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• • • Artificial intelligence ( AI, also machine intelligence, MI) is displayed by, in contrast with the natural intelligence ( NI) displayed by humans and other animals. In AI research is defined as the study of ': any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term 'artificial intelligence' is applied when a machine mimics 'cognitive' functions that humans associate with other, 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, leading to the quip 'AI is whatever hasn't been done yet.' For instance, is frequently excluded from 'artificial intelligence', having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully, competing at a high level in systems (such as and ),, intelligent routing in,, and interpreting complex data, including images and videos. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an '), followed by new approaches, success and renewed funding.

[ ] For most of its history, AI research has been divided into subfields that often fail to communicate with each other. The traditional problems (or goals) of AI research include,,,,, and the ability to move and manipulate objects. Is among the field's long-term goals.

Approaches include,, and. Many tools are used in AI, including versions of, and. The AI field draws upon,,,,,, and many others. The field was founded on the claim that 'can be so precisely described that a machine can be made to simulate it'. This raises philosophical arguments about the nature of the and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by, and since.

Some people also consider AI if it progresses unabatedly. Others believe that it is primarily a risk to employment: a frequently cited paper by Michael Osborne and found that almost half of U.S. Jobs are at risk to automation due to AI. In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in, large amounts of, and theoretical understanding; and AI techniques have become an essential part of the, helping to solve many challenging problems in computer science.

Main articles: and While thought-capable appeared as in antiquity, the idea of actually trying to build a machine to perform useful reasoning may have begun with (c. With his, extended the concept of the ( engineered the first one around 1623), intending to perform operations on concepts rather than numbers. Since the 19th century, artificial beings are common in fiction, as in 's or 's. The study of mechanical or began with and mathematicians in antiquity. The study of mathematical logic led directly to 's, 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. [ ] Along with concurrent discoveries in, and, this led researchers to consider the possibility of building an electronic brain.

The first work that is now generally recognized as AI was and ' 1943 formal design for 'artificial neurons'. The field of AI research was born at at in 1956. Attendees (), (), (), () and () became the founders and leaders of AI research.

They and their students produced programs that the press described as 'astonishing': computers were winning at the game checkers, solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. Was heavily funded by the and laboratories had been established around the world. AI's founders were optimistic about the future: predicted, 'machines will be capable, within twenty years, of doing any work a man can do'. Agreed, writing, 'within a generation. The problem of creating 'artificial intelligence' will substantially be solved'. They failed to recognize the difficulty of some of the remaining tasks.

Progress slowed and in 1974, in response to the criticism of 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 ', a period when obtaining funding for AI projects was difficult. In the early 1980s, AI research was revived by the commercial success of, 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 project inspired the U.S and British governments to restore funding for academic research. However, beginning with the collapse of the market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began. In the late 1990s and early 21st century, AI began to be used for logistics,, and other areas. The success was due to increasing computational power (see ), greater emphasis on solving specific problems, new ties between AI and other fields and a commitment by researchers to mathematical methods and scientific standards.

Became the first computer chess-playing system to beat a reigning world chess champion, on 11 May 1997. Advanced statistical techniques (loosely known as ), access to and enabled advances in and perception. [ ] By the mid 2010s, machine learning applications were used throughout the world. [ ] In a exhibition match, 's,, defeated the two greatest Jeopardy champions, and, by a significant margin. The, which provides a 3D body–motion interface for the and the Xbox One use algorithms that emerged from lengthy AI research as do in.

In March 2016, won 4 out of 5 games of in a match with Go champion, becoming the first to beat a professional Go player without. In the 2017, won a with, who at the time continuously held the world No. 1 ranking for two years. This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess. According to 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.

He attributes this to an increase in affordable, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.

Goals [ ] 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. Reasoning, problem solving [ ] Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with or incomplete information, employing concepts from and. For difficult problems, algorithms can require enormous computational resources—most experience a ': the amount of memory or computer time required becomes astronomical for problems of a certain size. The search for more efficient problem-solving algorithms is a high priority.

Human beings ordinarily use fast, intuitive judgments rather than step-by-step deduction that early AI research was able to model. AI has progressed using 'sub-symbolic' problem solving: approaches emphasize the importance of skills to higher reasoning; research attempts to simulate the structures inside the brain that give rise to this skill; mimic the human ability to guess. Knowledge representation [ ]. Main articles: and and are central to AI research.

Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects; situations, events, states and time; causes and effects; knowledge about knowledge (what we know about what other people know); and many other, less well researched domains. A representation of 'what exists' is an: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them.

The of these are captured as concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the. The most general ontologies are called, which attempt to provide a foundation for all other knowledge by acting as mediators between that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations are suitable for content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery via automated reasoning (inferring new statements based on explicitly stated knowledge), etc. Video events are often represented as rules, which can be used, among others, to automatically generate subtitles for constrained videos. Among the most difficult problems in knowledge representation are: and the Many of the things people know take the form of 'working assumptions'.

For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds.

Identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires.

AI research has explored a number of solutions to this problem. The breadth of commonsense knowledge The number of atomic facts that the average person knows is very large.

Research projects that attempt to build a complete knowledge base of (e.g., ) require enormous amounts of laborious —they must be built, by hand, one complicated concept at a time. A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the Internet, and thus be able to add to its own ontology. [ ] The subsymbolic form of some commonsense knowledge Much of what people know is not represented as 'facts' or 'statements' that they could express verbally.

For example, a chess master will avoid a particular chess position because it 'feels too exposed' or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.

Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that,, or will provide ways to represent this kind of knowledge. Planning [ ]. Main article: Intelligent agents must be able to set goals and achieve them.

They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the (or 'value') of available choices. In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.

However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment. Uses the and competition of many agents to achieve a given goal. Such as this is used by and.

Learning [ ]. Main article: Machine learning, a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience.

Is the ability to find patterns in a stream of input. Includes both and numerical.

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. Cartier Serial Numbers Year. In 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. These three types of learning can be analyzed in terms of, using concepts like.

The mathematical analysis of machine learning algorithms and their performance is a branch of known as. [ ] Within, 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.). Natural language processing [ ]. Main article: gives machines the ability to read and human language. A sufficiently powerful natural language processing system would enable and the acquisition of knowledge directly from human-written sources, such as newswire texts.

Some straightforward applications of natural language processing include,, and. A common method of processing and extracting meaning from natural language is through. Although these indexes require a large volume of user input, it is expected that increases in processor speeds and decreases in data storage costs will result in greater efficiency. Perception [ ]. Main article: The field of is closely related to AI.

Intelligence is required for robots to handle tasks such as object manipulation and, with sub-problems such as,, and. These systems require that an agent is able to: Be spatially cognizant of its surroundings, learn from and build a map of its environment, figure out how to get from one point in space to another, and execute that movement (which often involves compliant motion, a process where movement requires maintaining physical contact with an object). Social intelligence [ ]. A robot with rudimentary social skills Affective computing is the study and development of systems that can recognize, interpret, process, and simulate human. It is an interdisciplinary field spanning,, and. While the origins of the field may be traced as far back as the early philosophical inquiries into, the more modern branch of computer science originated with 's 1995 paper on 'affective computing'. A motivation for the research is the ability to simulate, where the machine would be able to interpret human emotions and adapts its behavior to give an appropriate response to those emotions.

Emotion and social skills are important to an intelligent agent for two reasons. First, being able to predict the actions of others by understanding their motives and emotional states allow an agent to make better decisions.

Concepts such as,, necessitate that an agent be able to detect and model human emotions. Second, in an effort to facilitate, an intelligent machine may want to display emotions (even if it does not experience those emotions itself) to appear more sensitive to the emotional dynamics of human interaction.

Creativity [ ]. Main articles: and Many researchers think that their work will eventually be incorporated into a machine with, combining all the skills mentioned above and even exceeding human ability in most or all these areas. A few believe that features like or an may be required for such a project. Many of the problems above also require that general intelligence be solved. For example, even specific straightforward tasks, like, require that a machine read and write in both languages (), follow the author's argument (), know what is being talked about (), and faithfully reproduce the author's original intent (). A problem like machine translation is considered ', but all of these problems need to be solved simultaneously in order to reach human-level machine performance. Approaches [ ] There is no established unifying theory or that guides AI research.

Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying? Or is as irrelevant to AI research as bird biology is to? Can intelligent behavior be described using simple, elegant principles (such as or )? Or does it necessarily require solving a large number of completely unrelated problems? Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require 'sub-symbolic' processing?

John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as, a term which has since been adopted by some non-GOFAI researchers. Stuart Shapiro divides AI research into three approaches, which he calls computational psychology, computational philosophy, and computer science. Computational psychology is used to make computer programs that mimic human behavior. Computational philosophy, is used to develop an adaptive, free-flowing computer mind.

Implementing computer science serves the goal of creating computers that can perform tasks that only people could previously accomplish. Together, the humanesque behavior, mind, and actions make up artificial intelligence. Cybernetics and brain simulation [ ]. Main article: 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:, and, and each one developed its own style of research. Named these approaches to AI 'good old fashioned AI' or '.

During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on or were abandoned or pushed into the background. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with and considered this the goal of their field.

Cognitive simulation [ ] Economist and 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, and. Their research team used the results of experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at would eventually culminate in the development of the architecture in the middle 1980s. Logic-based [ ] Unlike and, 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. His laboratory at () focused on using formal to solve a wide variety of problems, including, and.

Logic was also the focus of the work at the and elsewhere in Europe which led to the development of the programming language and the science of. Anti-logic or scruffy [ ] Researchers at (such as and ) found that solving difficult problems in and required ad-hoc solutions – they argued that there was no simple and general principle (like ) that would capture all the aspects of intelligent behavior. Described their 'anti-logic' approaches as ' (as opposed to the ' paradigms at and ). (such as 's ) are an example of 'scruffy' AI, since they must be built by hand, one complicated concept at a time. Knowledge-based [ ] When computers with large memories became available around 1970, researchers from all three traditions began to build into AI applications. This 'knowledge revolution' led to the development and deployment of (introduced by ), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic [ ] 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,, and. A number of researchers began to look into 'sub-symbolic' approaches to specific AI problems.

Sub-symbolic methods manage to approach intelligence without specific representations of knowledge. Embodied intelligence [ ] This includes,,, and. Researchers from the related field of, such as, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early researchers of the 1950s and reintroduced the use of in AI. This coincided with the development of the in the related field of: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. Computational intelligence and soft computing [ ] Interest in and ' was revived by and others in the middle of the 1980s. Neural networks are an example of --- they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.

Other approaches to AI include, and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of. Statistical [ ] In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like, economics or ).

And describe this movement as nothing less than a 'revolution' and 'the victory of the '. Critics argue that these techniques (with few exceptions ) are too focused on particular problems and have failed to address the long-term goal of general intelligence. There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between and.

Integrating the approaches [ ] Intelligent agent paradigm An is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as ). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.

And Researchers have designed systems to build intelligent systems out of interacting in a. A system with both symbolic and sub-symbolic components is a, and the study of such systems is. A provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. ' was an early proposal for such a hierarchical system. [ ] Tools [ ] In the course of 60 or so years of research, AI has developed a large number of tools to solve the most difficult problems in. A few of the most general of these methods are discussed below. Search and optimization [ ].

Main articles:,, and Many problems in AI can be solved in theory by intelligently searching through many possible solutions: can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from to, where each step is the application of an. Algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called. Algorithms for moving limbs and grasping objects use in. Many algorithms use search algorithms based on. Simple exhaustive searches are rarely sufficient for most real world problems: the (the number of places to search) quickly grows to. The result is a search that is or never completes.

The solution, for many problems, is to use ' 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 ' the '). Supply the program with a 'best guess' for the path on which the solution lies. Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of. 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: 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, and. 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, only the fittest to survive each generation (refining the guesses). Forms of include algorithms (such as or ) and (such as,, and ).

Main articles: and is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the algorithm uses logic for and is a method for. Several different forms of logic are used in AI research.

Or is the logic of statements which can be true or false. Also allows the use of and, and can express facts about objects, their properties, and their relations with each other., is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0).

Can be used for uncertain reasoning and have been widely used in modern industrial and consumer. [ ] models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence., and are forms of logic designed to help with default reasoning and the.

Several extensions of logic have been designed to handle specific domains of, such as:;, and (for representing events and time);; belief calculus; and. Probabilistic methods for uncertain reasoning [ ]. Main articles:,,,,, and 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 theory and economics.

Are a very general tool that can be used for a large number of problems: reasoning (using the algorithm), (using the ), (using ) and (using ). Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping systems to analyze processes that occur over time (e.g., or ). A key concept from the science of economics is ': 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,, and. These tools include models such as, dynamic, and. Classifiers and statistical learning methods [ ]. Main articles:,, and 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. Are functions that use to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class.

A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are many statistical and approaches. The most widely used classifiers are the, such as the,,,, and. The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified.

There is no single classifier that works best on all given problems; this is also referred to as the ' theorem. Determining a suitable classifier for a given problem is still more an art than science. Neural networks [ ]. A neural network is an interconnected group of nodes, akin to the vast network of in the.

Neural networks are modeled after the neurons in the human brain, where a trained algorithm determines an output response for input signals. The study of non-learning began in the decade before the field of AI research was founded, in the work of and. Invented the, a learning network with a single layer, similar to the old concept of. Early pioneers also include,,,, Christoph von der Malsburg, David Willshaw,,,,, and others. The main categories of networks are acyclic or (where the signal passes in only one direction) and (which allow feedback and short-term memories of previous input events).

Among the most popular feedforward networks are, and. Neural networks can be applied to the problem of (for robotics) or, using such techniques as,. Today, neural networks are often trained by the algorithm, which had been around since 1970 as the reverse mode of published by, and was introduced to neural networks.

Is an approach that models some of the structural and algorithmic properties of the. Deep feedforward neural networks [ ]. Main article: in with many layers has transformed many important subfields of artificial intelligence, including,, and others. According to a survey, the expression 'Deep Learning' was introduced to the community by in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to in 2000. The first functional Deep Learning networks were published by and V. Lapa in 1965.

[ ] These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by and Ruslan Salakhutdinov introduced another way of pre-training many-layered (FNNs) one layer at a time, treating each layer in turn as an, then using for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships.

Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses (CNNs), whose origins can be traced back to the introduced by in 1980. In 1989, and colleagues applied 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. Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions.

Deep feedforward neural networks were used in conjunction with by, Google Deepmind's program that was the first to beat a professional human player. Deep recurrent neural networks [ ].

Main article: Early on, was also applied to sequence learning with (RNNs) which are general computers 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. RNNs can be trained by but suffer from the. In 1992, it was shown that unsupervised pre-training of a stack of can speed up subsequent supervised learning of deep sequential problems. Numerous researchers now use variants of a deep learning recurrent NN called the (LSTM) network published by Hochreiter & Schmidhuber in 1997. LSTM is often trained by Connectionist Temporal Classification (CTC).

At Google, Microsoft and Baidu this approach has revolutionised. For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through to billions of smartphone users. Google also used LSTM to improve machine translation, Language Modeling and Multilingual Language Processing. LSTM combined with CNNs also improved automatic image captioning and a plethora of other applications. Control theory [ ]. Main article: In 1950, proposed a general procedure to test the intelligence of an agent now known as the.

This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results. [ ] For example, performance at (i.e.

Checkers) is optimal, [ ] performance at chess is high-human and nearing super-human (see ) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human. A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from and. Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers. A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart ().

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. Applications [ ]. Main article: 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. High-profile examples of AI include autonomous vehicles (such as and ), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as ), online assistants (such as ), image recognition in photographs, spam filtering, prediction of judicial decisions and targeting online advertisements. 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, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. Competitions and prizes [ ].

A patient side surgical arm of. 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. 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, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. Another study is using artificial intelligence to try 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.

According to, there was a recent study by surgeons at the Children's National Medical Center in Washington which successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.

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

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

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle. Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings. Some self-driving cars are not equipped with steering wheels or brakes, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions. Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high risk situations.

These situations could include a head on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.

The programing of the car in these situations is crucial to a successful driver-less automobile. Finance and Economics [ ] have long used systems to detect charges or claims outside of the norm, flagging these for human investigation.

The use of AI in can be traced back to 1987 when in USA 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. In August 2001, robots beat humans in a simulated competition. AI has also reduced fraud and financial crimes by monitoring of users for any abnormal changes or anomalies.

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories. For example, AI based buying and selling platforms have changed the law of in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce 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,,,, and. Video games [ ].

Main article: Artificial intelligence is used to generate intelligent behaviors primarily in (NPCs), often simulating human-like intelligence. Platforms [ ] A (or ') is defined as 'some sort of hardware architecture or software framework (including application frameworks), that allows software to run'. As Rodney Brooks pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.

A wide variety of platforms has allowed different aspects of AI to develop, ranging from such as to to robot platforms such as the with open interface. Recent advances in deep and distributed computing have led to a proliferation of software libraries, including,, and. Collective AI is a platform architecture that combines individual AI into a collective entity, in order to achieve global results from individual behaviors. With its collective structure, developers can crowdsource information and extend the functionality of existing AI domains on the platform for their own use, as well as continue to create and share new domains and capabilities for the wider community and greater good. As developers continue to contribute, the overall platform grows more intelligent and is able to perform more requests, providing a scalable model for greater communal benefit. Organizations like Inc. And the have used this collaborative AI model.

Education in AI [ ] A study found a shortage of 1.5 million highly trained data and AI professionals and managers and a number of private bootcamps have developed programs to meet that demand, including free programs like or paid programs like. Partnership on AI [ ] Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence. They stated: 'This partnership on AI will conduct research, organize discussions, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning.' Apple joined other tech companies as a founding member of the Partnership on AI in January 2017. The corporate members will make financial and research contributions to the group, while engaging with the scientific community to bring academics onto the board. Philosophy and ethics [ ].

Main articles: and There are three philosophical questions related to AI: • Is possible? Can a machine solve any problem that a human being can solve using intelligence? Or are there hard limits to what a machine can accomplish? • Are intelligent machines dangerous? How can we ensure that machines behave ethically and that they are used ethically?

• Can a machine have a, and in exactly the same sense that human beings do? Can a machine be, and thus deserve certain rights?

Can a machine cause harm? The limits of artificial general intelligence [ ]. — A common concern about the development of artificial intelligence is the potential threat it could pose to humanity. This concern has recently gained attention after mentions by celebrities including,, and.

A group of prominent tech titans including, Amazon Web Services and Musk have committed $1billion to a nonprofit company aimed at championing responsible AI development. The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. In his book, provides an argument that artificial intelligence will pose a threat to mankind. He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit behavior such as acquiring resources or protecting itself from being shut down.

If this AI's goals do not reflect humanity's - one example is an AI told to compute as many digits of pi as possible - it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal. For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching. Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence. Concern over risk from artificial intelligence has led to some high-profile donations and investments. In January 2015, donated ten million dollars to the to fund research on understanding AI decision making. The goal of the institute is to 'grow wisdom with which we manage' the growing power of technology. Musk also funds companies developing artificial intelligence such as and to 'just keep an eye on what's going on with artificial intelligence.

I think there is potentially a dangerous outcome there.' Development of militarized artificial intelligence is a related concern. Currently, 50+ countries are researching battlefield robots, including the United States, China, Russia, and the United Kingdom. Many people concerned about risk from superintelligent AI also want to limit the use of artificial soldiers.

Devaluation of humanity [ ]. Main article: wrote that AI applications cannot, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as or was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as ). To Weizenbaum these points suggest that AI research devalues human life. Decrease in demand for human labor [ ] Martin Ford, author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, and others argue that specialized artificial intelligence applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers to perform most routine and repetitive jobs. Ford predicts that many knowledge-based occupations—and in particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible to.

[ ] Artificial moral agents [ ] This raises the issue of how ethically the machine should behave towards both humans and other AI agents. This issue was addressed by Wendell Wallach in his book titled Moral Machines in which he introduced the concept of (AMA). For Wallach, AMAs have become a part of the research landscape of artificial intelligence as guided by its two central questions which he identifies as 'Does Humanity Want Computers Making Moral Decisions' and 'Can (Ro)bots Really Be Moral'. For Wallach the question is not centered on the issue of whether machines can demonstrate the equivalent of moral behavior in contrast to the constraints which society may place on the development of AMAs. Machine ethics [ ]. Main article: The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.

The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines. In all cases, only human beings have engaged in ethical reasoning. The time has come for adding an ethical dimension to at least some machines. Recognition of the ethical ramifications of behavior involving machines, as well as recent and potential developments in machine autonomy, necessitate this.

In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines. Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence. Further, investigation of machine ethics could enable the discovery of problems with current ethical theories, advancing our thinking about Ethics.' Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality.

A variety of perspectives of this nascent field can be found in the collected edition 'Machine Ethics' that stems from the AAAI Fall 2005 Symposium on Machine Ethics. Malevolent and friendly AI [ ]. Main article: Political scientist believes that AI can be neither designed nor guaranteed to be benevolent. He argues that 'any sufficiently advanced benevolence may be indistinguishable from malevolence.' Humans should not assume machines or robots would treat us favorably, because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share). Hyper-intelligent software may not necessarily decide to support the continued existence of humanity, and would be extremely difficult to stop. This topic has also recently begun to be discussed in academic publications as a real source of risks to civilization, humans, and planet Earth.

Physicist, founder, and founder have expressed concerns about the possibility that AI could evolve to the point that humans could not control it, with Hawking theorizing that this could '. One proposal to deal with this is to ensure that the first generally intelligent AI is ', and will then be able to control subsequently developed AIs. Some question whether this kind of check could really remain in place. Leading AI researcher writes, 'I think it is a mistake to be worrying about us developing malevolent AI anytime in the next few hundred years. I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence.' Machine consciousness, sentience and mind [ ]. Main articles: and Computationalism is the position in the that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing.

Computationalism argues that the relationship between mind and body is similar or identical to the relationship between software and hardware and thus may be a solution to the. This philosophical position was inspired by the work of AI researchers and cognitive scientists in the 1960s and was originally proposed by philosophers and. Strong AI hypothesis [ ]. Main article: 's considers a key issue in the: if a machine can be created that has intelligence, could it also? If it can feel, does it have the same rights as a human?

The idea also appears in modern science fiction, such as the film, in which humanoid machines have the ability to feel emotions. This issue, now known as ', is currently being considered by, for example, California's, although many critics believe that the discussion is premature. Some critics of argue that any hypothetical robot rights would lie on a spectrum with and human rights. The subject is profoundly discussed in the 2010 documentary film. Superintelligence [ ]. Main articles: and If research into produced sufficiently intelligent software, it might be able to reprogram and improve itself.

The improved software would be even better at improving itself, leading to. The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer named this scenario '. Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.

Has used (which describes the relentless exponential improvement in digital technology) to calculate that will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045. Transhumanism [ ]. —, April 1985 Robot designer, cyberneticist and inventor have predicted that humans and machines will merge in the future into that are more capable and powerful than either.

This idea, called, which has roots in and, has been illustrated in fiction as well, for example in the and the science-fiction series. In the 1980s artist 's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later 'the Gynoids' book followed that was used by or influenced movie makers including and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.

Argues that 'artificial intelligence is the next stage in evolution', an idea first proposed by 's ' (1863), and expanded upon by in his book of the same name in 1998. In fiction [ ].

Main article: Thought-capable artificial beings have appeared as storytelling devices since antiquity. The implications of a constructed machine exhibiting artificial intelligence have been a persistent theme in since the twentieth century. Early stories typically revolved around intelligent robots. The word 'robot' itself was coined by in his 1921 play, the title standing for '. Later, the SF writer developed the. He subsequently explored these in his many books, most notably the 'Multivac' series about a super-intelligent computer of the same name.

Asimov's laws are often brought up during layman discussions of machine ethics; while almost all artificial intelligence researchers are familiar with Asimov's laws through popular culture, they generally consider the laws useless for many reasons, one of which is their ambiguity. The novel, by, tells a science fiction story about Androids and humans clashing in a futuristic world.

Elements of artificial intelligence include the empathy box, mood organ, and the androids themselves. Throughout the novel, Dick portrays the idea that human subjectivity is altered by technology created with artificial intelligence. Nowadays AI is firmly rooted in popular culture; intelligent robots appear in innumerable works., the murderous computer in charge of the spaceship in (1968), is an example of the common 'robotic rampage' archetype in science fiction movies. (1984) and (1999) provide additional widely familiar examples. In contrast, the rare loyal robots such as Gort from (1951) and Bishop from (1986) are less prominent in popular culture. See also [ ] • • • • • • • • • • Notes [ ]. Berlin: Springer..

Introduction to Artificial Intelligence (2nd ed.). • Neapolitan, Richard; Jiang, Xia (2012).. Chapman & Hall/CRC..

Artificial Intelligence: A New Synthesis. Morgan Kaufmann.. •; (2003), (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall,. Upper Saddle River, New Jersey: Prentice Hall... New York: Oxford University Press..

Artificial Intelligence. Reading, MA: Addison-Wesley.. Artificial Intelligence.

Artificial Intelligence: An Introductory Course (2nd ed.). Edinburgh University Press..

Cambridge University Press.. History of AI [ ].

Sector Number% of total Agriculture 11 4 Business groups 8 3 Electricity 7 3 Forestry 47 17 Individual 103 37 Industrial processors 11 4 Iwi/Māori 9 3 Liquid fossil fuels (transport) 10 4 Local government 8 3 NGO and community groups 19 7 Research and tertiary organisation 7 3 Stationary energy (excluding electricity) 9 3 Waste 13 5 Wood processors and manufacturers 2 1 Other 14 5 TOTAL 278 The unique submissions and the form submission templates are available in the tables below. Some information is withheld at the request of submitters and contact details of all submitters are withheld under section 9(2)(a) of the Official Information Act 1982.

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