence and discretion, the ECB is accountable to the European Parliament (and to a lesser extent to the European Court of Auditors, the European Ombudsman and the Court of Justice of the EU (CJEU). Although no interinstitutional agreement exists between the European Parliament and the ECB to regulate the ECB\'s accountability framework, it has been inspired by a resolution of the European Parliament adopted in 1998 which was then informally agreed with the ECB and incorporated into the Parliament\'s rule of procedure.The accountability framework involves five main mechanisms:\n\nAnnual report: the ECB is bound to publish reports on its activities and has to address its annual report to the European Parliament, the European Commission, the Council of the European Union and the European Council. In return, the European Parliament evaluates the past activities to the ECB via its annual report on the European Central Bank (which is essentially a non legally-binding list of resolutions).\nQuarterly hearings: the Economic and Monetary affairs Committee of the European Parliament organises a hearing (the "Monetary Dialogue") with the ECB every quarter, allowing members of parliament to address oral questions to the ECB president.\nParliamentary questions: all Members of the European Parliament have the right to address written questions to the ECB president. The ECB president provides a written answer in about 6 weeks.\nAppointments: The European Parliament is consulted during the appointment process of executive board members of the ECB.\nLegal proceedings: the ECB\'s own legal personality allows civil society or public institutions to file complaints against the ECB to the Court of Justice of the EU.In 2013, an interinstitutional agreement was reached between the ECB and the European Parliament in the context of the establishment of the ECB\'s Banking Supervision. This agreement sets broader powers to the European Parliament then the established practice on the monetary policy side of the ECB\'s activities. For example, under the agreement, the Parliament can veto the appointment of the Chair and Vice-Chair of the ECB\'s supervisory board, and may approve removals if requested by the ECB.\n\n\n== Monetary policy tools ==\nThe principal monetary policy tool of the European central bank is collateralised borrowing or repo agreements. These tools are also used by the United States Federal Reserve Bank, but the Fed does more direct purchasing of financial assets than its European counterpart. The collateral used by the ECB is typically high quality public and private sector debt.The criteria for determining "high quality" for public debt have been preconditions for membership in the European Union: total debt must not be too large in relation to gross domestic product, for example, and deficits in any given year must not become too large. Though these criteria are fairly simple, a number of accounting techniques may hide the underlying reality of fiscal solvency—or the lack of same.In central banking, the privileged status of the central bank is that it can make as much money as it deems needed. In the United States Federal Reserve Bank, the Federal Reserve buys assets: typically, bonds issued by the Federal government. There is no limit on the bonds that it can buy and one of the tools at its disposal in a financial crisis is to take such extraordinary measures as the purchase of large amounts of assets such as commercial paper. The purpose of such operations is to ensure that adequate liquidity is available for functioning of the financial system.\n\n\n=== Regulatory reliance on credit ratings ===\nThink-tanks such as the World Pensions Council have also argued that European legislators have pushed somewhat dogmatically for the adoption of the Basel II recommendations, adopted in 2005, transposed in European Union law through the Capital Requirements Directive (CRD), effective since 2008. In essence, they forced European banks, and, more importantly, the European Central Bank itself (e.g. when gauging the solvency of financial institutions) to rely more than ever on standardised assessments of credit risk marketed by two non-European private agencies: Moody\'s and S&P.\n\n\n=== Difference with US Federal Reserve ===\nIn the United States, the Federal Reserve System purchases Treasury securities in order to inject liquidity into the economy. The Eurosystem, on the other hand, uses a different method. There are about 1,500 eligible banks which may bid for short-term repo contracts of two weeks to three months duration.The banks in effect borrow cash and must pay it back; the short durations allow interest rates to be adjusted continually. When the repo notes come due the participating banks bid again. An increase in the quantity of notes offered at auction allows an increase in liquidity in the economy. A decrease has the contrary effect. The contracts are carried on the asset side of the European Central Bank\'s balance sheet and the resulting deposits in member banks are carried as a liability. In layman terms, the liability of the central bank is money, and an increase in deposits in member banks, carried as a liability by the central bank, means that more money has been put into the economy.To qualify for participation in the auctions, banks must be able to offer proof of appropriate collateral in the form of loans to other entities. These can be the public debt of member states, but a fairly wide range of private banking securities are also accepted.  The fairly stringent membership requirements for the European Union, especially with regard to sovereign debt as a percentage of each member state\'s gross domestic product, are designed to ensure that assets offered to the bank as collateral are, at least in theory, all equally good, and all equally protected from the risk of inflation.\n\n\n== The ECB\'s response to the euro crisis ==\nFrom late 2009 a handful of mainly southern eurozone member states started being unable to repay their national Euro-denominated government debt or to finance the bail-out of troubled financial sectors under their national supervision without the assistance of third parties. This so-called European debt crisis began after Greece\'s new elected government stopped masking its true indebtedness and budget deficit and openly communicated the imminent danger of a Greek sovereign default.\nForeseeing a possible sovereign default in the eurozone, the general public, international and European institutions, and the financial community reassessed the economic situation and creditworthiness of some Eurozone member states, in particular Southern countries. Consequently, sovereign bonds yields of several Eurozone countries started to rise sharply. This provoked a self-fulfilling panic on financial markets: the more Greek bonds yields rose, the more likely a default became possible, the more bond yields increased in turn.This panic was also aggravated because of the inability of the ECB to react and intervene on sovereign bonds markets for two reasons. First, because the ECB\'s legal framework normally forbids the purchase of sovereign bonds (Article 123. TFEU), This prevented the ECB from implementing quantitative easing like the Federal Reserve and the Bank of England did as soon as 2008, which played an important role in stabilizing markets. Secondly, a decision by the ECB made in 2005 introduced a minimum credit rating (BBB-) for all Eurozone sovereign bonds to be eligible as collateral to the ECB\'s open market operations. This meant that if a private rating agencies were to downgrade a sovereign bond below that threshold, many banks would suddenly become illiquid because they would lose access to ECB refinancing operations. According to former member of the governing council of the ECB Athanasios Orphanides, this change in the ECB\'s collateral framework "planted the seed" of the euro crisis.Faced with those regulatory constraints, the ECB led by Jean-Claude Trichet in 2010 was reluctant to intervene to calm down financial markets. Up until May 6, 2010, Trichet formally denied at several press conferences the possibility of the ECB to embark into sovereign bonds purchases, even though Greece, Portugal, Spain and Italy faced waves of credit rating downgrades and increasing interest rate spreads.\n\n\n=== Securities Market Programme ===\n\nOn 10 May 2010, the ECB announced the launch of a "Securities Market Programme" (SMP) which involved the discretionary purchase of sovereign bonds in secondary markets. Extraordinarily, the decision was taken by the Governing Council during a teleconference call only three days after the ECB\'s usual meeting of May 6 (when Trichet still denied the possibility of purchasing sovereign bonds). The ECB justified this decision by the necessity to "address severe tensions in financial markets." The decision also coincided with the EU leaders decision of May 10 to establish the European Financial Stabilisation mechanism, which would serve as a crisis fighting fund to safeguard the euro area from future sovereign debt crisis.The\'s ECB bond buying focused primarily on Spanish and Italian debt. They were intended to dampen international speculation against those countries, and thus avoid a contagion of the Greek crisis towards other Eurozone countries. The assumption is that speculative activity will decrease over time and the value of the assets increase.\nAlthough SMP did involve an injection of new money into financial markets, all ECB injections were "sterilized" through weekly liquidity absorption. So the operation was neutral for the overall money supply.When the ECB buys bonds from other creditors such as European banks, the ECB does not disclose the transaction prices. Creditors profit of bargains with bonds sold at prices that exceed market\'s quotes.\nAs of 18 June 2012, the ECB in total had spent €212.1bn (equal to 2.2% of the Eurozone GDP) for bond purchases covering outright debt, as part of  the Securities Markets Programme. Controversially, the ECB made substantial profits out of SMP, which were largely redistributed to Eurozone countries. In 2013, the Eurogroup decided to refund those profits to Greece, however the payments were suspended over 2014 until 2017 over the conflict between Yanis Varoufakis and ministers of the Eurogroup. In 2018, profits refunds were reinstalled by the Eurogroup. However, several NGOs complained that a substantial part of the ECB profits would never be refunded to Greece.\n\n\n=== ECB reaction to the Irish banking crisis ===\nIn November 2010, it became clear that Ireland would not be able to afford to bail out its failing banks, and Anglo Irish Bank in particular which needed around 30 billions euros, a sum the government obviously neither could it borrow from financial markets when its bond yields were soaring to comparable levels with the Greek bonds. Instead, the governments issued a 31bn EUR "promissory note" (an IOU) to Anglo – which it had nationalized. In turn, the bank supplied the promissory note as collateral to the Central Bank of Ireland as collateral, so it could access emergency liquidity assistance (ELA). This way, Anglo was able to repay its bondholders. The operation became very controversial, as it basically shifted Anglo\'s private debts onto the government\'s balance sheet.\nIt became clear later that the ECB played a key role in making sure the Irish Government did not let Anglo default on its debts, in order to avoid a financial instability risks. In 15 October and 6 November 2010, the ECB President Jean-Claude Trichet sent two secret letters to the Irish finance Minister which essentially informed the Irish government of the possible suspension of ELA\'s credit lines, unless the Government requested a financial assistance programme to the Eurogroup under condition of further reforms and fiscal consolidation. Over 2012 and 2013, the ECB repeatedly insisted that the promissory note should be repaid in full, and refused the Government\'s proposal to swap the notes with a long-term (and less costly) bond until February 2013. In addition, the ECB insisted that no debt restructuring (or bail-in) should be applied to the nationalized banks\' bondholders, a measure which could have saved Ireland 8 billions euros.In short, fearing a new financial panic, the ECB took extraordinary measures to avoid at all cost debt restructuring in Ireland, which resulted in higher public debt in Ireland.\n\n\n=== Long-term refinancing operation ===\n\nSoon after Mario Draghi took over the presidency of the ECB, the bank announced on 8 December 2011 a new round of 1% interest loans with a term of three years (36 months) – the Long-term Refinancing operations (LTRO).\nThanks to this operation, 523 Banks tapped as much as €489.2 bn (US$640 bn). The loans were not offered to European states, but government securities issued by European states would be acceptable collateral as would mortgage-backed securities and other commercial paper that have a sufficient rating by credit agencies. Observers were surprised by the volume of the loans made when it was implemented. By far biggest amount of €325bn was tapped by banks in Greece, Ireland, Italy and Spain. This way the ECB tried to make sure that banks have enough cash to pay off €200bn of their own maturing debts in the first three months of 2012, and at the same time keep operating and loaning to businesses so that a credit crunch does not choke off economic growth. It also hoped that banks would use some of the money to buy government bonds, effectively easing the debt crisis.On 29 February 2012, the ECB held a second 36-month auction, LTRO2, providing eurozone banks with further €529.5 billion in low-interest loans. This second long term refinancing operation auction saw 800 banks take part. Net new borrowing under the February auction was around €313 billion – out of a total of €256bn existing ECB lending €215bn was rolled into LTRO2.\n\n\n=== Outright Monetary Transactions ===\nIn July 2012, in the midst of renewed fears about sovereigns in the eurozone, Draghi stated in a panel discussion in London that the ECB " ready to do whatever it takes to preserve the Euro. And believe me, it will be enough." This statement led to a steady decline in bond yields for eurozone countries, in particular Spain, Italy and France. In light of slow political progress on solving the eurozone crisis, Draghi\'s statement has been seen as a key turning point in the fortunes of the eurozone.\nFollowing up on Draghi\'s speech, the Governing Council of the European Central Bank (ECB) announced on 2 August 2012, that it "may undertake outright open market operations of a size adequate to reach its objective" in order to "safeguarding an appropriate monetary policy transmission and the singleness of the monetary policy". The technical framework of these operations was formulated on 6 September 2012 when the ECB announced the launch of the Outright Monetary Transactions programme (OMT). On the same date, the bank\'s Securities Markets Programme (SMP) was terminated.\nWhile the duration of the previous SMP was temporary, OMT has no ex-ante time or size limit. However, the activation of the purchases remains conditioned to the adherence by the benefitting country to an adjustment programme to the ESM. To date, OMT was never actually implemented by the ECB. However it is considered that its announcement (together with the "whatever it takes" speech) significantly contributed in stabilizing financial markets and ended the sovereign debt crisis.\n\n\n=== Quantitative Easing ===\nAlthough the sovereign debt crisis was almost solved by 2014, the ECB started to face a repeated decline in the Eurozone inflation rate, indicating that the economy was going towards a deflation. Responding to this threat, the ECB announced on 4 September 2014 the launch of two bond buying purchases programmes: the Covered Bond Purchasing Programme (CBPP3) and Asset-Backed Securities Programme (ABSPP).On 22 January 2015, the ECB announced an extension of those programmes within a full-fledge "quantitative easing" programme which also included sovereign bonds, to the tune of 60 billion euros per month up until at least September 2016. The programme was started on 9 March 2015. The program was repeatedly extended to reach about €2,500 billons and is currently expected to last until at least end of 2018.\n\n\n== Location ==\n\nThe bank is based in Ostend (East End), Frankfurt am Main. The city is the largest financial centre in the Eurozone and the bank\'s location in it is fixed by the Amsterdam Treaty. The bank moved to a new purpose-built headquarters in 2014, designed by a Vienna-based architectural office, Coop Himmelbau. The building is approximately 180 metres (591 ft) tall and is to be accompanied by other secondary buildings on a landscaped site on the site of the former wholesale market in the eastern part of Frankfurt am Main. The main construction on a 120,000 m² total site area began in October 2008, and it was expected that the building would become an architectural symbol for Europe. While it was designed to accommodate double the number of staff who operated in the former Eurotower, that building has been retained by the ECB, owing to more space being required since it took responsibility for banking supervision.\n\n\n== See also ==\n\nEconomics\nEuropean Banking Authority\nEuropean Systemic Risk Board\nOpen market operation\n\n\n== Notes ==\n\n\n== References ==\n\n\n== External links ==\nEuropean Central Bank, official website.\nThe origins and development of the European organisations: The European Central Bank, website.\nEuropean Central Bank: history, role and functions, ECB website.\nStatute of the European Central Bank (2012)',
 'A bank is a financial institution that accepts deposits from the public and creates credit. Lending activities can be performed either directly or indirectly through capital markets. Due to their importance in the financial stability of a country, banks are highly regulated in most countries. Most nations have institutionalized a system known as fractional reserve banking under which banks hold liquid assets equal to only a portion of their current liabilities. In addition to other regulations intended to ensure liquidity, banks are generally subject to minimum capital requirements based on an international set of capital standards, known as the Basel Accords.\nBanking in its modern sense evolved in the 14th century in the prosperous cities of Renaissance Italy but in many ways was a continuation of ideas and concepts of credit and lending that had their roots in the ancient world. In the history of banking, a number of banking dynasties – notably, the Medicis, the Fuggers, the Welsers, the Berenbergs, and the Rothschilds – have played a central role over many centuries. The oldest existing retail bank is Banca Monte dei Paschi di Siena, while the oldest existing merchant bank is Berenberg Bank.\n\n\n== History ==\n\nThe concept of banking may have begun in ancient Babylonia and Old sangvi, with merchants offering loans of grain as collateral within a barter system. Lenders in ancient Greece and during the Roman Empire added two important innovations: they accepted deposits and changed money. Archaeology from this period in ancient China and India also shows evidence of money lending.\nMore modern banking can be traced to medieval and early Renaissance Italy, to the rich cities in the centre and north like Florence, Lucca, Siena, Venice and Genoa. The Bardi and Peruzzi families dominated banking in 14th-century Florence, establishing branches in many other parts of Europe. One of the most famous Italian banks was the Medici Bank, set up by Giovanni di Bicci de\' Medici in 1397. The earliest known state deposit bank, Banco di San Giorgio (Bank of St. George), was founded in 1407 at Genoa, Italy.Modern banking practices, including fractional reserve banking and the issue of banknotes, emerged in the 17th and 18th centuries. Merchants started to store their gold with the goldsmiths of London, who possessed private vaults, and charged a fee for that service. In exchange for each deposit of precious metal, the goldsmiths issued receipts certifying the quantity and purity of the metal they held as a bailee; these receipts could not be assigned, only the original depositor could collect the stored goods.\n\nGradually the goldsmiths began to lend the money out on behalf of the depositor, which led to the development of modern banking practices; promissory notes (which evolved into banknotes) were issued for money deposited as a loan to the goldsmith. The goldsmith paid interest on these deposits. Since the promissory notes were payable on demand, and the advances (loans) to the goldsmith\'s customers were repayable over a longer time period, this was an early form of fractional reserve banking. The promissory notes developed into an assignable instrument which could circulate as a safe and convenient form of money backed by the goldsmith\'s promise to pay, allowing goldsmiths to advance loans with little risk of default. Thus, the goldsmiths of London became the forerunners of banking by creating new money based on credit.\nThe Bank of England was the first to begin the permanent issue of banknotes, in 1695. The Royal Bank of Scotland established the first overdraft facility in 1728. By the beginning of the 19th century a bankers\' clearing house was established in London to allow multiple banks to clear transactions. The Rothschilds pioneered international finance on a large scale, financing the purchase of the Suez canal for the British government.\n\n\n== Etymology ==\nThe word  bank was taken Middle English from Middle French banque, from Old Italian banco, meaning "table", from Old High German banc, bank "bench, counter". Benches were used as makeshift desks or exchange counters during the Renaissance by Jewish Florentine bankers, who used to make their transactions atop desks covered by green tablecloths.\n\n\n== Definition ==\nThe definition of a bank varies from country to country. See the relevant country pages for more information.\nUnder English common law, a banker is defined as a person who carries on the business of banking by conducting current accounts for his customers, paying cheques drawn on him/her and also collecting cheques for his/her customers.\n\nIn most common law jurisdictions there is a Bills of Exchange Act that codifies the law in relation to negotiable instruments, including cheques, and this Act contains a statutory definition of the term banker: banker includes a body of persons, whether incorporated or not, who carry on the business of banking\' (Section 2, Interpretation). Although this definition seems circular, it is actually functional, because it ensures that the legal basis for bank transactions such as cheques does not depend on how the bank is structured or regulated.\nThe business of banking is in many English common law countries not defined by statute but by common law, the definition above. In other English common law jurisdictions there are statutory definitions of the business of banking or banking business. When looking at these definitions it is important to keep in mind that they are defining the business of banking for the purposes of the legislation, and not necessarily in general. In particular, most of the definitions are from legislation that has the purpose of regulating and supervising banks rather than regulating the actual business of banking. However, in many cases the statutory definition closely mirrors the common law one. Examples of statutory definitions:\n\n"banking business" means the business of receiving money on current or deposit account, paying and collecting cheques drawn by or paid in by customers, the making of advances to customers, and includes such other business as the Authority may prescribe for the purposes of this Act; (Banking Act (Singapore), Section 2, Interpretation).\n"banking business" means the business of either or both of the following:receiving from the general public money on current, deposit, savings or other similar account repayable on demand or within less than [3 months] ... or with a period of call or notice of less than that period;\npaying or collecting cheques drawn by or paid in by customers.Since the advent of EFTPOS (Electronic Funds Transfer at Point Of Sale), direct credit, direct debit and internet banking, the cheque has lost its primacy in most banking systems as a payment instrument. This has led legal theorists to suggest that the cheque based definition should be broadened to include financial institutions that conduct current accounts for customers and enable customers to pay and be paid by third parties, even if they do not pay and collect cheques .\n\n\n=== Standard business ===\n\nBanks act as payment agents by conducting checking or current accounts for customers, paying cheques drawn by customers in the bank, and collecting cheques deposited to customers\' current accounts. Banks also enable customer payments via other payment methods such as Automated Clearing House (ACH), Wire transfers or telegraphic transfer, EFTPOS, and automated teller machines (ATMs).\nBanks borrow money by accepting funds deposited on current accounts, by accepting term deposits, and by issuing debt securities such as banknotes and bonds. Banks lend money by making advances to customers on current accounts, by making installment loans, and by investing in marketable debt securities and other forms of money lending.\nBanks provide different payment services, and a bank account is considered indispensable by most businesses and individuals. Non-banks that provide payment services such as remittance companies are normally not considered as an adequate substitute for a bank account.\nBanks can create new money when they make a loan. New loans throughout the banking system generate new deposits elsewhere in the system. The money supply is usually increased by the act of lending, and reduced when loans are repaid faster than new ones are generated. In the United Kingdom between 1997 and 2007, there was an increase in the money supply, largely caused by much more bank lending, which served to push up property prices and increase private debt. The amount of money in the economy as measured by M4 in the UK went from £750 billion to £1700 billion between 1997 and 2007, much of the increase caused by bank lending. If all the banks increase their lending together, then they can expect new deposits to return to them and the amount of money in the economy will increase. Excessive or risky lending can cause borrowers to default, the banks then become more cautious, so there is less lending and therefore less money so that the economy can go from boom to bust as happened in the UK and many other Western economies after 2007.\n\n\n=== Range of activities ===\nActivities undertaken by banks include personal banking, corporate banking, investment banking, private banking, transaction banking, insurance, consumer finance, foreign exchange trading, commodity trading, trading in equities, futures and options trading and money market trading.\n\n\n=== Channels ===\n\nBanks offer many different channels to access their banking and other services:\n\nBranch, in-person banking in a retail location\nAutomated teller machine banking adjacent to or remote from the bank\nBank by mail: Most banks accept cheque deposits via mail and use mail to communicate to their customers\nOnline banking over the Internet to perform multiple types of transactions\nMobile banking is using one\'s mobile phone to conduct banking transactions\nTelephone banking allows customers to conduct transactions over the telephone with an automated attendant, or when requested, with a telephone operator\nVideo banking performs banking transactions or professional banking consultations via a remote video and audio connection. Video banking can be performed via purpose built banking transaction machines (similar to an Automated teller machine) or via a video conference enabled bank branch clarification\nRelationship manager, mostly for private banking or business banking, who visits customers at their homes or businesses\nDirect Selling Agent, who works for the bank based on a contract, whose main job is to increase the customer base for the bank\n\n\n=== Business models ===\nA bank can generate revenue in a variety of different ways including interest, transaction fees and financial advice. Traditionally, the most significant method is via charging interest on the capital it lends out to customers. The bank profits from the difference between the level of interest it pays for deposits and other sources of funds, and the level of interest it charges in its lending activities.\nThis difference is referred to as the spread between the cost of funds and the loan interest rate. Historically, profitability from lending activities has been cyclical and dependent on the needs and strengths of loan customers and the stage of the economic cycle. Fees and financial advice constitute a more stable revenue stream and banks have therefore placed more emphasis on these revenue lines to smooth their financial performance.\nIn the past 20 years, American banks have taken many measures to ensure that they remain profitable while responding to increasingly changing market conditions.\n\nFirst, this includes the Gramm–Leach–Bliley Act, which allows banks again to merge with investment and insurance houses. Merging banking, investment, and insurance functions allows traditional banks to respond to increasing consumer demands for "one-stop shopping" by enabling cross-selling of products (which, the banks hope, will also increase profitability).\nSecond, they have expanded the use of risk-based pricing from business lending to consumer lending, which means charging higher interest rates to those customers that are considered to be a higher credit risk and thus increased chance of default on loans. This helps to offset the losses from bad loans, lowers the price of loans to those who have better credit histories, and offers credit products to high risk customers who would otherwise be denied credit.\nThird, they have sought to increase the methods of payment processing available to the general public and business clients. These products include debit cards, prepaid cards, smart cards, and credit cards. They make it easier for consumers to conveniently make transactions and smooth their consumption over time (in some countries with underdeveloped financial systems, it is still common to deal strictly in cash, including carrying suitcases filled with cash to purchase a home).However, with the convenience of easy credit, there is also increased risk that consumers will mismanage their financial resources and accumulate excessive debt. Banks make money from card products through interest charges and fees charged to cardholders, and transaction fees to retailers who accept the bank\'s credit and/or debit cards for payments.This helps in making a profit and facilitates economic development as a whole.Recently, as banks have been faced with pressure from fintechs, new and additional business models have been suggested such as freemium, monetization of data, white-labeling of banking and payment applications, or the cross-selling of complementary products.\n\n\n=== Products ===\n\n\n==== Retail ====\nSavings account\nRecurring deposit account\nFixed deposit account\nMoney market account\nCertificate of deposit (CD)\nIndividual retirement account (IRA)\nCredit card\nDebit card\nMortgage\nMutual fund\nPersonal loan\nTime deposits\nATM card\nCurrent accounts\nCheque books\nAutomated Teller Machine (ATM)\n\n\n==== Business (or commercial/investment) banking ====\nBusiness loan\nCapital raising (equity / debt / hybrids)\nRevolving credit\nRisk management (foreign exchange (FX)), interest rates, commodities, derivatives\nTerm loan\nCash management services (lock box, remote deposit capture, merchant processing)\nCredit services\n\n\n== Capital and risk ==\nBanks face a number of risks in order to conduct their business, and how well these risks are managed and understood is a key driver behind profitability, and how much capital a bank is required to hold. Bank capital consists principally of equity, retained earnings and subordinated debt.\nAfter the 2007-2009 financial crisis, regulators force banks to issue Contingent convertible bonds (CoCos).These are hybrid capital securities that absorb losses in accordance with their contractual terms when the capital of the issuing bank falls below a certain level. Then debt is reduced and bank capitalization gets a boost. Owing to their capacity to absorb losses, CoCos have the potential to satisfy regulatory capital requirement.Some of the main risks faced by banks include:\n\nCredit risk: risk of loss arising from a borrower who does not make payments as promised.\nLiquidity risk: risk that a given security or asset cannot be traded quickly enough in the market to prevent a loss (or make the required profit).\nMarket risk: risk that the value of a portfolio, either an investment portfolio or a trading portfolio, will decrease due to the change in value of the market risk factors.\nOperational risk: risk arising from execution of a company\'s business functions.\nReputational risk: a type of risk related to the trustworthiness of business.\nMacroeconomic risk: risks related to the aggregate economy the bank is operating in.The capital requirement is a bank regulation, which sets a framework within which a bank or depository institution must manage its balance sheet. The categorization of assets and capital is highly standardized so that it can be risk weighted.\n\n\n== Banks in the economy ==\n\n\n=== Economic functions ===\nThe economic functions of banks include:\n\nIssue of money, in the form of banknotes and current accounts subject to cheque or payment at the customer\'s order. These claims on banks can act as money because they are negotiable or repayable on demand, and hence valued at par. They are effectively transferable by mere delivery, in the case of banknotes, or by drawing a cheque that the payee may bank or cash.\nNetting and settlement of payments – banks act as both collection and paying agents for customers, participating in interbank clearing and settlement systems to collect, present, be presented with, and pay payment instruments. This enables banks to economize on reserves held for settlement of payments, since inward and outward payments offset each other. It also enables the offsetting of payment flows between geographical areas, reducing the cost of settlement between them.\nCredit intermediation – banks borrow and lend back-to-back on their own account as middle men.\nCredit quality improvement – banks lend money to ordinary commercial and personal borrowers (ordinary credit quality), but are high quality borrowers. The improvement comes from diversification of the bank\'s assets and capital which provides a buffer to absorb losses without defaulting on its obligations. However, banknotes and deposits are generally unsecured; if the bank gets into difficulty and pledges assets as security, to raise the funding it needs to continue to operate, this puts the note holders and depositors in an economically subordinated position.\nAsset liability mismatch/Maturity transformation – banks borrow more on demand debt and short term debt, but provide more long term loans. In other words, they borrow short and lend long. With a stronger credit quality than most other borrowers, banks can do this by aggregating issues (e.g. accepting deposits and issuing banknotes) and redemptions (e.g. withdrawals and redemption of banknotes), maintaining reserves of cash, investing in marketable securities that can be readily converted to cash if needed, and raising replacement funding as needed from various sources (e.g. wholesale cash markets and securities markets).\nMoney creation/destruction – whenever a bank gives out a loan in a fractional-reserve banking system, a new sum of money is created and conversely, whenever the principal on that loan is repaid money is destroyed.\n\n\n== Bank crisis ==\n\nBanks are susceptible to many forms of risk which have triggered occasional systemic crises. These include liquidity risk (where many depositors may request withdrawals in excess of available funds), credit risk (the chance that those who owe money to the bank will not repay it), and interest rate risk (the possibility that the bank will become unprofitable, if rising interest rates force it to pay relatively more on its deposits than it receives on its loans).\nBanking crises have developed many times throughout history when one or more risks have emerged for a banking sector as a whole. Prominent examples include the bank run that occurred during the Great Depression, the U.S. Savings and Loan crisis in the 1980s and early 1990s, the Japanese banking crisis during the 1990s, and the sub-prime mortgage crisis in the 2000s.\n\n\n=== Size of global banking industry ===\nAssets of the largest 1,000 banks in the world grew by 6.8% in the 2008/2009 financial year to a record US$96.4 trillion while profits declined by 85% to US$115 billion. Growth in assets in adverse market conditions was largely a result of recapitalization. EU banks held the largest share of the total, 56% in 2008/2009, down from 61% in the previous year. Asian banks\' share increased from 12% to 14% during the year, while the share of US banks increased from 11% to 13%. Fee revenue generated by global investment banking totalled US$66.3 billion in 2009, up 12% on the previous year.The United States has the most banks in the world in terms of institutions (5,330 as of 2015) and possibly branches (81,607 as of 2015). This is an indicator of the geography and regulatory structure of the US, resulting in a large number of small to medium-sized institutions in its banking system. As of November 2009, China\'s top 4 banks have in excess of 67,000 branches (ICBC:18000+, BOC:12000+, CCB:13000+, ABC:24000+) with an additional 140 smaller banks with an undetermined number of branches.\nJapan had 129 banks and 12,000 branches. In 2004, Germany, France, and Italy each had more than 30,000 branches – more than double the 15,000 branches in the UK.\n\n\n=== Mergers and acquisitions ===\nBetween 1985 and 2018 banks engaged in around 28,798 mergers or acquisitions, either as the acquirer or the target company. The overall known value of these deals cumulates to around 5,169 bil. USD. In terms of value, there have been two major waves (1999 and 2007) which both peaked at around 460 bil. USD followed by a steep decline (-82% from 2007 until 2018).\nHere is a list of the largest deals in history in terms of value with participation from at least one bank:\n\n\n== Regulation ==\n\nCurrently, commercial banks are regulated in most jurisdictions by government entities and require a special bank license to operate.\n\nUsually, the definition of the business of banking for the purposes of regulation is extended to include acceptance of deposits, even if they are not repayable to the customer\'s order – although money lending, by itself, is generally not included in the definition.\nUnlike most other regulated industries, the regulator is typically also a participant in the market, being either a publicly or privately governed central bank. Central banks also typically have a monopoly on the business of issuing banknotes. However, in some countries this is not the case. In the UK, for example, the Financial Services Authority licenses banks, and some commercial banks (such as the Bank of Scotland) issue their own banknotes in addition to those issued by the Bank of England, the UK government\'s central bank.\n\nBanking law is based on a contractual analysis of the relationship between the bank (defined above) and the customer – defined as any entity for which the bank agrees to conduct an account.\nThe law implies rights and obligations into this relationship as follows:\n\nThe bank account balance is the financial position between the bank and the customer: when the account is in credit, the bank owes the balance to the customer; when the account is overdrawn, the customer owes the balance to the bank.\nThe bank agrees to pay the customer\'s checks up to the amount standing to the credit of the customer\'s account, plus any agreed overdraft limit.\nThe bank may not pay from the customer\'s account without a mandate from the customer, e.g. a cheque drawn by the customer.\nThe bank agrees to promptly collect the cheques deposited to the customer\'s account as the customer\'s agent, and to credit the proceeds to the customer\'s account.\nThe bank has a right to combine the customer\'s accounts, since each account is just an aspect of the same credit relationship.\nThe bank has a lien on cheques deposited to the customer\'s account, to the extent that the customer is indebted to the bank.\nThe bank must not disclose details of transactions through the customer\'s account – unless the customer consents, there is a public duty to disclose, the bank\'s interests require it, or the law demands it.\nThe bank must not close a customer\'s account without reasonable notice, since cheques are outstanding in the ordinary course of business for several days.These implied contractual terms may be modified by express agreement between the customer and the bank. The statutes and regulations in force within a particular jurisdiction may also modify the above terms and/or create new rights, obligations or limitations relevant to the bank-customer relationship.\nSome types of financial institution, such as building societies and credit unions, may be partly or wholly exempt from bank license requirements, and therefore regulated under separate rules.\nThe requirements for the issue of a bank license vary between jurisdictions but typically include:\n\nMinimum capital\nMinimum capital ratio\n\'Fit and Proper\' requirements for the bank\'s controllers, owners, directors, or senior officers\nApproval of the bank\'s business plan as being sufficiently prudent and plausible.\n\n\n== Types of banking ==\nBanks\' activities can be divided into:\n\nretail banking, dealing directly with individuals and small businesses;\nbusiness banking, providing services to mid-market business;\ncorporate banking, directed at large business entities;\nprivate banking, providing wealth management services to high-net-worth individuals and families;\ninvestment banking, relating to activities on the financial markets.Most banks are profit-making, private enterprises. However, some are owned by government, or are non-profit organizations.\n\n\n=== various banks ===\n\nCommercial banks: the term used for a normal bank to distinguish it from an investment bank. After the Great Depression, the U.S. Congress required that banks only engage in banking activities, whereas investment banks were limited to capital market activities. Since the two no longer have to be under separate ownership, some use the term "commercial bank" to refer to a bank or a division of a bank that mostly deals with deposits and loans from corporations or large businesses.\nCommunity banks: locally operated financial institutions that empower employees to make local decisions to serve their customers and the partners.\nCommunity development banks: regulated banks that provide financial services and credit to under-served markets or populations.\nLand development banks: The special banks providing long-term loans are called land development banks (LDB). The history of LDB is quite old. The first LDB was started at Jhang in Punjab in 1920. The main objective of the LDBs are to promote the development of land, agriculture and increase the agricultural production. The LDBs provide long-term finance to members  directly through their branches.\nCredit unions or co-operative banks: not-for-profit cooperatives owned by the depositors and often offering rates more favourable than for-profit banks. Typically, membership is restricted to employees of a particular company, residents of a defined area, members of a certain union or religious organizations, and their immediate families.\nPostal savings banks: savings banks associated with national postal systems.\nPrivate banks: banks that manage the assets of high-net-worth individuals. Historically a minimum of US$1 million was required to open an account, however, over the last years many private banks have lowered their entry hurdles to US$350,000 for private investors.\nOffshore banks: banks located in jurisdictions with low taxation and regulation. Many offshore banks are essentially private banks.\nSavings bank: in Europe, savings banks took their roots in the 19th or sometimes even in the 18th century. Their original objective was to provide easily accessible savings products to all strata of the population. In some countries, savings banks were created on public initiative; in others, socially committed individuals created foundations to put in place the necessary infrastructure. Nowadays, European savings banks have kept their focus on retail banking: payments, savings products, credits and insurances for individuals or small and medium-sized enterprises. Apart from this retail focus, they also differ from commercial banks by their broadly decentralized distribution network, providing local and regional outreach – and by their socially responsible approach to business and society.\nBuilding societies and Landesbanks: institutions that conduct retail banking.\nEthical banks: banks that prioritize the transparency of all operations and make only what they consider to be socially responsible investments.\nA direct or internet-only bank is a banking operation without any physical bank branches. Transactions are usually accomplished using ATMs and electronic transfers and direct deposits through an online interface.\n\n\n=== Types of investment banks ===\nInvestment banks "underwrite" (guarantee the sale of) stock and bond issues, trade for their own accounts, make markets, provide investment management, and advise corporations on capital market activities such as mergers and acquisitions.\nMerchant banks were traditionally banks which engaged in trade finance. The modern definition, however, refers to banks which provide capital to firms in the form of shares rather than loans. Unlike venture caps, they tend not to invest in new companies.\n\n\n=== Both combined ===\n\nUniversal banks, more commonly known as financial services companies, engage in several of these activities. These big banks are very diversified groups that, among other services, also distribute insurance –  hence the term bancassurance, a portmanteau word combining "banque or bank" and "assurance", signifying that both banking and insurance are provided by the same corporate entity.\n\n\n=== Other types of banks ===\nCentral banks are normally government-owned and charged with quasi-regulatory responsibilities, such as supervising commercial banks, or controlling the cash interest rate. They generally provide liquidity to the banking system and act as the lender of last resort in event of a crisis.\nIslamic banks adhere to the concepts of Islamic law. This form of banking revolves around several well-established principles based on Islamic canons. All banking activities must avoid interest, a concept that is forbidden in Islam. Instead, the bank earns profit (markup) and fees on the financing facilities that it extends to customers.\n\n\n== Challenges within the banking industry ==\n\n\n=== United States ===\n\nThe United States banking industry is one of the most heavily regulated and guarded in the world, with multiple specialized and focused regulators. All banks with FDIC-insured deposits have the Federal Deposit Insurance Corporation (FDIC) as a regulator. However, for soundness examinations (i.e., whether a bank is operating in a sound manner), the Federal Reserve is the primary federal regulator for Fed-member state banks; the Office of the Comptroller of the Currency (OCC) is the primary federal regulator for national banks. State non-member banks are examined by the state agencies as well as the FDIC. National banks have one primary regulator – the OCC.\nEach regulatory agency has their own set of rules and regulations to which banks and thrifts must adhere.\nThe Federal Financial Institutions Examination Council (FFIEC) was established in 1979 as a formal inter-agency body empowered to prescribe uniform principles, standards, and report forms for the federal examination of financial institutions. Although the FFIEC has resulted in a greater degree of regulatory consistency between the agencies, the rules and regulations are constantly changing.\nIn addition to changing regulations, changes in the industry have led to consolidations within the Federal Reserve, FDIC, OTS, and OCC. Offices have been closed, supervisory regions have been merged, staff levels have been reduced and budgets have been cut. The remaining regulators face an increased burden with increased workload and more banks per regulator. While banks struggle to keep up with the changes in the regulatory environment, regulators struggle to manage their workload and effectively regulate their banks. The impact of these changes is that banks are receiving less hands-on assessment by the regulators, less time spent with each institution, and the potential for more problems slipping through the cracks, potentially resulting in an overall increase in bank failures across the United States.\nThe changing economic environment has a significant impact on banks and thrifts as they struggle to effectively manage their interest rate spread in the face of low rates on loans, rate competition for deposits and the general market changes, industry trends and economic fluctuations. It has been a challenge for banks to effectively set their growth strategies with the recent economic market. A rising interest rate environment may seem to help financial institutions, but the effect of the changes on consumers and businesses is not predictable and the challenge remains for banks to grow and effectively manage the spread to generate a return to their shareholders.\nThe management of the banks’ asset portfolios also remains a challenge in today\'s economic environment. Loans are a bank\'s primary asset category and when loan quality becomes suspect, the foundation of a bank is shaken to the core. While always an issue for banks, declining asset quality has become a big problem for financial institutions.\n\nThere are several reasons for this, one of which is the lax attitude some banks have adopted because of the years of “good times.” The potential for this is exacerbated by the reduction in the regulatory oversight of banks and in some cases depth of management. Problems are more likely to go undetected, resulting in a significant impact on the bank when they are discovered. In addition, banks, like any business, struggle to cut costs and have consequently eliminated certain expenses, such as adequate employee training programs.\nBanks also face a host of other challenges such as ageing ownership groups. Across the country, many banks’ management teams and board of directors are ageing. Banks also face ongoing pressure by shareholders, both public and private, to achieve earnings and growth projections. Regulators place added pressure on banks to manage the various categories of risk. Banking is also an extremely competitive industry. Competing in the financial services industry has become tougher with the entrance of such players as insurance agencies, credit unions, cheque cashing services, credit card companies, etc.\nAs a reaction, banks have developed their activities in financial instruments, through financial market operations such as brokerage and have become big players in such activities.\nAnother major challenge is the ageing infrastructure, also called legacy IT. Backend systems were built decades ago and are incompatible to new applications. Fixing bugs and creating interfaces costs huge sums, as knowledgeable programmers become scarce.\n\n\n=== Loan activities of banks ===\nTo be able to provide home buyers and builders with the funds needed, banks must compete for deposits. The phenomenon of disintermediation had to dollars moving from savings accounts and into direct market instruments such as U.S. Department of Treasury obligations, agency securities, and corporate debt. One of the greatest factors in recent years in the movement of deposits was the tremendous growth of money market funds whose higher interest rates attracted consumer deposits.To compete for deposits, US savings institutions offer many different types of plans:\nPassbook or ordinary deposit accounts  –  permit any amount to be added to or withdrawn from the account at any time.\nNOW and Super NOW accounts  –  function like checking accounts but earn interest. A minimum balance may be required on Super NOW accounts.\nMoney market accounts  –  carry a monthly limit of preauthorized transfers to other accounts or persons and may require a minimum or average balance.\nCertificate accounts  –  subject to loss of some or all interest on withdrawals before maturity.\nNotice accounts  –  the equivalent of certificate accounts with an indefinite term. Savers agree to notify the institution a specified time before withdrawal.\nIndividual retirement accounts (IRAs) and Keogh plans  –  a form of retirement savings in which the funds deposited and interest earned are exempt from income tax until after withdrawal.\nChecking accounts  –  offered by some institutions under definite restrictions.\nAll withdrawals and deposits are completely the sole decision and responsibility of the account owner unless the parent or guardian is required to do otherwise for legal reasons.\nClub accounts and other savings accounts  –  designed to help people save regularly to meet certain goals.\n\n\n== Types of accounts ==\n\nBank statements are accounting records produced by banks under the various accounting standards of the world. Under GAAP there are two kinds of accounts: debit and credit. Credit accounts are Revenue, Equity and Liabilities. Debit Accounts are Assets and Expenses. The bank credits a credit account to increase its balance, and debits a credit account to decrease its balance.The customer debits his or her savings/bank (asset) account in his ledger when making a deposit (and the account is normally in debit), while the customer credits a credit card (liability) account in his ledger every time he spends money (and the account is normally in credit). When the customer reads his bank statement, the statement will show a credit to the account for deposits, and debits for withdrawals of funds. The customer with a positive balance will see this balance reflected as a credit balance on the bank statement. If the customer is overdrawn, he will have a negative balance, reflected as a debit balance on the bank statement.\n\n\n=== Brokered deposits ===\nOne source of deposits for banks is brokers who deposit large sums of money on behalf of investors through trust corporations. This money will generally go to the banks which offer the most favourable terms, often better than those offered local depositors. It is possible for a bank to engage in business with no local deposits at all, all funds being brokered deposits. Accepting a significant quantity of such deposits, or "hot money" as it is sometimes called, puts a bank in a difficult and sometimes risky position, as the funds must be lent or invested in a way that yields a return sufficient to pay the high interest being paid on the brokered deposits. This may result in risky decisions and even in eventual failure of the bank. Banks which failed during 2008 and 2009 in the United States during the global financial crisis had, on average, four times more brokered deposits as a percent of their deposits than the average bank. Such deposits, combined with risky real estate investments, factored into the savings and loan crisis of the 1980s. Regulation of brokered deposits is opposed by banks on the grounds that the practice can be a source of external funding to growing communities with insufficient local deposits. There are different types of accounts: saving, recurring and current accounts.\n\n\n=== Custodial accounts ===\nCustodial accounts are accounts in which assets are held for a third party. For example, businesses that accept custody of funds for clients prior to their conversion, return or transfer may have a custodial account at a bank for this purposes.\n\n\n== Globalization in the banking industry ==\nIn modern time there has been huge reductions to the barriers of global competition in the banking industry. Increases in telecommunications and other financial technologies, such as Bloomberg, have allowed banks to extend their reach all over the world, since they no longer have to be near customers to manage both their finances and their risk. The growth in cross-border activities has also increased the demand for banks that can provide various services across borders to different nationalities.\nHowever, despite these reductions in barriers and growth in cross-border activities, the banking industry is nowhere near as globalized as some other industries. In the US, for instance, very few banks even worry about the Riegle–Neal Act, which promotes more efficient interstate banking. In the vast majority of nations around the globe the market share for foreign owned banks is currently less than a tenth of all market shares for banks in a particular nation.\nOne reason the banking industry has not been fully globalized is that it is more convenient to have local banks provide loans to small business and individuals. On the other hand, for large corporations, it is not as important in what nation the bank is in, since the corporation\'s financial information is available around the globe.\n\n\n== See also ==\n\n\n== References ==\n\n\n== External links ==\n\nGuardian Datablog – World\'s Biggest Banks\nBanking, Banks, and Credit Unions from UCB Libraries GovPubs\nA Guide to the National Banking System (PDF). Office of the Comptroller of the Currency (OCC), Washington, D.C. Provides an overview of the national banking system of the US, its regulation, and the OCC.',
 'Financial technology, often shortened to fintech, is the technology and innovation that aims to compete with traditional financial methods in the delivery of financial services. It is an emerging industry that uses technology to improve activities in finance. The use of smartphones for mobile banking, investing services and cryptocurrency are examples of technologies aiming to make financial services more accessible to the general public. Financial technology companies consist of both startups and established financial institutions and technology companies trying to replace or enhance the usage of financial services provided by existing financial companies. \n\n\n== Definition ==\nAfter reviewing more than 200 scientific papers citing the term "fintech," a study on the definition of fintech concluded that "fintech is a new financial industry that applies technology to improve financial activities."  Fintech is the new applications, processes, products, or business models in the financial services industry, composed of one or more complementary financial services and provided as an end-to-end process via the Internet. Fintech can also be considered as “any innovative ideas that improve financial service processes by proposing technology solutions according to different business situations, while the ideas could also lead to new business models or even new businesses."  The goal of "fintech" companies, both old and new, is to improve financial processes and increase automation in the industry. "Fintech" technology use can exist in the front, middle, and back office operations of companies. \n\n\n== Key areas ==\nFinancial technology has been used to automate insurance, trading, banking services, and risk management.The services may originate from various independent service providers including at least one licensed bank or insurer. The interconnection is enabled through open APIs and open banking and supported by regulations such as the European Payment Services Directive.\nIn trading on capital markets, innovative electronic trading platforms facilitate trades online and in real time. Social trading networks allow investors to observe the trading behavior of their peers and expert traders and to follow their investment strategies on currency exchange and capital markets. The platforms require little or no knowledge about financial markets, and have been described as disruptors which provide "a low-cost, sophisticated alternative to traditional wealth managers" by the World Economic Forum.Robo-advisers are a class of automated financial adviser that provide financial advice or investment management online with moderate to minimal human intervention. They provide digital financial advice based on mathematical rules or algorithms, and thus can provide a low-cost alternative to a human advisers.\nGlobal investment in financial technology increased more than 2,200% from $930 million in 2008 to more than $22 billion in 2015. The nascent financial technology industry in London has seen rapid growth over the last few years, according to the office of the Mayor of London. Forty percent of the City of London\'s workforce is employed in financial and technology services.In Europe, $1.5 billion was invested in financial technology companies in 2014, with London-based companies receiving $539 million, Amsterdam-based companies $306 million, and Stockholm-based companies receiving $266 million in investment. After London, Stockholm is the second highest funded city in Europe in the past 10 years. Europe\'s fintech deals reached a five-quarter high, rising from 37 in Q4 2015 to 47 in Q1 2016. Lithuania is starting to become a northern European hub for financial technology companies since the news in 2016 about the possible exit of Britain from the European Union. Lithuania has issued 51 fintech licenses since 2016, 32 of those in 2017. Fintech companies in the United States raised $12.4 billion in 2018, a 43% increase over 2017 figures. In the Asia Pacific region, the growth will see a new financial technology hub to be opened in Sydney, in April 2015. According to KPMG, Sydney\'s financial services sector in 2017 creates 9 per cent of national GDP and is bigger than the financial services sector in either Hong Kong or Singapore. A financial technology innovation lab was launched in Hong Kong in 2015.  In 2015, the Monetary Authority of Singapore launched an initiative named Fintech and Information Group to draw in start-ups from around the world. It pledged to spend $225 million in the fintech sector over the next five years.\n\n\n== Technologies ==\nWithin the financial services industry, many technologies have emerged to be some of the top "fintech" technologies. Some of these technologies include the Internet of Thing (IoT). Artificial Intelligence (AI), Big Data, Robotic Process Automation (RPA), Blockchain.\nFor the Internet of Things, the technology is used by the customers of financial institutions. For example, IoT devices are entering the homes of consumers, these devices allow people to be more connected. IoT creates an opportunity for companies to create pay-by-use business strategies. For financial institutions, they would be able to offer customers with the means to track their utility usage so that they would be able to save money. The Internet of Things is also changing the way people make payments. In the past, a credit card or cash was needed to pay for something or if you wanted to deposit money into your bank account you had to physically go into a bank. IoT eliminates all of this. With connected devices such as smartphones and watches, people have access to their bank accounts wherever and whenever. This enables financial institutions to offer mobile pay and mobile banking. Artificial Intelligence is a blanket term for many different technologies. In terms of the "fintech" industry, AI used in various forms. AI algorithms can be used to predict changes in the stock market and give insight into the economy. AI is used to provide insight on customer spending habits and allows financial institutions to better understand their clients.  Chatbots are another AI-driven tool that banks are starting to use to help with customer service. These chatbots can understand and interpret human text and provide customers with a solution or connect them with a representative to solve the issue.\nBig Data is another "fintech" technology that financial institutions utilize. Data is becoming a keystone of many businesses and financial institutions. In the finance sector, big data can be used to predict client investments and market changes and create new strategies and portfolios. Big Data can be used to analyze customer spending habits and therefore improve fraud detection. Big Data can essentially be utilized in every facet of a finance company. In the back office it can be used to analyze and mitigate risk, in the front and middle offices it can be used to provide more personalized customer services.  Big Data helps banks create segmented marketing strategies and can be used to optimize the operations of a company. Robotic Process Automation is an artificial intelligence technology that focuses on automating specific repetitive tasks. In terms of "fintech", RPA is used to perform manual tasks that often are repetitive and completed daily. These tasks just involve the input of information into a system and do not require much skill thus companies are replacing them with RPA which can complete the task quicker and more efficiently. RPA helps to process financial information such as accounts payable and receivable more efficiently than the manual process and often more accurately. RPA can be used to increase the productivity of the financial company.Blockchain is another AI technology that is beginning to be used in the finance industry. Out of all the "fintech" technologies, blockchain was developed for finance purposes and thus has direct ties to financial institutions. Blockchain is still being an emerging a technology, but many companies are recognize the impact that it will have. Blockchain is being more accepted by fintech companies and other companies throughout the industry and therefore more investment are being made into the technology. \n\n\n== Awards and recognition ==\nFinancial magazine Forbes created a list of the leading disruptors in financial technology for its Forbes 2019 global Fintech 50. In Europe there is a list called the FinTech 50, which aims to recognise the most innovative companies in fintech.A report published in February 2016 by EY commissioned by the UK Treasury compared seven leading fintech hubs: the United Kingdom, California, New York City, Singapore, Germany, Australia and Hong Kong. It ranked California first for \'talent\' and \'capital\', the United Kingdom first for \'government policy\' and New York City first for \'demand\'.For the past few years, PwC has posted a report called the "Global Fintech Report". In the 2019, the report covers many topics revolving around the financial technology sector. The report discusses the landscape of the "Fintech" industry and some of the emerging technologies in the sector. It provides strategies for financial institutions on how to incorporate more "fintech" technologies into their business. \n\n\n== Outlook ==\nFinance is seen as one of the industries most vulnerable to disruption by software because financial services, much like publishing, are made of information rather than concrete goods. In particular blockchains have the potential to reduce the cost of transacting in a financial system.  While finance has been shielded by regulation until now, and weathered the dot-com boom without major upheaval, a new wave of startups is increasingly "disaggregating" global banks. However, aggressive enforcement of the Bank Secrecy Act and money transmission regulations represents an ongoing threat to fintech companies. In response, the International Monetary Fund (IMF) and the World Bank jointly presented Bali Fintech Agenda on October 11, 2018 which consists of 12 policy elements acting as a guidelines for various governments and central banking institutions to adopt and deploy "rapid advances in financial technology".The New York Venture Capital Association (NYVCA) hosts annual summits to educate those interested in learning more about fintech. In 2018 alone, fintech was responsible for over 1,700 deals worth over 40 billion dollars.\n\n\n== Challenges and solutions ==\nIn addition to established competitors, fintech companies often face doubts from financial regulators like issuing banks and the Federal Government.Data security is another issue regulators are concerned about because of the threat of hacking as well as the need to protect sensitive consumer and corporate financial data. Leading global fintech companies are proactively turning to cloud technology to meet increasingly stringent compliance regulations.The Federal Trade Commission provides free resources for corporations of all sizes to meet their legal obligations of protecting sensitive data. Several private initiatives suggest that multiple layers of defense can help isolate and secure financial data.In the European Union, fintech companies must adhere to data protection laws, such as GDPR. Companies need to proactively protect users and companies data or face fines of 20 million euros, or in the case of an undertaking, up to 4% of their total global turnover. In addition to GDPR, European financial institutions including fintech firms have to update their regulatory affairs departments with the Payment Services Directive (PSD2), meaning they must organise their revenue structure around a central goal of privacy.Any data breach, no matter how small, can result in direct liability to a company (see the Gramm–Leach–Bliley Act) and ruin a fintech company\'s reputation.The online financial sector is also an increasing target of distributed denial of service extortion attacks. This security challenge is also faced by historical bank companies since they do offer Internet-connected customer services.\n\n\n== References and notes ==\n\n\n== External links ==\n Media related to Financial technology at Wikimedia Commons',
 'The International Monetary Fund (IMF), also known as the Fund,  is an international organization headquartered in Washington, D.C., consisting of 189 countries working to foster global monetary cooperation, secure financial stability, facilitate international trade, promote high employment and sustainable economic growth, and reduce poverty around the world while periodically depending on World Bank for its resources. Formed in 1944 at the Bretton Woods Conference primarily by the ideas of Harry Dexter White and John Maynard Keynes, it came into formal existence in 1945 with 29 member countries and the goal of reconstructing the international payment system. It now plays a central role in the management of balance of payments difficulties and international financial crises. Countries contribute funds to a pool through a quota system from which countries experiencing balance of payments problems can borrow money. As of 2016, the fund had XDR 477 billion (about US$667 billion).Through the fund and other activities such as the gathering of statistics and analysis, surveillance of its members\' economies, and the demand for particular policies, the IMF works to improve the economies of its member countries. The organization\'s objectives stated in the Articles of Agreement are: to promote international monetary co-operation, international trade, high employment, exchange-rate stability, sustainable economic growth, and making resources available to member countries in financial difficulty. IMF funds come from two major sources: quotas and loans. Quotas, which are pooled funds of member nations, generate most IMF funds. The size of a member\'s quota depends on its economic and financial importance in the world. Nations with larger economic importance have larger quotas. The quotas are increased periodically as a means of boosting the IMF\'s resources.\nThe current Managing Director (MD) and Chairwoman of the International Monetary Fund is Bulgarian Economist Kristalina Georgieva, who has held the post since 1 October 2019.Gita Gopinath was appointed as Chief Economist of IMF from 1 October 2018. She received her PhD in economics from Princeton University. Prior to her IMF appointment she was economic adviser to the Chief Minister of Kerala, India.\n\n\n== Functions ==\nAccording to the IMF itself, it works to foster global growth and economic stability by providing policy advice and financing the members by working with developing nations  helps them achieve macroeconomic stability and reduce poverty. The rationale for this is that private international capital markets function imperfectly and many countries have limited access to financial markets. Such market imperfections, together with balance-of-payments financing, provide the justification for official financing, without which many countries could only correct large external payment imbalances through measures with adverse economic consequences. The IMF provides alternate sources of financing.\nUpon the founding of the IMF, its three primary functions were: to oversee the fixed exchange rate arrangements between countries, thus helping national governments manage their exchange rates and allowing these governments to prioritize economic growth, and to provide short-term capital to aid the balance of payments. This assistance was meant to prevent the spread of international economic crises. The IMF was also intended to help mend the pieces of the international economy after the Great Depression and World War II. As well, to provide capital investments for economic growth and projects such as infrastructure.\nThe IMF\'s role was fundamentally altered by the floating exchange rates post-1971. It shifted to examining the economic policies of countries with IMF loan agreements to determine if a shortage of capital was due to economic fluctuations or economic policy. The IMF also researched what types of government policy would ensure economic recovery. A particular concern of the IMF was to prevent financial crisis, such as those in Mexico 1982, Brazil in 1987, East Asia in 1997–98 and Russia in 1998, from spreading and threatening the entire global financial and currency system. The challenge was to promote and implement policy that reduced the frequency of crises among the emerging market countries, especially the middle-income countries which are vulnerable to massive capital outflows. Rather than maintaining a position of oversight of only exchange rates, their function became one of surveillance of the overall macroeconomic performance of member countries. Their role became a lot more active because the IMF now manages economic policy rather than just exchange rates.\nIn addition, the IMF negotiates conditions on lending and loans under their policy of conditionality, which was established in the 1950s. Low-income countries can borrow on concessional terms, which means there is a period of time with no interest rates, through the Extended Credit Facility (ECF), the Standby Credit Facility (SCF) and the Rapid Credit Facility (RCF). Nonconcessional loans, which include interest rates, are provided mainly through Stand-By Arrangements (SBA), the Flexible Credit Line (FCL), the Precautionary and Liquidity Line (PLL), and the Extended Fund Facility. The IMF provides emergency assistance via the Rapid Financing Instrument (RFI) to members facing urgent balance-of-payments needs.\n\n\n=== Surveillance of the global economy ===\nThe IMF is mandated to oversee the international monetary and financial system and monitor the economic and financial policies of its member countries. This activity is known as surveillance and facilitates international co-operation. Since the demise of the Bretton Woods system of fixed exchange rates in the early 1970s, surveillance has evolved largely by way of changes in procedures rather than through the adoption of new obligations. The responsibilities changed from those of guardian to those of overseer of members\' policies.\nThe Fund typically analyses the appropriateness of each member country\'s economic and financial policies for achieving orderly economic growth, and assesses the consequences of these policies for other countries and for the global economy.\n\nIn 1995 the International Monetary Fund began work on data dissemination standards with the view of guiding IMF member countries to disseminate their economic and financial data to the public. The International Monetary and Financial Committee (IMFC) endorsed the guidelines for the dissemination standards and they were split into two tiers: The General Data Dissemination System (GDDS) and the Special Data Dissemination Standard (SDDS).\nThe executive board approved the SDDS and GDDS in 1996 and 1997 respectively, and subsequent amendments were published in a revised Guide to the General Data Dissemination System. The system is aimed primarily at statisticians and aims to improve many aspects of statistical systems in a country. It is also part of the World Bank Millennium Development Goals and Poverty Reduction Strategic Papers.\nThe primary objective of the GDDS is to encourage member countries to build a framework to improve data quality and statistical capacity building to evaluate statistical needs, set priorities in improving the timeliness, transparency, reliability and accessibility of financial and economic data. Some countries initially used the GDDS, but later upgraded to SDDS.\nSome entities that are not themselves IMF members also contribute statistical data to the systems:\n\nPalestinian Authority – GDDS\nHong Kong – SDDS\nMacau – GDDS\nEU institutions:\nthe European Central Bank for the Eurozone – SDDS\nEurostat for the whole EU – SDDS, thus providing data from Cyprus (not using any DDSystem on its own) and Malta (using only GDDS on its own)\n\n\n=== Conditionality of loans ===\nIMF conditionality is a set of policies or conditions that the IMF requires in exchange for financial resources. The IMF does require collateral from countries for loans but also requires the government seeking assistance to correct its macroeconomic imbalances in the form of policy reform. If the conditions are not met, the funds are withheld. The concept of conditionality was introduced in a 1952 Executive Board decision and later incorporated into the Articles of Agreement.\nConditionality is associated with economic theory as well as an enforcement mechanism for repayment. Stemming primarily from the work of Jacques Polak, the theoretical underpinning of conditionality was the "monetary approach to the balance of payments".\n\n\n==== Structural adjustment ====\n\nSome of the conditions for structural adjustment can include:\n\nCutting expenditures, also known as austerity.\nFocusing economic output on direct export and resource extraction,\nDevaluation of currencies,\nTrade liberalisation, or lifting import and export restrictions,\nIncreasing the stability of investment (by supplementing foreign direct investment with the opening of domestic stock markets),\nBalancing budgets and not overspending,\nRemoving price controls and state subsidies,\nPrivatization, or divestiture of all or part of state-owned enterprises,\nEnhancing the rights of foreign investors vis-a-vis national laws,\nImproving governance and fighting corruption.These conditions are known as the Washington Consensus.\n\n\n==== Benefits ====\nThese loan conditions ensure that the borrowing country will be able to repay the IMF and that the country will not attempt to solve their balance-of-payment problems in a way that would negatively impact the international economy. The incentive problem of moral hazard—when economic agents maximise their own utility to the detriment of others because they do not bear the full consequences of their actions—is mitigated through conditions rather than providing collateral; countries in need of IMF loans do not generally possess internationally valuable collateral anyway.Conditionality also reassures the IMF that the funds lent to them will be used for the purposes defined by the Articles of Agreement and provides safeguards that country will be able to rectify its macroeconomic and structural imbalances. In the judgment of the IMF, the adoption by the member of certain corrective measures or policies will allow it to repay the IMF, thereby ensuring that the resources will be available to support other members.As of 2004, borrowing countries have had a very good track record for repaying credit extended under the IMF\'s regular lending facilities with full interest over the duration of the loan. This indicates that IMF lending does not impose a burden on creditor countries, as lending countries receive market-rate interest on most of their quota subscription, plus any of their own-currency subscriptions that are loaned out by the IMF, plus all of the reserve assets that they provide the IMF.\n\n\n== History ==\n\nThe IMF was originally laid out as a part of the Bretton Woods system exchange agreement in 1944. During the Great Depression, countries sharply raised barriers to trade in an attempt to improve their failing economies. This led to the devaluation of national currencies and a decline in world trade.\n\nThis breakdown in international monetary co-operation created a need for oversight. The representatives of 45 governments met at the Bretton Woods Conference in the Mount Washington Hotel in Bretton Woods, New Hampshire, in the United States, to discuss a framework for postwar international economic co-operation and how to rebuild Europe.\nThere were two views on the role the IMF should assume as a global economic institution. American delegate Harry Dexter White foresaw an IMF that functioned more like a bank, making sure that borrowing states could repay their debts on time. Most of White\'s plan was incorporated into the final acts adopted at Bretton Woods. British economist John Maynard Keynes, on the other hand, imagined that the IMF would be a cooperative fund upon which member states could draw to maintain economic activity and employment through periodic crises. This view suggested an IMF that helped governments and to act as the United States government had during the New Deal in response to World War II.\n\nThe IMF formally came into existence on 27 December 1945, when the first 29 countries ratified its Articles of Agreement. By the end of 1946 the IMF had grown to 39 members. On 1 March 1947, the IMF began its financial operations, and on 8 May France became the first country to borrow from it.\n\nThe IMF was one of the key organisations of the international economic system; its design allowed the system to balance the rebuilding of international capitalism with the maximisation of national economic sovereignty and human welfare, also known as embedded liberalism. The IMF\'s influence in the global economy steadily increased as it accumulated more members. The increase reflected in particular the attainment of political independence by many African countries and more recently the 1991 dissolution of the Soviet Union because most countries in the Soviet sphere of influence did not join the IMF.The Bretton Woods exchange rate system prevailed until 1971, when the United States government suspended the convertibility of the US$ (and dollar reserves held by other governments) into gold. This is known as the Nixon Shock. The changes to the IMF articles of agreement reflecting these changes were ratified by the 1976 Jamaica Accords. Later in the 1970s, large commercial banks began lending to states because they were awash in cash deposited by oil exporters.  The lending of the so-called money center banks led to the IMF changing its role in the 1980s after a world recession provoked a crisis that brought the IMF back into global financial governance.\n\n\n=== 21st century ===\nThe IMF provided two major lending packages in the early 2000s to Argentina (during the 1998–2002 Argentine great depression) and Uruguay (after the 2002 Uruguay banking crisis). However, by the mid-2000s, IMF lending was at its lowest share of world GDP since the 1970s.In May 2010, the IMF participated, in 3:11 proportion, in the first Greek bailout that totalled €110 billion, to address the great accumulation of public debt, caused by continuing large public sector deficits. As part of the bailout, the Greek government agreed to adopt austerity measures that would reduce the deficit from 11% in 2009 to "well below 3%" in 2014. The bailout did not include debt restructuring measures such as a haircut, to the chagrin of the Swiss, Brazilian, Indian, Russian, and Argentinian Directors of the IMF, with the Greek authorities themselves (at the time, PM George Papandreou and Finance Minister Giorgos Papakonstantinou) ruling out a haircut.A second bailout package of more than €100 billion was agreed over the course of a few months from October 2011, during which time Papandreou was forced from office. The so-called Troika, of which the IMF is part, are joint managers of this programme, which was approved by the Executive Directors of the IMF on 15 March 2012 for XDR 23.8 billion and saw private bondholders take a haircut of upwards of 50%. In the interval between May 2010 and February 2012 the private banks of Holland, France and Germany reduced exposure to Greek debt from €122 billion to €66 billion.As of January 2012, the largest borrowers from the IMF in order were Greece, Portugal, Ireland, Romania, and Ukraine.On 25 March 2013, a €10 billion international bailout of Cyprus was agreed by the Troika, at the cost to the Cypriots of its agreement: to close the country\'s second-largest bank; to impose a one-time bank deposit levy on Bank of Cyprus uninsured deposits. No insured deposit of €100k or less were to be affected under the terms of a novel bail-in scheme.The topic of sovereign debt restructuring was taken up by the IMF in April 2013 for the first time since 2005, in a report entitled "Sovereign Debt Restructuring: Recent Developments and Implications for the Fund\'s Legal and Policy Framework". The paper, which was discussed by the board on 20 May, summarised the recent experiences in Greece, St Kitts and Nevis, Belize, and Jamaica. An explanatory interview with Deputy Director Hugh Bredenkamp was published a few days later, as was a deconstruction by Matina Stevis of the Wall Street Journal.In the October 2013 Fiscal Monitor publication, the IMF suggested that a capital levy capable of reducing Euro-area government debt ratios to "end-2007 levels" would require a very high tax rate of about 10%.The Fiscal Affairs department of the IMF, headed at the time by Acting Director Sanjeev Gupta, produced a January 2014 report entitled "Fiscal Policy and Income Inequality" that stated that "Some taxes levied on wealth, especially on immovable property, are also an option for economies seeking more progressive taxation ... Property taxes are equitable and efficient, but underutilized in many economies ... There is considerable scope to exploit this tax more fully, both as a revenue source and as a redistributive instrument."At the end of March 2014, the IMF secured an $18 billion bailout fund for the provisional government of Ukraine in the aftermath of the 2014 Ukrainian revolution.\n\n\n== Member countries ==\n\nNot all member countries of the IMF are sovereign states, and therefore not all "member countries" of the IMF are members of the United Nations. Amidst "member countries" of the IMF that are not member states of the UN are non-sovereign areas with special jurisdictions that are officially under the sovereignty of full UN member states, such as Aruba, Curaçao, Hong Kong, and Macau, as well as Kosovo. The corporate members appoint ex-officio voting members, who are listed below.  All members of the IMF are also International Bank for Reconstruction and Development (IBRD) members and vice versa.Former members are Cuba (which left in 1964), and the Republic of China (Taiwan), which was ejected from the UN in 1980 after losing the support of then United States President Jimmy Carter and was replaced by the People\'s Republic of China. However, "Taiwan Province of China" is still listed in the official IMF indices.Apart from Cuba, the other UN states that do not belong to the IMF are Andorra, Liechtenstein, Monaco and North Korea.\nThe former Czechoslovakia was expelled in 1954 for "failing to provide required data" and was readmitted in 1990, after the Velvet Revolution. Poland withdrew in 1950—allegedly pressured by the Soviet Union—but returned in 1986.\n\n\n=== Qualifications ===\nAny country may apply to be a part of the IMF. Post-IMF formation, in the early postwar period, rules for IMF membership were left relatively loose. Members needed to make periodic membership payments towards their quota, to refrain from currency restrictions unless granted IMF permission, to abide by the Code of Conduct in the IMF Articles of Agreement, and to provide national economic information. However, stricter rules were imposed on governments that applied to the IMF for funding.The countries that joined the IMF between 1945 and 1971 agreed to keep their exchange rates secured at rates that could be adjusted only to correct a "fundamental disequilibrium" in the balance of payments, and only with the IMF\'s agreement.\n\n\n=== Benefits ===\nMember countries of the IMF have access to information on the economic policies of all member countries, the opportunity to influence other members\' economic policies, technical assistance in banking, fiscal affairs, and exchange matters, financial support in times of payment difficulties, and increased opportunities for trade and investment.\n\n\n== Leadership ==\n\n\n=== Board of Governors ===\nThe Board of Governors consists of one governor and one alternate governor for each member country. Each member country appoints its two governors. The Board normally meets once a year and is responsible for electing or appointing executive directors to the Executive Board. While the Board of Governors is officially responsible for approving quota increases, special drawing right allocations, the admittance of new members, compulsory withdrawal of members, and amendments to the Articles of Agreement and By-Laws, in practice it has delegated most of its powers to the IMF\'s Executive Board.The Board of Governors is advised by the International Monetary and Financial Committee and the Development Committee. The International Monetary and Financial Committee has 24 members and monitors developments in global liquidity and the transfer of resources to developing countries. The Development Committee has 25 members and advises on critical development issues and on financial resources required to promote economic development in developing countries. They also advise on trade and environmental issues.\nThe Board of Governors reports directly to the Managing Director of the IMF, Kristalina Georgieva.\n\n\n=== Executive Board ===\n24 Executive Directors make up the Executive Board. The Executive Directors represent all 189 member countries in a geographically based roster. Countries with large economies have their own Executive Director, but most countries are grouped in constituencies representing four or more countries.Following the 2008 Amendment on Voice and Participation which came into effect in March 2011, seven countries each appoint an Executive Director: the United States, Japan, China, Germany, France, the United Kingdom, and Saudi Arabia. The remaining 17 Directors represent constituencies consisting of 2 to 23 countries. This Board usually meets several times each week. The Board membership and constituency is scheduled for periodic review every eight years.\n\n\n=== Managing Director ===\nThe IMF is led by a managing director, who is head of the staff and serves as Chairman of the Executive Board. The managing director is assisted by a First Deputy managing director and three other Deputy Managing Directors. Historically the IMF\'s managing director has been European and the president of the World Bank has been from the United States. However, this standard is increasingly being questioned and competition for these two posts may soon open up to include other qualified candidates from any part of the world. In August, 2019, the International Monetary Fund has removed the aged limit which is 65 or over for its managing director position.In 2011 the world\'s largest developing countries, the BRIC nations, issued a statement declaring that the tradition of appointing a European as managing director undermined the legitimacy of the IMF and called for the appointment to be merit-based.\n\n\n==== List of Managing Directors ====\n\nPrevious managing director Dominique Strauss-Kahn was arrested in connection with charges of sexually assaulting a New York hotel room attendant and resigned on 18 May. The charges were later dropped. On 28 June 2011 Christine Lagarde was confirmed as managing director of the IMF for a five-year term starting on 5 July 2011.  She was re-elected by consensus for a second five-year term, starting 5 July 2016, being the only candidate nominated for the post of Managing Director.\n\n\n== Voting power ==\nVoting power in the IMF is based on a quota system. Each member has a number of basic votes (each member\'s number of basic votes equals 5.502% of the total votes), plus one additional vote for each special drawing right (SDR) of 100,000 of a member country\'s quota. The special drawing right is the unit of account of the IMF and represents a claim to currency. It is based on a basket of key international currencies. The basic votes generate a slight bias in favour of small countries, but the additional votes determined by SDR outweigh this bias. Changes in the voting shares require approval by a super-majority of 85% of voting power.\nIn December 2015, the United States Congress adopted a legislation authorising the 2010 Quota and Governance Reforms. As a result,\n\nall 188 members\' quotas will increase from a total of about XDR 238.5 billion to about XDR 477 billion, while the quota shares and voting power of the IMF\'s poorest member countries will be protected.\nmore than 6 percent of quota shares will shift to dynamic emerging market and developing countries and also from over-represented to under-represented members.\nfour emerging market countries (Brazil, China, India, and Russia) will be among the ten largest members of the IMF. Other top 10 members are the United States, Japan, Germany, France, the United Kingdom and Italy.\n\n\n=== Effects of the quota system ===\nThe IMF\'s quota system was created to raise funds for loans. Each IMF member country is assigned a quota, or contribution, that reflects the country\'s relative size in the global economy. Each member\'s quota also determines its relative voting power. Thus, financial contributions from member governments are linked to voting power in the organisation.This system follows the logic of a shareholder-controlled organisation: wealthy countries have more say in the making and revision of rules. Since decision making at the IMF reflects each member\'s relative economic position in the world, wealthier countries that provide more money to the IMF have more influence than poorer members that contribute less; nonetheless, the IMF focuses on redistribution.\n\n\n=== Inflexibility of voting power ===\nQuotas are normally reviewed every five years and can be increased when deemed necessary by the Board of Governors. IMF voting shares are relatively inflexible: countries that grow economically have tended to become under-represented as their voting power lags behind. Currently, reforming the representation of developing countries within the IMF has been suggested. These countries\' economies represent a large portion of the global economic system but this is not reflected in the IMF\'s decision making process through the nature of the quota system. Joseph Stiglitz argues, "There is a need to provide more effective voice and representation for developing countries, which now represent a much larger portion of world economic activity since 1944, when the IMF was created." In 2008, a number of quota reforms were passed including shifting 6% of quota shares to dynamic emerging markets and developing countries.\n\n\n=== Overcoming borrower/creditor divide ===\nThe IMF\'s membership is divided along income lines: certain countries provide the financial resources while others use these resources. Both developed country "creditors" and developing country "borrowers" are members of the IMF. The developed countries provide the financial resources but rarely enter into IMF loan agreements; they are the creditors. Conversely, the developing countries use the lending services but contribute little to the pool of money available to lend because their quotas are smaller; they are the borrowers. Thus, tension is created around governance issues because these two groups, creditors and borrowers, have fundamentally different interests.The criticism is that the system of voting power distribution through a quota system institutionalises borrower subordination and creditor dominance. The resulting division of the IMF\'s membership into borrowers and non-borrowers has increased the controversy around conditionality because the borrowers are interested in increasing loan access while creditors want to maintain reassurance that the loans will be repaid.\n\n\n== Use ==\nA recent source revealed that the average overall use of IMF credit per decade increased, in real terms, by 21% between the 1970s and 1980s, and increased again by just over 22% from the 1980s to the 1991–2005 period. Another study has suggested that since 1950 the continent of Africa alone has received $300 billion from the IMF, the World Bank, and affiliate institutions.A study by Bumba Mukherjee found that developing democratic countries benefit more from IMF programs than developing autocratic countries because policy-making, and the process of deciding where loaned money is used, is more transparent within a democracy. One study done by Randall Stone found that although earlier studies found little impact of IMF programs on balance of payments, more recent studies using more sophisticated methods and larger samples "usually found IMF programs improved the balance of payments".\n\n\n=== Exceptional Access Framework – sovereign debt ===\nThe Exceptional Access Framework was created in 2003 when John B. Taylor was Under Secretary of the US Treasury for International Affairs.  The new Framework became fully operational in February 2003 and it was applied in the subsequent decisions on Argentina and Brazil. Its purpose was to place some sensible rules and limits on the way the IMF makes loans to support governments with debt problem—especially in emerging markets—and thereby move away from the bailout mentality of the 1990s. Such a reform was essential for ending the crisis atmosphere that then existed in emerging markets.  The reform was closely related to, and put in place nearly simultaneously with, the actions of several emerging market countries to place collective action clauses in their bond contracts.\nIn 2010, the framework was abandoned so the IMF could make loans to Greece in an unsustainable and political situation.The topic of sovereign debt restructuring was taken up by IMF staff in April 2013 for the first time since 2005, in a report entitled "Sovereign Debt Restructuring: Recent Developments and Implications for the Fund\'s Legal and Policy Framework". The paper, which was discussed by the board on 20 May, summarised the recent experiences in Greece, St Kitts and Nevis, Belize and Jamaica. An explanatory interview with Deputy Director Hugh Bredenkamp was published a few days later, as was a deconstruction by Matina Stevis of the Wall Street Journal.The staff was directed to formulate an updated policy, which was accomplished on 22 May 2014 with a report entitled "The Fund\'s Lending Framework and Sovereign Debt: Preliminary Considerations", and taken up by the Executive Board on 13 June. The staff proposed that "in circumstances where a (Sovereign) member has lost market access and debt is considered sustainable ... the IMF would be able to provide Exceptional Access on the basis of a debt operation that involves an extension of maturities", which was labelled a "reprofiling operation". These reprofiling operations would "generally be less costly to the debtor and creditors—and thus to the system overall—relative to either an upfront debt reduction operation or a bail-out that is followed by debt reduction ... (and) would be envisaged only when both (a) a member has lost market access and (b) debt is assessed to be sustainable, but not with high probability ... Creditors will only agree if they understand that such an amendment is necessary to avoid a worse outcome: namely, a default and/or an operation involving debt reduction ... Collective action clauses, which now exist in most—but not all—bonds, would be relied upon to address collective action problems."\n\n\n== IMF and globalization ==\nGlobalization encompasses three institutions: global financial markets and transnational companies, national governments linked to each other in economic and military alliances led by the United States, and rising "global governments" such as World Trade Organization (WTO), IMF, and World Bank. Charles Derber argues in his book People Before Profit, "These interacting institutions create a new global power system where sovereignty is globalized, taking power and constitutional authority away from nations and giving it to global markets and international bodies". Titus Alexander argues that this system institutionalises global inequality between western countries and the Majority World in a form of global apartheid, in which the IMF is a key pillar.The establishment of globalised economic institutions has been both a symptom of and a stimulus for globalisation. The development of the World Bank, the IMF regional development banks such as the European Bank for Reconstruction and Development (EBRD), and multilateral trade institutions such as the WTO signals a move away from the dominance of the state as the primary actor analysed in international affairs. Globalization has thus been transformative in terms of a reconceptualising of state sovereignty.Following United States President Bill Clinton\'s administration\'s aggressive financial deregulation campaign in the 1990s, globalisation leaders overturned long standing restrictions by governments that limited foreign ownership of their banks, deregulated currency exchange, and eliminated restrictions on how quickly money could be withdrawn by foreign investors.An IMF report from May 2015 estimated that the world\'s governments directly and indirectly subsidise fossil fuel companies with $5.3tn (£3.4tn) a year. The measurement accounts for the unpaid costs that polluters impose on governments by the burning of coal, oil, and gas. The projected impacts of fossil fuel subsidies on populations—air pollution, health problems, floods, droughts, and storms driven by climate change—account for over half of the reported global expenditure.\n\n\n== Criticisms ==\nOverseas Development Institute (ODI) research undertaken in 1980 included criticisms of the IMF which support the analysis that it is a pillar of what activist Titus Alexander calls global apartheid.\nDeveloped countries were seen to have a more dominant role and control over less developed countries (LDCs).\nThe Fund worked on the incorrect assumption that all payments disequilibria were caused domestically. The Group of 24 (G-24), on behalf of LDC members, and the United Nations Conference on Trade and Development (UNCTAD) complained that the IMF did not distinguish sufficiently between disequilibria with predominantly external as opposed to internal causes. This criticism was voiced in the aftermath of the 1973 oil crisis. Then LDCs found themselves with payments deficits due to adverse changes in their terms of trade, with the Fund prescribing stabilisation programmes similar to those suggested for deficits caused by government over-spending. Faced with long-term, externally generated disequilibria, the G-24 argued for more time for LDCs to adjust their economies.\nSome IMF policies may be anti-developmental; the report said that deflationary effects of IMF programmes quickly led to losses of output and employment in economies where incomes were low and unemployment was high. Moreover, the burden of the deflation is disproportionately borne by the poor.\nThe IMF\'s initial policies were based in theory and influenced by differing opinions and departmental rivalries. Critics suggest that its intentions to implement these policies in countries with widely varying economic circumstances were misinformed and lacked economic rationale.ODI conclusions were that the IMF\'s very nature of promoting market-oriented approaches attracted unavoidable criticism. On the other hand, the IMF could serve as a scapegoat while allowing governments to blame international bankers. The ODI conceded that the IMF was insensitive to political aspirations of LDCs, while its policy conditions were inflexible.Argentina, which had been considered by the IMF to be a model country in its compliance to policy proposals by the Bretton Woods institutions, experienced a catastrophic economic crisis in 2001, which some believe to have been caused by IMF-induced budget restrictions—which undercut the government\'s ability to sustain national infrastructure even in crucial areas such as health, education, and security—and privatisation of strategically vital national resources. Others attribute the crisis to Argentina\'s misdesigned fiscal federalism, which caused subnational spending to increase rapidly. The crisis added to widespread hatred of this institution in Argentina and other South American countries, with many blaming the IMF for the region\'s economic problems. The current—as of early 2006—trend toward moderate left-wing governments in the region and a growing concern with the development of a regional economic policy largely independent of big business pressures has been ascribed to this crisis.\nIn 2006, a senior ActionAid policy analyst Akanksha Marphatia stated that IMF policies in Africa undermine any possibility of meeting the Millennium Development Goals (MDGs) due to imposed restrictions that prevent spending on important sectors, such as education and health.In an interview (2008-05-19), the former Romanian Prime Minister Călin Popescu-Tăriceanu claimed that "Since 2005, IMF is constantly making mistakes when it appreciates the country\'s economic performances". Former Tanzanian President Julius Nyerere, who claimed that debt-ridden African states were ceding sovereignty to the IMF and the World Bank, famously asked, "Who elected the IMF to be the ministry of finance for every country in the world?"Former chief economist of IMF and former Reserve Bank of India (RBI) Governor Raghuram Rajan who predicted Financial crisis of 2007–08 criticised IMF for remaining a sideline player to the developed world. He criticised IMF for praising the monetary policies of the US, which he believed were wreaking havoc in emerging markets. He had been critical of the ultra-loose money policies of the Western nations and IMF.Countries such as Zambia have not received proper aid with long-lasting effects, leading to concern from economists. Since 2005, Zambia (as well as 29 other African countries) did receive debt write-offs, which helped with the country\'s medical and education funds. However, Zambia returned to a debt of over half its GDP in less than a decade. American economist William Easterly, sceptical of the IMF\'s methods, had initially warned that "debt relief would simply encourage more reckless borrowing by crooked governments unless it was accompanied by reforms to speed up economic growth and improve governance," according to The Economist.\n\n\n=== Conditionality ===\nThe IMF has been criticised for being "out of touch" with local economic conditions, cultures, and environments in the countries they are requiring policy reform. The economic advice the IMF gives might not always take into consideration the difference between what spending means on paper and how it is felt by citizens.  Countries charge that with excessive conditionality, they do not "own" the programs and the links are broken between a recipient country\'s people, its government, and the goals being pursued by the IMF.Jeffrey Sachs argues that the IMF\'s "usual prescription is \'budgetary belt tightening to countries who are much too poor to own belts\'". Sachs wrote that the IMF\'s role as a generalist institution specialising in macroeconomic issues needs reform. Conditionality has also been criticised because a country can pledge collateral of "acceptable assets" to obtain waivers—if one assumes that all countries are able to provide "acceptable collateral".One view is that conditionality undermines domestic political institutions. The recipient governments are sacrificing policy autonomy in exchange for funds, which can lead to public resentment of the local leadership for accepting and enforcing the IMF conditions. Political instability can result from more leadership turnover as political leaders are replaced in electoral backlashes. IMF conditions are often criticised for reducing government services, thus increasing unemployment.Another criticism is that IMF programs are only designed to address poor governance, excessive government spending, excessive government intervention in markets, and too much state ownership. This assumes that this narrow range of issues represents the only possible problems; everything is standardised and differing contexts are ignored. A country may also be compelled to accept conditions it would not normally accept had they not been in a financial crisis in need of assistance.On top of that, regardless of what methodologies and data sets used, it comes to same the conclusion of exacerbating income inequality. With Gini coefficient, it became clear that countries with IMF programs face increased income inequality.It is claimed that conditionalities retard social stability and hence inhibit the stated goals of the IMF, while Structural Adjustment Programs lead to an increase in poverty in recipient countries. The IMF sometimes advocates "austerity programmes", cutting public spending and increasing taxes even when the economy is weak, to bring budgets closer to a balance, thus reducing budget deficits. Countries are often advised to lower their corporate tax rate. In Globalization and Its Discontents, Joseph E. Stiglitz, former chief economist and senior vice-president at the World Bank, criticises these policies. He argues that by converting to a more monetarist approach, the purpose of the fund is no longer valid, as it was designed to provide funds for countries to carry out Keynesian reflations, and that the IMF "was not participating in a conspiracy, but it was reflecting the interests and ideology of the Western financial community."Stiglitz concludes, "Modern high-tech warfare is designed to remove physical contact: dropping bombs from 50,000 feet ensures that one does not \'feel\' what one does. Modern economic management is similar: from one\'s luxury hotel, one can callously impose policies about which one would think twice if one knew the people whose lives one was destroying."The researchers Eric Toussaint and Damien Millet argue that the IMF \'s policies amount to a new form of colonization that does not need a military presence: \n\nFollowing the exigencies of the governments of the richest companies, the IMF, permitted countries in crisis to borrow in order to avoid default on their repayments. Caught in the debt\'s downward spiral, developing countries soon had no other recourse than to take on new debt in order to repay the old debt. Before providing them with new loans, at higher interest rates, future leaders asked the IMF, to intervene with the guarantee of ulterior reimbursement, asking for a signed agreement with the said countries. The IMF  thus agreed to restart the flow of the \'finance pump\' on condition that the concerned countries first use this money to reimburse banks and other private lenders, while restructuring their economy at the IMF\'s discretion: these were the famous conditionalities, detailed in the Structural Adjustment Programs. The IMF and its ultra-liberal experts took control of the borrowing countries\' economic policies. A new form of colonization was thus instituted. It was not even necessary to establish an administrative or military presence; the debt alone maintained this new form of submission."\nInternational politics play an important role in IMF decision making. The clout of member states is roughly proportional to its contribution to IMF finances. The United States has the greatest number of votes and therefore wields the most influence. Domestic politics often come into play, with politicians in developing countries using conditionality to gain leverage over the opposition to influence policy.\n\n\n=== Reform ===\n\n\n==== Function and policies ====\nThe IMF is only one of many international organisations, and it is a generalist institution that deals only with macroeconomic issues; its core areas of concern in developing countries are very narrow. One proposed reform is a movement towards close partnership with other specialist agencies such as UNICEF, the Food and Agriculture Organization (FAO), and the United Nations Development Program (UNDP).Jeffrey Sachs argues in The End of Poverty that the IMF and the World Bank have "the brightest economists and the lead in advising poor countries on how to break out of poverty, but the problem is development economics". Development economics needs the reform, not the IMF. He also notes that IMF loan conditions should be paired with other reforms—e.g., trade reform in developed nations, debt cancellation, and increased financial assistance for investments in basic infrastructure. IMF loan conditions cannot stand alone and produce change; they need to be partnered with other reforms or other conditions as applicable.\n\n\n==== US influence and voting reform ====\nThe scholarly consensus is that IMF decision-making is not simply technocratic, but also guided by political and economic concerns. The United States is the IMF\'s most powerful member, and its influence reaches even into decision-making concerning individual loan agreements. The United States has historically been openly opposed to losing what Treasury Secretary Jacob Lew described in 2015 as its "leadership role" at the IMF, and the United States\' "ability to shape international norms and practices".Reforms to give more powers to emerging economies were agreed by the G20 in 2010. The reforms could not pass, however, until they were ratified by the US Congress, since 85% of the Fund\'s voting power was required for the reforms to take effect, and the Americans held more than 16% of voting power at the time. After repeated criticism, the United States finally ratified the voting reforms at the end of 2015. The OECD countries maintained their overwhelming majority of voting share, and the United States in particular retained its share at over 16%.The criticism of the US-and-Europe-dominated IMF has led to what some consider \'disenfranchising the world\' from the governance of the IMF. Raúl Prebisch, the founding secretary-general of the UN Conference on Trade and Development (UNCTAD), wrote that one of "the conspicuous deficiencies of the general economic theory, from the point of view of the periphery, is its false sense of universality."\n\n\n=== Support of dictatorships ===\nThe role of the Bretton Woods institutions has been controversial since the late Cold War, because of claims that the IMF policy makers supported military dictatorships friendly to American and European corporations, but also other anti-communist and Communist regimes (such as Mobutu\'s Zaire and Ceaușescu\'s Romania, respectively). Critics also claim that the IMF is generally apathetic or hostile to human rights, and labour rights. The controversy has helped spark the anti-globalization movement.\nAn example of IMF\'s support for a dictatorship was its ongoing support for Mobutu\'s rule in Zaire, although its own envoy, Erwin Blumenthal, provided a sobering report about the entrenched corruption and embezzlement and the inability of the country to pay back any loans.Arguments in favour of the IMF say that economic stability is a precursor to democracy; however, critics highlight various examples in which democratised countries fell after receiving IMF loans.A 2017 study found no evidence of IMF lending programs undermining democracy in borrowing countries. To the contrary, it found "evidence for modest but definitively positive conditional differences in the democracy scores of participating and non-participating countries."\n\n\n=== Impact on access to food ===\nA number of civil society organisations have criticised the IMF\'s policies for their impact on access to food, particularly in developing countries. In October 2008, former United States president Bill Clinton delivered a speech to the United Nations on World Food Day, criticising the World Bank and IMF for their policies on food and agriculture:\n\nWe need the World Bank, the IMF, all the big foundations, and all the governments to admit that, for 30 years, we all blew it, including me when I was president. We were wrong to believe that food was like some other product in international trade, and we all have to go back to a more responsible and sustainable form of agriculture.\n\nThe FPIF remarked that there is a recurring pattern: "the destabilization of peasant producers by a one-two punch of IMF-World Bank structural adjustment programs that gutted government investment in the countryside followed by the massive influx of subsidized U.S. and European Union agricultural imports after the WTO\'s Agreement on Agriculture pried open markets."\n\n\n=== Impact on public health ===\nA 2009 study concluded that the strict conditions resulted in thousands of deaths in Eastern Europe by tuberculosis as public health care had to be weakened. In the 21 countries to which the IMF had given loans, tuberculosis deaths rose by 16.6%.In 2009, a book by Rick Rowden titled The Deadly Ideas of Neoliberalism: How the IMF has Undermined Public Health and the Fight Against AIDS, claimed that the IMF\'s monetarist approach towards prioritising price stability (low inflation) and fiscal restraint (low budget deficits) was unnecessarily restrictive and has prevented developing countries from scaling up long-term investment in public health infrastructure. The book claimed the consequences have been chronically underfunded public health systems, leading to demoralising working conditions that have fuelled a "brain drain" of medical personnel, all of which has undermined public health and the fight against HIV/AIDS in developing countries.In 2016, the IMF\'s research department published a report titled "Neoliberalism: Oversold?" which, while praising some aspects of the "neoliberal agenda," claims that the organisation has been "overselling" fiscal austerity policies and financial deregulation, which they claim has exacerbated both financial crises and economic inequality around the world.\n\n\n=== Impact on environment ===\nIMF policies have been repeatedly criticised for making it difficult for indebted countries to say no to environmentally harmful projects that nevertheless generate revenues such as oil, coal, and forest-destroying lumber and agriculture projects. Ecuador, for example, had to defy IMF advice repeatedly to pursue the protection of its rainforests, though paradoxically this need was cited in the IMF argument to provide support to Ecuador. The IMF acknowledged this paradox in the 2010 report that proposed the IMF Green Fund, a mechanism to issue special drawing rights directly to pay for climate harm prevention and potentially other ecological protection as pursued generally by other environmental finance.While the response to these moves was generally positive possibly because ecological protection and energy and infrastructure transformation are more politically neutral than pressures to change social policy. Some experts voiced concern that the IMF was not representative, and that the IMF proposals to generate only US$200 billion a year by 2020 with the SDRs as seed funds, did not go far enough to undo the general incentive to pursue destructive projects inherent in the world commodity trading and banking systems—criticisms often levelled at the World Trade Organization and large global banking institutions.\nIn the context of the European debt crisis, some observers noted that Spain and California, two troubled economies within Europe and the United States, and also Germany, the primary and politically most fragile supporter of a euro currency bailout would benefit from IMF recognition of their leadership in green technology, and directly from Green Fund-generated demand for their exports, which could also improve their credit ratings.\n\n\n=== Impact on  gender equality ===\nIMF supports women\'s empowerment and promotes their rights in countries with a significant gender gap.\n\n\n== Scandals ==\nBoth Christine Lagarde and her two predecessors Strauss-Kahn and Rato have been investigated by the authorities and have either faced trial or are scheduled to go on trial for a variety of offences.Lagarde had been accused of giving preferential treatment to businessman-turned-politician Bernard Tapie as he pursued a legal challenge against the French government. At the time, Lagarde was the French economic minister. Within hours of her conviction, in which she escaped any punishment, the fund\'s 24-member executive board put to rest any speculation that she might have to resign, praising her "outstanding leadership" and the "wide respect" she commands around the world.Rato was arrested on 16 April 2015 for alleged fraud, embezzlement and money laundering. On 23 February 2017, the Audiencia Nacional found Rato guilty of embezzlement and sentenced to 4\u200b1⁄2 years\' imprisonment. In September 2018, the sentence was confirmed by the Supreme Court of Spain.\n\n\n== Alternatives ==\nIn March 2011, the Ministers of Economy and Finance of the African Union proposed to establish an African Monetary Fund.At the 6th BRICS summit in July 2014 the BRICS nations (Brazil, Russia, India, China, and South Africa) announced the BRICS Contingent Reserve Arrangement (CRA) with an initial size of US$100 billion, a framework to provide liquidity through currency swaps in response to actual or potential short-term balance-of-payments pressures.In 2014, the China-led Asian Infrastructure Investment Bank was established.\n\n\n== In the media ==\nLife and Debt, a documentary film, deals with the IMF\'s policies\' influence on Jamaica and its economy from a critical point of view. Debtocracy, a 2011 independent Greek documentary film, also criticises the IMF.  Portuguese musician José Mário Branco\'s 1982 album FMI is inspired by the IMF\'s intervention in Portugal through monitored stabilisation programs in 1977–78. In the 2015 film, Our Brand Is Crisis, the IMF is mentioned as a point of political contention, where the Bolivian population fears its electoral interference.\n\n\n== See also ==\n\n\n== Notes ==\n\n\n== References ==\n\n\n=== Footnotes ===\n\n\n=== Bibliography ===\n\n\n== Further reading ==\nBordo, M.D. (1993).  Bordo, M.D.; Eichengreen, Barry (eds.). The Bretton Woods International Monetary System: A Historical Overview. A Retrospective on the Bretton Woods System. London. doi:10.7208/chicago/9780226066905.001.0001. ISBN 9780226065878.\ndeVries, Margaret Garritsen. The IMF in a Changing World, 1945–85, International Monetary Fund, 1986.\nJames, H. International Monetary Cooperation since Bretton Woods, Oxford, 1996.\nJoicey, N. and Pickford, S. "The International Monetary Fund and Global Economic Cooperation" in Nicholas Bayne and Stephen Woolcock, The New Economic Diplomacy: Decision-Making and Negotiation in International Relations, (Ashgate Publishing, 2011).\nKeynes, J.M. The Collected Writings, Vol. XXVI. Activities 1941–1946: Shaping the Post-War World: Bretton Woods and Reparations, Cambridge, 1980.\nMoschella, M. Governing Risk: The IMF and Global Financial Crises (Palgrave Macmillan; 2010).\nSkidelsky, R. John Maynard Keynes: Fighting for Britain, London, 2000.\nTruman, E. Strengthening IMF Surveillance: A Comprehensive Proposal, Policy Brief 10–29, Peterson Institute for International Economics, 2010.\nWeiss, Martin A. The International Monetary Fund. (Washington, DC: Congressional Research Service, 24 May 2018).\nWoods, N. The Globalizers: The IMF, the World Bank, and Their Borrowers, Ithaca, 2006\n\n\n== External links ==\nOfficial website\nIMF Publications page\nWorld Economic Outlook – quarterly staff periodical\nrecurrent Regional Economic Reports – published annually\nbiennial Global Financial Stability Report\nStaff Discussion Notes – publication as and when desired\nFiscal Monitor publication as and when desired\nFinance and Development – quarterly academic journal\nInternational Monetary Fund at Curlie\nIFIWatchNet (Web resource for analysis and commentary critical of the IMF and similar institutions)\n"IMF-Supported Macroeconomic Policies and the World Recession: A Look at Forty-One Borrowing Countries", from the Center for Economic and Policy Research, October 2009\n"Proceedings and Documents of the United Nations Monetary and Financial Conference, Bretton Woods, New Hampshire, July 1–22, 1944"\n"IMF\'s Effect on Third World Countries" from the Dean Peter Krogh Foreign Affairs Digital Archives\n"IMF and Ukraine found a compromise"',
 'Basketball is a team sport in which two teams, most commonly of five players each, opposing one another on a rectangular court, compete with the primary objective of shooting a basketball (approximately 9.4 inches (24 cm) in diameter) through the defender\'s hoop (a basket 18 inches (46 cm) in diameter mounted 10 feet (3.048 m) high to a backboard at each end of the court) while preventing the opposing team from shooting through their own hoop.  A field goal is worth two points, unless made from behind the three-point line, when it is worth three.  After a foul, timed play stops and the player fouled or designated to shoot a technical foul is given one or more one-point free throws.  The team with the most points at the end of the game wins, but if regulation play expires with the score tied, an additional period of play (overtime) is mandated.\nPlayers advance the ball by bouncing it while walking or running (dribbling) or by passing it to a teammate, both of which require considerable skill. On offense, players may use a variety of shots—the lay-up, the jump shot, or a dunk; on defense, they may steal the ball from a dribbler, intercept passes, or block shots; either offense or defense may collect a rebound, that is, a missed shot that bounces from rim or backboard.  It is a violation to lift or drag one\'s pivot foot without dribbling the ball, to carry it, or to hold the ball with both hands then resume dribbling.\nThe five players on each side at a time fall into five playing positions: the tallest player is usually the center, the tallest and strongest is the power forward, a slightly shorter but more agile big man is the small forward, and the shortest players or the best ball handlers are the shooting guard and the point guard, who implements the coach\'s game plan by managing the execution of offensive and defensive plays (player positioning).  Informally, players may play three-on-three, two-on-two, and one-on-one.\nInvented in 1891 by Canadian-American gym teacher James Naismith in Springfield, Massachusetts, United States, basketball has evolved to become one of the world\'s most popular and widely viewed sports. The National Basketball Association (NBA) is the most significant professional basketball league in the world in terms of popularity, salaries, talent, and level of competition. Outside North America, the top clubs from national leagues qualify to continental championships such as the EuroLeague and the Basketball Champions League Americas. The FIBA Basketball World Cup and Men\'s Olympic Basketball Tournament are the major international events of the sport and attract top national teams from around the world. Each continent hosts regional competitions for national teams, like EuroBasket and FIBA AmeriCup.\nThe FIBA Women\'s Basketball World Cup and Women\'s Olympic Basketball Tournament feature top national teams from continental championships. The main North American league is the WNBA (NCAA Women\'s Division I Basketball Championship is also popular), whereas strongest European clubs participate in the EuroLeague Women.\n\n\n== History ==\n\n\n=== Creation ===\n\nIn early December 1891, Canadian James Naismith, a physical education professor and instructor at the International Young Men\'s Christian Association Training School (YMCA) (today, Springfield College) in Springfield, Massachusetts, was trying to keep his gym class active on a rainy day. He sought a vigorous indoor game to keep his students occupied and at proper levels of fitness during the long New England winters. After rejecting other ideas as either too rough or poorly suited to walled-in gymnasiums, he wrote the basic rules and nailed a peach basket onto a 10-foot (3.0 m) elevated track. In contrast with modern basketball nets, this peach basket retained its bottom, and balls had to be retrieved manually after each "basket" or point scored; this proved inefficient, however, so the bottom of the basket was removed, allowing the balls to be poked out with a long dowel each time.\n\nBasketball was originally played with a soccer ball. These round balls from "association football" were made, at the time, with a set of laces to close off the hole needed for inserting the inflatable bladder after the other sewn-together segments of the ball\'s cover had been flipped outside-in. These laces could cause bounce passes and dribbling to be unpredictable. Eventually a lace-free ball construction method was invented, and this change to the game was endorsed by Naismith. (Whereas in American football, the lace construction proved to be advantageous for gripping and remains to this day.) The first balls made specifically for basketball were brown, and it was only in the late 1950s that Tony Hinkle, searching for a ball that would be more visible to players and spectators alike, introduced the orange ball that is now in common use. Dribbling was not part of the original game except for the "bounce pass" to teammates. Passing the ball was the primary means of ball movement. Dribbling was eventually introduced but limited by the asymmetric shape of early balls. Dribbling was common by 1896, with a rule against the double dribble by 1898.The peach baskets were used until 1906 when they were finally replaced by metal hoops with backboards. A further change was soon made, so the ball merely passed through. Whenever a person got the ball in the basket, his team would gain a point. Whichever team got the most points won the game. The baskets were originally nailed to the mezzanine balcony of the playing court, but this proved impractical when spectators in the balcony began to interfere with shots. The backboard was introduced to prevent this interference; it had the additional effect of allowing rebound shots. Naismith\'s handwritten diaries, discovered by his granddaughter in early 2006, indicate that he was nervous about the new game he had invented, which incorporated rules from a children\'s game called duck on a rock, as many had failed before it.\nFrank Mahan, one of the players from the original first game, approached Naismith after the Christmas break, in early 1892, asking him what he intended to call his new game. Naismith replied that he hadn\'t thought of it because he had been focused on just getting the game started. Mahan suggested that it be called "Naismith ball", at which he laughed, saying that a name like that would kill any game. Mahan then said, "Why not call it basketball?" Naismith replied, "We have a basket and a ball, and it seems to me that would be a good name for it." The first official game was played in the YMCA gymnasium in Albany, New York, on January 20, 1892, with nine players. The game ended at 1–0; the shot was made from 25 feet (7.6 m), on a court just half the size of a present-day Streetball or National Basketball Association (NBA) court.\nAt the time, football was being played with 10 to a team (which was increased to 11). When winter weather got too icy to play football, teams were taken indoors, and it was convenient to have them split in half and play basketball with five on each side.  By 1897–1898 teams of five became standard.\n\n\n=== College basketball ===\n\nBasketball\'s early adherents were dispatched to YMCAs throughout the United States, and it quickly spread through the United States and Canada. By 1895, it was well established at several women\'s high schools. While the YMCA was responsible for initially developing and spreading the game, within a decade it discouraged the new sport, as rough play and rowdy crowds began to detract from the YMCA\'s primary mission. However, other amateur sports clubs, colleges, and professional clubs quickly filled the void. In the years before World War I, the Amateur Athletic Union and the Intercollegiate Athletic Association of the United States (forerunner of the NCAA) vied for control over the rules for the game. The first pro league, the National Basketball League, was formed in 1898 to protect players from exploitation and to promote a less rough game. This league only lasted five years.\nJames Naismith was instrumental in establishing college basketball. His colleague C.O. Beamis fielded the first college basketball team just a year after the Springfield YMCA game at the suburban Pittsburgh Geneva College. Naismith himself later coached at the University of Kansas for six years, before handing the reins to renowned coach Forrest "Phog" Allen. Naismith\'s disciple Amos Alonzo Stagg brought basketball to the University of Chicago, while Adolph Rupp, a student of Naismith\'s at Kansas, enjoyed great success as coach at the University of Kentucky. On February 9, 1895, the first intercollegiate 5-on-5 game was played at Hamline University between Hamline and the School of Agriculture, which was affiliated with the University of Minnesota. The School of Agriculture won in a 9–3 game.\nIn 1901, colleges, including the University of Chicago, Columbia University, Cornell University, Dartmouth College, the University of Minnesota, the U.S. Naval Academy, the University of Colorado and Yale University began sponsoring men\'s games. In 1905, frequent injuries on the football field prompted President Theodore Roosevelt to suggest that colleges form a governing body, resulting in the creation of the Intercollegiate Athletic Association of the United States (IAAUS). In 1910, that body would change its name to the National Collegiate Athletic Association (NCAA). The first Canadian interuniversity basketball game was played at the YMCA in Kingston, Ontario on February 6, 1904, when McGill University—Naismith\'s alma mater—visited Queen\'s University. McGill won 9–7 in overtime; the score was 7–7 at the end of regulation play, and a ten-minute overtime period settled the outcome. A good turnout of spectators watched the game.The first men\'s national championship tournament, the National Association of Intercollegiate Basketball tournament, which still exists as the National Association of Intercollegiate Athletics (NAIA) tournament, was organized in 1937. The first national championship for NCAA teams, the National Invitation Tournament (NIT) in New York, was organized in 1938; the NCAA national tournament would begin one year later. College basketball was rocked by gambling scandals from 1948 to 1951, when dozens of players from top teams were implicated in match fixing and point shaving. Partially spurred by an association with cheating, the NIT lost support to the NCAA tournament.\n\n\n=== High school basketball ===\n\nBefore widespread school district consolidation, most American high schools were far smaller than their present-day counterparts. During the first decades of the 20th century, basketball quickly became the ideal interscholastic sport due to its modest equipment and personnel requirements. In the days before widespread television coverage of professional and college sports, the popularity of high school basketball was unrivaled in many parts of America. Perhaps the most legendary of high school teams was Indiana\'s Franklin Wonder Five, which took the nation by storm during the 1920s, dominating Indiana basketball and earning national recognition.\nToday virtually every high school in the United States fields a basketball team in varsity competition. Basketball\'s popularity remains high, both in rural areas where they carry the identification of the entire community, as well as at some larger schools known for their basketball teams where many players go on to participate at higher levels of competition after graduation. In the 2016–17 season, 980,673 boys and girls represented their schools in interscholastic basketball competition, according to the National Federation of State High School Associations. The states of Illinois, Indiana and Kentucky are particularly well known for their residents\' devotion to high school basketball, commonly called Hoosier Hysteria in Indiana; the critically acclaimed film Hoosiers shows high school basketball\'s depth of meaning to these communities.\nThere is currently no tournament to determine a national high school champion. The most serious effort was the National Interscholastic Basketball Tournament at the University of Chicago from 1917 to 1930. The event was organized by Amos Alonzo Stagg and sent invitations to state champion teams. The tournament started out as a mostly Midwest affair but grew. In 1929 it had 29 state champions. Faced with opposition from the National Federation of State High School Associations and North Central Association of Colleges and Schools that bore a threat of the schools losing their accreditation the last tournament was in 1930. The organizations said they were concerned that the tournament was being used to recruit professional players from the prep ranks. The tournament did not invite minority schools or private/parochial schools.\nThe National Catholic Interscholastic Basketball Tournament ran from 1924 to 1941 at Loyola University. The National Catholic Invitational Basketball Tournament from 1954 to 1978 played at a series of venues, including Catholic University, Georgetown and George Mason. The National Interscholastic Basketball Tournament for Black High Schools was held from 1929 to 1942 at Hampton Institute. The National Invitational Interscholastic Basketball Tournament was held from 1941 to 1967 starting out at Tuskegee Institute. Following a pause during World War II it resumed at Tennessee State College in Nashville. The basis for the champion dwindled after 1954 when Brown v. Board of Education began an integration of schools. The last tournaments were held at Alabama State College from 1964 to 1967.\n\n\n=== Professional basketball ===\n\nTeams abounded throughout the 1920s. There were hundreds of men\'s professional basketball teams in towns and cities all over the United States, and little organization of the professional game. Players jumped from team to team and teams played in armories and smoky dance halls. Leagues came and went. Barnstorming squads such as the Original Celtics and two all-African American teams, the New York Renaissance Five ("Rens") and the (still existing) Harlem Globetrotters played up to two hundred games a year on their national tours.\nIn 1946, the Basketball Association of America (BAA) was formed. The first game was played in Toronto, Ontario, Canada between the Toronto Huskies and New York Knickerbockers on November 1, 1946. Three seasons later, in 1949, the BAA merged with the National Basketball League (NBL) to form the National Basketball Association (NBA). By the 1950s, basketball had become a major college sport, thus paving the way for a growth of interest in professional basketball. In 1959, a basketball hall of fame was founded in Springfield, Massachusetts, site of the first game. Its rosters include the names of great players, coaches, referees and people who have contributed significantly to the development of the game. The hall of fame has people who have accomplished many goals in their career in basketball. An upstart organization, the American Basketball Association, emerged in 1967 and briefly threatened the NBA\'s dominance until the ABA-NBA merger in 1976. Today the NBA is the top professional basketball league in the world in terms of popularity, salaries, talent, and level of competition.\nThe NBA has featured many famous players, including George Mikan, the first dominating "big man"; ball-handling wizard Bob Cousy and defensive genius Bill Russell of the Boston Celtics; charismatic center Wilt Chamberlain, who originally played for the barnstorming Harlem Globetrotters; all-around stars Oscar Robertson and Jerry West; more recent big men Kareem Abdul-Jabbar, Shaquille O\'Neal, Hakeem Olajuwon and Karl Malone; playmakers John Stockton, Isiah Thomas and Steve Nash; crowd-pleasing forwards Julius Erving and Charles Barkley; European stars Dirk Nowitzki, Pau Gasol and Tony Parker; more recent superstars LeBron James, Allen Iverson and Kobe Bryant; and the three players who many credit with ushering the professional game to its highest level of popularity during the 1980s and 1990s: Larry Bird, Earvin "Magic" Johnson, and Michael Jordan.\nIn 2001, the NBA formed a developmental league, the National Basketball Development League (later known as the NBA D-League and then the NBA G League after a branding deal with Gatorade). As of the 2018–19 season, the G League has 27 teams.\n\n\n=== International basketball ===\n\nFIBA (International Basketball Federation) was formed in 1932 by eight founding nations: Argentina, Czechoslovakia, Greece, Italy, Latvia, Portugal, Romania and Switzerland. At this time, the organization only oversaw amateur players. Its acronym, derived from the French Fédération Internationale de Basket-ball Amateur, was thus "FIBA". Men\'s basketball was first included at the Berlin 1936 Summer Olympics, although a demonstration tournament was held in 1904. The United States defeated Canada in the first final, played outdoors. This competition has usually been dominated by the United States, whose team has won all but three titles. The first of these came in a controversial final game in Munich in 1972 against the Soviet Union, in which the ending of the game was replayed three times until the Soviet Union finally came out on top. In 1950 the first FIBA World Championship for men, now known as the FIBA Basketball World Cup, was held in Argentina. Three years later, the first FIBA World Championship for women, now known as the FIBA Women\'s Basketball World Cup, was held in Chile. Women\'s basketball was added to the Olympics in 1976, which were held in Montreal, Quebec, Canada with teams such as the Soviet Union, Brazil and Australia rivaling the American squads.\nIn 1989, FIBA allowed professional NBA players to participate in the Olympics for the first time. Prior to the 1992 Summer Olympics, only European and South American teams were allowed to field professionals in the Olympics. The United States\' dominance continued with the introduction of the original Dream Team. In the 2004 Athens Olympics, the United States suffered its first Olympic loss while using professional players, falling to Puerto Rico (in a 19-point loss) and Lithuania in group games, and being eliminated in the semifinals by Argentina. It eventually won the bronze medal defeating Lithuania, finishing behind Argentina and Italy. The Redeem Team, won gold at the 2008 Olympics, and the B-Team, won gold at the 2010 FIBA World Championship in Turkey despite featuring no players from the 2008 squad. The United States continued its dominance as they won gold at the 2012 Olympics, 2014 FIBA World Cup and the 2016 Olympics.\n\nWorldwide, basketball tournaments are held for boys and girls of all age levels. The global popularity of the sport is reflected in the nationalities represented in the NBA. Players from all six inhabited continents currently play in the NBA. Top international players began coming into the NBA in the mid-1990s, including Croatians Dražen Petrović and Toni Kukoč, Serbian Vlade Divac, Lithuanians Arvydas Sabonis and Šarūnas Marčiulionis, Dutchman Rik Smits and German Detlef Schrempf.\nIn the Philippines, the Philippine Basketball Association\'s first game was played on April 9, 1975 at the Araneta Coliseum in Cubao, Quezon City. Philippines. It was founded as a "rebellion" of several teams from the now-defunct Manila Industrial and Commercial Athletic Association, which was tightly controlled by the Basketball Association of the Philippines (now defunct), the then-FIBA recognized national association. Nine teams from the MICAA participated in the league\'s first season that opened on April 9, 1975. The NBL is Australia\'s pre-eminent men\'s professional basketball league. The league commenced in 1979, playing a winter season (April–September) and did so until the completion of the 20th season in 1998. The 1998–99 season, which commenced only months later, was the first season after the shift to the current summer season format (October–April). This shift was an attempt to avoid competing directly against Australia\'s various football codes. It features 8 teams from around Australia and one in New Zealand. A few players including Luc Longley, Andrew Gaze, Shane Heal, Chris Anstey and Andrew Bogut made it big internationally, becoming poster figures for the sport in Australia. The Women\'s National Basketball League began in 1981.\n\n\n=== Women\'s basketball ===\n\nWomen\'s basketball began in 1892 at Smith College when Senda Berenson, a physical education teacher, modified Naismith\'s rules for women. Shortly after she was hired at Smith, she went to Naismith to learn more about the game. Fascinated by the new sport and the values it could teach, she organized the first women\'s collegiate basketball game on March 21, 1893, when her Smith freshmen and sophomores played against one another. However, the first women\'s interinstitutional game was played in 1892 between the University of California and Miss Head\'s School. Berenson\'s rules were first published in 1899, and two years later she became the editor of A. G. Spalding\'s first Women\'s Basketball Guide. Berenson\'s freshmen played the sophomore class in the first women\'s intercollegiate basketball game at Smith College, March 21, 1893. The same year, Mount Holyoke and Sophie Newcomb College (coached by Clara Gregory Baer) women began playing basketball. By 1895, the game had spread to colleges across the country, including Wellesley, Vassar, and Bryn Mawr. The first intercollegiate women\'s game was on April 4, 1896. Stanford women played Berkeley, 9-on-9, ending in a 2–1 Stanford victory.\nWomen\'s basketball development was more structured than that for men in the early years. In 1905, the Executive Committee on Basket Ball Rules (National Women\'s Basketball Committee) was created by the American Physical Education Association. These rules called for six to nine players per team and 11 officials. The International Women\'s Sports Federation (1924) included a women\'s basketball competition. 37 women\'s high school varsity basketball or state tournaments were held by 1925. And in 1926, the Amateur Athletic Union backed the first national women\'s basketball championship, complete with men\'s rules. The Edmonton Grads, a touring Canadian women\'s team based in Edmonton, Alberta, operated between 1915 and 1940. The Grads toured all over North America, and were exceptionally successful. They posted a record of 522 wins and only 20 losses over that span, as they met any team that wanted to challenge them, funding their tours from gate receipts. The Grads also shone on several exhibition trips to Europe, and won four consecutive exhibition Olympics tournaments, in 1924, 1928, 1932, and 1936; however, women\'s basketball was not an official Olympic sport until 1976. The Grads\' players were unpaid, and had to remain single. The Grads\' style focused on team play, without overly emphasizing skills of individual players. The first women\'s AAU All-America team was chosen in 1929. Women\'s industrial leagues sprang up throughout the United States, producing famous athletes, including Babe Didrikson of the Golden Cyclones, and the All American Red Heads Team, which competed against men\'s teams, using men\'s rules. By 1938, the women\'s national championship changed from a three-court game to two-court game with six players per team.\n\nThe NBA-backed Women\'s National Basketball Association (WNBA) began in 1997. Though it had shaky attendance figures, several marquee players (Lisa Leslie, Diana Taurasi, and Candace Parker among others) have helped the league\'s popularity and level of competition. Other professional women\'s basketball leagues in the United States, such as the American Basketball League (1996–98), have folded in part because of the popularity of the WNBA. The WNBA has been looked at by many as a niche league. However, the league has recently taken steps forward. In June 2007, the WNBA signed a contract extension with ESPN. The new television deal ran from 2009 to 2016. Along with this deal, came the first ever rights fees to be paid to a women\'s professional sports league. Over the eight years of the contract, "millions and millions of dollars" were "dispersed to the league\'s teams." In a March 12, 2009 article, NBA commissioner David Stern said that in the bad economy, "the NBA is far less profitable than the WNBA. We\'re losing a lot of money among a large number of teams. We\'re budgeting the WNBA to break even this year."\n\n\n== Rules and regulations ==\n\nMeasurements and time limits discussed in this section often vary among tournaments and organizations; international and NBA rules are used in this section.\nThe object of the game is to outscore one\'s opponents by throwing the ball through the opponents\' basket from above while preventing the opponents from doing so on their own. An attempt to score in this way is called a shot. A successful shot is worth two points, or three points if it is taken from beyond the three-point arc 6.75 metres (22 ft 2 in) from the basket in international games and 23 feet 9 inches (7.24 m) in NBA games. A one-point shot can be earned when shooting from the foul line after a foul is made.\n\n\n=== Playing regulations ===\nGames are played in four quarters of 10 (FIBA) or 12 minutes (NBA). College men\'s games use two 20-minute halves, college women\'s games use 10-minute quarters, and most United States high school varsity games use 8-minute quarters; however, this varies from state to state. 15 minutes are allowed for a half-time break under FIBA, NBA, and NCAA rules and 10 minutes in United States high schools. Overtime periods are five minutes in length except for high school, which is four minutes in length. Teams exchange baskets for the second half. The time allowed is actual playing time; the clock is stopped while the play is not active. Therefore, games generally take much longer to complete than the allotted game time, typically about two hours.\nFive players from each team may be on the court at one time. Substitutions are unlimited but can only be done when play is stopped. Teams also have a coach, who oversees the development and strategies of the team, and other team personnel such as assistant coaches, managers, statisticians, doctors and trainers.\nFor both men\'s and women\'s teams, a standard uniform consists of a pair of shorts and a jersey with a clearly visible number, unique within the team, printed on both the front and back. Players wear high-top sneakers that provide extra ankle support. Typically, team names, players\' names and, outside of North America, sponsors are printed on the uniforms.\nA limited number of time-outs, clock stoppages requested by a coach (or sometimes mandated in the NBA) for a short meeting with the players, are allowed. They generally last no longer than one minute (100 seconds in the NBA) unless, for televised games, a commercial break is needed.\nThe game is controlled by the officials consisting of the referee (referred to as crew chief in the NBA), one or two umpires (referred to as referees in the NBA) and the table officials. For college, the NBA, and many high schools, there are a total of three referees on the court. The table officials are responsible for keeping track of each teams scoring, timekeeping, individual and team fouls, player substitutions, team possession arrow, and the shot clock.\n\n\n=== Equipment ===\n\nThe only essential equipment in a basketball game is the ball and the court: a flat, rectangular surface with baskets at opposite ends. Competitive levels require the use of more equipment such as clocks, score sheets, scoreboard(s), alternating possession arrows, and whistle-operated stop-clock systems.\n\nA regulation basketball court in international games is 91.9 feet (28.0 meters) long and 49.2 feet (15 meters) wide. In the NBA and NCAA the court is 94 by 50 feet (29 by 15 meters). Most courts have wood flooring, usually constructed from maple planks running in the same direction as the longer court dimension. The name and logo of the home team is usually painted on or around the center circle.\nThe basket is a steel rim 18 inches (46 cm) diameter with an attached net affixed to a backboard that measures 6 by 3.5 feet (1.8 by 1.1 meters) and one basket is at each end of the court. The white outlined box on the backboard is 18 inches (46 cm) high and 2 feet (61 cm) wide. At almost all levels of competition, the top of the rim is exactly 10 feet (3.05 meters) above the court and 4 feet (1.22 meters) inside the baseline. While variation is possible in the dimensions of the court and backboard, it is considered important for the basket to be of the correct height – a rim that is off by just a few inches can have an adverse effect on shooting.\nThe size of the basketball is also regulated. For men, the official ball is 29.5 inches (75 cm) in circumference (size 7, or a "295 ball") and weighs 22 oz (623.69 grams). If women are playing, the official basketball size is 28.5 inches (72 cm) in circumference (size 6, or a "285 ball") with a weight of 20 oz (567 grams). In 3x3, a formalized version of the halfcourt 3-on-3 game, a dedicated ball with the circumference of a size 6 ball but the weight of a size 7 ball is used in all competitions (men\'s, women\'s, and mixed teams).\n\n\n=== Violations ===\nThe ball may be advanced toward the basket by being shot, passed between players, thrown, tapped, rolled or dribbled (bouncing the ball while running).\nThe ball must stay within the court; the last team to touch the ball before it travels out of bounds forfeits possession. The ball is out of bounds if it touches a boundary line, or touches any player or object that is out of bounds.\nThere are limits placed on the steps a player may take without dribbling, which commonly results in an infraction known as traveling. Nor may a player stop his dribble and then resume dribbling. A dribble that touches both hands is considered stopping the dribble, giving this infraction the name double dribble. Within a dribble, the player cannot carry the ball by placing his hand on the bottom of the ball; doing so is known as carrying the ball. A team, once having established ball control in the front half of their court, may not return the ball to the backcourt and be the first to touch it. A violation of these rules results in loss of possession.\nThe ball may not be kicked, nor be struck with the fist. For the offense, a violation of these rules results in loss of possession; for the defense, most leagues reset the shot clock and the offensive team is given possession of the ball out of bounds.\nThere are limits imposed on the time taken before progressing the ball past halfway (8 seconds in FIBA and the NBA; 10 seconds in NCAA and high school for both sexes), before attempting a shot (24 seconds in FIBA, the NBA, and U Sports (Canadian universities) play for both sexes, and 30 seconds in NCAA play for both sexes), holding the ball while closely guarded (5 seconds), and remaining in the restricted area known as the free-throw lane, (or the "key") (3 seconds). These rules are designed to promote more offense.\nBasket interference, or goaltending is a violation charged when a player illegally interferes with a shot. This violation is incurred when a player touches the ball on its downward trajectory to the basket, unless it is obvious that the ball has no chance of entering the basket, if a player touches the ball while it is in the rim, or in the area extended upwards from the basket, or if a player reaches through the basket to interfere with the shot. When a defensive player is charged with goaltending, the basket is awarded. If an offensive player commits the infraction, the basket is cancelled. In either case possession of the ball is turned over to the defensive team.\n\n\n=== Fouls ===\n\nAn attempt to unfairly disadvantage an opponent through certain types of physical contact is illegal and is called a personal foul. These are most commonly committed by defensive players; however, they can be committed by offensive players as well. Players who are fouled either receive the ball to pass inbounds again, or receive one or more free throws if they are fouled in the act of shooting, depending on whether the shot was successful. One point is awarded for making a free throw, which is attempted from a line 15 feet (4.6 m) from the basket.\nThe referee is responsible for judging whether contact is illegal, sometimes resulting in controversy. The calling of fouls can vary between games, leagues and referees.\nThere is a second category of fouls called technical fouls, which may be charged for various rules violations including failure to properly record a player in the scorebook, or for unsportsmanlike conduct. These infractions result in one or two free throws, which may be taken by any of the five players on the court at the time. Repeated incidents can result in disqualification. A blatant foul involving physical contact that is either excessive or unnecessary is called an intentional foul (flagrant foul in the NBA). In FIBA and NCAA women\'s basketball, a foul resulting in ejection is called a disqualifying foul, while in leagues other than the NBA, such a foul is referred to as flagrant.\nIf a team exceeds a certain limit of team fouls in a given period (quarter or half) – four for NBA, NCAA women\'s, and international games – the opposing team is awarded one or two free throws on all subsequent non-shooting fouls for that period, the number depending on the league. In the US college men\'s game and high school games for both sexes, if a team reaches 7 fouls in a half, the opposing team is awarded one free throw, along with a second shot if the first is made. This is called shooting "one-and-one". If a team exceeds 10 fouls in the half, the opposing team is awarded two free throws on all subsequent fouls for the half.\nWhen a team shoots foul shots, the opponents may not interfere with the shooter, nor may they try to regain possession until the last or potentially last free throw is in the air.\nAfter a team has committed a specified number of fouls, the other team is said to be "in the bonus". On scoreboards, this is usually signified with an indicator light reading "Bonus" or "Penalty" with an illuminated directional arrow or dot indicating that team is to receive free throws when fouled by the opposing team. (Some scoreboards also indicate the number of fouls committed.)\nIf a team misses the first shot of a two-shot situation, the opposing team must wait for the completion of the second shot before attempting to reclaim possession of the ball and continuing play.\nIf a player is fouled while attempting a shot and the shot is unsuccessful, the player is awarded a number of free throws equal to the value of the attempted shot. A player fouled while attempting a regular two-point shot thus receives two shots, and a player fouled while attempting a three-point shot receives three shots.\nIf a player is fouled while attempting a shot and the shot is successful, typically the player will be awarded one additional free throw for one point. In combination with a regular shot, this is called a "three-point play" or "four-point play" (or more colloquially, an "and one") because of the basket made at the time of the foul (2 or 3 points) and the additional free throw (1 point).\n\n\n== Common techniques and practices ==\n\n\n=== Positions ===\n\nAlthough the rules do not specify any positions whatsoever, they have evolved as part of basketball. During the early years of basketball\'s evolution, two guards, two forwards, and one center were used. In more recent times specific positions evolved, but the current trend, advocated by many top coaches including Mike Krzyzewski is towards positionless basketball, where big guys are free to shoot from outside and dribble if their skill allows it. Popular descriptions of positions include:\nPoint guard (often called the "1") : usually the fastest player on the team, organizes the team\'s offense by controlling the ball and making sure that it gets to the right player at the right time.\nShooting guard (the "2") : creates a high volume of shots on offense, mainly long-ranged; and guards the opponent\'s best perimeter player on defense.\nSmall forward (the "3") : often primarily responsible for scoring points via cuts to the basket and dribble penetration; on defense seeks rebounds and steals, but sometimes plays more actively.\nPower forward (the "4"): plays offensively often with their back to the basket; on defense, plays under the basket (in a zone defense) or against the opposing power forward (in man-to-man defense).\nCenter (the "5"): uses height and size to score (on offense), to protect the basket closely (on defense), or to rebound.\nThe above descriptions are flexible. For most teams today, the shooting guard and small forward have very similar responsibilities and are often called the wings, as do the power forward and center, who are often called post players. While most teams describe two players as guards, two as forwards, and one as a center, on some occasions teams choose to call them by different designations.\n\n\n=== Strategy ===\n\nThere are two main defensive strategies: zone defense and man-to-man defense. In a zone defense, each player is assigned to guard a specific area of the court. Zone defenses often allow the defense to double team the ball, a manoeuver known as a trap. In a man-to-man defense, each defensive player guards a specific opponent.\nOffensive plays are more varied, normally involving planned passes and movement by players without the ball. A quick movement by an offensive player without the ball to gain an advantageous position is known as a cut. A legal attempt by an offensive player to stop an opponent from guarding a teammate, by standing in the defender\'s way such that the teammate cuts next to him, is a screen or pick. The two plays are combined in the pick and roll, in which a player sets a pick and then "rolls" away from the pick towards the basket. Screens and cuts are very important in offensive plays; these allow the quick passes and teamwork, which can lead to a successful basket. Teams almost always have several offensive plays planned to ensure their movement is not predictable. On court, the point guard is usually responsible for indicating which play will occur.\n\n\n=== Shooting ===\n\nShooting is the act of attempting to score points by throwing the ball through the basket, methods varying with players and situations.\nTypically, a player faces the basket with both feet facing the basket. A player will rest the ball on the fingertips of the dominant hand (the shooting arm) slightly above the head, with the other hand supporting the side of the ball. The ball is usually shot by jumping (though not always) and extending the shooting arm. The shooting arm, fully extended with the wrist fully bent, is held stationary for a moment following the release of the ball, known as a follow-through. Players often try to put a steady backspin on the ball to absorb its impact with the rim. The ideal trajectory of the shot is somewhat controversial, but generally a proper arc is recommended. Players may shoot directly into the basket or may use the backboard to redirect the ball into the basket.\n\nThe two most common shots that use the above described setup are the set shot and the jump shot. The set shot is taken from a standing position, with neither foot leaving the floor, typically used for free throws, and in other circumstances while the jump shot is taken in mid-air, the ball released near the top of the jump. This provides much greater power and range, and it also allows the player to elevate over the defender. Failure to release the ball before the feet return to the floor is considered a traveling violation.\nAnother common shot is called the lay-up. This shot requires the player to be in motion toward the basket, and to "lay" the ball "up" and into the basket, typically off the backboard (the backboard-free, underhand version is called a finger roll). The most crowd-pleasing and typically highest-percentage accuracy shot is the slam dunk, in which the player jumps very high and throws the ball downward, through the basket while touching it.\n\nAnother shot that is becoming common is the "circus shot". The circus shot is a low-percentage shot that is flipped, heaved, scooped, or flung toward the hoop while the shooter is off-balance, airborne, falling down, and/or facing away from the basket. A back-shot is a shot taken when the player is facing away from the basket, and may be shot with the dominant hand, or both; but there is a very low chance that the shot will be successful.\nA shot that misses both the rim and the backboard completely is referred to as an air ball. A particularly bad shot, or one that only hits the backboard, is jocularly called a brick. The hang time is the length of time a player stays in the air after jumping, either to make a slam dunk, lay-up or jump shot.\n\n\n=== Rebounding ===\n\nThe objective of rebounding is to successfully gain possession of the basketball after a missed field goal or free throw, as it rebounds from the hoop or backboard. This plays a major role in the game, as most possessions end when a team misses a shot. There are two categories of rebounds: offensive rebounds, in which the ball is recovered by the offensive side and does not change possession, and defensive rebounds, in which the defending team gains possession of the loose ball. The majority of rebounds are defensive, as the team on defense tends to be in better position to recover missed shots.\n\n\n=== Passing ===\n\nA pass is a method of moving the ball between players. Most passes are accompanied by a step forward to increase power and are followed through with the hands to ensure accuracy.\nA staple pass is the chest pass. The ball is passed directly from the passer\'s chest to the receiver\'s chest. A proper chest pass involves an outward snap of the thumbs to add velocity and leaves the defence little time to react.\nAnother type of pass is the bounce pass. Here, the passer bounces the ball crisply about two-thirds of the way from his own chest to the receiver. The ball strikes the court and bounces up toward the receiver. The bounce pass takes longer to complete than the chest pass, but it is also harder for the opposing team to intercept (kicking the ball deliberately is a violation). Thus, players often use the bounce pass in crowded moments, or to pass around a defender.\nThe overhead pass is used to pass the ball over a defender. The ball is released while over the passer\'s head.\nThe outlet pass occurs after a team gets a defensive rebound. The next pass after the rebound is the outlet pass.\nThe crucial aspect of any good pass is it being difficult to intercept. Good passers can pass the ball with great accuracy and they know exactly where each of their other teammates prefers to receive the ball. A special way of doing this is passing the ball without looking at the receiving teammate. This is called a no-look pass.\nAnother advanced style of passing is the behind-the-back pass, which, as the description implies, involves throwing the ball behind the passer\'s back to a teammate. Although some players can perform such a pass effectively, many coaches discourage no-look or behind-the-back passes, believing them to be difficult to control and more likely to result in turnovers or violations.\n\n\n=== Dribbling ===\n\nDribbling is the act of bouncing the ball continuously with one hand and is a requirement for a player to take steps with the ball. To dribble, a player pushes the ball down towards the ground with the fingertips rather than patting it; this ensures greater control.\nWhen dribbling past an opponent, the dribbler should dribble with the hand farthest from the opponent, making it more difficult for the defensive player to get to the ball. It is therefore important for a player to be able to dribble competently with both hands.\nGood dribblers (or "ball handlers") tend to bounce the ball low to the ground, reducing the distance of travel of the ball from the floor to the hand, making it more difficult for the defender to "steal" the ball. Good ball handlers frequently dribble behind their backs, between their legs, and switch directions suddenly, making a less predictable dribbling pattern that is more difficult to defend against. This is called a crossover, which is the most effective way to move past defenders while dribbling.\nA skilled player can dribble without watching the ball, using the dribbling motion or peripheral vision to keep track of the ball\'s location. By not having to focus on the ball, a player can look for teammates or scoring opportunities, as well as avoid the danger of having someone steal the ball away from him/her.\n\n\n=== Blocking ===\n\nA block is performed when, after a shot is attempted, a defender succeeds in altering the shot by touching the ball. In almost all variants of play, it is illegal to touch the ball after it is in the downward path of its arc; this is known as goaltending. It is also illegal under NBA and Men\'s NCAA basketball to block a shot after it has touched the backboard, or when any part of the ball is directly above the rim. Under international rules it is illegal to block a shot that is in the downward path of its arc or one that has touched the backboard until the ball has hit the rim. After the ball hits the rim, it is again legal to touch it even though it is no longer considered as a block performed.\nTo block a shot, a player has to be able to reach a point higher than where the shot is released. Thus, height can be an advantage in blocking. Players who are taller and playing the power forward or center positions generally record more blocks than players who are shorter and playing the guard positions. However, with good timing and a sufficiently high vertical leap, even shorter players can be effective shot blockers.\n\n\n== Height ==\nAt the professional level, most male players are above 6 feet 3 inches (1.91 m) and most women above 5 feet 7 inches (1.70 m). Guards, for whom physical coordination and ball-handling skills are crucial, tend to be the smallest players. Almost all forwards in the top men\'s pro leagues are 6 feet 6 inches (1.98 m) or taller. Most centers are over 6 feet 10 inches (2.08 m) tall. According to a survey given to all NBA teams, the average height of all NBA players is just under 6 feet 7 inches (2.01 m), with the average weight being close to 222 pounds (101 kg). The tallest players ever in the NBA were Manute Bol and Gheorghe Mureșan, who were both 7 feet 7 inches (2.31 m) tall. At 7 feet 2 inches (2.18 m), Margo Dydek was the tallest player in the history of the WNBA.\nThe shortest player ever to play in the NBA is Muggsy Bogues at 5 feet 3 inches (1.60 m). Other short players have thrived at the pro level. Anthony "Spud" Webb was just 5 feet 7 inches (1.70 m) tall, but had a 42-inch (1.1 m) vertical leap, giving him significant height when jumping. While shorter players are often at a disadvantage in certain aspects of the game, their ability to navigate quickly through crowded areas of the court and steal the ball by reaching low are strengths.\nPlayers regularly inflate their height. Many prospects exaggerate their height while in high school or college to make themselves more appealing to coaches and scouts, who prefer taller players. Charles Barkley stated; "I\'ve been measured at 6-5, 6-4 \u200b3⁄4. But I started in college at 6-6." Sam Smith, a former writer from The Chicago Tribune, said: "We sort of know the heights, because after camp, the sheet comes out. But you use that height, and the player gets mad. And then you hear from his agent. Or you file your story with the right height, and the copy desk changes it because they have the \'official\' N.B.A. media guide, which is wrong. So you sort of go along with the joke." In the NBA, there is no standard on whether a player\'s listed height uses their measurement with shoes on or without. The NBA Draft Combine, which most players attend before the draft, provides both measurements. Thereafter, a player\'s team is solely responsible for their listed height, which can vary depending on the process selected.Notable players who overstated their height include:\n\nKobe Bryant whose listed height is 6 feet 6 inches (1.98 m), while his actual height is 6 feet 4 inches (1.93 m)\nCharles Barkley whose listed height is 6 feet 6 inches (1.98 m), while his actual height is just under 6 feet 5 inches (1.96 m)\nKevin Love  whose listed height is 6 feet 10 inches (2.08 m), while his actual height is just under 6 feet 8 inches (2.03 m)\nJason Collins whose listed height is 7 feet 0 inches (2.13 m), while his actual height is 6 feet 8 inches (2.03 m)\nDwight Howard whose listed height is 6 feet 11 inches (2.11 m), while his actual height is 6 feet 9 inches (2.06 m)On rare occasions, some players will understate their actual heights, not to be repositioned. One example is Kevin Durant, whose listed height is 6 feet 9 inches (2.06 m), while his actual height is 7 feet 0 inches (2.13 m). Durant\'s reasoning was, "Really, that\'s the prototypical size for a small forward. Anything taller than that, and they\'ll start saying, \'Ah, he\'s a power forward."\n\n\n== Variations and similar games ==\n\nVariations of basketball are activities based on the game of basketball, using common basketball skills and equipment (primarily the ball and basket). Some variations are only superficial rules changes, while others are distinct games with varying degrees of basketball influences. Other variations include children\'s games, contests or activities meant to help players reinforce skills.\nThere are principal basketball sports with variations on basketball including Wheelchair basketball, Water basketball, Beach basketball, Slamball, Streetball and Unicycle basketball. An earlier version of basketball, played primarily by women and girls, was Six-on-six basketball. Horseball is a game played on horseback where a ball is handled and points are scored by shooting it through a high net (approximately 1.5m×1.5m). The sport is like a combination of polo, rugby, and basketball. There is even a form played on donkeys known as Donkey basketball, but that version has come under attack from animal rights groups.\n\n\n=== Half-court ===\nPerhaps the single most common variation of basketball is the half-court game, played in informal settings without referees or strict rules. Only one basket is used, and the ball must be "taken back" or "cleared" – passed or dribbled outside the three-point line each time possession of the ball changes from one team to the other. Half-court games require less cardiovascular stamina, since players need not run back and forth a full court. Half-court raises the number of players that can use a court or, conversely, can be played if there is an insufficient number to form full 5-on-5 teams.\nHalf-court basketball is usually played 1-on-1, 2-on-2 or 3-on-3. The latter variation is gradually gaining official recognition as 3x3, originally known as FIBA 33. It was first tested at the 2007 Asian Indoor Games in Macau and the first official tournaments were held at the 2009 Asian Youth Games and the 2010 Youth Olympics, both in Singapore. The first FIBA 3x3 Youth World Championships were held in Rimini, Italy in 2011, with the first FIBA 3x3 World Championships for senior teams following a year later in Athens. The sport is highly tipped to become an Olympic sport as early as 2016. In the summer of 2017, the BIG3 basketball league, a professional 3x3 half court basketball league that features former NBA players, began. The BIG3 features several rule variants including a four-point field goal.\n\n\n=== Other variations ===\nThere are also other basketball sports, such as:\n\nOne-on-One -  It is a variation in which two players will use only a small section of the court (often no more than a half of a court) and compete to play the ball into a single hoop. Such games tend to emphasize individual dribbling and ball stealing skills over shooting and team play.\nWater basketball - Water basketball, played in a swimming pool, merges basketball and water polo rules.\nBeach basketball - A modified version of basketball, played on beaches, was invented by Philip Bryant. Beach basketball is played in a circular court with no backboard on the goal, no out-of-bounds rule with the ball movement to be done via passes or 21/2 steps, as dribbling is next to impossible on a soft surface. Beach basketball has grown to a very popular, widespread competitive sport. 15 Annual World Championships have been organized.\nDunk Hoops - Dunk Hoops (a.k.a. Dunk Ball) is a variation of the game of basketball, played on basketball hoops with lowered (under basketball regulation 10 feet) rims. It originated when the popularity of the slam dunk grew and was developed to create better chances for dunks with lowered rims and using altered goaltending rules.\nSlamball - Slamball is full-contact basketball, with trampolines. Points are scored by playing the ball through the net, as in basketball, though the point-scoring rules are modified. The main differences from the parent sport is the court; below the padded basketball rim and backboard are four trampolines set into the floor, which serve to propel players to great heights for slam dunks. The rules also permit some physical contact between the members of the four-player teams.\nStreetball - Streetball is a less formal variant of basketball, played on playgrounds and in gymnasiums across the world. Often only one half of the court is used, but otherwise, the rules of the game are very similar to those of basketball. The number of participants in a game, or a run, may range from one defender and one person on offense (known as one on one) to two full teams of five each. Streetball is a very popular game worldwide, and some cities in the United States have organized streetball programs, such as midnight basketball. Many cities also host their own weekend-long streetball tournaments.\nUnicycle basketball - Unicycle basketball is played using a regulation basketball on a regular basketball court with the same rules, for example, one must dribble the ball while riding. There are a number of rules that are particular to unicycle basketball as well, for example, a player must have at least one foot on a pedal when in-bounding the ball. Unicycle basketball is usually played using 24" or smaller unicycles, and using plastic pedals, both to preserve the court and the players\' shins. In North America, popular unicycle basketball games are organized.Spin-offs from basketball that are now separate sports include:\n\nRingball, a traditional South African sport that stems from basketball, has been played since 1907.  The sport is now promoted in South Africa, Namibia, Botswana, Lesotho, India, and Mauritius to establish Ringball as an international sport.\nKorfball (Dutch: Korfbal, korf meaning \'basket\') started in the Netherlands and is now played worldwide as a mixed gender team ball game, similar to mixed netball and basketball\nNetball (formerly known as Women basketball but now played by both males and females), a limited-contact team sport in which two teams of seven try to score points against one another by placing a ball through a high hoop. Australia New Zealand champions (so called ANZ Championship) is very famous in Australia and New Zealand as the premier netball league.\n\n\n== Social forms of basketball ==\n\nBasketball has been adopted by various social groups, which have established their own environments and sometimes their own rules. Such socialized forms of basketball include the following.\n\n\n=== Recreational basketball ===\nBasketball is played widely casually in schools and colleges where fun, entertainment and camaraderie rule rather than winning a game.\n\nBasketball Schools and Academies, where students are trained in developing basketball fundamentals, undergo fitness and endurance exercises and learn various basketball skills. Basketball students learn proper ways of passing, ball handling, dribbling, shooting from various distances, rebounding, offensive moves, defense, layups, screens, basketball rules and basketball ethics. Also popular are the basketball camps organized for various occasions, often to get prepared for basketball events, and basketball clinics for improving skills.\nCollege and University basketball played in educational institutions of higher learning. This includes National Collegiate Athletic Association (NCAA) intercollegiate basketball.\n\n\n=== Disabled basketball ===\nDisabled basketball is played by various disabled groups, such as the deaf and physically crippled people.\n\nDeaf basketball - One of several deaf sports,  deaf basketball relies on signing for communication. Any deaf sporting event that happens, its purpose is to serve as a catalyst for the socialization of a low-incidence and geographically dispersed population.\nWheelchair basketball - A sport based on basketball but designed for disabled people in wheelchairs and considered one of the major disabled sports practiced.There is a functional classification system that is used to help determine if the wheelchair basketball player classification system reflects the existing differences in the performance of elite female players. what this system does is it gives an analysis of the players\' functional resources through field-testing and game observation. During this system\'s process, players are assigned a score of 1 to 4.5.\n\n\n=== Ethnic and Religion-based basketball ===\nExamples of ethnic basketball include Indo-Pak or Russian or Armenian leagues in the United States or Canada, for example, or Filipino expatriate basketball leagues in the Gulf or the United States. Religion-based basketball includes, most notably, church-related Christian basketball leagues, Jewish, Muslim and Hindu basketball leagues, and so on. or denominational leagues like Coptic, Syriac/Assyrian basketball leagues in the United States or Canada.\n\n\n=== Prison basketball ===\nPrison basketball is practiced in prisons and penitentiary institutions. Active religious basketball missionary groups also play basketball with prisoners. Some prisons have developed their own prison basketball leagues. At times, non-prisoners may play in such leagues, provided all home and away games are played within prison courts. Film director Jason Moriarty has released a documentary relating to the sport, entitled Prison Ball. Prison systems in the United States makes about $74 billion a year. The reason why prisons make so much money in a year is the fact that over 2.3 million Americans are currently incarcerated. It all started in 1972, the number of people in the United States prison has increased over 700% and now charges the taxpayer $39 billion to maintain every year. US population is only a little smaller than countries like Iceland, Estonia and Cyprus combined, so it was not a surprise that the US penal system has developed its own type of subculture. There is a lot of people that when they hear "correctional facility" think about men behind bars and playing card and lifting weights in the yard. That\'s almost correct but most people don\'t know that there is a wide range of athletic opportunities available in today\'s penitentiaries. Prison leagues are becoming the best way and most popular way inmates spend their time. For younger offenders, basketball is a good way to get their lives back on track, but for long term prisoners, these basketball leagues are a great way for them to get away from all their daily problems. Some prison leagues sometimes get blessed with their own celebrities. "Pee Wee" Kirkland who was drafted by the Chicago Bulls, but turned it down because he was making way more money in the drug world. Eventually caught up and arrested, he then found himself playing in the Anthracite Basketball League, representing Lewis-burgh penitentiary. Also, another celebrity Michael Vick used the football prison league to stay in shape. Some other big names that played in prison basketball leagues are Allen Iverson, JR Smith and Plaxico Burress, for some prison leagues can be a valuable training ground.\n\n\n=== Show basketball ===\nShow basketball is performed by entertainment basketball show teams, the prime example being the Harlem Globetrotters. There are even specialized entertainment teams, such as teams of celebrities, people with short heights and others.\n\nCelebrity basketball teams made of celebrities (actors, singers, and so on.) playing in their own leagues or in public, often for entertainment and charity events;\nMidget basketball teams made up of athletes of short stature offering shows using basketball;\nSlamball offered as entertainment events. Slamball is a very intense form of basketball game actually it is an elevated game of basketball that is infused with football tactics and involves bouncing of a trampolines. This game is very popular in places like Europe and Australia, this intense game is full of contact just like football the trampolines are embedded in the floor around the baskets. The objective of the game is just like any sport you have to score points by getting the ball into the basket, this is mostly done by the player using the trampoline to go fly high in the air to come down with slam dunk. Not only do they use tactics from football but they also do the same style of substitution as hockey well you can say it\'s very similar. The potions of slamball are stopper, gunner and handler. The stopper is the defender and is in control of the whole defensive plan. The handler is basically the point guard of the game, their in control of setting plays on the offensive end and helps a little on defensive end as well. The last position the gunner is the one that does most of the scoring and all also helps out on the defensive end.\n\n\n=== Other forms ===\nGay basketball played in LGBTQIA+ communities in gay basketball leagues. The sport of basketball is a major part of events during the Gay Games, World Outgames and EuroGames.\nMidnight basketball, a basketball initiative to curb inner-city crime in the United States and elsewhere by keeping urban youth off the streets and engaging them with sports alternatives to drugs and crime.\nMini basketball played by underage children.\nMaxi Basketball played by more elderly individuals.\nRezball, short for reservation ball, is the avid Native American following of basketball, particularly a style of play particular to Native American teams of some areas.\nSchool or High school basketball, the sport of basketball being one of the most frequently exercised and popular sports in all school systems.\n\n\n== Fantasy basketball ==\n\nFantasy basketball was popularized during the 1990s after the advent of the Internet. Those who play this game are sometimes referred to as General Managers, who draft actual NBA players and compute their basketball statistics. The game was popularized by ESPN Fantasy Sports,, and Yahoo! Fantasy Sports. Other sports websites provided the same format keeping the game interesting with participants actually owning specific players.\n\n\n== See also ==\n\n\n== Notes ==\n\n\n== References ==\n\nGeneral references\n\n\n== Further reading ==\n\n\n== External links ==\n\nHistoricalBasketball Hall of Fame – Springfield, MA\nNational Basketball Foundation—runs the Naismith Museum in Ontario\nHometown Sports HeroesOrganizationsBasketball at the Olympic Games\nInternational Basketball Federation\nNational Basketball Association\nWomen\'s National Basketball Association\nContinental Basketball Association (oldest professional basketball league in the world)\nNational Wheelchair Basketball AssociationOther Sources"Basketball". Encyclopædia Britannica Online.\nBasketball at Curlie\nEurobasket website\ Basketball Statistics, Analysis and History\nOntario\'s Historical Plaques – Dr. James Naismith (1861–1939)',
 'Swimming is the self-propulsion of a person through water, usually for recreation, sport, exercise, or survival.  Locomotion is achieved through coordinated movement of the limbs, the body, or both.  Humans can hold their breath underwater and undertake rudimentary locomotive swimming within weeks of birth, as a survival response.Swimming is consistently among the top public recreational activities, and in some countries, swimming lessons are a compulsory part of the educational curriculum.  As a formalized sport, swimming features in a range of local, national, and international competitions, including every modern Summer Olympics.\n\n\n== Science ==\n\nSwimming relies on the nearly neutral buoyancy of the human body. On average, the body has a relative density of 0.98 compared to water, which causes the body to float. However, buoyancy varies on the basis of body composition, lung inflation, and the salinity of the water. Higher levels of body fat and saltier water both lower the relative density of the body and increase its buoyancy.\nSince the human body is only slightly less dense than water, water supports the weight of the body during swimming. As a result, swimming is “low-impact” compared to land activities such as running. The density and viscosity of water also create resistance for objects moving through the water. Swimming strokes use this resistance to create propulsion, but this same resistance also generates drag on the body.\nHydrodynamics is important to stroke technique for swimming faster, and swimmers who want to swim faster or exhaust less try to reduce the drag of the body\'s motion through the water. To be more hydrodynamic, swimmers can either increase the power of their strokes or reduce water resistance, though power must increase by a factor of three to achieve the same effect as reducing resistance. Efficient swimming by reducing water resistance involves a horizontal water position, rolling the body to reduce the breadth of the body in the water, and extending the arms as far as possible to reduce wave resistance.Just before plunging into the pool, swimmers may perform exercises such as squatting. Squatting helps in enhancing a swimmer’s start by warming up the thigh muscles.\n\n\n== Infant swimming ==\n\nHuman babies demonstrate an innate swimming or diving reflex from newborn until the age of approximately 6 months. Other mammals also demonstrate this phenomenon (see mammalian diving reflex).  The diving response involves apnea, reflex bradycardia, and peripheral vasoconstriction; in other words, babies immersed in water spontaneously hold their breath, slow their heart rate, and reduce blood circulation to the extremities (fingers and toes).\nBecause infants are innately able to swim, classes for babies of about 6 months old are offered in many locations. This helps build muscle memory and makes strong swimmers from a young age.\n\n\n== Technique ==\n\nSwimming can be undertaken using a wide range of styles, known as \'strokes,\' and these strokes are used for different purposes, or to distinguish between classes in competitive swimming.  It is not necessary to use a defined stroke for propulsion through the water, and untrained swimmers may use a \'doggy paddle\' of arm and leg movements, similar to the way four-legged animals swim.\nThere are four main strokes used in competition and recreation swimming: the front crawl, also known as freestyle, the breaststroke, the backstroke and the butterfly.  Competitive swimming in Europe started around 1800, mostly using the breaststroke. In 1873, John Arthur Trudgen introduced the trudgen to Western swimming competitions.  The butterfly stroke developed in the 1930s, and was considered a variant of the breaststroke until accepted as a separate style in 1953. Butterfly is considered the hardest stroke by many people, but it is the most effective for all-around toning and the building of muscles. It also burns the most calories.Other strokes exist for specific purposes, such as training or rescue, and it is also possible to adapt strokes to avoid using parts of the body, either to isolate certain body parts, such as swimming with arms only or legs only to train them harder, or for use by amputees or those affected by paralysis.\n\n\n== History ==\n\nSwimming has been recorded since prehistoric times, and the earliest records of swimming date back to Stone Age paintings from around 7,000 years ago. Written references date from 2000 BC.  Some of the earliest references include the Epic of Gilgamesh, the Iliad, the Odyssey, the Bible (Ezekiel 47:5, Acts 27:42, Isaiah 25:11), Beowulf, and other sagas.\nThe coastal tribes living in the volatile Low Countries were known as excellent swimmers by the Romans. Men and horses of the Batavi tribe could cross the Rhine without losing formation, according to Tacitus. Dio Cassius describes one surprise tactic employed by Aulus Plautius against the Celts at the Battle of the Medway:\nThe [British Celts] thought that Romans would not be able to cross it without a bridge, and consequently bivouacked in rather careless fashion on the opposite bank; but he sent across a detachment of [Batavii], who were accustomed to swim easily in full armour across the most turbulent streams. . . . Thence the Britons retired to the river Thames at a point near where it empties into the ocean and at flood-tide forms a lake. This they easily crossed because they knew where the firm ground and the easy passages in this region were to be found, but the Romans in attempting to follow them were not so successful. However, the [Batavii] swam across again and some others got over by a bridge a little way up-stream, after which they assailed the barbarians from several sides at once and cut down many of them."\nIn 1538, Nikolaus Wynmann, a Swiss–German professor of languages, wrote the earliest known complete book about swimming, Colymbetes, sive de arte natandi dialogus et festivus et iucundus lectu (The Swimmer, or A Dialogue on the Art of Swimming and Joyful and Pleasant to Read).\n\n\n== Purpose ==\nThere are many reasons why people swim, from swimming as a recreational pursuit to swimming as a necessary part of a job or other activity. Swimming may also be used to rehabilitate injuries, especially various cardiovascular and muscle injuries.\n\n\n=== Recreation ===\n\nMany swimmers swim for recreation, with swimming consistently ranking as one of the physical activities people are most likely to take part in.  Recreational swimming can also be used for exercise, relaxation or rehabilitation.  The support of the water, and the reduction in impact, make swimming accessible for people who are unable to undertake activities such as running.\n\n\n=== Health ===\n\nSwimming is primarily a cardiovascular/aerobic exercise due to the long exercise time, requiring a constant oxygen supply to the muscles, except for short sprints where the muscles work anaerobically. Furthermore, swimming can help tone and strengthen muscles. Swimming allows sufferers of arthritis to exercise affected joints without worsening their symptoms. However, swimmers with arthritis may wish to avoid swimming breaststroke, as improper technique can exacerbate knee pain caused by arthritis. As with most aerobic exercise, swimming reduces the harmful effects of stress. Swimming is also effective in improving health for people with cardiovascular problems and chronic illnesses. It is proven to positively impact the mental health of pregnant women and mothers. Swimming can even improve mood.\n\n\n==== Disabled swimmers ====\nAs of 2013, the Americans with Disabilities Act requires that swimming pools in the United States be accessible to disabled swimmers.\n\n\n==== Elderly swimmers ====\n"Water-based exercise can benefit older adults by improving quality of life and decreasing disability. It also improves or maintains the bone health of post-menopausal women."\n\n\n=== Sport ===\n\nSwimming as a sport predominantly involves participants competing to be the fastest over a given distance in a certain period of time.  Competitors swim different distances in different levels of competition. For example, swimming has been an Olympic sport since 1896, and the current program includes events from 50 m to 1500 m in length, across all four main strokes and medley.\nThe sport is governed internationally by the Fédération Internationale de Natation (FINA), and competition pools for FINA events are 25 or 50 meters in length. In the United States, a pool 25 yards in length is commonly used for competition.\nOther swimming and water-related sporting disciplines include open water swimming, diving, synchronized swimming, water polo, triathlon, and the modern pentathlon.\n\n\n=== Occupation ===\nSome occupations require workers to swim, such as abalone and pearl diving, and spearfishing.\nSwimming is used to rescue people in the water who are in distress, including exhausted swimmers, non-swimmers who have accidentally entered the water, and others who have come to harm on the water.  Lifeguards or volunteer lifesavers are deployed at many pools and beaches worldwide to fulfill this purpose, and they, as well as rescue swimmers, may use specific swimming styles for rescue purposes.\nSwimming is also used in marine biology to observe plants and animals in their natural habitat. Other sciences use swimming; for example, Konrad Lorenz swam with geese as part of his studies of animal behavior.\nSwimming also has military purposes. Military swimming is usually done by special operation forces, such as Navy SEALs and US Army Special Forces. Swimming is used to approach a location, gather intelligence, engage in sabotage or combat, and subsequently depart. This may also include airborne insertion into water or exiting a submarine while it is submerged. Due to regular exposure to large bodies of water, all recruits in the United States Navy, Marine Corps, and Coast Guard are required to complete basic swimming or water survival training.\nSwimming is also a professional sport. Companies sponsor swimmers who have the skills to compete at the international level. Many swimmers compete competitively to represent their home countries in the Olympics. Professional swimmers may also earn a living as entertainers, performing in water ballets.\n\n\n=== Locomotion ===\nLocomotion by swimming over brief distances is frequent when alternatives are precluded. There have been cases of political refugees swimming in the Baltic Sea and of people jumping in the water and swimming ashore from vessels not intended to reach land where they planned to go.\n\n\n== Risks ==\n\nThere are many risks associated with voluntary or involuntary human presence in water, which may result in death directly or through drowning asphyxiation. Swimming is both the goal of much voluntary presence and the prime means of regaining land in accidental situations.\nMost recorded water deaths fall into these categories:\n\nPanic occurs when an inexperienced swimmer or a nonswimmer becomes mentally overwhelmed by the circumstances of their immersion, leading to sinking and drowning. Occasionally, panic kills through hyperventilation, even in shallow water.\nExhaustion can make a person unable to sustain efforts to swim or tread water, often leading to death through drowning. An adult with fully developed and extended lungs has generally positive or at least neutral buoyancy, and can float with modest effort when calm and in still water. A small child has negative buoyancy and must make a sustained effort to avoid sinking rapidly.\nHypothermia, in which a person loses critical core temperature, can lead to unconsciousness or heart failure.\nDehydration from prolonged exposure to hypertonic salt water—or, less frequently, salt water aspiration syndrome where inhaled salt water creates foam in the lungs that restricts breathing—can cause loss of physical control or kill directly without actual drowning. Hypothermia and dehydration also kill directly, without causing drowning, even when the person wears a life vest.\nBlunt trauma in a fast moving flood or river water can kill a swimmer outright, or lead to their drowning.Adverse effects of swimming can include:\n\nExostosis, an abnormal bony overgrowth narrowing the ear canal due to frequent, long-term splashing or filling of cold water into the ear canal, also known as surfer\'s ear\nInfection from water-borne bacteria, viruses, or parasites\nChlorine inhalation (in swimming pools)\nHeart attacks while swimming (the primary cause of sudden death among triathlon participants, occurring at the rate of 1 to 2 per 100,000 participations.)\nAdverse encounters with aquatic life:\nStings from sea lice, jellyfish, fish, seashells, and some species of coral\nPuncture wounds caused by crabs, lobsters, sea urchins, zebra mussels, stingrays, flying fish, sea birds, and debris\nHemorrhaging bites from fish, marine mammals, and marine reptiles, occasionally resulting from predation\nVenomous bites from sea snakes and certain species of octopus\nElectrocution or mild shock from electric eels and electric raysAround any pool area, safety equipment is often important, and is a zoning requirement for most residential pools in the United States. Supervision by personnel trained in rescue techniques is required at most competitive swimming meets and public pools.\n\n\n== Lessons ==\n\nTraditionally, children were considered not able to swim independently until 4 years of age,\nalthough now infant swimming lessons are recommended to prevent drowning.In Sweden, Denmark, Norway, Estonia and Finland, the curriculum for the fifth grade (fourth grade in Estonia) states that all children should learn how to swim as well as how to handle emergencies near water. Most commonly, children are expected to be able to swim 200 metres (660 ft)—of which at least 50 metres (160 ft) on their back – after first falling into deep water and getting their head under water. Even though about 95 percent of Swedish school children know how to swim, drowning remains the third most common cause of death among children.In both the Netherlands and Belgium swimming lessons under school time (schoolzwemmen, school swimming) are supported by the government. Most schools provide swimming lessons. There is a long tradition of swimming lessons in the Netherlands and Belgium, the Dutch translation for the breaststroke swimming style is even schoolslag (schoolstroke). In France, swimming is a compulsory part of the curriculum for primary schools. Children usually spend one semester per year learning swimming during CP/CE1/CE2/CM1 (1st, 2nd, 3rd and 4th grade).\nIn many places, swimming lessons are provided by local swimming pools, both those run by the local authority and by private leisure companies. Many schools also include swimming lessons into their Physical Education curricula, provided either in the schools\' own pool or in the nearest public pool.\nIn the UK, the "Top-ups scheme" calls for school children who cannot swim by the age of 11 to receive intensive daily lessons. Children who have not reached Great Britain\'s National Curriculum standard of swimming 25 meters by the time they leave primary school receive a half-hour lesson every day for two weeks during term-time.In Canada and Mexico there has been a call to include swimming in public school curriculum.In the United States there is the Infant Swimming Resource (ISR) initiative that provides lessons for infant children, to cope with an emergency where they have fallen into the water. They are taught how to roll-back-to-float (hold their breath underwater, to roll onto their back, to float unassisted, rest and breathe until help arrives).\nIn Switzerland, swimming lessons for babies are popular, to help them getting used to be in another element. At the competition level, unlike in other countries - such as the Commonwealth countries, swimming teams are not related to educational institutions (high-schools and universities), but rather to cities or regions. \n\n\n== Clothing and equipment ==\n\n\n=== Swimsuits ===\n\nStandard everyday clothing is usually impractical for swimming and is unsafe under some circumstances. Most cultures today expect swimmers to wear swimsuits.\nMen\'s swimsuits commonly resemble shorts, or briefs. Casual men\'s swimsuits (for example, boardshorts) are rarely skintight, unlike competitive swimwear, like jammers or diveskins. In most cases, boys and men swim with their upper body exposed, except in countries where custom or law prohibits it in a public setting, or for practical reasons such as sun protection.\nModern women\'s swimsuits are generally skintight, covering the pubic region and the breasts (See bikini). Women\'s swimwear may also cover the midriff as well. Women\'s swimwear is often a fashion statement, and whether it is modest or not is a subject of debate by many groups, religious and secular.\nCompetitive swimwear is built so that the wearer can swim faster and more efficiently. Modern competitive swimwear is skintight and lightweight. There are many kinds of competitive swimwear for each gender. It is used in aquatic competitions, such as water polo, swim racing, diving, and rowing.\nWetsuits provide both thermal insulation and flotation. Many swimmers lack buoyancy in the leg. The wetsuit reduces density and therefore improves buoyancy while swimming. It provides insulation by absorbing some of the surrounding water, which then heats up when in direct contact with skin. The wetsuit is the usual choice for those who swim in cold water for long periods of time, as it reduces susceptibility to hypothermia.\nSome people also choose to wear no clothing while swimming; this is known as skinny dipping. It was common for males to swim naked in a public setting up to the early 20th century. Today, skinny dipping can be a rebellious activity or merely a casual one.\n\n\n=== Accessories ===\nEar plugs can prevent water from getting in the ears.\nNoseclips can prevent water from getting in the nose. However, this is generally only used for synchronized swimming. Using nose clips in competitive swimming can cause a disadvantage to most swimmers. It is for this reason that nose clips are only used for synchronized swimming and recreational swimming.\nGoggles protect the eyes from chlorinated water, and can improve underwater visibility. Tinted goggles protect the eyes from sunlight that reflects from the bottom of the pool.\nSwim caps keep the body streamlined and protect the hair from chlorinated water.\nKickboards are used to keep the upper body afloat while exercising the lower body.\nPull buoys are used to keep the lower body afloat while exercising the upper body.\nSwimfins are used to elongate the kick and improve technique and speed. Fins also build upper calf muscles.\nHand paddles are used to increase resistance during arm movements, with the goal of improving technique and power.\nSnorkels are used to help improve and maintain a good head position in the water.\nPool noodles are used to keep the user afloat during the time in the water.\nSafety fencing and equipment is mandatory at public pools and a zoning requirement at most residential pools in the United States.\n\n\n== See also ==\n\n\n== References ==\n\n\n== Bibliography ==\nCox, Lynne (2005). Swimming to Antarctica: Tales of a Long-Distance Swimmer. Harvest Books. ISBN 978-0-15-603130-1.\nManiscalco F., Il nuoto nel mondo greco romano, Naples 1993.\nMehl H., Antike Schwimmkunst, Munchen 1927.\nSchuster G., Smits W. & Ullal J., Thinkers of the Jungle. Tandem Verlag 2008.\nSprawson, Charles (2000). Haunts of the Black Masseur - The Swimmer as Hero. University of Minnesota Press. ISBN 978-0-8166-3539-9.svin\nTarpinian, Steve (1996). The Essential Swimmer. The Lyons Press. ISBN 978-1-55821-386-9.\n\n\n== External links ==\n Media related to Swimming at Wikimedia Commons\, Overview of 150 historical and less known swimming-strokes',
 'Tennis is a racket sport that can be played individually against a single opponent (singles) or between two teams of two players each (doubles). Each player uses a tennis racket that is strung with cord to strike a hollow rubber ball covered with felt over or around a net and into the opponent\'s court. The object of the game is to maneuver the ball in such a way that the opponent is not able to play a valid return. The player who is unable to return the ball will not gain a point, while the opposite player will.\nTennis is an Olympic sport and is played at all levels of society and at all ages. The sport can be played by anyone who can hold a racket, including wheelchair users. The modern game of tennis originated in Birmingham, England, in the late 19th century as lawn tennis. It had close connections both to various field (lawn) games such as croquet and bowls as well as to the older racket sport today called real tennis. During most of the 19th century, in fact, the term tennis referred to real tennis, not lawn tennis.\nThe rules of modern tennis have changed little since the 1890s. Two exceptions are that from 1908 to 1961 the server had to keep one foot on the ground at all times, and the adoption of the tiebreak in the 1970s. A recent addition to professional tennis has been the adoption of electronic review technology coupled with a point-challenge system, which allows a player to contest the line call of a point, a system known as Hawk-Eye.\nTennis is played by millions of recreational players and is also a popular worldwide spectator sport. The four Grand Slam tournaments (also referred to as the Majors) are especially popular: the Australian Open played on hard courts, the French Open played on red clay courts, Wimbledon played on grass courts, and the US Open also played on hard courts.\n\n\n== History ==\n\n\n=== Predecessors ===\n\nHistorians believe that the game\'s ancient origin lay in 12th century northern France, where a ball was struck with the palm of the hand. Louis X of France was a keen player of jeu de paume ("game of the palm"), which evolved into real tennis, and became notable as the first person to construct indoor tennis courts in the modern style. Louis was unhappy with playing tennis outdoors and accordingly had indoor, enclosed courts made in Paris "around the end of the 13th century". In due course this design spread across royal palaces all over Europe. In June 1316 at Vincennes, Val-de-Marne and following a particularly exhausting game, Louis drank a large quantity of cooled wine and subsequently died of either pneumonia or pleurisy, although there was also suspicion of poisoning. Because of the contemporary accounts of his death, Louis X is history\'s first tennis player known by name. Another of the early enthusiasts of the game was King Charles V of France, who had a court set up at the Louvre Palace.It was not until the 16th century that rackets came into use and the game began to be called "tennis", from the French term tenez, which can be translated as "hold!", "receive!" or "take!", an interjection used as a call from the server to his opponent. It was popular in England and France, although the game was only played indoors where the ball could be hit off the wall. Henry VIII of England was a big fan of this game, which is now known as real tennis. During the 18th and early 19th centuries, as real tennis declined, new racket sports emerged in England.The invention of the first lawn mower in 1830, in Britain, is believed to have been a catalyst, for the preparation of modern-style grass courts, sporting ovals, playing fields, pitches, greens, etc. This in turn led to the codification of modern rules for many sports, including lawn tennis, most football codes, lawn bowls and others.\n\n\n=== Origins of the modern game ===\n\nBetween 1859 and 1865 Harry Gem, a solicitor and his friend Augurio Perera developed a game that combined elements of racquets and the Basque ball game pelota, which they played on Perera\'s croquet lawn in Birmingham in England. In 1872, along with two local doctors, they founded the world\'s first tennis club on Avenue Road, Leamington Spa. This is where "lawn tennis" was used as a name of activity by a club for the first time. After Leamington, the second club to take up the game of lawn tennis appears to have been the Edgbaston Archery and Croquet Society, also in Birmingham.\nIn Tennis: A Cultural History, Heiner Gillmeister reveals that on December 8, 1874, British army officer Walter Clopton Wingfield wrote to Harry Gem, commenting that he (Wingfield) had been experimenting with his version of lawn tennis “for a year and a half”. In December 1873, Wingfield designed and patented a game which he called sphairistikè (Greek: σφαιριστική, meaning "ball-playing"), and was soon known simply as "sticky" – for the amusement of guests at a garden party on his friend\'s estate of Nantclwyd Hall, in Llanelidan, Wales. According to R. D. C. Evans, turfgrass agronomist, "Sports historians all agree that [Wingfield] deserves much of the credit for the development of modern tennis." According to Honor Godfrey, museum curator at Wimbledon, Wingfield "popularized this game enormously. He produced a boxed set which included a net, poles, rackets, balls for playing the game – and most importantly you had his rules. He was absolutely terrific at marketing and he sent his game all over the world. He had very good connections with the clergy, the law profession, and the aristocracy and he sent thousands of sets out in the first year or so, in 1874." The world\'s oldest annual tennis tournament took place at Leamington Lawn Tennis Club in Birmingham in 1874. This was three years before the All England Lawn Tennis and Croquet Club would hold its first championships at Wimbledon, in 1877. The first Championships culminated in a significant debate on how to standardise the rules.\n\nIn the U.S. in 1874 Mary Ewing Outerbridge, a young socialite, returned from Bermuda with a sphairistikè set. She became fascinated by the game of tennis after watching British army officers play. She laid out a tennis court at the Staten Island Cricket Club at Camp Washington, Tompkinsville, Staten Island, New York. The first American National championship was played there in September 1880. An Englishman named O.E. Woodhouse won the singles title, and a silver cup worth $100, by defeating Canadian I. F. Hellmuth. There was also a doubles match which was won by a local pair. There were different rules at each club. The ball in Boston was larger than the one normally used in New York.\nOn 21 May 1881, the oldest nationwide tennis organization in the world was formed, the United States National Lawn Tennis Association (now the United States Tennis Association) in order to standardize the rules and organize competitions. The U.S. National Men\'s Singles Championship, now the US Open, was first held in 1881 at the Newport Casino, Newport, Rhode Island. The U.S. National Women\'s Singles Championships were first held in 1887 in Philadelphia.\n\nTennis also became popular in France, where the French Championships dates to 1891 although until 1925 it was open only to tennis players who were members of French clubs. Thus, Wimbledon, the US Open, the French Open, and the Australian Open (dating to 1905) became and have remained the most prestigious events in tennis. Together these four events are called the Majors or Slams (a term borrowed from bridge rather than baseball).\n\nIn 1913, the International Lawn Tennis Federation (ILTF), now the International Tennis Federation (ITF), was founded and established three official tournaments as the major championships of the day. The World Grass Court Championships were awarded to Great Britain.  The World Hard Court Championships were awarded to France; the term "hard court" was used for clay courts at the time. Some tournaments were held in Belgium instead. And the World Covered Court Championships for indoor courts was awarded annually; Sweden, France, Great Britain, Denmark, Switzerland and Spain each hosted the tournament. At a meeting held on 16 March 1923 in Paris, the title \'World Championship\' was dropped and a new category of Official Championship was created for events in Great Britain, France, the United States, and Australia – today\'s Grand Slam events. The impact on the four recipient nations to replace the ‘world championships’ with ‘official\nchampionships’ was simple in a general sense: each became a major nation of the federation with enhanced voting power and each now operated a major event.The comprehensive rules promulgated in 1924 by the ILTF, have remained largely stable in the ensuing eighty years, the one major change being the addition of the tiebreak system designed by Jimmy Van Alen. That same year, tennis withdrew from the Olympics after the 1924 Games but returned 60 years later as a 21-and-under demonstration event in 1984. This reinstatement was credited by the efforts by the then ITF President Philippe Chatrier, ITF General Secretary David Gray and ITF Vice President Pablo Llorens, and support from IOC President Juan Antonio Samaranch. The success of the event was overwhelming and the IOC decided to reintroduce tennis as a full medal sport at Seoul in 1988.\n\nThe Davis Cup, an annual competition between men\'s national teams, dates to 1900. The analogous competition for women\'s national teams, the Fed Cup, was founded as the Federation Cup in 1963 to celebrate the 50th anniversary of the founding of the ITF.In 1926, promoter C. C. Pyle established the first professional tennis tour with a group of American and French tennis players playing exhibition matches to paying audiences. The most notable of these early professionals were the American Vinnie Richards and the Frenchwoman Suzanne Lenglen. Once a player turned pro he or she was no longer permitted to compete in the major (amateur) tournaments.In 1968, commercial pressures and rumors of some amateurs taking money under the table led to the abandonment of this distinction, inaugurating the Open Era, in which all players could compete in all tournaments, and top players were able to make their living from tennis. With the beginning of the Open Era, the establishment of an international professional tennis circuit, and revenues from the sale of television rights, tennis\'s popularity has spread worldwide, and the sport has shed its middle-class English-speaking image (although it is acknowledged that this stereotype still exists).In 1954, Van Alen founded the International Tennis Hall of Fame, a non-profit museum in Newport, Rhode Island. The building contains a large collection of tennis memorabilia as well as a hall of fame honouring prominent members and tennis players from all over the world. Each year, a grass court tournament and an induction ceremony honoring new Hall of Fame members are hosted on its grounds.\n\n\n== Equipment ==\n\nPart of the appeal of tennis stems from the simplicity of equipment required for play. Beginners need only a racket and balls.\n\n\n=== Rackets ===\n\nThe components of a tennis racket include a handle, known as the grip, connected to a neck which joins a roughly elliptical frame that holds a matrix of tightly pulled strings. For the first 100 years of the modern game, rackets were made of wood and of standard size, and strings were of animal gut. Laminated wood construction yielded more strength in rackets used through most of the 20th century until first metal and then composites of carbon graphite, ceramics, and lighter metals such as titanium were introduced. These stronger materials enabled the production of oversized rackets that yielded yet more power. Meanwhile, technology led to the use of synthetic strings that match the feel of gut yet with added durability.\n\nUnder modern rules of tennis, the rackets must adhere to the following guidelines;\nThe hitting area, composed of the strings, must be flat and generally uniform.\nThe frame of the hitting area may not be more than 29 inches (74 cm) in length and 12.5 inches (32 cm) in width.\nThe entire racket must be of a fixed shape, size, weight, and weight distribution. There may not be any energy source built into the rackets.\nThe rackets must not provide any kind of communication, instruction or advice to the player during the match.The rules regarding rackets have changed over time, as material and engineering advances have been made. For example, the maximum length of the frame had been 32 inches (81 cm) until 1997, when it was shortened to 29 inches (74 cm).Many companies manufacture and distribute tennis rackets. Wilson, Head and Babolat are some of the more commonly used brands; however, many more companies exist. The same companies sponsor players to use these rackets in the hopes that the company name will become more well known by the public.\n\n\n=== Balls ===\n\nTennis balls were originally made of cloth strips stitched together with thread and stuffed with feathers. Modern tennis balls are made of hollow vulcanized rubber with a felt coating. Traditionally white, the predominant colour was gradually changed to optic yellow in the latter part of the 20th century to allow for improved visibility. Tennis balls must conform to certain criteria for size, weight, deformation, and bounce to be approved for regulation play. The International Tennis Federation (ITF) defines the official diameter as 65.41–68.58 mm (2.575–2.700 in). Balls must weigh between 56.0 and 59.4 g (1.98 and 2.10 oz). Tennis balls were traditionally manufactured in the United States and Europe. Although the process of producing the balls has remained virtually unchanged for the past 100 years, the majority of manufacturing now takes place in the Far East. The relocation is due to cheaper labour costs and materials in the region. Tournaments that are played under the ITF Rules of Tennis must use balls that are approved by the International Tennis Federation (ITF) and be named on the official ITF list of approved tennis balls.\n\n\n=== Miscellaneous ===\nAdvanced players improve their performance through a number of accoutrements. Vibration dampeners may be interlaced in the proximal part of the string array for improved feel. Racket handles may be customized with absorbent or rubber-like materials to improve the players\' grip. Players often use sweat bands on their wrists to keep their hands dry and head bands or bandanas to keep the sweat out of their eyes as well. Finally, although the game can be played in a variety of shoes, specialized tennis shoes have wide, flat soles for stability and a built-up front structure to avoid excess wear.\n\n\n== Manner of play ==\n\n\n=== Court ===\n\nTennis is played on a rectangular, flat surface. The court is 78 feet (23.77 m) long, and 27 feet (8.2 m) wide for singles matches and 36 ft (11 m) for doubles matches. Additional clear space around the court is required in order for players to reach overrun balls. A net is stretched across the full width of the court, parallel with the baselines, dividing it into two equal ends. It is held up by either a cord or metal cable of diameter no greater than 0.8 cm (1⁄3 in). The net is 3 feet 6 inches (1.07 m) high at the posts and 3 feet (0.91 m) high in the center. The net posts are 3 feet (0.91 m) outside the doubles court on each side or, for a singles net, 3 feet (0.91 m) outside the singles court on each side.\nThe modern tennis court owes its design to Major Walter Clopton Wingfield. In 1873, Wingfield patented a court much the same as the current one for his stické tennis (sphairistike). This template was modified in 1875 to the court design that exists today, with markings similar to Wingfield\'s version, but with the hourglass shape of his court changed to a rectangle.Tennis is unusual in that it is played on a variety of surfaces. Grass, clay, and hardcourts of concrete or asphalt topped with acrylic are the most common. Occasionally carpet is used for indoor play, with hardwood flooring having been historically used. Artificial turf courts can also be found.\n\n\n==== Lines ====\nThe lines that delineate the width of the court are called the baseline (farthest back) and the service line (middle of the court). The short mark in the center of each baseline is referred to as either the hash mark or the center mark. The outermost lines that make up the length are called the doubles sidelines; they are the boundaries for doubles matches. The lines to the inside of the doubles sidelines are the singles sidelines, and are the boundaries in singles play. The area between a doubles sideline and the nearest singles sideline is called the doubles alley, playable in doubles play. The line that runs across the center of a player\'s side of the court is called the service line because the serve must be delivered into the area between the service line and the net on the receiving side. Despite its name, this is not where a player legally stands when making a serve.The line dividing the service line in two is called the center line or center service line. The boxes this center line creates are called the service boxes; depending on a player\'s position, they have to hit the ball into one of these when serving. A ball is out only if none of it has hit the area inside the lines, or the line, upon its first bounce. All lines are required to be between 1 and 2 inches (25 and 51 mm) in width, with the exception of the baseline which can be up to 4 inches (100 mm) wide, although in practice it is often the same width as the others.\n\n\n=== Play of a single point ===\n\nThe players or teams start on opposite sides of the net. One player is designated the server, and the opposing player is the receiver. The choice to be server or receiver in the first game and the choice of ends is decided by a coin toss before the warm-up starts. Service alternates game by game between the two players or teams. For each point, the server starts behind the baseline, between the center mark and the sideline. The receiver may start anywhere on their side of the net. When the receiver is ready, the server will serve, although the receiver must play to the pace of the server.\nFor a service to be legal, the ball must travel over the net without touching it into the diagonally opposite service box. If the ball hits the net but lands in the service box, this is a let or net service, which is void, and the server retakes that serve. The player can serve any number of let services in a point and they are always treated as voids and not as faults. A fault is a serve that falls long or wide of the service box, or does not clear the net. There is also a "foot fault" when a player\'s foot touches the baseline or an extension of the center mark before the ball is hit. If the second service, after a fault, is also a fault, the server double faults, and the receiver wins the point. However, if the serve is in, it is considered a legal service.\nA legal service starts a rally, in which the players alternate hitting the ball across the net. A legal return consists of a player hitting the ball so that it falls in the server\'s court, before it has bounced twice or hit any fixtures except the net. A player or team cannot hit the ball twice in a row. The ball must travel over the net into the other players\' court. A ball that hits the net during a rally is considered a legal return as long as it crosses into the opposite side of the court. The first player or team to fail to make a legal return loses the point. The server then moves to the other side of the service line at the start of a new point.\n\n\n=== Scoring ===\n\n\n==== Game, set, match ====\n\n\n===== Game =====\nA game consists of a sequence of points played with the same player serving. A game is won by the first player to have won at least four points in total and at least two points more than the opponent. The running score of each game is described in a manner peculiar to tennis: scores from zero to three points are described as "love", "15", "30", and "40", respectively. If at least three points have been scored by each player, making the player\'s scores equal at 40 apiece, the score is not called out as "40–40", but rather as "deuce". If at least three points have been scored by each side and a player has one more point than his opponent, the score of the game is "advantage" for the player in the lead. During informal games, "advantage" can also be called "ad in" or "van in" when the serving player is ahead, and "ad out" or "van out" when the receiving player is ahead.\n\nThe score of a tennis game during play is always read with the serving player\'s score first. In tournament play, the chair umpire calls the point count (e.g., "15-love") after each point. At the end of a game, the chair umpire also announces the winner of the game and the overall score.\n\n\n===== Set =====\nA set consists of a sequence of games played with service alternating between games, ending when the count of games won meets certain criteria. Typically, a player wins a set by winning at least six games and at least two games more than the opponent. If one player has won six games and the opponent five, an additional game is played. If the leading player wins that game, the player wins the set 7–5. If the trailing player wins the game (tying the set 6–6) a tie-break is played. A tie-break, played under a separate set of rules, allows one player to win one more game and thus the set, to give a final set score of 7–6. A "love" set means that the loser of the set won zero games, colloquially termed a \'jam donut\' in the US. In tournament play, the chair umpire announces the winner of the set and the overall score. The final score in sets is always read with the winning player\'s score first, e.g. "6–2, 4–6, 6–0, 7–5".\n\n\n===== Match =====\nA match consists of a sequence of sets. The outcome is determined through a best of three or five sets system. On the professional circuit, men play best-of-five-set matches at all four Grand Slam tournaments, Davis Cup, and the final of the Olympic Games and best-of-three-set matches at all other tournaments, while women play best-of-three-set matches at all tournaments. The first player to win two sets in a best-of-three, or three sets in a best-of-five, wins the match. Only in the final sets of matches at the Australian Open, the French Open, Wimbledon, the Olympic Games, Davis Cup (until 2015), and Fed Cup are tie-breaks not played. In these cases, sets are played indefinitely until one player has a two-game lead, occasionally leading to some remarkably long matches.\nIn tournament play, the chair umpire announces the end of the match with the well-known phrase "Game, set, match" followed by the winning person\'s or team\'s name.\n\n\n==== Special point terms ====\n\n\n===== Game point =====\nA game point occurs in tennis whenever the player who is in the lead in the game needs only one more point to win the game. The terminology is extended to sets (set point), matches (match point), and even championships (championship point). For example, if the player who is serving has a score of 40-love, the player has a triple game point (triple set point, etc.) as the player has three consecutive chances to win the game. Game points, set points, and match points are not part of official scoring and are not announced by the chair umpire in tournament play.\n\n\n===== Break point =====\nA break point occurs if the receiver, not the server, has a chance to win the game with the next point. Break points are of particular importance because serving is generally considered advantageous, with servers being expected to win games in which they are serving. A receiver who has one (score of 30–40 or advantage), two (score of 15–40) or three (score of love-40) consecutive chances to win the game has break point, double break point or triple break point, respectively. If the receiver does, in fact, win their break point, the game is awarded to the receiver, and the receiver is said to have converted their break point. If the receiver fails to win their break point it is called a failure to convert. Winning break points, and thus the game, is also referred to as breaking serve, as the receiver has disrupted, or broken the natural advantage of the server. If in the following game the previous server also wins a break point it is referred to as breaking back. Except where tie-breaks apply, at least one break of serve is required to win a set.\n\n\n=== Rule variations ===\n\nNo adFrom \'No advantage\'. Scoring method created by Jimmy Van Alen. The first player or doubles team to win four points wins the game, regardless of whether the player or team is ahead by two points. When the game score reaches three points each, the receiver chooses which side of the court (advantage court or deuce court) the service is to be delivered on the seventh and game-deciding point. Utilized by World Team Tennis professional competition, ATP tours, WTA tours, ITF Pro Doubles and ITF Junior Doubles.Pro setInstead of playing multiple sets, players may play one "pro set". A pro set is first to 8 (or 10) games by a margin of two games, instead of first to 6 games. A 12-point tie-break is usually played when the score is 8–8 (or 10–10). These are often played with no-ad scoring.Match tie-breakThis is sometimes played instead of a third set. A match tie-break (also called super tie-break) is played like a regular tie-break, but the winner must win ten points instead of seven. Match tie-breaks are used in the Hopman Cup, Grand Slams (excluding Wimbledon) and the Olympic Games for mixed doubles; on the ATP (since 2006), WTA (since 2007) and ITF (excluding four Grand Slam tournaments and the Davis Cup) tours for doubles and as a player\'s choice in USTA league play.Fast4Fast4 is a shortened format that offers a "fast" alternative, with four points, four games and four rules: there are no advantage scores, lets are played, tie-breakers apply at three games all and the first to four games wins the set.Another, however informal, tennis format is called Canadian doubles. This involves three players, with one person playing a doubles team. The single player gets to utilize the alleys normally reserved only for a doubles team. Conversely, the doubles team does not use the alleys when executing a shot. The scoring is the same as a regular game. This format is not sanctioned by any official body.\n"Australian doubles", another informal and unsanctioned form of tennis, is played with similar rules to the Canadian doubles style, only in this version, players rotate court position after each game. As such, each player plays doubles and singles over the course of a match, with the singles player always serving. Scoring styles vary, but one popular method is to assign a value of 2 points to each game, with the server taking both points if he or she holds serve and the doubles team each taking one if they break serve.\nWheelchair tennis can be played by able-bodied players as well as people who require a wheelchair for mobility. An extra bounce is permitted. This rule makes it possible to have mixed wheelchair and able-bodied matches. It is possible for a doubles team to consist of a wheelchair player and an able-bodied player (referred to as "one-up, one-down"), or for a wheelchair player to play against an able-bodied player. In such cases, the extra bounce is permitted for the wheelchair users only.\n\n\n== Officials ==\n\nIn most professional play and some amateur competition, there is an officiating head judge or chair umpire (usually referred to as the umpire), who sits in a raised chair to one side of the court. The umpire has absolute authority to make factual determinations. The umpire may be assisted by line judges, who determine whether the ball has landed within the required part of the court and who also call foot faults. There also may be a net judge who determines whether the ball has touched the net during service. The umpire has the right to overrule a line judge or a net judge if the umpire is sure that a clear mistake has been made.In some tournaments, line judges who would be calling the serve, were assisted by electronic sensors that beeped to indicate the serve was out. This system was called "Cyclops". Cyclops has since largely been replaced by the Hawk-Eye system. In professional tournaments using this system, players are allowed three unsuccessful appeals per set, plus one additional appeal in the tie-break to challenge close line calls by means of an electronic review. The US Open, Miami Masters, US Open Series, and World Team Tennis started using this challenge system in 2006 and the Australian Open and Wimbledon introduced the system in 2007. In clay-court matches, such as at the French Open, a call may be questioned by reference to the mark left by the ball\'s impact on the court surface.\nThe referee, who is usually located off the court, is the final authority about tennis rules. When called to the court by a player or team captain, the referee may overrule the umpire\'s decision if the tennis rules were violated (question of law) but may not change the umpire\'s decision on a question of fact. If, however, the referee is on the court during play, the referee may overrule the umpire\'s decision. (This would only happen in Davis Cup or Fed Cup matches, not at the World Group level, when a chair umpire from a non-neutral country is in the chair).Ball boys and girls may be employed to retrieve balls, pass them to the players, and hand players their towels. They have no adjudicative role. In rare events (e.g., if they are hurt or if they have caused a hindrance), the umpire may ask them for a statement of what actually happened. The umpire may consider their statements when making a decision. In some leagues, especially junior leagues, players make their own calls, trusting each other to be honest. This is the case for many school and university level matches. The referee or referee\'s assistant, however, can be called on court at a player\'s request, and the referee or assistant may change a player\'s call. In unofficiated matches, a ball is out only if the player entitled to make the call is sure that the ball is out.\n\n\n== Junior tennis ==\n\nIn tennis, a junior is a player under 18 who is still legally protected by a parent or guardian. Players on the main adult tour who are under 18 must have documents signed by a parent or guardian. These players, however, are still eligible to play in junior tournaments.\nThe International Tennis Federation (ITF) conducts a junior tour that allows juniors to establish a world ranking and an Association of Tennis Professionals (ATP) or Women\'s Tennis Association (WTA) ranking. Most juniors who enter the international circuit do so by progressing through ITF, Satellite, Future, and Challenger tournaments before entering the main circuit. The latter three circuits also have adults competing in them. Some juniors, however, such as Australian Lleyton Hewitt and Frenchman Gaël Monfils, have catapulted directly from the junior tour to the ATP tour by dominating the junior scene or by taking advantage of opportunities given to them to participate in professional tournaments.\nIn 2004, the ITF implemented a new rankings scheme to encourage greater participation in doubles, by combining two rankings (singles and doubles) into one combined tally. Junior tournaments do not offer prize money except for the Grand Slam tournaments, which are the most prestigious junior events. Juniors may earn income from tennis by participating in the Future, Satellite, or Challenger tours. Tournaments are broken up into different tiers offering different amounts of ranking points, culminating with Grade A.\nLeading juniors are allowed to participate for their nation in the Junior Fed Cup and Davis Cup competitions. To succeed in tennis often means having to begin playing at a young age. To facilitate and nurture a junior\'s growth in tennis, almost all tennis playing nations have developed a junior development system. Juniors develop their play through a range of tournaments on all surfaces, accommodating all different standards of play. Talented juniors may also receive sponsorships from governing bodies or private institutions.\n\n\n== Match play ==\n\n\n=== Continuity ===\nA tennis match is intended to be continuous. Because stamina is a relevant factor, arbitrary delays are not permitted. In most cases, service is required to occur no more than 20 seconds after the end of the previous point. This is increased to 90 seconds when the players change ends (after every odd-numbered game), and a 2-minute break is permitted between sets. Other than this, breaks are permitted only when forced by events beyond the players\' control, such as rain, damaged footwear, damaged racket, or the need to retrieve an errant ball. Should a player be determined to be stalling repeatedly, the chair umpire may initially give a warning followed by subsequent penalties of "point", "game", and default of the match for the player who is consistently taking longer than the allowed time limit.In the event of a rain delay, darkness or other external conditions halting play, the match is resumed at a later time, with the same score as at the time of the delay, and the players at the same end of the court when rain halted play, or at the same position (north or south) if play is resumed on a different court.\n\n\n=== Ball changes ===\nBalls wear out quickly in serious play and, therefore, in ATP and WTA tournaments, they are changed after every nine games with the first change occurring after only seven games, because the first set of balls is also used for the pre-match warm-up. As a courtesy to the receiver, the server will often signal to the receiver before the first serve of the game in which new balls are used as a reminder that they are using new balls. However, in ITF tournaments like Fed Cup, the balls are changed in a 9–11 style. Continuity of the balls\' condition is considered part of the game, so if a re-warm-up is required after an extended break in play (usually due to rain), then the re-warm-up is done using a separate set of balls, and use of the match balls is resumed only when play resumes.\n\n\n=== On-court coaching ===\nA recent rule change is to allow coaching on court on a limited basis during a match. This has been introduced in women\'s tennis for WTA Tour events in 2009 and allows the player to request her coach once per set.\n\n\n== Stance ==\nStance refers to the way a player prepares themselves in order to best be able to return a shot. Essentially, it enables them to move quickly in order to achieve a particular stroke. There are four main stances in modern tennis: open, semi-open, closed, and neutral. All four stances involve the player crouching in some manner: as well as being a more efficient striking posture, it allows them to isometrically preload their muscles in order to play the stroke more dynamically. What stance is selected is strongly influenced by shot selection. A player may quickly alter their stance depending on the circumstances and the type of shot they intend to play. Any given stance also alters dramatically based upon the actual playing of the shot with dynamic movements and shifts of body weight occurring.\n\n\n=== Open stance ===\nThis is the most common stance in tennis. The player’s feet are placed parallel to the net. They may be pointing sideways, directly at the net or diagonally towards it. This stance allows for a high degree of torso rotation which can add significant power to the stroke. This process is sometimes likened to the coiling and uncoiling of a spring. i.e the torso is rotated as a means of preloading the muscular system in preparation for playing the stroke: this is the coiling phase. When the stroke is played the torso rotates to face forwards again, called uncoiling, and adds significant power to the stroke. A disadvantage of this stance is that it does not always allow ‘for proper weight transfer and maintenance of balance’ when making powerful strokes. It is commonly used for forehand strokes; double-handed backhands can also be made effectively from it.\n\n\n=== Semi-open stance ===\nThis stance is somewhere between open and closed and is a very flexible stance. The feet are aligned diagonally towards the net. It allows for a lot of shoulder rotation and the torso can be coiled, before being uncoiled into the shot in order to increase the power of the shot. It is commonly used in modern tennis especially by ‘top professional players on the forehand’. Two-handed backhands can also be employed from this stance.\n\n\n=== Closed stance ===\nThe closed stance is the least commonly used of the three main stances. One foot is placed further towards the net with the other foot further from it; there is a diagonal alignment between the feet.  It allows for effective torso rotation in order to increase the power of the shot. It is usually used to play backhand shots and it is rare to see forehand shots played from it. A stroke from this stance may entail the rear foot coming completely off the floor with bodyweight being transferred entirely to the front foot.\n\n\n=== Neutral stance ===\nThis is sometimes also referred to as the square stance. One foot is positioned closer to the net and ahead of the other which is  behind and in line with it. Both feet are aligned at a 90 degree angle to the net. The neutral stance is often taught early because ‘It allows beginners to learn about shifting weight and rotation of the body.’ Forehands and backhands may be made from it.\n\n\n== Shots ==\n\nA competent tennis player has eight basic shots in his or her repertoire: the serve, forehand, backhand, volley, half-volley, overhead smash, drop shot, and lob.\n\n\n=== Grip ===\n\nA grip is a way of holding the racket in order to hit shots during a match. The grip affects the angle of the racket face when it hits the ball and influences the pace, spin, and placement of the shot. Players use various grips during play, including the Continental (The "Handshake Grip"), Eastern (Can be either semi-eastern or full eastern. Usually used for backhands.), and Western (semi-western or full western, usually for forehand grips) grips. Most players change grips during a match depending on what shot they are hitting; for example, slice shots and serves call for a Continental grip.\n\n\n=== Serve ===\n\nA serve (or, more formally, a "service") in tennis is a shot to start a point. The serve is initiated by tossing the ball into the air and hitting it (usually near the apex of its trajectory) into the diagonally opposite service box without touching the net. The serve may be hit under- or overhand although underhand serving remains a rarity. If the ball hits the net on the first serve and bounces over into the correct diagonal box then it is called a "let" and the server gets two more additional serves to get it in. There can also be a let if the server serves the ball and the receiver isn\'t prepared. If the server misses his or her first serve and gets a let on the second serve, then they get one more try to get the serve in the box.\nExperienced players strive to master the conventional overhand serve to maximize its power and placement. The server may employ different types of serve including flat serve, topspin serve, slice serve, and kick (American twist) serve. A reverse type of spin serve is hit in a manner that spins the ball opposite the natural spin of the server, the spin direction depending upon right- or left-handedness. If the ball is spinning counterclockwise, it will curve right from the hitter\'s point of view and curve left if spinning clockwise.Some servers are content to use the serve simply to initiate the point; however, advanced players often try to hit a winning shot with their serve. A winning serve that is not touched by the opponent is called an "ace".\n\n\n=== Forehand ===\n\nFor a right-handed player, the forehand is a stroke that begins on the right side of the body, continues across the body as contact is made with the ball, and ends on the left side of the body. There are various grips for executing the forehand, and their popularity has fluctuated over the years. The most important ones are the continental, the eastern, the semi-western, and the western. For a number of years, the small, frail 1920s player Bill Johnston was considered by many to have had the best forehand of all time, a stroke that he hit shoulder-high using a western grip. Few top players used the western grip after the 1920s, but in the latter part of the 20th century, as shot-making techniques and equipment changed radically, the western forehand made a strong comeback and is now used by many modern players. No matter which grip is used, most forehands are generally executed with one hand holding the racket, but there have been fine players with two-handed forehands. In the 1940s and 50s, the Ecuadorian/American player Pancho Segura used a two-handed forehand to achieve a devastating effect against larger, more powerful players. Players such as Monica Seles or France\'s Fabrice Santoro and Marion Bartoli are also notable players known for their two-handed forehands.\n\n\n=== Backhand ===\n\nFor right-handed players, the backhand is a stroke that begins on the left side of their body, continues across their body as contact is made with the ball, and ends on the right side of their body. It can be executed with either one hand or with both and is generally considered more difficult to master than the forehand. For most of the 20th century, the backhand was performed with one hand, using either an eastern or a continental grip. The first notable players to use two hands were the 1930s Australians Vivian McGrath and John Bromwich, but they were lonely exceptions. The two-handed grip gained popularity in the 1970s as Björn Borg, Chris Evert, Jimmy Connors, and later Mats Wilander and Marat Safin used it to great effect, and it is now used by a large number of the world\'s best players, including Rafael Nadal and Serena Williams.Two hands give the player more control, while one hand can generate a slice shot, applying backspin on the ball to produce a low trajectory bounce. Reach is also limited with the two-handed shot. The player long considered to have had the best backhand of all time, Don Budge, had a powerful one-handed stroke in the 1930s and 1940s that imparted topspin onto the ball. Ken Rosewall, another player noted for his one-handed backhand, used a very accurate slice backhand through the 1950s and 1960s. A small number of players, notably Monica Seles, use two hands on both the backhand and forehand sides.\n\n\n=== Other shots ===\nA volley is a shot returned to the opponent in mid-air before the ball bounces, generally performed near the net, and is usually made with a stiff-wristed punching motion to hit the ball into an open area of the opponent\'s court. The half volley is made by hitting the ball on the rise just after it has bounced, also generally in the vicinity of the net, and played with the racket close to the ground. The swinging volley is hit out of the air as the player approaches the net. It is an offensive shot used to take preparation time away from the opponent, as it returns the ball into the opponent\'s court much faster than a standard volley.\nFrom a poor defensive position on the baseline, the lob can be used as either an offensive or defensive weapon, hitting the ball high and deep into the opponent\'s court to either enable the lobber to get into better defensive position or to win the point outright by hitting it over the opponent\'s head. If the lob is not hit deeply enough into the other court, however, an opponent near the net may then hit an overhead smash, a hard, serve-like shot, to try to end the point.\nA difficult shot in tennis is the return of an attempted lob over the backhand side of a player. When the contact point is higher than the reach of a two-handed backhand, most players will try to execute a high slice (under the ball or sideways). Fewer players attempt the backhand sky-hook or smash. Rarely, a player will go for a high topspin backhand, while themselves in the air. A successful execution of any of these alternatives requires balance and timing, with less margin of error than the lower contact point backhands, since this shot is a break in the regular pattern of play.\nIf an opponent is deep in his court, a player may suddenly employ an unexpected drop shot, by softly tapping the ball just over the net so that the opponent is unable to run in fast enough to retrieve it. Advanced players will often apply back spin to a drop shot, causing the ball to "skid" upon landing and bounce sideways, with less forward momentum toward their opponent, or even backwards towards the net, thus making it even more difficult to return.\n\n\n== Injuries ==\n\nMuscle strain is one of the most common injuries in tennis. When an isolated large-energy appears during the muscle contraction and at the same time body weight apply huge amount of pressure to the lengthened muscle, muscle strain can occur. Inflammation and bleeding are triggered when muscle strain occurs, which can result in redness, pain and swelling. Overuse is also common in tennis players of all levels. Muscle, cartilage, nerves, bursae, ligaments and tendons may be damaged from overuse. The repetitive use of a particular muscle without time for repair and recovery is the most common cause of injury.\n\n\n== Tournaments ==\n\nTournaments are often organized by gender and number of players. Common tournament configurations include men\'s singles, women\'s singles, and doubles, where two players play on each side of the net. Tournaments may be organized for specific age groups, with upper age limits for youth and lower age limits for senior players. Example of this include the Orange Bowl and Les Petits As junior tournaments. There are also tournaments for players with disabilities, such as wheelchair tennis and deaf tennis. In the four Grand Slam tournaments, the singles draws are limited to 128 players for each gender.\nMost large tournaments seed players, but players may also be matched by their skill level. According to how well a person does in sanctioned play, a player is given a rating that is adjusted periodically to maintain competitive matches. For example, the United States Tennis Association administers the National Tennis Rating Program (NTRP), which rates players between 1.0 and 7.0 in 1/2 point increments. Average club players under this system would rate 3.0–4.5 while world class players would be 7.0 on this scale.\n\n\n=== Grand Slam tournaments ===\nThe four Grand Slam tournaments are considered to be the most prestigious tennis events in the world. They are held annually and comprise, in chronological order, the Australian Open, the French Open, Wimbledon, and the US Open. Apart from the Olympic Games, Davis Cup, Fed Cup, and Hopman Cup, they are the only tournaments regulated by the International Tennis Federation (ITF). The ITF\'s national associations, Tennis Australia (Australian Open), the Fédération Française de Tennis (French Open), the Lawn Tennis Association (Wimbledon) and the United States Tennis Association (US Open) are delegated the responsibility to organize these events.Aside from the historical significance of these events, they also carry larger prize funds than any other tour event and are worth double the number of ranking points to the champion than in the next echelon of tournaments, the Masters 1000 (men) and Premier events (women). Another distinguishing feature is the number of players in the singles draw. There are 128, more than any other professional tennis tournament. This draw is composed of 32 seeded players, other players ranked in the world\'s top 100, qualifiers, and players who receive invitations through wild cards. Grand Slam men\'s tournaments have best-of-five set matches while the women play best-of-three. Grand Slam tournaments are among the small number of events that last two weeks, the others being the Indian Wells Masters and the Miami Masters.\nCurrently, the Grand Slam tournaments are the only tour events that have mixed doubles contests. Grand Slam tournaments are held in conjunction with wheelchair tennis tournaments and junior tennis competitions. These tournaments also contain their own idiosyncrasies. For example, players at Wimbledon are required to wear predominantly white. Andre Agassi chose to skip Wimbledon from 1988 through 1990 citing the event\'s traditionalism, particularly its "predominantly white" dress code. Wimbledon has its own particular methods for disseminating tickets, often leading tennis fans to follow complex procedures to obtain tickets.\n* The international tournament began in 1925\n\n\n=== Men\'s tournament structure ===\n\n\n==== Masters 1000 ====\nThe ATP World Tour Masters 1000 is a group of nine tournaments that form the second-highest echelon in men\'s tennis. Each event is held annually, and a win at one of these events is worth 1000 ranking points. When the ATP, led by Hamilton Jordan, began running the men\'s tour in 1990, the directors designated the top nine tournaments, outside of the Grand Slam events, as "Super 9" events. In 2000 this became the Tennis Masters Series and in 2004 the ATP Masters Series. In November at the end of the tennis year, the world\'s top eight players compete in the ATP World Tour Finals, a tournament with a rotating locale. It is currently held in London, England.In August 2007 the ATP announced major changes to the tour that were introduced in 2009. The Masters Series was renamed to the "Masters 1000", the addition of the number 1000 referring to the number of ranking points earned by the winner of each tournament. Contrary to earlier plans, the number of tournaments was not reduced from nine to eight and the Monte Carlo Masters remains part of the series although, unlike the other events, it does not have a mandatory player commitment. The Hamburg Masters has been downgraded to a 500-point event. The Madrid Masters moved to May and onto clay courts, and a new tournament in Shanghai took over Madrid\'s former indoor October slot. As of 2011 six of the nine "1000" level tournaments are combined ATP and WTA events.\n\n\n==== 250 and 500 Series ====\nThe third and fourth tier of men\'s tennis tournaments are formed by the ATP World Tour 500 series, consisting of 11 tournaments, and the ATP World Tour 250 series with 40 tournaments. Like the ATP World Tour Masters 1000, these events offer various amounts of prize money and the numbers refer to the amount of ranking points earned by the winner of a tournament. The Dubai Tennis Championships offer the largest financial incentive to players, with total prize money of US$2,313,975 (2012). These series have various draws of 28, 32, 48 and 56 for singles and 16 and 24 for doubles. It is mandatory for leading players to enter at least four 500 events, including at least one after the US Open.\n\n\n==== Challenger Tour and Futures tournaments ====\nThe Challenger Tour for men is the lowest level of tournament administered by the ATP. It is composed of about 150 events and, as a result, features a more diverse range of countries hosting events. The majority of players use the Challenger Series at the beginning of their career to work their way up the rankings. Andre Agassi, between winning Grand Slam tournaments, plummeted to World No. 141 and used Challenger Series events for match experience and to progress back up the rankings. The Challenger Series offers prize funds of between US$25,000 and US$150,000.\nBelow the Challenger Tour are the Futures tournaments, events on the ITF Men\'s Circuit. These tournaments also contribute towards a player\'s ATP rankings points. Futures Tournaments offer prize funds of between US$10,000 and US$15,000. Approximately 530 Futures Tournaments are played each year.\n\n\n=== Women\'s tournament structure ===\n\n\n==== Premier events ====\nPremier events for women form the most prestigious level of events on the Women\'s Tennis Association Tour after the Grand Slam tournaments. These events offer the largest rewards in terms of points and prize money. Within the Premier category are Premier Mandatory, Premier 5, and Premier tournaments. The Premier events were introduced in 2009 replacing the previous Tier I and II tournament categories. Currently four tournaments are Premier Mandatory, five tournaments are Premier 5, and twelve tournaments are Premier. The first tiering system in women\'s tennis was introduced in 1988. At the time of its creation, only two tournaments, the Lipton International Players Championships in Florida and the German Open in Berlin, comprised the Tier I category.\n\n\n==== International events ====\nInternational tournaments are the second main tier of the WTA tour and consist of 31 tournaments, with a prize money for every event at U.S.$220,000, except for the year-ending Commonwealth Bank Tournament of Champions in Bali, which has prize money of U.S.$600,000.\n\n\n== Players ==\n\n\n=== Professional players ===\nProfessional tennis players enjoy the same relative perks as most top sports personalities: clothing, equipment and endorsements. Like players of other individual sports such as golf, they are not salaried, but must play and finish highly in tournaments to obtain prize money.\nIn recent years, some controversy has surrounded the involuntary or deliberate noise caused by players\' grunting.\n\n\n==== Singles and doubles professional careers ====\nWhile players are gradually less competitive in singles by their late 20s and early 30s, they can still continue competitively in doubles (as instanced by Martina Navratilova and John McEnroe, who won doubles titles in their 40s).\nIn the Open Era, several female players such as Martina Navratilova, Margaret Court, Martina Hingis, Serena Williams, and Venus Williams (the latter two sisters playing together) have been prolific at both singles and doubles events throughout their careers. John McEnroe is one of the very few professional male players to be top ranked in both singles and doubles at the same time, and Yevgeny Kafelnikov is the most recent male player to win multiple Grand Slams in both singles and doubles during the same period of his career.\nIn terms of public attention and earnings (see below), singles champions have far surpassed their doubles counterparts. The Open Era, particularly the men\'s side, has seen many top-ranked singles players that only sparingly compete in doubles, while having "doubles specialists" who are typically being eliminated early in the singles draw but do well in the doubles portion of a tournament. Notable doubles pairings include The Woodies (Todd Woodbridge and Mark Woodforde) and the Bryan Brothers (identical twin brothers Robert Charles "Bob" Bryan and Michael Carl "Mike" Bryan). Woodbridge has disliked the term "doubles ‘specialists’", saying that he and Woodforde "set a singles schedule and doubles fitted in around that", although later in Woodbridge\'s career he focused exclusively on doubles as his singles ranking fell too low that it was no longer financially viable to recover at that age. Woodbridge noted that while top singles players earn enough that they don\'t need to nor want to play doubles, he suggested that lower-ranked singles players outside the Top Ten should play doubles to earn more playing time and money.\n\n\n==== Olympics ====\nThe Olympics doubles tennis tournament necessitates that both members of a doubles pairing be from the same country, hence several top professional pairs such as Jamie Murray and Bruno Soares cannot compete in the Olympics. Top-ranked singles players that are usually rivals on the professional circuit, such as Boris Becker and Michael Stich, and Roger Federer and Stan Wawrinka have formed a rare doubles partnership for the Olympics. Unlike professional tennis tournaments (see below) where singles players receive much more prize money than doubles players, an Olympic medal for both singles and doubles has similar prestige. The Olympics is more of a priority for doubles champions while singles champions often skip the tournament. While the ATP has voted for Olympic results to count towards player ranking points, WTA players voted against it.For the 2000 Olympics, Lisa Raymond was passed over for Team USA in favor of Serena Williams by captain Billie Jean King, even though Raymond was the top-ranked doubles player in the world at the time, and Raymond unsuccessfully challenged the selection.\n\n\n==== Prize money ====\nIn professional tennis tournaments such as Wimbledon, the singles competition receives the most prize money and coverage, followed by doubles, and then mixed doubles usually receive the lowest monetary awards. For instance in the US Open as of 2018, the men\'s and women\'s singles prize money (US$40,912,000) accounts for 80.9 percent of total player base compensation, while men\'s and women\'s doubles (US$6,140,840), men\'s and women\'s singles qualifying (US$3,008,000), and mixed doubles (US$505,000) account for 12.1 percent, 5.9 percent, and 1.0 percent, respectively. The singles winner receives US$3,800,000, while the doubles winning pair receives $700,000 and the mixed doubles winning pair receives US$155,000.\n\n\n=== Grand Slam tournament winners ===\n\nThe following players have won at least five singles titles at Grand Slam tournaments:\n\n\n=== Greatest male players ===\n\nA frequent topic of discussion among tennis fans and commentators is who was the greatest male singles player of all time. By a large margin, an Associated Press poll in 1950 named Bill Tilden as the greatest player of the first half of the 20th century. From 1920 to 1930, Tilden won singles titles at Wimbledon three times and the U.S. Championships seven times. In 1938, however, Donald Budge became the first person to win all four major singles titles during the same calendar year, the Grand Slam, and won six consecutive major titles in 1937 and 1938. Tilden called Budge "the finest player 365 days a year that ever lived." In his 1979 autobiography, Jack Kramer said that, based on consistent play, Budge was the greatest player ever. Some observers, however, also felt that Kramer deserved consideration for the title. Kramer was among the few who dominated amateur and professional tennis during the late 1940s and early 1950s. Tony Trabert has said that of the players he saw before the start of the open era, Kramer was the best male champion.\n\nBy the 1960s, Budge and others had added Pancho Gonzales and Lew Hoad to the list of contenders. Budge reportedly believed that Gonzales was the greatest player ever. Gonzales said about Hoad, "When Lew\'s game was at its peak nobody could touch him. ... I think his game was the best game ever. Better than mine. He was capable of making more shots than anybody. His two volleys were great. His overhead was enormous. He had the most natural tennis mind with the most natural tennis physique."Before and during the open era, Rod Laver remains the only male player in history to have won the calendar year Grand Slam twice in 1962 and 1969  and also the calendar year Professional Grand Slam in 1967. More recently Björn Borg and Pete Sampras were regarded by many of their contemporaries as among the greatest ever. Andre Agassi, the first of two male players in history to have achieved a Career Golden Slam in singles tennis (followed by Rafael Nadal), has been called the best service returner in the history of the game. He is the first man to win grand slams on all modern surfaces (previous holders of all grand slam tournaments played in an era of grass and clay only), and is regarded by a number of critics and fellow players to be among the greatest players of all time. However it must be noted that both Rod Laver and Ken Rosewall also won major Pro Slam tournaments on all three surfaces (grass, clay, wood) Rosewall in 1963 and Laver in 1967.More recently, Roger Federer is considered by many observers to have the most "complete" game in modern tennis. He has won 20 grand slam titles and 6 World Tour Finals, the most for any male player. Many experts of tennis, former tennis players and his own tennis peers believe Federer is the greatest player in the history of the game. Federer\'s biggest rival Rafael Nadal is regarded as the greatest competitor in tennis history by some former players and is regarded to have the potential to be the greatest of all time. Nadal is regarded as the greatest clay court player of all time.\n\n\n=== Greatest female players ===\n\nAs with the men there are frequent discussions about who is the greatest female singles player of all time with Steffi Graf, Martina Navratilova and Serena Williams being the three players most often nominated.\nIn March 2012 the TennisChannel published a combined list of the 100 greatest men and women tennis players of all time. It ranked Steffi Graf as the greatest female player (in 3rd place overall), followed by Martina Navratilova (4th place) and Margaret Court (8th place). The rankings were determined by an international panel.\n\nSportswriter John Wertheim of Sports Illustrated stated in an article in July 2010 that Serena Williams is the greatest female tennis player ever with the argument that "Head-to-head, on a neutral surface (i.e. hard courts), everyone at their best, I can\'t help feeling that she crushes the other legends.". In a reaction to this article Yahoo sports blog Busted Racket published a list of the top-10 women\'s tennis players of all time placing Martina Navratilova in first spot. This top-10 list was similar to the one published in June 2008 by the Bleacher Report who also ranked Martina Navratilova as the top female player of all time.Steffi Graf is considered by some to be the greatest female player. Billie Jean King said in 1999, "Steffi is definitely the greatest women\'s tennis player of all time." Martina Navratilova has included Graf on her list of great players. In December 1999, Graf was named the greatest female tennis player of the 20th century by a panel of experts assembled by the Associated Press. Tennis writer Steve Flink, in his book The Greatest Tennis Matches of the Twentieth Century, named her as the best female player of the 20th century, directly followed by Martina Navratilova.Tennis magazine selected Martina Navratilova as the greatest female tennis player for the years 1965 through 2005. Tennis historian and journalist Bud Collins has called Navratilova "arguably, the greatest player of all time." Billie Jean King said about Navratilova in 2006, "She\'s the greatest singles, doubles and mixed doubles player who\'s ever lived."\n\n\n== In popular culture ==\n"Tennis balles" are mentioned by William Shakespeare in his play Henry V (1599), when a basket of them is given to King Henry as a mockery of his youth and playfulness.\nDavid Foster Wallace, an amateur tennis player himself at Urbana High School in Illinois, included tennis in many of his works of nonfiction and fiction including "Tennis Player Michael Joyce\'s Professional Artistry as a Paradigm of Certain Stuff about Choice, Freedom, Discipline, Joy, Grotesquerie, and Human Completeness," the autobiographical piece "Derivative Sport in Tornado Alley," and Infinite Jest, which is partially set at the fictional "Enfield Tennis Academy" in Massachusetts.\nJapanese Manga series The Prince of Tennis revolves around the tennis prodigy Echizen Ryoma and tennis matches between rival schools.\nThe Royal Tenenbaums (2001) features Richie Tenenbaum (Luke Wilson), a tennis pro who suffers from depression and has a breakdown on court in front of thousands of fans.\nWimbledon (2004) is a film about a discouraged pro tennis player (Paul Bettany) who meets a young woman on the women\'s tennis circuit (Kirsten Dunst) who helps him find his drive to go and win Wimbledon.\nIn The Squid and the Whale (2005), Joan (Laura Linney) has an affair with her kids\' tennis coach, Ivan (William Baldwin). In a symbolic scene, Joan\'s ex-husband, Bernard (Jeff Daniels), loses a tennis match against Ivan in front of the kids.\nWoody Allen\'s Match Point (2005) features a love affair between a former tennis pro (Jonathan Rhys Meyers) and his best friend\'s fiancé (Scarlett Johansson).\nConfetti (2006) is a mockumentary which sees three couples competing to win the title of "Most Original Wedding of the Year". One competing couple (Meredith MacNeill and Stephen Mangan) are a pair of hyper-competitive professional tennis players holding a tennis-themed wedding.\nThere are several tennis video games including Mario Tennis, the TopSpin series, Wii Sports, and Grand Slam Tennis.\n\n\n== See also ==\n\nOutline of tennis\nGlossary of tennis\nTennis games\nTennis injuries\nTennis strategy\nTennis technology\nTennis statistics\n\n\n== References ==\n\n\n== Further reading ==\n\n\n== External links ==\n\nInternational organizationsInternational Tennis Federation (ITF)\nAssociation of Tennis Players (ATP) – men\'s professional tennis organization\nWomen\'s Tennis Association (WTA) – women\'s professional tennis organizationTeam competitionsDavis Cup\nFed CupOtherTennis at Curlie\nInternational Tennis Hall of Fame\nTennis Grand Slam tournaments history']

TF-IDF example

In [0]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(stop_words={'english'})
X = vectorizer.fit_transform(wiki_lst)
In [4]:
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(10, 8668)
In [5]:
  (0, 6465)	0.0037030254583112652
  (0, 964)	0.004882267731937409
  (0, 8125)	0.010015751043877716
  (0, 4343)	0.0059475617701261
  (0, 2218)	0.0059475617701261
  (0, 1730)	0.006622703136533057
  (0, 2868)	0.008514303426096251
  (0, 3054)	0.010015751043877716
  (0, 1942)	0.005376737878309773
  (0, 1839)	0.008514303426096251
  (0, 5661)	0.0074490093879075615
  (0, 2138)	0.004055961480562905
  (0, 6719)	0.010015751043877716
  (0, 5023)	0.010015751043877716
  (0, 6898)	0.0059475617701261
  (0, 1257)	0.008514303426096251
  (0, 3008)	0.008514303426096251
  (0, 7599)	0.010015751043877716
  (0, 4400)	0.008514303426096251
  (0, 8035)	0.006622703136533057
  (0, 6616)	0.010015751043877716
  (0, 2860)	0.0074490093879075615
  (0, 5867)	0.006622703136533057
  (0, 692)	0.006622703136533057
  (0, 8273)	0.004446114152344639
  :	:
  (0, 826)	0.07776353462453657
  (0, 1827)	0.0178426853103783
  (0, 6870)	0.004055961480562905
  (0, 7842)	0.37770859674774904
  (0, 8245)	0.008514303426096251
  (0, 7528)	0.0074490093879075615
  (0, 3451)	0.025921178208178856
  (0, 4183)	0.010015751043877716
  (0, 4481)	0.006622703136533057
  (0, 3118)	0.010015751043877716
  (0, 7936)	0.13330891649920554
  (0, 7721)	0.021506951513239093
  (0, 677)	0.2814299348316562
  (0, 594)	0.013245406273066114
  (0, 6112)	0.006622703136533057
  (0, 5024)	0.031122799066412472
  (0, 6928)	0.019868109409599172
  (0, 8284)	0.014646803195812226
  (0, 7841)	0.05554538187466898
  (0, 3244)	0.0475804941610088
  (0, 2485)	0.02003150208775543
  (0, 5179)	0.0074490093879075615
  (0, 4315)	0.07776353462453657
  (0, 6926)	0.49008003210344625
  (0, 2181)	0.5077558441214906
In [0]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
In [7]:
Sum_of_squared_distances = []
K = range(2,10)
for k in K:
    km = KMeans(n_clusters=k, max_iter=200, n_init=10)
    km =

plt.plot(K, Sum_of_squared_distances, 'bx-')
plt.title('Elbow Method For Optimal k')
In [8]:
true_k = 6
model = KMeans(n_clusters=true_k, init='k-means++', max_iter=200, n_init=10)
                         title  cluster
3        European Central Bank        0
4                         Bank        0
6  International Monetary Fund        0
1      Artificial intelligence        1
2             Machine Learning        1
8                     Swimming        2
0                 Data Science        3
7                   Basketball        4
9                       Tennis        4
5         Financial technology        5
In [9]:
from wordcloud import WordCloud
for k in range(0,true_k):
    text=s['wiki']' ')
    text=' '.join([word for word in text.split()])
    wordcloud = WordCloud(max_font_size=50, max_words=100, background_color="white").generate(text)
    print('Cluster: {}'.format(k))
    plt.imshow(wordcloud, interpolation="bilinear")
Cluster: 0
       European Central Bank
 International Monetary Fund
Cluster: 1
 Artificial intelligence
        Machine Learning
Cluster: 2
Cluster: 3
 Data Science