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Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer 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.[1] More in detail, Kaplan and Haenlein define AI as a systems 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.[2] Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.”[4] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[5] 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),[7] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[8] Analytical AI has only characteristics consistent with cognitive intelligence generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive as well as emotional intelligence, understanding, in addition to cognitive elements, also human emotions considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[9][10] followed by disappointment and the loss of funding (known as an “AI winter”),[11][12] followed by new approaches, success and renewed funding.[10][13] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[14] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[15] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[16][17][18] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[14]

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.[15] General intelligence is among the field’s long-term goals.[19] 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 others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[20] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[21] Some people also consider AI to be a danger to humanity if it progresses unabated.[22] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[23]

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.[24][13]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[25] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[26] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[21]

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 ChurchTuring thesis.[27] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[28] 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.[30] 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.[31] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[33] (and by 1959 were reportedly playing better than the average human),[34] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[35] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[36] and laboratories had been established around the world.[37] 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”.[9]

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”,[11] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[39] 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.[10] 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.[12]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[24] 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.[40] 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.[43] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[44] as do intelligent personal assistants in smartphones.[45] 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.[7][46] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[47] who at the time continuously held the world No. 1 ranking for two years.[48][49] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[50] 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.[13] 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.[50] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[51][52] 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”.[53][54]

A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] An AI’s intended goal function can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do actions mathematically similar to the actions that got you rewards in the past”). Goals can be explicitly defined, or can be induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior and punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems; this is similar to how animals evolved to innately desire certain goals such as finding food, or how dogs can be bred via artificial selection to possess desired traits. Some AI systems, such as nearest-neighbor, instead reason by analogy; these systems are not generally given goals, except to the degree that goals are somehow 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.[57]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe:

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, if given infinite data, time, and memory, learn to approximate any function, including whatever combination of mathematical functions would best describe the entire world. These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it 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 swaths of possibilities that are unlikely to be fruitful.[59] 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 an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[61]

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;[62] the best approach is often different depending on the problem.[64]

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, to be successful, 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. Settling 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.[c][67][68][69]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave 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 inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[72][73][74] 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.[75][76][77]

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.[15]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[78] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[79]

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.[59] 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.[80]

Knowledge representation[81] and knowledge engineering[82] 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;[83] situations, events, states and time;[84] causes and effects;[85] knowledge about knowledge (what we know about what other people know);[86] 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.[87] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[88] 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,[89] scene interpretation,[90] clinical decision support,[91] knowledge discovery (mining “interesting” and actionable inferences from large databases),[92] and other areas.[93]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[100] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[101]

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.[102] 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.[103]

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.[104]

Machine learning, a fundamental concept of AI research since the field’s inception,[105] is the study of computer algorithms that improve automatically through experience.[106][107]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[108] 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, after seeing 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.[107] 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.[109] In reinforcement learning[110] 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.

Natural language processing[111] (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[112] and machine translation.[113] 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.[114]

Machine perception[115] 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,[116] facial recognition, and object recognition.[117] 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.[118]

AI is heavily used in robotics.[119] 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.[120] 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.[122][123] 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”.[124][125] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[126]

Moravec’s paradox can be extended to many forms of social intelligence.[128][129] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[130] 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.[134]

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 humancomputer interaction.[135] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[136]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) 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).[137] 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.[19][138] 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.[139][140][141] Besides transfer learning,[142] 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.[144][145]

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.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[146] 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?[16]Can 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?[17]

In 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.[147] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 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”.[148] 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.[149]Researchers 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.

Economist 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.[150][151]

Unlike 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 of whether people used the same algorithms.[16] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[152] 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.[153]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[154] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey 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).[17] 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.[155]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[156] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[39] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[157] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By 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.[18] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This 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.[158] Their work revived the non-symbolic viewpoint 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.

Within 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.).[159][160]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[163] Artificial neural networks are an example of soft computingthey 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, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[164]

Much 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).[d] 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 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.[40][165] 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.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[174] 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.[175] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[176] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[120] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[177] 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.[178] 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.[179]

Evolutionary 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.[180] 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).[181][182]

Logic[183] 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[184] and inductive logic programming is a method for learning.[185]

Several different forms of logic are used in AI research. Propositional logic[186] involves truth functions such as “or” and “not”. First-order logic[187] 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.[e][189][190]

Default logics, non-monotonic logics and circumscription[95] 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;[83] situation calculus, event calculus and fluent calculus (for representing events and time);[84] causal calculus;[85] belief calculus;[191] and modal logics.[86]

Overall, qualitiative 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.[193]

Many 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.[194]

Bayesian networks[195] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[196] learning (using the expectation-maximization algorithm),[f][198] planning (using decision networks)[199] and perception (using dynamic Bayesian networks).[200] 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).[200] 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,[201] and information value theory.[101] These tools include models such as Markov decision processes,[202] dynamic decision networks,[200] game theory and mechanism design.[203]

The 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.[204]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[205] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[207]k-nearest neighbor algorithm,[g][209]kernel methods such as the support vector machine (SVM),[h][211]Gaussian mixture model,[212] and the extremely popular naive Bayes classifier.[i][214] 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.[215]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple 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 net forms “concepts” that are distributed among a subnetwork of shared[j] 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 nets 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.[k] 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.[218][219]

The study of non-learning artificial neural networks[207] 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.

The 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.[220] 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.[221]

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,[222][223] and was introduced to neural networks by Paul Werbos.[224][225][226]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[227]

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”.[228]

Deep 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.[229] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[230][231][229]

According to one overview,[232] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[233] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[234] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[235][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[236] 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.[238]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[239] 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.[240]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[229]

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.[241]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[242] which are in theory Turing complete[243] 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.[229] RNNs can be trained by gradient descent[244][245][246] but suffer from the vanishing gradient problem.[230][247] 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.[248]

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.[249] LSTM is often trained by Connectionist Temporal Classification (CTC).[250] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[251][252][253] 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.[254] Google also used LSTM to improve machine translation,[255] Language Modeling[256] and Multilingual Language Processing.[257] LSTM combined with CNNs also improved automatic image captioning[258] and a plethora of other applications.

AI, 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.[259] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[260][261] 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.”[262] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[126]

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.[263][264] E-sports such as StarCraft continue to provide additional public benchmarks.[265][266] 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.[267]

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.[268] 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.[270][271]

AI 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,[274] prediction of judicial decisions[275] and targeting online advertisements.[276][277]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[278] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[279]

AI is being applied to the high cost problem of dosage issueswhere 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.[280]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[281] 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.[282] Another study is using artificial intelligence to try and 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.[283] 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% percent accuracy.[284]

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.[285] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[286] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[287]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[288]

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.[289]

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.[290] 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.[291]

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.[292] 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.[293]

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.[294] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial 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 unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks 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.[295] In August 2001, robots beat humans in a simulated financial trading competition.[296] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[297]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[298] 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 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).[299][300]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[301][302] Military drones capable of autonomous action are widely considered a useful asset. In 2017, Vladimir Putin stated that “Whoever becomes the leader in (artificial intelligence) will become the ruler of the world”.[303][304] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[305]

For 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.[306]

It 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.[307] A documented case reports that online gambling companies were using AI to improve customer targeting.[308]

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.[309]

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Artificial intelligence – Wikipedia

What is Cloud Computing? | Rackspace Managed Cloud

Description Multi-tenant environment with pay-as-you-grow scalability Scalability plus the enhanced security and control of a single-tenant environment For predictable workloads that require enhanced security and control Connect the public cloud to your private cloud or dedicated servers even in your own data center Best for Non-sensitive, public-facing operations and unpredictable traffic Sensitive, business-critical operations Sensitive, business-critical operations, plus demanding performance, security and compliance requirements Combine public, private and/or dedicated servers, for the best of each

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What is Cloud Computing? | Rackspace Managed Cloud

What is Cloud Computing? – Amazon Web Services

Whether you are running applications that share photos to millions of mobile users or youre supporting the critical operations of your business, a cloud services platform provides rapid access to flexible and low cost IT resources. With cloud computing, you dont need to make large upfront investments in hardware and spend a lot of time on the heavy lifting of managing that hardware. Instead, you can provision exactly the right type and size of computing resources you need to power your newest bright idea or operate your IT department. You can access as many resources as you need, almost instantly, and only pay for what you use.

Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.

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What is Cloud Computing? – Amazon Web Services

Types of Cloud Computing – Amazon Web Services (AWS)

Platforms as a service remove the need for organizations to manage the underlying infrastructure (usually hardware and operating systems) and allow you to focus on the deployment and management of your applications. This helps you be more efficient as you dont need to worry about resource procurement, capacity planning, software maintenance, patching, or any of the other undifferentiated heavy lifting involved in running your application.

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Types of Cloud Computing – Amazon Web Services (AWS)

Benefits of cloud computing | IBM Cloud

If you are considering adopting cloud technologies and practices, you will receive a ton of different guidance about the benefits you might see.

Infrastructure and workloads

Many companies position the low initial costs and pay-as-you-go attributes as a very significant cost savings. Theyll note the considerable cost of building and operating data centers and argue for avoiding that to save money. Numbers can get astronomical depending on how you calculate them.

SaaS and cloud dev platforms

A software-as-a-service provider may discuss the savings from paying for application access versus purchasing off-the-shelf software. Software providers will add those “cloud attribute” benefits to the specifics of their software. Recently, there has been more discussion regarding the savings that cloud-based platforms can offer developers.

Speed and productivity

How much is it worth to your business if you can get a new application up and running in 30 hours rather than six to nine months? Likewise, the generic “staff productivity” doesn’t do justice to the capabilities that cloud dashboards, real-time statistics and active analytics can bring to reducing administration burden. How much does a person hour cost your company?

Risk exposure

I like to think of this simply. What is the impact if you are wrong?

When the negative impact to trying new things is low, meaning that the risk is low, you will try many more things. The more you attempt, the more successes you will have.

If you asked me how to benefit from adopting cloud services, my first question would be, “Which services?” Every user and every organization is going to get a different set of benefits. The most important thing I can suggest is to think across the spectrum. Evaluate the potential savings, but also think about the soft benefits: improved productivity, more speed and lowered risk.

As hockey great Wayne Gretzky observed, you will miss 100 percent of the shots that you dont take. How much of a benefit is it to take your shot?

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Benefits of cloud computing | IBM Cloud

Cloud computing – Simple English Wikipedia, the free encyclopedia

In Computer science, cloud computing describes a type of outsourcing of computer services, similar to the way in which electricity supply is outsourced. Users can simply use it. They do not need to worry where the electricity is from, how it is made, or transported. Every month, they pay for what they consumed.

The idea behind cloud computing is similar: The user can simply use storage, computing power, or specially crafted development environments, without having to worry how these work internally. Cloud computing is usually Internet-based computing. The cloud is a metaphor for the Internet based on how the internet is described in computer network diagrams; which means it is an abstraction hiding the complex infrastructure of the internet.[1] It is a style of computing in which IT-related capabilities are provided as a service,[2] allowing users to access technology-enabled services from the Internet (“in the cloud”)[3] without knowledge of, or control over the technologies behind these servers.[4]

According to a paper published by IEEE Internet Computing in 2008 “Cloud Computing is a paradigm in which information is permanently stored in servers on the Internet and cached temporarily on clients that include computers, laptops, handhelds, sensors, etc.”[5]

Cloud computing is a general concept that utilizes software as a service (SaaS), such as Web 2.0 and other technology trends, all of which depend on the Internet for satisfying users’ needs. For example, Google Apps provides common business applications online that are accessed from a web browser, while the software and data are stored on the Internet servers.

Cloud computing is often confused with other ideas:

Cloud computing often uses grid computing, has autonomic characteristics and is billed like utilities, but cloud computing can be seen as a natural next step from the grid-utility model.[8] Some successful cloud architectures have little or no centralised infrastructure or billing systems including peer-to-peer networks like BitTorrent and Skype.[9]

The majority of cloud computing infrastructure currently consists of reliable services delivered through data centers that are built on computer and storage virtualization technologies. The services are accessible anywhere in the world, with The Cloud appearing as a single point of access for all the computing needs of consumers. Commercial offerings need to meet the quality of service requirements of customers and typically offer service level agreements.[10] Open standards and open source software are also critical to the growth of cloud computing.[11]

As customers generally do not own the infrastructure or know all details about it, mainly they are accessing or renting, so they can consume resources as a service, and may be paying for what they do not need, instead of what they actually do need to use. Many cloud computing providers use the utility computing model which is analogous to how traditional public utilities like electricity are consumed, while others are billed on a subscription basis. By sharing consumable and “intangible” computing power between multiple “tenants”, utilization rates can be improved (as servers are not left idle) which can reduce costs significantly while increasing the speed of application development.

A side effect of this approach is that “computer capacity rises dramatically” as customers do not have to engineer for peak loads.[12] Adoption has been enabled by “increased high-speed bandwidth” which makes it possible to receive the same response times from centralized infrastructure at other sites.

Cloud computing is being driven by providers including Google, Amazon.com, and Yahoo! as well as traditional vendors including IBM, Intel,[13] Microsoft[14] and SAP.[15] It can adopted by all kinds of users, be they individuals or large enterprises. Most internet users are currently using cloud services, even if they do not realize it. Webmail for example is a cloud service, as are Facebook and Wikipedia and contact list synchronization and online data backups.

The Cloud[16] is a metaphor for the Internet,[17] or more generally components and services which are managed by others.[1]

The underlying concept dates back to 1960 when John McCarthy expressed his opinion that “computation may someday be organized as a public utility” and the term Cloud was already in commercial use in the early 1990s to refer to large ATM networks.[18] By the turn of the 21st century, cloud computing solutions had started to appear on the market,[19] though most of the focus at this time was on Software as a service.

Amazon.com played a key role in the development of cloud computing when upgrading their data centers after the dot-com bubble and providing access to their systems by way of Amazon Web Services in 2002 on a utility computing basis. They found the new cloud architecture resulted in significant internal efficiency improvements.[20]

2007 observed increased activity, including Google, IBM and a number of universities starting large scale cloud computing research project,[21] around the time the term started gaining popularity in the mainstream press. It was a hot topic by mid-2008 and numerous cloud computing events had been scheduled.[22]

In August 2008 Gartner observed that “organizations are switching from company-owned hardware and software assets to per-use service-based models” and that the “projected shift to cloud computing will result in dramatic growth in IT products in some areas and in significant reductions in other areas”.[23]

Clouds cross many country borders and “may be the ultimate form of globalisation”.[24] As such it is the subject of complex geopolitical issues, whereby providers must satisfy many legal restrictions in order to deliver service to a global market. This dates back to the early days of the Internet, where libertarian thinkers felt that “cyberspace was a distinct place calling for laws and legal institutions of its own”; author Neal Stephenson envisaged this as a tiny island data haven in his science-fiction classic novel Cryptonomicon.[24]

Although there have been efforts to match the legal environment (such as US-EU Safe Harbor), providers like Amazon Web Services usually deal with international markets (typically the United States and European Union) by deploying local infrastructure and allowing customers to select their countries.[25] However, there are still concerns about security and privacy for individual through various governmental levels, (for example the USA PATRIOT Act and use of national security letters and title II of the Electronic Communications Privacy Act, the Stored Communications Act).

In March 2007, Dell applied to trademark the term ‘”cloud computing” in the United States. It received a “Notice of Allowance” in July 2008 which was subsequently canceled on August 6, resulting in a formal rejection of the trademark application in less than a week later.

In November 2007, the Free Software Foundation released the Affero General Public License (abbreviated as Affero GPL and AGPL), a version of GPLv3 designed to close a perceived legal loophole associated with Free software designed to be run over a network, particularly software as a service. According to the AGPL license application service providers are required to release any changes they make to an AGPL open source code.

Cloud architecture[26] is the systems architecture of the software systems involved in the delivery of cloud computing (e.g. hardware, software) as designed by a cloud architect who typically works for a cloud integrator. It typically involves multiple cloud components communicating with each other over application programming interfaces (usually web services).[27]

This is very similar to the Unix philosophy of having multiple programs doing one thing well and working together over universal interfaces. Complexity is controlled and the resulting systems are more manageable than their monolithic counterparts.

Cloud architecture extends to the client where web browsers and/or software applications are used to access cloud applications.

Cloud storage architecture is loosely coupled where metadata operations are centralized enabling the data nodes to scale into the hundreds, each independently delivering data to applications or users.

A cloud application influences The Cloud model of software architecture, often eliminating the need to install and run the application on the customer’s own computer, thus reducing software maintenance, ongoing operations, and support. For example:

A cloud client is computer hardware and/or computer software which relies on The Cloud for application delivery, or which is specifically designed for delivery of cloud services, and which is in either case essentially useless without a Cloud.[33] For example:

Cloud infrastructure (e.g. Infrastructure as a service) is the delivery of computer infrastructure (typically a platform virtualization environment) as a service.[41] For example:

A cloud platform (e.g. Platform as a service) (the delivery of a computing platform and/or solution stack as a service) [42] facilitates deployment of applications without the cost and complexity of buying and managing the underlying hardware and software layers.[43] For example:

A cloud service (e.g. Web Service) is “software system[s] designed to support interoperable machine-to-machine interaction over a network”[44] which may be accessed by other cloud computing components, software (e.g. Software plus services) or end users directly.[45] For example:

Cloud storage is the delivery of data storage as a service (including database-like services), often billed on a utility computing basis (e.g. per gigabyte per month).[46] For example:

Traditional storage vendors have recently begun to offer their own flavor of cloud storage, sometimes in conjunction with their existing software products (e.g. Symantec’s Online Storage for Backup Exec). Others focus on providing a new kind of back-end storage optimally designed for delivering cloud storage (EMC’s Atmos), categorically known as Cloud Optimized Storage.

A cloud computing provider or cloud computing service provider owns and operates cloud computing systems serve someone else. Usually this needs building and managing new data centers. Some organisations get some of the benefits of cloud computing by becoming “internal” cloud providers and servicing themselves, though they do not benefit from the same economies of scale and still have to engineer for peak loads. The barrier to entry is also significantly higher with capital expenditure required and billing and management creates some overhead. However, significant operational efficiency and quickness advantages can be achieved even by small organizations, and server consolidation and virtualization rollouts are already in progress.[47] Amazon.com was the first such provider, modernising its data centers which, like most computer networks were using as little as 10% of its capacity at any one time just to leave room for occasional spikes. This allowed small, fast-moving groups to add new features faster and easier, and they went on to open it up to outsiders as Amazon Web Services in 2002 on a utility computing basis.[20]

The companies listed in the Components section are providers.

A user is a consumer of cloud computing.[33] The privacy of users in cloud computing has become of increasing concern.[48][49] The rights of users is also an issue, which is being addressed via a community effort to create a bill of rights (currently in draft).[50][51]

A vendor sells products and services that facilitate the delivery, adoption and use of cloud computing.[52] For example:

A cloud standard is one of a number of existing (typically lightweight) open standards that have facilitated the growth of cloud computing, including:[57]

View post:

Cloud computing – Simple English Wikipedia, the free encyclopedia

What is Cloud Computing? – Amazon Web Services

Whether you are running applications that share photos to millions of mobile users or youre supporting the critical operations of your business, a cloud services platform provides rapid access to flexible and low cost IT resources. With cloud computing, you dont need to make large upfront investments in hardware and spend a lot of time on the heavy lifting of managing that hardware. Instead, you can provision exactly the right type and size of computing resources you need to power your newest bright idea or operate your IT department. You can access as many resources as you need, almost instantly, and only pay for what you use.

Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.

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What is Cloud Computing? – Amazon Web Services

What is Cloud Computing? | Rackspace Managed Cloud

Description Multi-tenant environment with pay-as-you-grow scalability Scalability plus the enhanced security and control of a single-tenant environment For predictable workloads that require enhanced security and control Connect the public cloud to your private cloud or dedicated servers even in your own data center Best for Non-sensitive, public-facing operations and unpredictable traffic Sensitive, business-critical operations Sensitive, business-critical operations, plus demanding performance, security and compliance requirements Combine public, private and/or dedicated servers, for the best of each

Original post:

What is Cloud Computing? | Rackspace Managed Cloud

What is Cloud Computing? – Amazon Web Services

Whether you are running applications that share photos to millions of mobile users or youre supporting the critical operations of your business, a cloud services platform provides rapid access to flexible and low cost IT resources. With cloud computing, you dont need to make large upfront investments in hardware and spend a lot of time on the heavy lifting of managing that hardware. Instead, you can provision exactly the right type and size of computing resources you need to power your newest bright idea or operate your IT department. You can access as many resources as you need, almost instantly, and only pay for what you use.

Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.

See original here:

What is Cloud Computing? – Amazon Web Services

Cloud computing – Simple English Wikipedia, the free encyclopedia

This article has many issues. Please help fix them or discuss these issues on the talk page.

In Computer science, cloud computing describes a type of outsourcing of computer services, similar to the way in which electricity supply is outsourced. Users can simply use it. They do not need to worry where the electricity is from, how it is made, or transported. Every month, they pay for what they consumed.

The idea behind cloud computing is similar: The user can simply use storage, computing power, or specially crafted development environments, without having to worry how these work internally. Cloud computing is usually Internet-based computing. The cloud is a metaphor for the Internet based on how the internet is described in computer network diagrams; which means it is an abstraction hiding the complex infrastructure of the internet.[1] It is a style of computing in which IT-related capabilities are provided as a service,[2] allowing users to access technology-enabled services from the Internet (“in the cloud”)[3] without knowledge of, or control over the technologies behind these servers.[4]

According to a paper published by IEEE Internet Computing in 2008 “Cloud Computing is a paradigm in which information is permanently stored in servers on the Internet and cached temporarily on clients that include computers, laptops, handhelds, sensors, etc.”[5]

Cloud computing is a general concept that utilizes software as a service (SaaS), such as Web 2.0 and other technology trends, all of which depend on the Internet for satisfying users’ needs. For example, Google Apps provides common business applications online that are accessed from a web browser, while the software and data are stored on the Internet servers.

Contents

Cloud computing is often confused with other ideas:

Cloud computing often uses grid computing, has autonomic characteristics and is billed like utilities, but cloud computing can be seen as a natural next step from the grid-utility model.[8] Some successful cloud architectures have little or no centralised infrastructure or billing systems including peer-to-peer networks like BitTorrent and Skype.[9]

The majority of cloud computing infrastructure currently consists of reliable services delivered through data centers that are built on computer and storage virtualization technologies. The services are accessible anywhere in the world, with The Cloud appearing as a single point of access for all the computing needs of consumers. Commercial offerings need to meet the quality of service requirements of customers and typically offer service level agreements.[10] Open standards and open source software are also critical to the growth of cloud computing.[11]

As customers generally do not own the infrastructure or know all details about it, mainly they are accessing or renting, so they can consume resources as a service, and may be paying for what they do not need, instead of what they actually do need to use. Many cloud computing providers use the utility computing model which is analogous to how traditional public utilities like electricity are consumed, while others are billed on a subscription basis. By sharing consumable and “intangible” computing power between multiple “tenants”, utilization rates can be improved (as servers are not left idle) which can reduce costs significantly while increasing the speed of application development.

A side effect of this approach is that “computer capacity rises dramatically” as customers do not have to engineer for peak loads.[12] Adoption has been enabled by “increased high-speed bandwidth” which makes it possible to receive the same response times from centralized infrastructure at other sites.

Cloud computing is being driven by providers including Google, Amazon.com, and Yahoo! as well as traditional vendors including IBM, Intel,[13] Microsoft[14] and SAP.[15] It can adopted by all kinds of users, be they individuals or large enterprises. Most internet users are currently using cloud services, even if they do not realize it. Webmail for example is a cloud service, as are Facebook and Wikipedia and contact list synchronization and online data backups.

The Cloud[16] is a metaphor for the Internet,[17] or more generally components and services which are managed by others.[1]

The underlying concept dates back to 1960 when John McCarthy expressed his opinion that “computation may someday be organized as a public utility” and the term Cloud was already in commercial use in the early 1990s to refer to large ATM networks.[18] By the turn of the 21st century, cloud computing solutions had started to appear on the market,[19] though most of the focus at this time was on Software as a service.

Amazon.com played a key role in the development of cloud computing when upgrading their data centers after the dot-com bubble and providing access to their systems by way of Amazon Web Services in 2002 on a utility computing basis. They found the new cloud architecture resulted in significant internal efficiency improvements.[20]

2007 observed increased activity, including Google, IBM and a number of universities starting large scale cloud computing research project,[21] around the time the term started gaining popularity in the mainstream press. It was a hot topic by mid-2008 and numerous cloud computing events had been scheduled.[22]

In August 2008 Gartner observed that “organizations are switching from company-owned hardware and software assets to per-use service-based models” and that the “projected shift to cloud computing will result in dramatic growth in IT products in some areas and in significant reductions in other areas”.[23]

Clouds cross many country borders and “may be the ultimate form of globalisation”.[24] As such it is the subject of complex geopolitical issues, whereby providers must satisfy many legal restrictions in order to deliver service to a global market. This dates back to the early days of the Internet, where libertarian thinkers felt that “cyberspace was a distinct place calling for laws and legal institutions of its own”; author Neal Stephenson envisaged this as a tiny island data haven in his science-fiction classic novel Cryptonomicon.[24]

Although there have been efforts to match the legal environment (such as US-EU Safe Harbor), providers like Amazon Web Services usually deal with international markets (typically the United States and European Union) by deploying local infrastructure and allowing customers to select their countries.[25] However, there are still concerns about security and privacy for individual through various governmental levels, (for example the USA PATRIOT Act and use of national security letters and title II of the Electronic Communications Privacy Act, the Stored Communications Act).

Cloud architecture[26] is the systems architecture of the software systems involved in the delivery of cloud computing (e.g. hardware, software) as designed by a cloud architect who typically works for a cloud integrator. It typically involves multiple cloud components communicating with each other over application programming interfaces (usually web services).[27]

This is very similar to the Unix philosophy of having multiple programs doing one thing well and working together over universal interfaces. Complexity is controlled and the resulting systems are more manageable than their monolithic counterparts.

Cloud architecture extends to the client where web browsers and/or software applications are used to access cloud applications.

Cloud storage architecture is loosely coupled where metadata operations are centralized enabling the data nodes to scale into the hundreds, each independently delivering data to applications or users.

A cloud application influences The Cloud model of software architecture, often eliminating the need to install and run the application on the customer’s own computer, thus reducing software maintenance, ongoing operations, and support. For example:

A cloud client is computer hardware and/or computer software which relies on The Cloud for application delivery, or which is specifically designed for delivery of cloud services, and which is in either case essentially useless without a Cloud.[33] For example:

Cloud infrastructure (e.g. Infrastructure as a service) is the delivery of computer infrastructure (typically a platform virtualization environment) as a service.[41] For example:

A cloud platform (e.g. Platform as a service) (the delivery of a computing platform and/or solution stack as a service) [42] facilitates deployment of applications without the cost and complexity of buying and managing the underlying hardware and software layers.[43] For example:

A cloud service (e.g. Web Service) is “software system[s] designed to support interoperable machine-to-machine interaction over a network”[44] which may be accessed by other cloud computing components, software (e.g. Software plus services) or end users directly.[45] For example:

Cloud storage is the delivery of data storage as a service (including database-like services), often billed on a utility computing basis (e.g. per gigabyte per month).[46] For example:

Traditional storage vendors have recently begun to offer their own flavor of cloud storage, sometimes in conjunction with their existing software products (e.g. Symantec’s Online Storage for Backup Exec). Others focus on providing a new kind of back-end storage optimally designed for delivering cloud storage (EMC’s Atmos), categorically known as Cloud Optimized Storage.

A cloud computing provider or cloud computing service provider owns and operates cloud computing systems serve someone else. Usually this needs building and managing new data centers. Some organisations get some of the benefits of cloud computing by becoming “internal” cloud providers and servicing themselves, though they do not benefit from the same economies of scale and still have to engineer for peak loads. The barrier to entry is also significantly higher with capital expenditure required and billing and management creates some overhead. However, significant operational efficiency and quickness advantages can be achieved even by small organizations, and server consolidation and virtualization rollouts are already in progress.[47] Amazon.com was the first such provider, modernising its data centers which, like most computer networks were using as little as 10% of its capacity at any one time just to leave room for occasional spikes. This allowed small, fast-moving groups to add new features faster and easier, and they went on to open it up to outsiders as Amazon Web Services in 2002 on a utility computing basis.[20]

The companies listed in the Components section are providers.

A user is a consumer of cloud computing.[33] The privacy of users in cloud computing has become of increasing concern.[48][49] The rights of users is also an issue, which is being addressed via a community effort to create a bill of rights (currently in draft).[50][51]

A vendor sells products and services that facilitate the delivery, adoption and use of cloud computing.[52] For example:

A cloud standard is one of a number of existing (typically lightweight) open standards that have facilitated the growth of cloud computing, including:[57]

Excerpt from:

Cloud computing – Simple English Wikipedia, the free encyclopedia

What is Cloud Computing? – Amazon Web Services

Whether you are running applications that share photos to millions of mobile users or youre supporting the critical operations of your business, a cloud services platform provides rapid access to flexible and low cost IT resources. With cloud computing, you dont need to make large upfront investments in hardware and spend a lot of time on the heavy lifting of managing that hardware. Instead, you can provision exactly the right type and size of computing resources you need to power your newest bright idea or operate your IT department. You can access as many resources as you need, almost instantly, and only pay for what you use.

Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A Cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.

Read more:

What is Cloud Computing? – Amazon Web Services

Cloud computing: Hardware & Software Security: Online …

Examples of cloud computing include Software as a Service, Platform as a Service, and Infrastructure as a Service. Generally, cloud computing services are run outside the walls of the customer organization, on a vendor’s infrastructure with vendor maintenance.

Although cloud-like services can be internal (e.g., IU’s Intelligent Infrastructure), this document refers exclusively to cloud services provided by third-party vendors over a network connection where at least part of the service resides outside the institution, regardless of whether those services are offered freely to the public or privately to paying or registered users.

Cloud computing represents an externalization of information technology applications and infrastructure beyond an organization’s data center walls. In the university context, cloud computing may be thought of as extra-campus or above-campus computing.

Cloud services are often available “on demand,” and utilize an infrastructure shared by the vendor’s customers. While some offer a flat fee model or consumption-based pricing, other cloud services are offered at no cost.

Within the university, the confidentiality, integrity, availability, use control, and accountability of institutional data and services are expected to be ensured by a suite of physical, technical, and administrative safeguards proportional to the sensitivity and criticality (i.e., risk) of those information assets and services.

These safeguards help protect the reputation of the university and reduce institutional exposure to legal and compliance risks. Much of the challenge in approaching cloud computing involves determining whether a service vendor has adequate safeguards in place commensurate with the value and risk associated with assets and services involved.

Once the high-level challenges are understood, the next step is to consider the risks and determine whether or how to appropriately mitigate those risks in the context of the proposed information and/or service.

The above factors should not be taken to suggest that cloud computing has no potential benefits; but rather that the benefits must be balanced with the risks involved when evaluating the use of cloud computing services.

Cloud computing services are similar to traditional outsourcing and can be approached analogously while accounting for their unique risks/benefits. The following recommendations and strategies are intended to assist units in their approach to evaluating the prudence and feasibility of leveraging cloud services.

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Cloud computing: Hardware & Software Security: Online …

Cloud Computing Trends: 2018 State of the Cloud Survey

In January 2018, RightScale conducted its seventh annual State of the Cloud Survey of the latest cloud computing trends, with a focus on infrastructure-as-a-service and platform-as-a-service.

Both public and private cloud adoption grew in 2018, with larger enterprises increasing their focus on public cloud. AWS is no longer the runaway leader as Azure has grown rapidly and is now a close second, especially among enterprise users. New to the survey this year is data on the large and growing spend on public cloud, which has driven cost optimization to the top of companies’ 2018 priority list. To gain control of growing spend, enterprise cloud teams are taking a stronger cloud governance role, including managing costs.

The State of the Cloud Survey is the largest survey on the use of cloud infrastructure thatis focused on cloud buyers and users, as opposed to cloud vendors. Their answers provide a comprehensive perspective on the state of the cloud today.

The survey asked 997 IT professionals about their adoption of cloud infrastructure and related technologies. Fifty-three percent of the respondents represented enterprises with more than 1,000 employees. The margin of error is 3.08 percent.

We highlight several key findings from the survey in this blog post. For the complete survey results, download the RightScale 2018 State of the Cloud Report.

Multi-Cloud Is the Preferred Strategy Among Enterprises

96 Percent of Respondents Use Cloud

More Enterprises Are Prioritizing Public Cloud in 2018

Organizations Leverage Almost 5 Clouds

Serverless Is the Top-Growing Extended Cloud Service

Enterprise Public Cloud Spend Is Significant and Growing Quickly

Enterprise Central IT Teams Shift Role to Governance and Brokering Cloud

Significant Wasted Cloud Spend Makes Optimizing Costs the Top Initiative

Container Use Is Up: Docker Is Used Most Broadly While Kubernetes Grows Quickly

Use of Configuration Tools Grows, with Ansible Showing Strongest Growth

Azure Continues to Grow Quickly and Reduce the AWS Lead, Especially Among Enterprises

Private Cloud Adoption Grows Across the Board

AWS Leads in Users with 50+ VMs While Azure Grows Its Footprint Faster

How AWS, Azure, Google Cloud, and IBM Cloud Stack Up Among Enterprises

In the 12 months since the last State of the Cloud Survey, a multi-cloud strategy remains the preference among enterprises even as the percentage of enterprises who use multiple clouds dropped slightly to 81 percent vs. 85 percent in 2017. Those planning a hybrid cloud strategy fell to 51 percent (from 58 percent in 2017). However, there was a slight increase in the number of enterprises are using multiple public clouds or multiple private clouds.

Both public and private cloud adoption have increased in the last year. The number of respondents now adopting public cloud is 92 percent, up from 89 percent in 2017, while the number of respondents now adopting private cloud is 75 percent, up from 72 percent in 2017. As a result, the overall portion of respondents using at least one public or private cloud is now 96 percent.

Among enterprises, the central IT team is typically tasked with assembling a hybrid portfolio of supported clouds. This year, many more enterprises see public cloud as their top priority, up from 29 percent in 2017 to 38 percent in 2018. Hybrid cloud still leads the to-do list, but has decreased as a top priority for enterprises, declining from 50 percent in 2017 to 45 percent in 2018.

Only 8 percent of enterprises are focusing on building a private cloud, and 9 percent see their top priority as using a hosted private cloud.

On average, survey respondents are using 4.8 clouds across both public and private. Respondents are already running applications in 3.1 clouds and experimenting with 1.7 more.

A significant number of public cloud users are now leveraging services beyond just the basic compute, storage, and network services. Year over year, serverless was the top-growing extended cloud service with a 75 percent increase over 2017 (12 to 21 percent adoption). Container-as-a-service was the second highest growth rate at 36 percent (14 to 19 percent adoption). DBaaS SQL and DBaaS NoSQL were third and fourth (26 and 22 percent growth rates, respectively), but achieved this growth starting from a much larger base of use, with 35 and 23 percent adoption, respectively, in 2017.

As use of public cloud has grown, so has the amount of spend. Public cloud spend is quickly becoming a significant new line item in IT budgets, especially among larger companies. Among all respondents, 13 percent spend at least $6 million annually on public cloud while 30 percent are spending at least $1.2 million per year. Among enterprises the spend is even higher, with 26 percent exceeding $6 million per year and more than half (52 percent) above $1.2 million per year.

Enterprises are not only using a lot of public cloud, but also planning to rapidly grow public cloud spend. Twenty percent of enterprises will more than double their public cloud spend in 2018, while 71 percent will grow spend at least 20 percent.

SMBs generally have fewer workloads overall and, as a result, smaller cloud bills (half spend under $120 thousand per year). However, 13 percent of SMBs still exceed $1.2 million in annual spend.

In contrast, private cloud use will grow more slowly for all sizes of organization. Only 7 percent of each group (enterprises and SMBs) is planning to double its use in 2018. Fewer than half of enterprises (47 percent) and 35 percent of SMBs plan to grow private cloud use by more than 20 percent.

As companies adopt cloud-first strategies, they are increasingly creating a centralized cloud team or a Center of Excellence for cloud. These teams provide centralized controls, tools, and best practices to help accelerate the use of cloud while reducing costs and risk.

Overall, 44 percent of companies already have a central cloud team. Enterprises have an even stronger need for centralized governance within their larger organizations: 57 percent of enterprises already have a central cloud team with another 24 percent planning one.

In 2018 we see enterprise central IT taking a stronger cloud governance role in advising on which applications move to cloud (69 percent in 2018 vs. 63 percent in 2017), managing costs (64 percent in 2018 vs. 55 percent in 2017), setting policies (60 percent in 2018 vs. 58 percent in 2017), and brokering cloud services (60 percent in 2018 vs. 54 percent in 2017).

Even though managing cloud costs is a top challenge, cloud users underestimate the amount of wasted cloud spend. Respondents estimate 30 percent waste, while RightScale has measured actual waste at 35 percent.

With significant wasted cloud spend, organizations are focusing on gaining control of costs. Optimizing cloud costs is the top initiative for the second year in a row, increasing from 53 percent of respondents in 2017 to 58 percent in 2018.

Despite an increased focus on cloud cost management, only a minority of companies have begun to implement automated policies to optimize cloud costs, such as shutting down unused workloads or selecting lower-cost cloud or regions. This represents an opportunity for increased efficiency and increased savings, since manual policies are difficult to monitor and enforce.

Docker adoption increased to 49 percent from 35 percent last year (a growth rate of 40 percent). Kubernetes, a container orchestration tool that leverages Docker, saw the fastest growth, almost doubling to reach 27 percent adoption.

Many users also choose container-as-a-service offerings from the public cloud providers.

The AWS container service (ECS/EKS) followed close behind Docker with 44 percent adoption (26 percent growth rate). Azure Container Service adoption reached 20 percent due to a strong growth of 82 percent, and Google Container Engine also grew strongly (75 percent) to reach adoption of 14 percent.

As part of adopting DevOps processes, companies often choose to implement configuration management tools that allow them to standardize and automate deployment and configuration of servers and applications. Among all respondents, Ansible and Chef are tied with 36 percent adoption each, followed by Puppet at 34 percent adoption.

Ansible showed the strongest growth since last year, up 71 percent in adoption. Chef grew 29 percent and Puppet grew 21 percent.

In 2018, AWS continues to lead in public cloud adoption, but other public clouds are growing more quickly. Azure especially is now nipping at the heels of AWS, especially in larger companies.

And 64 percent of respondents currently run applications in AWS, up from 57 percent in 2017 (12 percent growth rate).

Among enterprises, Azure did even better. Azure increased adoption significantly from 43 percent to 58 percent (35 percent growth rate) while AWS adoption in this group increased from 59 percent to 68 percent (15 percent growth rate). Among other cloud providers that were included in the survey last year, all saw increased adoption this year with Oracle growing fastest from 5 to 10 percent (100 percent growth rate), IBM Cloud from 10 to 15 percent (50 percent growth rate), and Google from 15 to 19 percent (27 percent growth rate).

Enterprise respondents with future projects (the combination of experimenting and planning to use) show the most interest in Google (41 percent).

In contrast to last years survey when we saw private cloud adoption flatten, the 2018 survey shows that adoption of private cloud increased across all providers.

Overall, VMware vSphere continues to lead with 50 percent adoption, up significantly from last year (42 percent). This includes respondents who view their vSphere environment as a private cloud whether or not it meets the accepted definition of cloud computing. OpenStack (24 percent), VMware vCloud Director (24 percent), Microsoft System Center (23 percent), and bare metal (22 percent) were all neck and neck. Azure Stack was in the sixth slot, but showed the highest percentage of respondents that were experimenting or planning to use the technology.

The cloud adoption numbers cited previously indicate the number of respondents that are running any workloads in a particular cloud. However, it is also important to look at the number of workloads or VMs that are running in each cloud. The following charts show the number of VMs being run across the top public and private clouds.

Among all respondents, 15 percent of respondents have more than 1,000+ VMs in vSphere as compared to 10 percent in AWS.

However, AWS leads in respondents with more than 50 VMs, (47 percent for AWS vs. 37 percent for VMware). In third position, Azure shows stronger growth, increasing respondents of more than 50 VMs from 21 to 29 percent.

While public cloud found its initial success in small forward-thinking organizations, over the past few years the battle has now shifted to larger enterprises. AWS has been moving quickly to address the needs of enterprises, and Microsoft has been working to bring its enterprise relationships to Azure. Google and IBM are also focusing on growing their infrastructure-as-a-service lines of business and continue to increase adoption.

The following public cloud scorecard provides a quick snapshot showing that AWS still maintains a lead among enterprises with the highest percentage adoption and largest VM footprint of the top public cloud providers. However, Azure is showing strength by growing much more quickly on already solid adoption numbers. IBM and Google are growing strongly as well but on a smaller base of users.

The 2018 State of the Cloud Survey shows that multi-cloud remains the preferred strategy. Almost every organization is using cloud at some level, with both public and private cloud adoption growing. On average, companies using or experimenting with nearly five public and private clouds with a majority of workloads now running in cloud.

However, public cloud is increasingly becoming the top focus among enterprises and, as a result, public cloud use is growing more quickly with the addition of new customers, an increase in workloads, and an increase in the number of services used.

This expansion in cloud use is driving public cloud spend higher, with large increases expected in 2018. Cost was the number one cloud challenge for intermediate and advanced cloud users. As a result, spend continues to be the top initiative for 2018 as even more organizations are turning their efforts to cost optimization efforts. There is still much room for improvement as 35 percent of cloud bills are wasted due to inefficiencies, and few organizations have yet implemented automated policies to help address these issues.

Enterprise central IT teams are taking a stronger role in cloud adoption, creating central cloud teams or a Center of Excellence. The role of these central teams is focused on cost management and governance as well as advising business units on workloads that should move to cloud. However, business units seek stronger autonomy, except in the area of cost optimization where they look to the central IT team for assistance.

The use of DevOps continues to increase, driving further adoption of container and configuration tools. Docker grew strongly again this year, and Kubernetes showed even stronger growth as a container orchestration solution. Many users are also adopting container-as-a-service offerings from AWS, Azure, and Google.

AWS still leads in public cloud adoption but Azure continues to grow more quickly and gains ground, especially with enterprise customers. Among enterprise cloud beginners, Azure is slightly ahead of AWS. Google maintains the third position, and VMware Cloud on AWS did well in its first year of availability. Adoption of Oracle Cloud is still small, but is growing well in the enterprise.

Cloud provider revenue is driven not just by adoption (percentage of companies using the cloud), but also the number of workloads (VMs) deployed, and the use of other extended cloud services.

Respondents continue to run more VMs in AWS than in other public clouds. However, Azure is growing quickly here as well to reduce AWSs lead.

VMware vSphere continues to lead as a private cloud option (both in adoption and number of VMs) followed by VMware vCloud Director. OpenStack is third, but Azure Pack (sixth place). stands out with the strongest interest level.

Download the RightScale 2018 State of the Cloud Report for the complete survey results.

Use of Charts and Data In This Report

We encourage the re-use of data, charts, and text published in this report under the terms of this Creative Commons Attribution 4.0 International License. You are free to share and make commercial use of this work as long as you attribute the RightScale 2018 State of the Cloud Report as stipulated in the terms of the license.

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Cloud Computing Trends: 2018 State of the Cloud Survey

Cloud computing | Define Cloud computing at Dictionary.com

noun

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Dictionary.com UnabridgedBased on the Random House Unabridged Dictionary, Random House, Inc. 2018

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Collins English Dictionary – Complete & Unabridged 2012 Digital Edition William Collins Sons & Co. Ltd. 1979, 1986 HarperCollins Publishers 1998, 2000, 2003, 2005, 2006, 2007, 2009, 2012

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Home – IEEE Cloud Computing

Aug 2018 -Listento Mitalee Sarker interview theCloudPerfectproject team

Jan 2016 -Listento Sekou Remy speak with Joy Johnson, VP for Mobile at AudioCommon

Aug 2015 – Listento Sekou Remy interview with Brian Dean about the USA Computing Olympiad and its continued use of cloud computing.

Jul 2015 -Listento three IT professionals from different industries,Wade McPherson,Robby Goswami, andDavid Samuel,share their perceptions of the cloud.

Apr 2015 -Listento theCloudsters(IEEE Cloud Computing volunteers)discuss theIEEE Cloud Computingmagazine article by Joe Weinman: “The Nuances of Cloud Economics.”

VisitiTunesto listen and subscribe to the latest podcasts or listen to more from the archive.

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Home – IEEE Cloud Computing

Cloud computing – CloudEXPO

Untitled DocumentCloud Computing EnablingDigital Transformation! Cloud-Native | Serverless DevOpsSummit FinTechEXPO – New York Blockchain Event CloudEXPO – Enterprise Cloud DXWorldEXPO – Digital Transformation (DX) Smart Cities | IoT | IIoT AI | Machine Learning | Cognitive Computing BigData | Analytics The API Enterprise | Mobility | Security Hot Topics | FinTech | WebRTC

Join Us at Cloud Expo Silicon Valley, June 24 – 26 Now is the time for a truly global DX event, to bring together the leading minds from the technology world in a conversation about Digital Transformation. DX encompasses the continuing technology revolution, and is addressing society’s most important issues throughout the entire $78 trillion 21st-century global economy.

DXWorldEXPO has organized these issues along 10 tracks, 22 keynotes and general sessions, and a faculty of 222 of the world’s top speakers.

DXWorldEXPO has three major themes on its conference agenda:

Technology – The Revolution Continues Economy – The 21st Century Emerges Society – The Big Issues

Global 2000 companies have more than US$40 trillion in annual revenue – more than 50% of the world’s entire GDP. The Global 2000 spends a total of US$2.4 trillion annually on enterprise IT. The average Global 2000 company has US$11 billion in annual revenue. The average Global 2000 company spends more than $600 million annually on enterprise IT. Governments throughout the world spend another US$500 billion on IT – much of it dedicated to new Smart City initiatives.

For the past 10 years CloudEXPO helped drive the migration to modern enterprise IT infrastructures, built upon the foundation of cloud computing. Today’s hybrid, multiple cloud IT infrastructures integrate Big Data, analytics, blockchain, the IoT, mobile devices, and the latest in cryptography and enterprise-grade security.

Digital Transformation is the key issue driving the global enterprise IT business. DX is most prominent among Global 2000 enterprises and government institutions.

Technology – The Revolution Continues DX Tech: Data-Driven Global 2000 DX Tech: The Blockchain ChallengeDX Tech: AI and Cognitive DX Tech: The Global Cloud

Economy – The 21st Century Emerges DX Econ: Software is Rewriting the World DX Econ: Smart Cities, Nations, and Regions DX Econ: FinTech and the Token Economy DX Econ: The Industrial Internet and Industrie 4.0

Society – The Big Issues DX Society: Environment DX Society: Education DX Society: Agriculture DX Society: Health Care

Benefits of Visiting the CloudEXPO Floor

CloudEXPOCloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday’s debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterprises are using some form of XaaS – software, platform, and infrastructure as a service.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.

Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers.

Companies are each developing their unique mix of cloud technologies and services, forming multi-cloud and hybrid cloud architectures and deployments across all major industries. Cloud-driven thinking has become the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, and the public sector.

DXWorldEXPO – Digital TransformationDXWorldEXPO LLC, the producer of the world’s most influential technology conferences and trade shows has announced the conference tracks forCloudEXPO|DXWorldEXPO2018 New York.Digital Transformation(DX) is a major focus with the introduction of DXWorldEXPOwithin the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term.

A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throughout enterprises of all sizes.

IoT and Smart CitiesThe Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago.

All major researchers estimate there will be tens of billions devices – computers, smartphones, tablets, and sensors – connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend Internet of Things at Cloud Expo in New York City. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as “IoT-Ready” as it can be!

DevOpsSUMMITThe widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long development cycles that produce software that is obsolete at launch. DevOps may be disruptive, but it is essential.

DevOps at Cloud Expo, will expand the DevOps community, enable a wide sharing of knowledge, and educate delegates and technology providers alike. Recent research has shown that DevOps dramatically reduces development time, the amount of enterprise IT professionals put out fires, and support time generally. Time spent on infrastructure development is significantly increased, and DevOps practitioners report more software releases and higher quality.

FinTech – New York Blockchain EventFinTech Is Now Part of the DXWorldEXPO | CloudEXPO Program. Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.

Accordingly, attendees at the upcoming 22ndCloudEXPO | DXWorldEXPO will find fresh new content in two new tracks called:

which will incorporate FinTech and Blockchain, as well asmachine learning,artificial intelligence anddeep learningin these two distinct tracks.

FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.

More than US$20 billion in venture capital is being invested in FinTech this year.DXWorldEXPO|CloudEXPO are pleased to bring you the latestFinTechdevelopments as an integral part of our program.

Conference Agenda, Keynotes & TracksDXWordEXPO New York 2018andCloudEXPO New York 2018agenda present 222 rockstar faculty members, 200 sessions and 22 keynotes and general sessions in 10 distinct conference tracks.

DXWorldEXPO|CloudEXPO 2018 New Yorkcover all of these tools, with the most comprehensive program and with 222 rockstar speakers throughout our industry presenting 22Keynotes and General Sessions, 200Breakout Sessions along 10 Tracks, as well as our signature Power Panels. Our Expo Floor brings together the world’s leading companies throughout the world of Cloud Computing, DevOps, FinTech, Digital Transformation, and all they entail.

As your enterprise creates a vision and strategy that enables you to create your own unique, long-term success, learning about all the technologies involved is essential. Companies today not only form multi-cloud and hybrid cloud architectures, but create them with built-in cognitive capabilities.

Cloud-Nativethinking is now the norm in financial services, manufacturing, telco, healthcare, transportation, energy, media, entertainment, retail and other consumer industries, as well as the public sector.

CloudEXPOisthe world’s most influentialtechnologyeventwhere Cloud Computing was coined over a decade ago and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals.

Sponsorship OpportunitiesDXWorldEXPO|CloudEXPOarethe single show where technology buyers and vendors can meet to experience and discus cloud computing and all that it entails. Sponsors ofDXWorldEXPO|CloudEXPOwill benefit from unmatched branding, profile building and lead generation opportunities through:

Faculty Highlight

By Yeshim Deniz

Dec. 18, 2018 11:45 PM EST

By Pat Romanski

Dec. 4, 2018 02:30 PM EST

Digital Transformation Blogs

By Yeshim Deniz

Dec. 18, 2018 11:45 PM EST

By Pat Romanski

Dec. 4, 2018 02:30 PM EST

By Yeshim Deniz

Nov. 26, 2018 01:30 PM EST

DevOpsSUMMIT Blogs

By Yeshim Deniz

Nov. 26, 2018 01:30 PM EST

By Zakia Bouachraoui

Nov. 12, 2018 12:00 PM EST

By Pat Romanski

Nov. 12, 2018 12:00 AM EST

CloudEXPO TV Power Panels

@SteveMar_Msft

@SOASoftwareInc

@Cnnct2me

@Flexential

Business Executives including CEOs, CMOs, & CIOs ,Presidents & SVPs,Directors of Business Development ,Directors of IT Operations,Product and Purchasing Managers,IT Managers.

If you would like to participate, please provide us with details of your website/s and event/s or your organization and please include basic audience demographics as well as relevant metrics such as ave. page views per month.

To get involved, email [emailprotected].

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Cloud computing – CloudEXPO

Cloud Computing, Cloud Software, Cloud Accounting – NetSuite

The concept behind cloud computing is simple: it lets you run computer applications over the Internet, without having to buy, install or manage your own servers. You can run your company’s IT operations with nothing more than a browser and an Internet connection. Applications, operating systems, servers and network switches all reside out of sight and within the metaphorical cloud, the Internet and are managed by your cloud computing vendor.

Cloud computing turns conventional software delivery on its head in a number of ways:

Best of all, cloud computing lets you focus on your business rather than on your software. You don’t have to use valuable IT resources to keep business systems on life support. Instead, you can re-deploy them to focus on more strategic business initiatives while leaving your cloud computing vendor to worry about scalability, security, uptime, application maintenance and system upgrades.

And you can be confident in taking your business public, or into new regions of the world, without outgrowing your cloud computing resources, thanks to the world-class datacenters typically provided by cloud computing vendors.

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Cloud Computing, Cloud Software, Cloud Accounting – NetSuite

Cloud Computing – ed2go

Gone are the days of saving files to a clunky harddrivecountless businesses and individuals are moving their information to the cloud. Many of us arent even aware of how much we depend on that technology, let alone how it works, so let your curiosity for cloud computing grow into a dynamic career by furthing your education with ed2go. Our cloud computing courses allow you to study from the comfort of your own home as they help prepare you for certifications and careers in cloud computing, as well as networking and engineering. Whether you want to pursue a career specifically in cloud services or youre hoping to build out your IT skillset, cloud computing training courses will help you stay at the forefront of this fast-paced field. Prepare for your CompTIA Cloud+ certification exam or study to become a Microsoft Certified Solutions Expert (MCSE)in private cloud technology, or master a number of in-demand skills. Like the cloud itself, your future is vast.

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Cloud Computing – ed2go

Learn Cloud Computing Tutorial – javatpoint

Cloud Computing tutorial provides basic and advanced concepts of Cloud Computing. Our Cloud Computing tutorial is designed for beginners and professionals.

Cloud Computing Tutorial with high end solution of IT infrastructure. Cloud computing is a virtualization based technology that reduces the cost of IT infrastructure. It provides a solution of IT infrastructure in low cost.

In this cloud tutorial, you will learn basics and advanced topics of cloud which is developed for beginners and professionals.

Cloud computing means on demand delivery of IT resources via the internet with pay-as-you-go pricing. It provides a solution of IT infrastructure in low cost.

Actually, Small as well as some large IT companies follows the traditional methods to provide the IT infrastructure. That means for any IT company, we need a Server Room that is the basic need of IT companies.

In that server room, there should be a database server, mail server, networking, firewalls, routers, modem, switches, QPS (Query Per Second means how much queries or load will be handled by the server) , configurable system, high net speed and the maintenance engineers.

To establish such IT infrastructure, we need to spend lots of money. To overcome all these problems and to reduce the IT infrastructure cost, Cloud Computing comes into existence.

The characteristics of cloud computing are given below:

The cloud works in the distributed computing environment. It shares resources among users and works very fast.

Availability of servers is high and more reliable, because chances of infrastructure failure are minimal.

Means “on-demand” provisioning of resources on a large scale, without having engineers for peak loads.

With the help of cloud computing, multiple users and applications can work more efficiently with cost reductions by sharing common infrastructure.

Cloud computing enables the users to access systems using a web browser regardless of their location or what device they use e.g. PC, mobile phone etc. As infrastructure is off-site (typically provided by a third-party) and accessed via the Internet, users can connect from anywhere.

Maintenance of cloud computing applications is easier, since they do not need to be installed on each user’s computer and can be accessed from different places. So, it reduces the cost also.

By using cloud computing, the cost will be reduced because to take the services of cloud computing, IT company need not to set its own infrastructure and pay-as-per usage of resources.

Application Programming Interfaces (APIs) are provided to the users so that they can access services on the cloud by using these APIs and pay the charges as per the usage of services.

Cloud Tutorial

Types of Cloud

Cloud Service Models

Virtualization

Amazon EC2

Interview

Before learning Cloud Computing, you must have the basic knowledge of Operating System.

Our Cloud Computing Tutorial is designed to help beginners and professionals.

We assure that you will not find any problem in this Cloud Computing tutorial. But if there is any mistake, please post the problem in contact form.

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Learn Cloud Computing Tutorial – javatpoint

Cloud Computing – Gartner IT Glossary

Gartner defines cloud computing as a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies.

See also CASB or cloud access security brokers

Gartner Enterprise Architecture & Technology Innovation Summit 2017 access the full range of insights critical for architects like you. From best practices in EA to creative ways to identify opportunities presented by disruptive technologies. Register by March 31 and save $325 off the standard registration rate. Learn more click here now!

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Cloud Computing – Gartner IT Glossary


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