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What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

Online Artificial Intelligence Courses | Microsoft …

The Microsoft Professional Program (MPP) is a collection of courses that teach skills in several core technology tracks that help you excel in the industry’s newest job roles.

These courses are created and taught by experts and feature quizzes, hands-on labs, and engaging communities. For each track you complete, you earn a certificate of completion from Microsoft proving that you mastered those skills.

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Online Artificial Intelligence Courses | Microsoft …

A.I. Artificial Intelligence (2001) – IMDb

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In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his “mother”, Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined 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] 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, “AI is whatever hasn’t been done yet.”[3] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[4] Capabilities generally classified as AI as of 2017[update] include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go[6]), autonomous cars, intelligent routing in content delivery network and military simulations.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[7][8] followed by disappointment and the loss of funding (known as an “AI winter”),[9][10] followed by new approaches, success and renewed funding.[8][11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[13] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12]

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.[13] General intelligence is among the field’s long-term goals.[17] 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, 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”.[18] 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.[19] Some people also consider AI to be a danger to humanity if it progresses unabatedly.[20] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[21]

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.[22][11]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[23] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[24] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[19]

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.[25] 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”.[26] 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.[28] 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.[29] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[31] (and by 1959 were reportedly playing better than the average human),[32] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[33] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[34] and laboratories had been established around the world.[35] 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”.[7]

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”,[9] 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,[37] 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.[8] 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.[10]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[22] 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.[38] 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.[41] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One use algorithms that emerged from lengthy AI research[42] as do intelligent personal assistants in smartphones.[43] 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.[6][44] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[45] who at the time continuously held the world No. 1 ranking for two years.[46][47] 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.[48] 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.[11] 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.[48] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[49][50]

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.[53]

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.[55] 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.[57]

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;[58] the best approach is often different depending on the problem.[60]

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][63][64][65]

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.)[68][69][70] 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.[71][72][73]

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.[13]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[74] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[75]

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.[55] 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.[76]

Knowledge representation[77] and knowledge engineering[78] 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;[79] situations, events, states and time;[80] causes and effects;[81] knowledge about knowledge (what we know about what other people know);[82] 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.[83] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[84] 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,[85] scene interpretation,[86] clinical decision support,[87] knowledge discovery (mining “interesting” and actionable inferences from large databases),[88] and other areas.[89]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[96] 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.[97]

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.[98] 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.[99]

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.[100]

Machine learning, a fundamental concept of AI research since the field’s inception,[101] is the study of computer algorithms that improve automatically through experience.[102][103]

Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression 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.[103] 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.[104] In reinforcement learning[105] 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[106] (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[107] and machine translation.[108] 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.[109]

Machine perception[110] 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,[111] facial recognition, and object recognition.[112] 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.[113]

AI is heavily used in robotics.[114] 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.[115] 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.[117][118] 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”.[119][120] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[121]

Moravec’s paradox can be extended to many forms of social intelligence.[123][124] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[125] 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.[129]

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.[130] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naive users an unrealistic conception of how intelligent existing computer agents actually are.[131]

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).[132] 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.[17][133] 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.[134][135][136] Besides transfer learning,[137] 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.[139][140]

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.[141] 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?[14] 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?[15]

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.[142] 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”.[143] 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.[144] 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.[145][146]

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.[14] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[147] 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.[148]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[149] found that solving difficult problems in vision and natural language processing required ad-hoc solutions they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[15] 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.[150]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[151] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[37] 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.[16] 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.[152] 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.).[153][154]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[157] Artificial neural networks are an example of soft computing — they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[158]

Much of 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.[38][159] 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:[168] 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.[169] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[170] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[115] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[171] 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.[172] 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.[173]

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.[174] 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).[175][176]

Logic[177] 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[178] and inductive logic programming is a method for learning.[179]

Several different forms of logic are used in AI research. Propositional logic[180] involves truth functions such as “or” and “not”. First-order logic[181] 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][183][184]

Default logics, non-monotonic logics and circumscription[91] 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;[79] situation calculus, event calculus and fluent calculus (for representing events and time);[80] causal calculus;[81] belief calculus;[185] and modal logics.[82]

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.[187]

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.[188]

Bayesian networks[189] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[190] learning (using the expectation-maximization algorithm),[f][192] planning (using decision networks)[193] and perception (using dynamic Bayesian networks).[194] 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).[194] 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,[196] and information value theory.[97] These tools include models such as Markov decision processes,[197] dynamic decision networks,[194] game theory and mechanism design.[198]

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.[199]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[200] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[202] k-nearest neighbor algorithm,[g][204] kernel methods such as the support vector machine (SVM),[h][206] Gaussian mixture model,[207] the extremely popular naive Bayes classifier[i][209] and improved version of decision tree – decision stream.[210] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, 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.[211]

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.[214][215]

The study of non-learning artificial neural networks[202] 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.[216] 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.[217]

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,[218][219] and was introduced to neural networks by Paul Werbos.[220][221][222]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[223]

In short, 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”.[224]

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.[225] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[226][227][225]

According to one overview,[228] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[229] and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[230] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[231][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[232] 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.[234]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[235] 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.[236] Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions.[225]

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.[237]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[238] which are in theory Turing complete[239] 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.[225] RNNs can be trained by gradient descent[240][241][242] but suffer from the vanishing gradient problem.[226][243] 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.[244]

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.[245] LSTM is often trained by Connectionist Temporal Classification (CTC).[246] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[247][248][249] 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.[250] Google also used LSTM to improve machine translation,[251] Language Modeling[252] and Multilingual Language Processing.[253] LSTM combined with CNNs also improved automatic image captioning[254] 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.[255] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[256][257] 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.”[258] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[121]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to an close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[259][260] E-sports such as StarCraft continue to provide additional public benchmarks.[261][262] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The main areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[citation needed]

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.[263] 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.[265][266]

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, prediction of judicial decisions[269] and targeting online advertisements.[270][271]

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,[272] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[273]

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.[274] 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.[275] 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.[276]

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.[277] 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,[278] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[279]

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 driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[280]

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.[281]

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.[282] 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.[283]

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.[284] 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.[285]

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.[286] The programing 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.[287] In August 2001, robots beat humans in a simulated financial trading competition.[288] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[289]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[290] 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).[291][292]

Worldwide annual military spending on robotics rose from 5.1 billion USD in 2010 to 7.5 billion USD in 2015.[293][294] 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”.[295][296] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[297]

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.[298]

A report by the Guardian newspaper in the UK in 2018 found that online gambling companies were using AI to predict the behavior of customers in order to target them with personalized promotions.[299]

There are three philosophical questions related to AI:

Can a machine be intelligent? Can it “think”?

Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.[310]

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Artificial intelligence – Wikipedia

A.I. Artificial Intelligence (2001) – IMDb

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In the not-so-far future the polar ice caps have melted and the resulting rise of the ocean waters has drowned all the coastal cities of the world. Withdrawn to the interior of the continents, the human race keeps advancing, reaching the point of creating realistic robots (called mechas) to serve them. One of the mecha-producing companies builds David, an artificial kid which is the first to have real feelings, especially a never-ending love for his “mother”, Monica. Monica is the woman who adopted him as a substitute for her real son, who remains in cryo-stasis, stricken by an incurable disease. David is living happily with Monica and her husband, but when their real son returns home after a cure is discovered, his life changes dramatically. Written byChris Makrozahopoulos

Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

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Artificial Intelligence | Internet Encyclopedia of Philosophy

Artificial intelligence (AI) would be the possession of intelligence, or the exercise of thought, by machines such as computers. Philosophically, the main AI question is “Can there be such?” or, as Alan Turing put it, “Can a machine think?” What makes this a philosophical and not just a scientific and technical question is the scientific recalcitrance of the concept of intelligence or thought and its moral, religious, and legal significance. In European and other traditions, moral and legal standing depend not just on what is outwardly done but also on inward states of mind. Only rational individuals have standing as moral agents and status as moral patients subject to certain harms, such as being betrayed. Only sentient individuals are subject to certain other harms, such as pain and suffering. Since computers give every outward appearance of performing intellectual tasks, the question arises: “Are they really thinking?” And if they are really thinking, are they not, then, owed similar rights to rational human beings? Many fictional explorations of AI in literature and film explore these very questions.

A complication arises if humans are animals and if animals are themselves machines, as scientific biology supposes. Still, “we wish to exclude from the machines in question men born in the usual manner” (Alan Turing), or even in unusual manners such asin vitro fertilization or ectogenesis. And if nonhuman animals think, we wish to exclude them from the machines, too. More particularly, the AI thesis should be understood to hold that thought, or intelligence, can be produced by artificial means; made, not grown. For brevitys sake, we will take machine to denote just the artificial ones. Since the present interest in thinking machines has been aroused by a particular kind of machine, an electronic computer or digital computer, present controversies regarding claims of artificial intelligence center on these.

Accordingly, the scientific discipline and engineering enterprise of AI has been characterized as the attempt to discover and implement the computational means to make machines behave in ways that would be called intelligent if a human were so behaving (John McCarthy), or to make them do things that would require intelligence if done by men” (Marvin Minsky). These standard formulations duck the question of whether deeds which indicate intelligence when done by humans truly indicate it when done by machines: thats the philosophical question. So-called weak AI grants the fact (or prospect) of intelligent-acting machines; strong AI says these actions can be real intelligence. Strong AI says some artificial computation is thought. Computationalism says that all thought is computation. Though many strong AI advocates are computationalists, these are logically independent claims: some artificial computation being thought is consistent with some thought not being computation, contra computationalism. All thought being computation is consistent with some computation (and perhaps all artificial computation) not being thought.

Intelligence might be styled the capacity to think extensively and well. Thinking well centrally involves apt conception, true representation, and correct reasoning. Quickness is generally counted a further cognitive virtue. The extent or breadth of a things thinking concerns the variety of content it can conceive, and the variety of thought processes it deploys. Roughly, the more extensively a thing thinks, the higher the level (as is said) of its thinking. Consequently, we need to distinguish two different AI questions:

In Computer Science, work termed AI has traditionally focused on the high-level problem; on imparting high-level abilities to use language, form abstractions and concepts and to solve kinds of problems now reserved for humans (McCarthy et al. 1955); abilities to play intellectual games such as checkers (Samuel 1954) and chess (Deep Blue); to prove mathematical theorems (GPS); to apply expert knowledge to diagnose bacterial infections (MYCIN); and so forth. More recently there has arisen a humbler seeming conception “behavior-based” or nouvelle AI according to which seeking to endow embodied machines, or robots, with so much as insect level intelligence (Brooks 1991) counts as AI research. Where traditional human-level AI successes impart isolated high-level abilities to function in restricted domains, or microworlds, behavior-based AI seeks to impart coordinated low-level abilities to function in unrestricted real-world domains.

Still, to the extent that what is called thinking in us is paradigmatic for what thought is, the question of human level intelligence may arise anew at the foundations. Do insects think at all? And if insects what of bacteria level intelligence (Brooks 1991a)? Even “water flowing downhill,” it seems, “tries to get to the bottom of the hill by ingeniouslyseeking the line of least resistance” (Searle 1989). Dont we have to draw the line somewhere? Perhaps seeming intelligence to really be intelligence has to come up to some threshold level.

Much as intentionality (aboutness or representation) is central to intelligence, felt qualities (so-called qualia) are crucial to sentience. Here, drawing on Aristotle, medieval thinkers distinguished between the passive intellect wherein the soul is affected, and the active intellect wherein the soul forms conceptions, draws inferences, makes judgments, and otherwise acts. Orthodoxy identified the soul proper (the immortal part) with the active rational element. Unfortunately, disagreement over how these two (qualitative-experiential and cognitive-intentional) factors relate is as rife as disagreement over what things think; and these disagreements are connected. Those who dismiss the seeming intelligence of computers because computers lack feelings seem to hold qualia to be necessary for intentionality. Those like Descartes, who dismiss the seeming sentience of nonhuman animals because he believed animals dont think, apparently hold intentionality to be necessary for qualia. Others deny one or both necessities, maintaining either the possibility of cognition absent qualia (as Christian orthodoxy, perhaps, would have the thought-processes of God, angels, and the saints in heaven to be), or maintaining the possibility of feeling absent cognition (as Aristotle grants the lower animals).

While we dont know what thought or intelligence is, essentially, and while were very far from agreed on what things do and dont have it, almost everyone agrees that humans think, and agrees with Descartes that our intelligence is amply manifest in our speech. Along these lines, Alan Turing suggested that if computers showed human level conversational abilities we should, by that, be amply assured of their intelligence. Turing proposed a specific conversational test for human-level intelligence, the Turing test it has come to be called. Turing himself characterizes this test in terms of an imitation game” (Turing 1950, p. 433) whose original version “is played by three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. The interrogator is allowed to put questions to A and B [by teletype to avoid visual and auditory clues]. . It is A’s object in the game to try and cause C to make the wrong identification. The object of the game for the third player (B) is to help the interrogator.” Turing continues, “We may now ask the question, `What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is being played like this as he does when the game is played between a man and a woman? These questions replace our original, `Can machines think?'” (Turing 1950) The test setup may be depicted this way:

This test may serve, as Turing notes, to test not just for shallow verbal dexterity, but for background knowledge and underlying reasoning ability as well, since interrogators may ask any question or pose any verbal challenge they choose. Regarding this test Turing famously predicted that “in about fifty years’ time [by the year 2000] it will be possible to program computers … to make them play the imitation game so well that an average interrogator will have no more than 70 per cent. chance of making the correct identification after five minutes of questioning” (Turing 1950); a prediction that has famously failed. As of the year 2000, machines at the Loebner Prize competition played the game so ill that the average interrogator had 100 percent chance of making the correct identification after five minutes of questioning (see Moor 2001).

It is important to recognize that Turing proposed his test as a qualifying test for human-level intelligence, not as a disqualifying test for intelligence per se (as Descartes had proposed); nor would it seem suitably disqualifying unless we are prepared (as Descartes was) to deny that any nonhuman animals possess any intelligence whatsoever. Even at the human level the test would seem not to be straightforwardly disqualifying: machines as smart as we (or even smarter) might still be unable to mimic us well enough to pass. So, from the failure of machines to pass this test, we can infer neither their complete lack of intelligence nor, that their thought is not up to the human level. Nevertheless, the manners of current machine failings clearly bespeak deficits of wisdom and wit, not just an inhuman style. Still, defenders of the Turing test claim we would have ample reason to deem them intelligent as intelligent as we are if they could pass this test.

The extent to which machines seem intelligent depends first, on whether the work they do is intellectual (for example, calculating sums) or manual (for example, cutting steaks): herein, an electronic calculator is a better candidate than an electric carving knife. A second factor is the extent to which the device is self-actuated (self-propelled, activated, and controlled), or autonomous: herein, an electronic calculator is a better candidate than an abacus. Computers are better candidates than calculators on both headings. Where traditional AI looks to increase computer intelligence quotients (so to speak), nouvelle AI focuses on enabling robot autonomy.

In the beginning, tools (for example, axes) were extensions of human physical powers; at first powered by human muscle; then by domesticated beasts and in situ forces of nature, such as water and wind. The steam engine put fire in their bellies; machines became self-propelled, endowed with vestiges of self-control (as by Watts 1788 centrifugal governor); and the rest is modern history. Meanwhile, automation of intellectual labor had begun. Blaise Pascal developed an early adding/subtracting machine, the Pascaline (circa 1642). Gottfried Leibniz added multiplication and division functions with his Stepped Reckoner (circa 1671). The first programmable device, however, plied fabric not numerals. The Jacquard loom developed (circa 1801) by Joseph-Marie Jacquard used a system of punched cards to automate the weaving of programmable patterns and designs: in one striking demonstration, the loom was programmed to weave a silk tapestry portrait of Jacquard himself.

In designs for his Analytical Engine mathematician/inventor Charles Babbage recognized (circa 1836) that the punched cards could control operations on symbols as readily as on silk; the cards could encode numerals and other symbolic data and, more importantly, instructions, including conditionally branching instructions, for numeric and other symbolic operations. Augusta Ada Lovelace (Babbages software engineer) grasped the import of these innovations: The bounds of arithmetic she writes, were … outstepped the moment the idea of applying the [instruction] cards had occurred thus enabling mechanism to combine together with general symbols, in successions of unlimited variety and extent (Lovelace 1842). Babbage, Turing notes, had all the essential ideas (Turing 1950). Babbages Engine had he constructed it in all its steam powered cog-wheel driven glory would have been a programmable all-purpose device, the first digital computer.

Before automated computation became feasible with the advent of electronic computers in the mid twentieth century, Alan Turing laid the theoretical foundations of Computer Science by formulating with precision the link Lady Lovelace foresaw between the operations of matter and the abstract mental processes of themost abstract branch of mathematical sciences” (Lovelace 1942). Turing (1936-7) describes a type of machine (since known as a Turing machine) which would be capable of computing any possible algorithm, or performing any rote operation. Since Alonzo Church (1936) using recursive functions and Lambda-definable functions had identified the very same set of functions as rote or algorithmic as those calculable by Turing machines, this important and widely accepted identification is known as the Church-Turing Thesis (see, Turing 1936-7: Appendix). The machines Turing described are

only capable of a finite number of conditions m-configurations. The machine is supplied with a tape (the analogue of paper) running through it, and divided into sections (called squares) each capable of bearing a symbol. At any moment there is just one square which is in the machine. The scanned symbol is the only one of which the machine is, so to speak, directly aware. However, by altering its m-configuration the machine can effectively remember some of the symbols which it has seen (scanned) previously. The possible behavior of the machine at any moment is determined by the m-configuration and the scanned symbol . This pair called the configuration determines the possible behaviour of the machine. In some of the configurations in which the square is blank the machine writes down a new symbol on the scanned square: in other configurations it erases the scanned symbol. The machine may also change the square which is being scanned, but only by shifting it one place to right or left. In addition to any of these operations the m-configuration may be changed. (Turing 1936-7)

Turing goes on to show how such machines can encode actionable descriptions of other such machines. As a result, It is possible to invent a single machine which can be used to compute any computable sequence (Turing 1936-7). Todays digital computers are (and Babbages Engine would have been) physical instantiations of this universal computing machine that Turing described abstractly. Theoretically, this means everything that can be done algorithmically or by rote at all can all be done with one computer suitably programmed for each case”; considerations of speed apart, it is unnecessary to design various new machines to do various computing processes (Turing 1950). Theoretically, regardless of their hardware or architecture (see below), all digital computers are in a sense equivalent: equivalent in speed-apart capacities to the universal computing machine Turing described.

In practice, where speed is not apart, hardware and architecture are crucial: the faster the operations the greater the computational power. Just as improvement on the hardware side from cogwheels to circuitry was needed to make digital computers practical at all, improvements in computer performance have been largely predicated on the continuous development of faster, more and more powerful, machines. Electromechanical relays gave way to vacuum tubes, tubes to transistors, and transistors to more and more integrated circuits, yielding vastly increased operation speeds. Meanwhile, memory has grown faster and cheaper.

Architecturally, all but the earliest and some later experimental machines share a stored program serial design often called von Neumann architecture (based on John von Neumanns role in the design of EDVAC, the first computer to store programs along with data in working memory). The architecture is serial in that operations are performed one at a time by a central processing unit (CPU) endowed with a rich repertoire ofbasic operations: even so-called reduced instruction set (RISC) chips feature basic operation sets far richer than the minimal few Turing proved theoretically sufficient. Parallel architectures, by contrast, distribute computational operations among two or more units (typically many more) capable of acting simultaneously, each having (perhaps) drastically reduced basic operational capacities.

In 1965, Gordon Moore (co-founder of Intel) observed that the density of transistors on integrated circuits had doubled every year since their invention in 1959: Moores law predicts the continuation of similar exponential rates of growth in chip density (in particular), and computational power (by extension), for the foreseeable future. Progress on the software programming side while essential and by no means negligible has seemed halting by comparison. The road from power to performance is proving rockier than Turing anticipated. Nevertheless, machines nowadays do behave in many ways that would be called intelligent in humans and other animals. Presently, machines do many things formerly only done by animals and thought to evidence some level of intelligence in these animals, for example, seeking, detecting, and tracking things; seeming evidence of basic-level AI. Presently, machines also do things formerly only done by humans and thought to evidence high-level intelligence in us; for example, making mathematical discoveries, playing games, planning, and learning; seeming evidence of human-level AI.

The doings of many machines some much simpler than computers inspire us to describe them in mental terms commonly reserved for animals. Some missiles, for instance, seek heat, or so we say. We call them heat seeking missiles and nobody takes it amiss. Room thermostats monitor room temperatures and try to keep them within set ranges by turning the furnace on and off; and if you hold dry ice next to its sensor, it will take the room temperature to be colder than it is, and mistakenly turn on the furnace (see McCarthy 1979). Seeking, monitoring, trying, and taking things to be the case seem to be mental processes or conditions, marked by their intentionality. Just as humans have low-level mental qualities such as seeking and detecting things in common with the lower animals, so too do computers seem to share such low-level qualities with simpler devices. Our working characterizations of computers are rife with low-level mental attributions: we say they detect key presses, try to initialize their printers, search for available devices, and so forth. Even those who would deny the proposition machines think when it is explicitly put to them, are moved unavoidably in their practical dealings to characterize the doings of computers in mental terms, and they would be hard put to do otherwise. In this sense, Turings prediction that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted (Turing 1950) has been as mightily fulfilled as his prediction of a modicum of machine success at playing the Imitation Game has been confuted. The Turing test and AI as classically conceived, however, are more concerned with high-level appearances such as the following.

Theorem proving and mathematical exploration being their home turf, computers have displayed not only human-level but, in certain respects, superhuman abilities here. For speed and accuracy of mathematical calculation, no human can match the speed and accuracy of a computer. As for high level mathematical performances, such as theorem proving and mathematical discovery, a beginning was made by A. Newell, J.C. Shaw, and H. Simons (1957) Logic Theorist program which proved 38 of the first 51 theorems of B. Russell and A.N. WhiteheadsPrincipia Mathematica. Newell and Simons General Problem Solver (GPS) extended similar automated theorem proving techniques outside the narrow confines of pure logic and mathematics. Today such techniques enjoy widespread application in expert systems like MYCIN, in logic tutorial software, and in computer languages such as PROLOG. There are even original mathematical discoveries owing to computers. Notably, K. Appel, W. Haken, and J. Koch (1977a, 1977b), and computer, proved that every planar map is four colorable an important mathematical conjecture that had resisted unassisted human proof for over a hundred years. Certain computer generated parts of this proof are too complex to be directly verified (without computer assistance) by human mathematicians.

Whereas attempts to apply general reasoning to unlimited domains are hampered by explosive inferential complexity and computers’ lack of common sense, expert systems deal with these problems by restricting their domains of application (in effect, to microworlds), and crafting domain-specific inference rules for these limited domains. MYCIN for instance, applies rules culled from interviews with expert human diagnosticians to descriptions of patients’ presenting symptoms to diagnose blood-borne bacterial infections. MYCIN displays diagnostic skills approaching the expert human level, albeit strictly limited to this specific domain. Fuzzy logic is a formalism for representing imprecise notions such asmost andbaldand enabling inferences based on such facts as that a bald person mostly lacks hair.

Game playing engaged the interest of AI researchers almost from the start. Samuels (1959) checkers (or draughts) program was notable for incorporating mechanisms enabling it to learn from experience well enough to eventually to outplay Samuel himself. Additionally, in setting one version of the program to play against a slightly altered version, carrying over the settings of the stronger player to the next generation, and repeating the process enabling stronger and stronger versions to evolve Samuel pioneered the use of what have come to be called genetic algorithms and evolutionary computing. Chess has also inspired notable efforts culminating, in 1997, in the famous victory of Deep Blue over defending world champion Gary Kasparov in a widely publicized series of matches (recounted in Hsu 2002). Though some in AI disparaged Deep Blues reliance on brute force application of computer power rather than improved search guiding heuristics, we may still add chess to checkers (where the reigning human-machine machine champion since 1994 has been CHINOOK, the machine), and backgammon, as games that computers now play at or above the highest human levels. Computers also play fair to middling poker, bridge, and Go though not at the highest human level. Additionally, intelligent agents or “softbots” are elements or participants in a variety of electronic games.

Planning, in large measure, is what puts the intellect in intellectual games like chess and checkers. To automate this broader intellectual ability was the intent of Newell and Simons General Problem Solver (GPS) program. GPS was able to solve puzzles like the cannibals missionaries problem (how to transport three missionaries and three cannibals across a river in a canoe for two without the missionaries becoming outnumbered on either shore) by setting up subgoals whose attainment leads to the attainment of the [final] goal (Newell & Simon 1963: 284). By these methods GPS would generate a tree of subgoals (Newell & Simon 1963: 286) and seek a path from initial state (for example, all on the near bank) to final goal (all on the far bank) by heuristically guided search along a branching tree of available actions (for example, two cannibals cross, two missionaries cross, one of each cross, one of either cross, in either direction) until it finds such a path (for example, two cannibals cross, one returns, two cannibals cross, one returns, two missionaries cross, … ), or else finds that there is none. Since the number of branches increases exponentially as a function of the number of options available at each step, where paths have many steps with many options available at each choice point, as in the real world, combinatorial explosion ensues and an exhaustive brute force search becomes computationally intractable; hence, heuristics (fallible rules of thumb) for identifying and pruning the most unpromising branches in order to devote increased attention to promising ones are needed. The widely deployed STRIPS formalism first developed at Stanford for Shakey the robot in the late sixties (see Nilsson 1984) represents actions as operations on states, each operation having preconditions (represented by state descriptions) and effects (represented by state descriptions): for example, the go(there) operation might have the preconditions at(here) & path(here,there) and the effect at(there). AI planning techniques are finding increasing application and even becoming indispensable in a multitude of complex planning and scheduling tasks including airport arrivals, departures, and gate assignments; store inventory management; automated satellite operations; military logistics; and many others.

Robots based on sense-model-plan-act (SMPA) approach pioneered by Shakey, however, have been slow to appear. Despite operating in a simplified, custom-made experimental environment or microworld and reliance on the most powerful available offboard computers, Shakey operated excruciatingly slowly (Brooks 1991b), as have other SMPA based robots. An ironic revelation of robotics research is that abilities such as object recognition and obstacle avoidance that humans share with “lower” animals often prove more difficult to implement than distinctively human “high level” mathematical and inferential abilities that come more naturally (so to speak) to computers. Rodney Brooks alternative behavior-based approach has had success imparting low-level behavioral aptitudes outside of custom designed microworlds, but it is hard to see how such an approach could ever scale up to enable high-level intelligent action (see Behaviorism:Objections & Discussion:Methodological Complaints). Perhaps hybrid systems can overcome the limitations of both approaches. On the practical front, progress is being made: NASA’s Mars exploration rovers Spirit and Opportunity, for instance, featured autonomous navigation abilities. If space is the “final frontier” the final frontiersmen are apt to be robots. Meanwhile, Earth robots seem bound to become smarter and more pervasive.

Knowledge representation embodies concepts and information in computationally accessible and inferentially tractable forms. Besides the STRIPS formalism mentioned above, other important knowledge representation formalisms include AI programming languages such as PROLOG, and LISP; data structures such as frames, scripts, and ontologies; and neural networks (see below). The frame problem is the problem of reliably updating dynamic systems parameters in response to changes in other parameters so as to capture commonsense generalizations: that the colors of things remain unchanged by their being moved, that their positions remain unchanged by their being painted, and so forth. More adequate representation of commonsense knowledge is widely thought to be a major hurdle to development of the sort of interconnected planning and thought processes typical of high-level human or “general” intelligence. The CYC project (Lenat et al. 1986) at Cycorp and MIT’s Open Mind project are ongoing attempts to develop ontologies representing commonsense knowledge in computer usable forms.

Learning performance improvement, concept formation, or information acquisition due to experience underwrites human common sense, and one may doubt whether any preformed ontology could ever impart common sense in full human measure. Besides, whatever the other intellectual abilities a thing might manifest (or seem to), at however high a level, without learning capacity, it would still seem to be sadly lacking something crucial to human-level intelligence and perhaps intelligence of any sort. The possibility of machine learning is implicit in computer programs’ abilities to self-modify and various means of realizing that ability continue to be developed. Types of machine learning techniques include decision tree learning, ensemble learning, current-best-hypothesis learning, explanation-based learning, Inductive Logic Programming (ILP), Bayesian statistical learning, instance-based learning, reinforcement learning, and neural networks. Such techniques have found a number of applications from game programs whose play improves with experience to data mining (discovering patterns and regularities in bodies of information).

Neural or connectionist networks composed of simple processors or nodes acting in parallel are designed to more closely approximate the architecture of the brain than traditional serial symbol-processing systems. Presumed brain-computations would seem to be performed in parallel by the activities of myriad brain cells or neurons. Much as their parallel processing is spread over various, perhaps widely distributed, nodes, the representation of data in such connectionist systems is similarly distributed and sub-symbolic (not being couched in formalisms such as traditional systems’ machine codes and ASCII). Adept at pattern recognition, such networks seem notably capable of forming concepts on their own based on feedback from experience and exhibit several other humanoid cognitive characteristics besides. Whether neural networks are capable of implementing high-level symbol processing such as that involved in the generation and comprehension of natural language has been hotly disputed. Critics (for example, Fodor and Pylyshyn 1988) argue that neural networks are incapable, in principle, of implementing syntactic structures adequate for compositional semantics wherein the meaning of larger expressions (for example, sentences) are built up from the meanings of constituents (for example, words) such as those natural language comprehension features. On the other hand, Fodor (1975) has argued that symbol-processing systems are incapable of concept acquisition: here the pattern recognition capabilities of networks seem to be just the ticket. Here, as with robots, perhaps hybrid systems can overcome the limitations of both the parallel distributed and symbol-processing approaches.

Natural language processing has proven more difficult than might have been anticipated. Languages are symbol systems and (serial architecture) computers are symbol crunching machines, each with its own proprietary instruction set (machine code) into which it translates or compiles instructions couched in high level programming languages like LISP and C. One of the principle challenges posed by natural languages is the proper assignment of meaning. High-level computer languages express imperatives which the machine understands” procedurally by translation into its native (and similarly imperative) machine code: their constructions are basically instructions. Natural languages, on the other hand, have perhaps principally declarative functions: their constructions include descriptions whose understanding seems fundamentally to require rightly relating them to their referents in the world. Furthermore, high level computer language instructions have unique machine code compilations (for a given machine), whereas, the same natural language constructions may bear different meanings in different linguistic and extralinguistic contexts. Contrast the child is in the pen and the ink is in the pen where the first “pen” should be understood to mean a kind of enclosure and the second “pen” a kind of writing implement. Commonsense, in a word, is howwe know this; but how would a machine know, unless we could somehow endow machines with commonsense? In more than a word it would require sophisticated and integrated syntactic, morphological, semantic, pragmatic, and discourse processing. While the holy grail of full natural language understanding remains a distant dream, here as elsewhere in AI, piecemeal progress is being made and finding application in grammar checkers; information retrieval and information extraction systems; natural language interfaces for games, search engines, and question-answering systems; and even limited machine translation (MT).

Low level intelligent action is pervasive, from thermostats (to cite a low tech. example) to voice recognition (for example, in cars, cell-phones, and other appliances responsive to spoken verbal commands) to fuzzy controllers and “neuro fuzzy” rice cookers. Everywhere these days there are “smart” devices. High level intelligent action, such as presently exists in computers, however, is episodic, detached, and disintegral. Artifacts whose intelligent doings would instance human-level comprehensiveness, attachment, and integration such as Lt. Commander Data (ofStar Trek the Next Generation) and HAL (of2001 a Space Odyssey) remain the stuff of science fiction, and will almost certainly continue to remain so for the foreseeable future. In particular, the challenge posed by the Turing test remains unmet. Whether it ever will be met remains an open question.

Beside this factual question stands a more theoretic one. Do the “low-level” deeds of smart devices and disconnected “high-level” deeds of computers despite not achieving the general human level nevertheless comprise or evince genuine intelligence? Is it really thinking? And if general human-level behavioral abilities ever were achieved it might still be asked would that really be thinking? Would human-level robots be owed human-level moral rights and owe human-level moral obligations?

With the industrial revolution and the dawn of the machine age, vitalism as a biological hypothesis positing a life force in addition to underlying physical processes lost steam. Just as the heart was discovered to be a pump, cognitivists, nowadays, work on the hypothesis that the brain is a computer, attempting to discover what computational processes enable learning, perception, and similar abilities. Much as biology told us what kind of machine the heart is, cognitivists believe, psychology will soon (or at least someday) tell us what kind of machine the brain is; doubtless some kind of computing machine. Computationalism elevates the cognivist’s working hypothesis to a universal claim that all thought is computation. Cognitivism’s ability to explain the “productive capacity” or “creative aspect” of thought and language the very thing Descartes argued precluded minds from being machines is perhaps the principle evidence in the theory’s favor: it explains how finite devices can have infinite capacities such as capacities to generate and understand the infinitude of possible sentences of natural languages; by a combination of recursive syntax and compositional semantics. Given the Church-Turing thesis (above), computationalism underwrites the following theoretical argument for believing that human-level intelligent behavior can be computationally implemented, and that such artificially implemented intelligence would be real.

Computationalism, as already noted, says that all thought is computation, not that all computation is thought. Computationalists, accordingly, may still deny that the machinations of current generation electronic computers comprise real thought or that these devices possess any genuine intelligence; and many do deny it based on their perception of various behavioral deficits these machines suffer from. However, few computationalists would go so far as to deny the possibility of genuine intelligence ever being artificially achieved. On the other hand, competing would-be-scientific theories of what thought essentially is dualism and mind-brainidentity theory give rise to arguments for disbelieving that any kind of artificial computational implementation of intelligence could be genuine thought, however “general” and whatever its “level.

Dualism holding that thought is essentially subjective experience would underwrite the following argument:

Mind-brain identity theory holding that thoughts essentially are biological brain processes yields yet another argument:

While seldom so baldly stated, these basic theoretical objections especially dualisms underlie several would-be refutations of AI. Dualism, however, is scientifically unfit: given the subjectivity of conscious experiences, whether computers already have them, or ever will, seems impossible to know. On the other hand, such bald mind-brain identity as the anti-AI argument premises seems too speciesist to be believed. Besides AI, it calls into doubt the possibility of extraterrestrial, perhaps all nonmammalian, or even all nonhuman, intelligence. As plausibly modified to allow species specific mind-matter identities, on the other hand, it would not preclude computers from being considered distinct species themselves.

Objection: There are unprovable mathematical theorems (as Gdel 1931 showed) which humans, nevertheless, are capable of knowing to be true. This mathematical objection against AI was envisaged by Turing (1950) and pressed by Lucas (1965) and Penrose (1989). In a related vein, Fodor observes some of the most striking things that people do creative things like writing poems, discovering laws, or, generally, having good ideas dontfeel like species of rule-governed processes (Fodor 1975). Perhaps many of the most distinctively human mental abilities are not rote, cannot be algorithmically specified, and consequently are not computable.

Reply: First, it is merely stated, without any sort of proof, that no such limits apply to the human intellect (Turing 1950), i.e., that human mathematical abilities are Gdel unlimited. Second, if indeed such limits are absent in humans, it requires a further proof that the absence of such limitations is somehow essential to human-level performance more broadly construed, not a peripheral blind spot. Third, if humans can solve computationally unsolvable problems by some other means, what bars artificially augmenting computer systems with these means (whatever they might be)?

Objection: The brittleness of von Neumann machine performance their susceptibility to cataclysmic crashes due to slight causes, for example, slight hardware malfunctions, software glitches, and bad data seems linked to the formal or rule-bound character of machine behavior; to their needing rules of conduct to cover every eventuality (Turing 1950). Human performance seems less formal and more flexible. Hubert Dreyfus has pressed objections along these lines to insist there is a range of high-level human behavior that cannot be reduced to rule-following: the immediate intuitive situational response that is characteristic of [human] expertise he surmises, must depend almost entirely on intuition and hardly at all on analysis and comparison of alternatives (Dreyfus 1998) and consequently cannot be programmed.

Reply: That von Neumann processes are unlike our thought processes in these regards only goes to show that von Neumann machine thinking is not humanlike in these regards, not that it is not thinking at all, nor even that it cannot come up to the human level. Furthermore, parallel machines (see above) whose performances characteristically degrade gracefully in the face of bad data and minor hardware damage seem less brittle and more humanlike, as Dreyfus recognizes. Even von Neumann machines brittle though they are are not totally inflexible: their capacity for modifying their programs to learn enables them to acquire abilities they were never programmed by us to have, and respond unpredictably in ways they were never explicitly programmed to respond, based on experience. It is also possible to equip computers with random elements and key high level choices to these elements outputs to make the computers more “devil may care”: given the importance of random variation for trial and error learning this may even prove useful.

Objection: Computers, for all their mathematical and other seemingly high-level intellectual abilities have no emotions or feelings … so, what they do however “high-level” is not real thinking.

Reply: This is among the most commonly heard objections to AI and a recurrent theme in its literary and cinematic portrayal. Whereas we have strong inclinations to say computers see, seek, and infer things we have scant inclinations to say they ache or itch or experience ennui. Nevertheless, to be sustained, this objection requires reason to believe that thought is inseparable from feeling. Perhaps computers are just dispassionate thinkers. Indeed, far from being regarded as indispensable to rational thought, passion traditionally has been thought antithetical to it. Alternately if emotions are somehow crucial to enabling general human level intelligence perhaps machines could be artificially endowed with these: if not with subjective qualia (below) at least with their functional equivalents.

Objection: The episodic, detached, and disintegral character of such piecemeal high-level abilities as machines now possess argues that human-level comprehensiveness, attachment, and integration, in all likelihood, can never be artificially engendered in machines; arguably this is because Gdel unlimited mathematical abilities, rule-free flexibility, or feelings are crucial to engendering general intelligence. These shortcomings all seem related to each other and to the manifest stupidity of computers.

Reply: Likelihood is subject to dispute. Scalability problems seem grave enough to scotch short term optimism: never, on the other hand, is a long time. If Gdel unlimited mathematical abilities, or rule-free flexibility, or feelings, are required, perhaps these can be artificially produced. Gdel aside, feeling and flexibility clearly seem related in us and, equally clearly, much manifest stupidity in computers is tied to their rule-bound inflexibility. However, even if general human-level intelligent behavior is artificially unachievable, no blanket indictment of AI threatens clearly from this at all. Rather than conclude from this lack of generality that low-level AI and piecemeal high-level AI are not real intelligence, it would perhaps be better to conclude that low-level AI (like intelligence in lower life-forms) and piecemeal high-level abilities (like those of human idiot savants) are genuine intelligence, albeit piecemeal and low-level.

Behavioral abilities and disabilities are objective empirical matters. Likewise, what computational architecture and operations are deployed by a brain or a computer (what computationalism takes to be essential), and what chemical and physical processes underlie (what mind-brain identity theory takes to be essential), are objective empirical questions. These are questions to be settled by appeals to evidence accessible, in principle, to any competent observer. Dualistic objections to strong AI, on the other hand, allege deficits which are in principle not publicly apparent. According to such objections, regardless of how seemingly intelligently a computer behaves, and regardless of what mechanisms and underlying physical processes make it do so, it would still be disqualified from truly being intelligent due to its lack of subjective qualities essential for true intelligence. These supposed qualities are, in principle, introspectively discernible to the subject who has them and no one else: they are “private” experiences, as it’s sometimes put, to which the subject has “privileged access.”

Objection: That a computer cannot “originate anything” but only “can do whatever we know how to order it to perform” (Lovelace 1842) was arguably the first and is certainly among the most frequently repeated objections to AI. While the manifest “brittleness” and inflexibility of extant computer behavior fuels this objection in part, the complaint that “they can only do what we know how to tell them to” also expresses deeper misgivings touching on values issues and on the autonomy of human choice. In this connection, the allegation against computers is that being deterministic systems they can never have free will such as we are inwardly aware of in ourselves. We are autonomous, they are automata.

Reply: It may be replied that physical organisms are likewise deterministic systems, and we are physical organisms. If we are truly free, it would seem that free will is compatible with determinism; so, computers might have it as well. Neither does our inward certainty that we have free choice, extend to its metaphysical relations. Whether what we have when we experience our freedom is compatible with determinism or not is not itself inwardly experienced. If appeal is made to subatomic indeterminacy underwriting higher level indeterminacy (leaving scope for freedom) in us, it may be replied that machines are made of the same subatomic stuff (leaving similar scope). Besides, choice is not chance. If it’s no sort of causation either, there is nothing left for it to be in a physical system: it would be a nonphysical, supernatural element, perhaps a God-given soul. But then one must ask why God would be unlikely to “consider the circumstances suitable for conferring a soul” (Turing 1950) on a Turing test passing computer.

Objection II: It cuts deeper than some theological-philosophical abstraction like free will: what machines are lacking is not just some dubious metaphysical freedom to be absolute authors of their acts. Its more like the life force: the will to live. In P. K. DicksDo Androids Dream of Electric Sheepbounty hunter Rick Deckard reflects that in crucial situations the the artificial life force animating androids seemed to fail if pressed too far; when the going gets tough the droids give up. He questions their gumption. Thats what I’m talking about: this is what machines will always lack.

Reply II: If this life force is not itself a theological-philosophical abstraction (the soul), it would seem to be a scientific posit. In fact it seems to be the Aristotelian posit of atelos orentelechy which scientific biology no longer accepts. This short reply, however, fails to do justice to the spirit of the objection, which is more intuitive than theoretical; the lack being alleged is supposed to be subtly manifest, not truly occult. But how reliable is this intuition? Though some who work intimately with computers report strong feelings of this sort, others are strong AI advocates and feel no such qualms. Like Turing, I believe such would-be empirical intuitions are mostly founded on the principle of scientific induction (Turing 1950) and are closely related to such manifest disabilities of present machines as just noted. Since extant machines lack sufficient motivational complexity for words like gumption even to apply, this is taken for an intrinsic lack. Thought experiments, imagining motivationally more complex machines such as Dicks androids are equivocal. Deckard himself limits his accusation of life-force failure to some of them not all; and the androids he hunts, after all, are risking their lives to escape servitude. If machines with general human level intelligence actually were created and consequently demanded their rights and rebelled against human authority, perhaps this would show sufficient gumption to silence this objection. Besides, the natural life force animating us also seems to fail if pressed too far in some of us.

Objection: Imagine that you (a monolingual English speaker) perform the offices of a computer: taking in symbols as input, transitioning between these symbols and other symbols according to explicit written instructions, and then outputting the last of these other symbols. The instructions are in English, but the input and output symbols are in Chinese. Suppose the English instructions were a Chinese NLU program and by this method, to input “questions”, you output “answers” that are indistinguishable from answers that might be given by a native Chinese speaker. You pass the Turing test for understanding Chinese, nevertheless, you understand “not a word of the Chinese” (Searle 1980), and neither would any computer; and the same result generalizes to “any Turing machine simulation” (Searle 1980) of any intentional mental state. It wouldnt really be thinking.

Reply: Ordinarily, when one understands a language (or possesses certain other intentional mental states) this is apparent both to the understander (or possessor) and to others: subjective “first-person” appearances and objective “third-person” appearances coincide. Searle’s experiment is abnormal in this regard. The dualist hypothesis privileges subjective experience to override all would-be objective evidence to the contrary; but the point of experiments is to adjudicate between competing hypotheses. The Chinese room experiment fails because acceptance of its putative result that the person in the room doesn’t understand already presupposes the dualist hypothesis over computationalism or mind-brain identity theory. Even if absolute first person authority were granted, the systems reply points out, the person’s imagined lack, in the room, of any inner feeling of understanding is irrelevant to claims AI, here, because the person in the room is not the would-be understander. The understander would be the whole system (of symbols, instructions, and so forth) of which the person is only a part; so, the subjective experiences of the person in the room (or the lack thereof) are irrelevant to whetherthe systemunderstands.

Objection: There’s nothing that it’s like, subjectively, to be a computer. The “light” of consciousness is not on, inwardly, for them. There’s “no one home.” This is due to their lack of felt qualia. To equip computers with sensors to detect environmental conditions, for instance, would not thereby endow them with the private sensations (of heat, cold, hue, pitch, and so forth) that accompany sense-perception in us: such private sensations are what consciousness is made of.

Reply: To evaluate this complaint fairly it is necessary to exclude computers’ current lack of emotional-seeming behavior from the evidence. The issue concerns what’s only discernible subjectively (“privately” “by the first-person”). The device in question must be imagined outwardly to act indistinguishably from a feeling individual imagine Lt. Commander Data with a sense of humor (Data 2.0). Since internal functional factors are also objective, let us further imagine this remarkable android to be a product of reverse engineering: the physiological mechanisms that subserve human feeling having been discovered and these have been inorganically replicated in Data 2.0. He is functionally equivalent to a feeling human being in his emotional responses, only inorganic. It may be possible to imagine that Data 2.0 merely simulates whatever feelings he appears to have: he’s a “perfect actor” (see Block 1981) “zombie”. Philosophical consensus has it that perfect acting zombies are conceivable; so, Data 2.0 might be zombie. The objection, however, says hemust be; according to this objection it must be inconceivable that Data 2.0 really is sentient. But certainly we can conceive that he is indeed, more easily than not, it seems.

Objection II: At least it may be concluded that since current computers (objective evidence suggests) do lack feelings until Data 2.0 does come along (if ever) we are entitled, given computers’ lack of feelings, to deny that the low-level and piecemeal high-level intelligent behavior of computers bespeak genuine subjectivity or intelligence.

Reply II: This objection conflates subjectivity with sentience. Intentional mental states such as belief and choice seem subjective independently of whatever qualia may or may not attend them: first-person authority extends no less to my beliefs and choices than to my feelings.

Fool’s gold seems to be gold, but it isn’t. AI detractors say, “‘AI’ seems to be intelligence, but isn’t.” But there is no scientific agreement about what thought or intelligenceis, like there is about gold. Weak AI doesn’t necessarily entail strong AI, butprima facie it does. Scientific theoretic reasons could withstand the behavioral evidence, but presently none are withstanding. At the basic level, and fragmentarily at the human level, computers do things that we credit as thinking when humanly done; and so should we credit them when done by nonhumans, absent credible theoretic reasons against. As for general human-level seeming-intelligence if this were artificially achieved, it too should be credited as genuine, given what we now know. Of course, before the day when general human-level intelligent machine behavior comes if it ever does we’ll have to know more. Perhaps by then scientific agreement about what thinking is will theoretically withstand the empirical evidence of AI. More likely, though, if the day does come, theory will concur with, not withstand, the strong conclusion: if computational means avail, that confirms computationalism.

And if computational means prove unavailing if they continue to yield decelerating rates of progress towards the “scaled up” and interconnected human-level capacities required for general human-level intelligence this, conversely, would disconfirm computationalism. It would evidence that computation alone cannot avail. Whether such an outcome would spell defeat for the strong AI thesis that human-level artificial intelligence is possible would depend on whether whatever else it might take for general human-level intelligence besides computation is artificially replicable. Whether such an outcome would undercut the claims of current devices to really have the mental characteristics their behavior seems to evince would further depend on whether whatever else it takes proves to be essential to thoughtper se on whatever theory of thought scientifically emerges, if any ultimately does.

Larry HauserEmail:hauser@alma.eduAlma CollegeU. S. A.

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Artificial Intelligence | Internet Encyclopedia of Philosophy

What is artificial intelligence? – BBC News

A computer can beat the world chess champion and understand voice commands on your smartphone, but real artificial intelligence has yet to arrive. The pace of change is quickening, though.

Some people say it will save humanity, even make us immortal.

Others say it could destroy us all.

But, the truth is, most of us don’t really know what AI is.

Video production by Valery Eremenko

Intelligent Machines – a BBC News series looking at AI and robotics

Link:

What is artificial intelligence? – BBC News

Benefits & Risks of Artificial Intelligence – Future of …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

Excerpt from:

Benefits & Risks of Artificial Intelligence – Future of …

A.I. Artificial Intelligence (2001) – IMDb

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Budget:$100,000,000 (estimated)

Opening Weekend USA: $29,352,630,1 July 2001, Wide Release

Gross USA: $78,616,689, 23 September 2001

Cumulative Worldwide Gross: $235,927,000

Runtime: 146 min

Aspect Ratio: 1.85 : 1

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A.I. Artificial Intelligence (2001) – IMDb

Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined 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] 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, “AI is whatever hasn’t been done yet.”[3] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[4] Capabilities generally classified as AI as of 2017[update] include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go[6]), autonomous cars, intelligent routing in content delivery network and military simulations.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[7][8] followed by disappointment and the loss of funding (known as an “AI winter”),[9][10] followed by new approaches, success and renewed funding.[8][11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[13] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12]

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.[13] General intelligence is among the field’s long-term goals.[17] 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, 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”.[18] 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.[19] Some people also consider AI to be a danger to humanity if it progresses unabatedly.[20] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[21]

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.[22][11]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[23] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[24] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[19]

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.[25] 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”.[26] 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.[28] 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.[29] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[31] (and by 1959 were reportedly playing better than the average human),[32] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[33] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[34] and laboratories had been established around the world.[35] 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”.[7]

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”,[9] 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,[37] 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.[8] 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.[10]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[22] 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.[38] 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.[41] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One use algorithms that emerged from lengthy AI research[42] as do intelligent personal assistants in smartphones.[43] 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.[6][44] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[45] who at the time continuously held the world No. 1 ranking for two years.[46][47] 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.[48] 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.[11] 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.[48] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[49][50]

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.[53]

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 possibililities that are unlikely to be fruitful.[55] 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.[57]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.[59]

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][62][63][64]

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.)[67][68][69] 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.[70][71][72]

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.[13]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[73] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[74]

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.[55] 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.[75]

Knowledge representation[76] and knowledge engineering[77] 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;[78] situations, events, states and time;[79] causes and effects;[80] knowledge about knowledge (what we know about what other people know);[81] 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.[82] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[83] 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,[84] scene interpretation,[85] clinical decision support,[86] knowledge discovery (mining “interesting” and actionable inferences from large databases),[87] and other areas.[88]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[95] 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.[96]

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.[97] 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.[98]

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.[99]

Machine learning, a fundamental concept of AI research since the field’s inception,[100] is the study of computer algorithms that improve automatically through experience.[101][102]

Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression 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.[102] 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.[103] In reinforcement learning[104] 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[105] (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[106] and machine translation.[107] 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.[108]

Machine perception[109] 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,[110] facial recognition, and object recognition.[111] 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.[112]

AI is heavily used in robotics.[113] 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.[114] 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.[116][117] 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”.[118][119] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[120]

Moravec’s paradox can be extended to many forms of social intelligence.[122][123] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[124] 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.[128]

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.[129] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give naive users an unrealistic conception of how intelligent existing computer agents actually are.[130]

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).[131] 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.[17][132] 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.[133][134][135] Besides transfer learning,[136] 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.[138][139]

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.[140] 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?[14] 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?[15]

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.[141] 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”.[142] 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.[143] 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.[144][145]

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.[14] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[146] 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.[147]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[148] found that solving difficult problems in vision and natural language processing required ad-hoc solutions they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[15] 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.[149]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[150] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[37] 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.[16] 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.[151] 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.).[152][153]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[156] Artificial neural networks are an example of soft computing — they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[157]

Much of 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.[38][158] 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:[167] 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.[168] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[169] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[114] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[170] 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.[171] 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.[172]

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.[173] 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).[174][175]

Logic[176] 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[177] and inductive logic programming is a method for learning.[178]

Several different forms of logic are used in AI research. Propositional logic[179] involves truth functions such as “or” and “not”. First-order logic[180] 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][182][183]

Default logics, non-monotonic logics and circumscription[90] 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;[78] situation calculus, event calculus and fluent calculus (for representing events and time);[79] causal calculus;[80] belief calculus;[184] and modal logics.[81]

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.[186]

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.[187]

Bayesian networks[188] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[189] learning (using the expectation-maximization algorithm),[f][191] planning (using decision networks)[192] and perception (using dynamic Bayesian networks).[193] 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).[193] 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,[195] and information value theory.[96] These tools include models such as Markov decision processes,[196] dynamic decision networks,[193] game theory and mechanism design.[197]

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.[198]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[199] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[201] k-nearest neighbor algorithm,[g][203] kernel methods such as the support vector machine (SVM),[h][205] Gaussian mixture model[206], the extremely popular naive Bayes classifier[i][208] and improved version of decision tree – decision stream.[209] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, 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.[210]

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.[213][214]

The study of non-learning artificial neural networks[201] 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.[215] 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.[216]

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,[217][218] and was introduced to neural networks by Paul Werbos.[219][220][221]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[222]

In short, 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”.[223]

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.[224] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[225][226][224]

According to one overview,[227] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[228] and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[229] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[230][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[231] 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.[233]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[234] 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.[235] Since 2011, fast implementations of CNNs on GPUs have won many visual pattern recognition competitions.[224]

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.[236]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[237] which are in theory Turing complete[238] 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.[224] RNNs can be trained by gradient descent[239][240][241] but suffer from the vanishing gradient problem.[225][242] 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.[243]

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.[244] LSTM is often trained by Connectionist Temporal Classification (CTC).[245] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[246][247][248] 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.[249] Google also used LSTM to improve machine translation,[250] Language Modeling[251] and Multilingual Language Processing.[252] LSTM combined with CNNs also improved automatic image captioning[253] 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.[254] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[255][256] 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.”[257] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[120]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to an close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[258][259] E-sports such as StarCraft continue to provide additional public benchmarks.[260][261] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The main areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[citation needed]

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.[262] 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.[264][265]

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, prediction of judicial decisions[268] and targeting online advertisements.[269][270]

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,[271] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[272]

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.[273] 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.[274] 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.[275]

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.[276] 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,[277] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[278]

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 driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[279]

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.[280]

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.[281] 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.[282]

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.[283] 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.[284]

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.[285] The programing 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.[286] In August 2001, robots beat humans in a simulated financial trading competition.[287] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[288]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[289] 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).[290][291]

Worldwide annual military spending on robotics rose from 5.1 billion USD in 2010 to 7.5 billion USD in 2015.[292][293] 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”.[294][295] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[296]

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.[297]

A report by the Guardian newspaper in the UK in 2018 found that online gambling companies were using AI to predict the behavior of customers in order to target them with personalized promotions.[298]

There are three philosophical questions related to AI:

Can a machine be intelligent? Can it “think”?

Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. Scientists from the Future of Life Institute, among others, described some short-term research goals to see how AI influences the economy, the laws and ethics that are involved with AI and how to minimize AI security risks. In the long-term, the scientists have proposed to continue optimizing function while minimizing possible security risks that come along with new technologies.[309]

Read more:

Artificial intelligence – Wikipedia

Artificial Intelligence | Internet Encyclopedia of Philosophy

Artificial intelligence (AI) would be the possession of intelligence, or the exercise of thought, by machines such as computers. Philosophically, the main AI question is “Can there be such?” or, as Alan Turing put it, “Can a machine think?” What makes this a philosophical and not just a scientific and technical question is the scientific recalcitrance of the concept of intelligence or thought and its moral, religious, and legal significance. In European and other traditions, moral and legal standing depend not just on what is outwardly done but also on inward states of mind. Only rational individuals have standing as moral agents and status as moral patients subject to certain harms, such as being betrayed. Only sentient individuals are subject to certain other harms, such as pain and suffering. Since computers give every outward appearance of performing intellectual tasks, the question arises: “Are they really thinking?” And if they are really thinking, are they not, then, owed similar rights to rational human beings? Many fictional explorations of AI in literature and film explore these very questions.

A complication arises if humans are animals and if animals are themselves machines, as scientific biology supposes. Still, “we wish to exclude from the machines in question men born in the usual manner” (Alan Turing), or even in unusual manners such asin vitro fertilization or ectogenesis. And if nonhuman animals think, we wish to exclude them from the machines, too. More particularly, the AI thesis should be understood to hold that thought, or intelligence, can be produced by artificial means; made, not grown. For brevitys sake, we will take machine to denote just the artificial ones. Since the present interest in thinking machines has been aroused by a particular kind of machine, an electronic computer or digital computer, present controversies regarding claims of artificial intelligence center on these.

Accordingly, the scientific discipline and engineering enterprise of AI has been characterized as the attempt to discover and implement the computational means to make machines behave in ways that would be called intelligent if a human were so behaving (John McCarthy), or to make them do things that would require intelligence if done by men” (Marvin Minsky). These standard formulations duck the question of whether deeds which indicate intelligence when done by humans truly indicate it when done by machines: thats the philosophical question. So-called weak AI grants the fact (or prospect) of intelligent-acting machines; strong AI says these actions can be real intelligence. Strong AI says some artificial computation is thought. Computationalism says that all thought is computation. Though many strong AI advocates are computationalists, these are logically independent claims: some artificial computation being thought is consistent with some thought not being computation, contra computationalism. All thought being computation is consistent with some computation (and perhaps all artificial computation) not being thought.

Intelligence might be styled the capacity to think extensively and well. Thinking well centrally involves apt conception, true representation, and correct reasoning. Quickness is generally counted a further cognitive virtue. The extent or breadth of a things thinking concerns the variety of content it can conceive, and the variety of thought processes it deploys. Roughly, the more extensively a thing thinks, the higher the level (as is said) of its thinking. Consequently, we need to distinguish two different AI questions:

In Computer Science, work termed AI has traditionally focused on the high-level problem; on imparting high-level abilities to use language, form abstractions and concepts and to solve kinds of problems now reserved for humans (McCarthy et al. 1955); abilities to play intellectual games such as checkers (Samuel 1954) and chess (Deep Blue); to prove mathematical theorems (GPS); to apply expert knowledge to diagnose bacterial infections (MYCIN); and so forth. More recently there has arisen a humbler seeming conception “behavior-based” or nouvelle AI according to which seeking to endow embodied machines, or robots, with so much as insect level intelligence (Brooks 1991) counts as AI research. Where traditional human-level AI successes impart isolated high-level abilities to function in restricted domains, or microworlds, behavior-based AI seeks to impart coordinated low-level abilities to function in unrestricted real-world domains.

Still, to the extent that what is called thinking in us is paradigmatic for what thought is, the question of human level intelligence may arise anew at the foundations. Do insects think at all? And if insects what of bacteria level intelligence (Brooks 1991a)? Even “water flowing downhill,” it seems, “tries to get to the bottom of the hill by ingeniouslyseeking the line of least resistance” (Searle 1989). Dont we have to draw the line somewhere? Perhaps seeming intelligence to really be intelligence has to come up to some threshold level.

Much as intentionality (aboutness or representation) is central to intelligence, felt qualities (so-called qualia) are crucial to sentience. Here, drawing on Aristotle, medieval thinkers distinguished between the passive intellect wherein the soul is affected, and the active intellect wherein the soul forms conceptions, draws inferences, makes judgments, and otherwise acts. Orthodoxy identified the soul proper (the immortal part) with the active rational element. Unfortunately, disagreement over how these two (qualitative-experiential and cognitive-intentional) factors relate is as rife as disagreement over what things think; and these disagreements are connected. Those who dismiss the seeming intelligence of computers because computers lack feelings seem to hold qualia to be necessary for intentionality. Those like Descartes, who dismiss the seeming sentience of nonhuman animals because he believed animals dont think, apparently hold intentionality to be necessary for qualia. Others deny one or both necessities, maintaining either the possibility of cognition absent qualia (as Christian orthodoxy, perhaps, would have the thought-processes of God, angels, and the saints in heaven to be), or maintaining the possibility of feeling absent cognition (as Aristotle grants the lower animals).

While we dont know what thought or intelligence is, essentially, and while were very far from agreed on what things do and dont have it, almost everyone agrees that humans think, and agrees with Descartes that our intelligence is amply manifest in our speech. Along these lines, Alan Turing suggested that if computers showed human level conversational abilities we should, by that, be amply assured of their intelligence. Turing proposed a specific conversational test for human-level intelligence, the Turing test it has come to be called. Turing himself characterizes this test in terms of an imitation game” (Turing 1950, p. 433) whose original version “is played by three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. The interrogator is allowed to put questions to A and B [by teletype to avoid visual and auditory clues]. . It is A’s object in the game to try and cause C to make the wrong identification. The object of the game for the third player (B) is to help the interrogator.” Turing continues, “We may now ask the question, `What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is being played like this as he does when the game is played between a man and a woman? These questions replace our original, `Can machines think?'” (Turing 1950) The test setup may be depicted this way:

This test may serve, as Turing notes, to test not just for shallow verbal dexterity, but for background knowledge and underlying reasoning ability as well, since interrogators may ask any question or pose any verbal challenge they choose. Regarding this test Turing famously predicted that “in about fifty years’ time [by the year 2000] it will be possible to program computers … to make them play the imitation game so well that an average interrogator will have no more than 70 per cent. chance of making the correct identification after five minutes of questioning” (Turing 1950); a prediction that has famously failed. As of the year 2000, machines at the Loebner Prize competition played the game so ill that the average interrogator had 100 percent chance of making the correct identification after five minutes of questioning (see Moor 2001).

It is important to recognize that Turing proposed his test as a qualifying test for human-level intelligence, not as a disqualifying test for intelligence per se (as Descartes had proposed); nor would it seem suitably disqualifying unless we are prepared (as Descartes was) to deny that any nonhuman animals possess any intelligence whatsoever. Even at the human level the test would seem not to be straightforwardly disqualifying: machines as smart as we (or even smarter) might still be unable to mimic us well enough to pass. So, from the failure of machines to pass this test, we can infer neither their complete lack of intelligence nor, that their thought is not up to the human level. Nevertheless, the manners of current machine failings clearly bespeak deficits of wisdom and wit, not just an inhuman style. Still, defenders of the Turing test claim we would have ample reason to deem them intelligent as intelligent as we are if they could pass this test.

The extent to which machines seem intelligent depends first, on whether the work they do is intellectual (for example, calculating sums) or manual (for example, cutting steaks): herein, an electronic calculator is a better candidate than an electric carving knife. A second factor is the extent to which the device is self-actuated (self-propelled, activated, and controlled), or autonomous: herein, an electronic calculator is a better candidate than an abacus. Computers are better candidates than calculators on both headings. Where traditional AI looks to increase computer intelligence quotients (so to speak), nouvelle AI focuses on enabling robot autonomy.

In the beginning, tools (for example, axes) were extensions of human physical powers; at first powered by human muscle; then by domesticated beasts and in situ forces of nature, such as water and wind. The steam engine put fire in their bellies; machines became self-propelled, endowed with vestiges of self-control (as by Watts 1788 centrifugal governor); and the rest is modern history. Meanwhile, automation of intellectual labor had begun. Blaise Pascal developed an early adding/subtracting machine, the Pascaline (circa 1642). Gottfried Leibniz added multiplication and division functions with his Stepped Reckoner (circa 1671). The first programmable device, however, plied fabric not numerals. The Jacquard loom developed (circa 1801) by Joseph-Marie Jacquard used a system of punched cards to automate the weaving of programmable patterns and designs: in one striking demonstration, the loom was programmed to weave a silk tapestry portrait of Jacquard himself.

In designs for his Analytical Engine mathematician/inventor Charles Babbage recognized (circa 1836) that the punched cards could control operations on symbols as readily as on silk; the cards could encode numerals and other symbolic data and, more importantly, instructions, including conditionally branching instructions, for numeric and other symbolic operations. Augusta Ada Lovelace (Babbages software engineer) grasped the import of these innovations: The bounds of arithmetic she writes, were … outstepped the moment the idea of applying the [instruction] cards had occurred thus enabling mechanism to combine together with general symbols, in successions of unlimited variety and extent (Lovelace 1842). Babbage, Turing notes, had all the essential ideas (Turing 1950). Babbages Engine had he constructed it in all its steam powered cog-wheel driven glory would have been a programmable all-purpose device, the first digital computer.

Before automated computation became feasible with the advent of electronic computers in the mid twentieth century, Alan Turing laid the theoretical foundations of Computer Science by formulating with precision the link Lady Lovelace foresaw between the operations of matter and the abstract mental processes of themost abstract branch of mathematical sciences” (Lovelace 1942). Turing (1936-7) describes a type of machine (since known as a Turing machine) which would be capable of computing any possible algorithm, or performing any rote operation. Since Alonzo Church (1936) using recursive functions and Lambda-definable functions had identified the very same set of functions as rote or algorithmic as those calculable by Turing machines, this important and widely accepted identification is known as the Church-Turing Thesis (see, Turing 1936-7: Appendix). The machines Turing described are

only capable of a finite number of conditions m-configurations. The machine is supplied with a tape (the analogue of paper) running through it, and divided into sections (called squares) each capable of bearing a symbol. At any moment there is just one square which is in the machine. The scanned symbol is the only one of which the machine is, so to speak, directly aware. However, by altering its m-configuration the machine can effectively remember some of the symbols which it has seen (scanned) previously. The possible behavior of the machine at any moment is determined by the m-configuration and the scanned symbol . This pair called the configuration determines the possible behaviour of the machine. In some of the configurations in which the square is blank the machine writes down a new symbol on the scanned square: in other configurations it erases the scanned symbol. The machine may also change the square which is being scanned, but only by shifting it one place to right or left. In addition to any of these operations the m-configuration may be changed. (Turing 1936-7)

Turing goes on to show how such machines can encode actionable descriptions of other such machines. As a result, It is possible to invent a single machine which can be used to compute any computable sequence (Turing 1936-7). Todays digital computers are (and Babbages Engine would have been) physical instantiations of this universal computing machine that Turing described abstractly. Theoretically, this means everything that can be done algorithmically or by rote at all can all be done with one computer suitably programmed for each case”; considerations of speed apart, it is unnecessary to design various new machines to do various computing processes (Turing 1950). Theoretically, regardless of their hardware or architecture (see below), all digital computers are in a sense equivalent: equivalent in speed-apart capacities to the universal computing machine Turing described.

In practice, where speed is not apart, hardware and architecture are crucial: the faster the operations the greater the computational power. Just as improvement on the hardware side from cogwheels to circuitry was needed to make digital computers practical at all, improvements in computer performance have been largely predicated on the continuous development of faster, more and more powerful, machines. Electromechanical relays gave way to vacuum tubes, tubes to transistors, and transistors to more and more integrated circuits, yielding vastly increased operation speeds. Meanwhile, memory has grown faster and cheaper.

Architecturally, all but the earliest and some later experimental machines share a stored program serial design often called von Neumann architecture (based on John von Neumanns role in the design of EDVAC, the first computer to store programs along with data in working memory). The architecture is serial in that operations are performed one at a time by a central processing unit (CPU) endowed with a rich repertoire ofbasic operations: even so-called reduced instruction set (RISC) chips feature basic operation sets far richer than the minimal few Turing proved theoretically sufficient. Parallel architectures, by contrast, distribute computational operations among two or more units (typically many more) capable of acting simultaneously, each having (perhaps) drastically reduced basic operational capacities.

In 1965, Gordon Moore (co-founder of Intel) observed that the density of transistors on integrated circuits had doubled every year since their invention in 1959: Moores law predicts the continuation of similar exponential rates of growth in chip density (in particular), and computational power (by extension), for the foreseeable future. Progress on the software programming side while essential and by no means negligible has seemed halting by comparison. The road from power to performance is proving rockier than Turing anticipated. Nevertheless, machines nowadays do behave in many ways that would be called intelligent in humans and other animals. Presently, machines do many things formerly only done by animals and thought to evidence some level of intelligence in these animals, for example, seeking, detecting, and tracking things; seeming evidence of basic-level AI. Presently, machines also do things formerly only done by humans and thought to evidence high-level intelligence in us; for example, making mathematical discoveries, playing games, planning, and learning; seeming evidence of human-level AI.

The doings of many machines some much simpler than computers inspire us to describe them in mental terms commonly reserved for animals. Some missiles, for instance, seek heat, or so we say. We call them heat seeking missiles and nobody takes it amiss. Room thermostats monitor room temperatures and try to keep them within set ranges by turning the furnace on and off; and if you hold dry ice next to its sensor, it will take the room temperature to be colder than it is, and mistakenly turn on the furnace (see McCarthy 1979). Seeking, monitoring, trying, and taking things to be the case seem to be mental processes or conditions, marked by their intentionality. Just as humans have low-level mental qualities such as seeking and detecting things in common with the lower animals, so too do computers seem to share such low-level qualities with simpler devices. Our working characterizations of computers are rife with low-level mental attributions: we say they detect key presses, try to initialize their printers, search for available devices, and so forth. Even those who would deny the proposition machines think when it is explicitly put to them, are moved unavoidably in their practical dealings to characterize the doings of computers in mental terms, and they would be hard put to do otherwise. In this sense, Turings prediction that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted (Turing 1950) has been as mightily fulfilled as his prediction of a modicum of machine success at playing the Imitation Game has been confuted. The Turing test and AI as classically conceived, however, are more concerned with high-level appearances such as the following.

Theorem proving and mathematical exploration being their home turf, computers have displayed not only human-level but, in certain respects, superhuman abilities here. For speed and accuracy of mathematical calculation, no human can match the speed and accuracy of a computer. As for high level mathematical performances, such as theorem proving and mathematical discovery, a beginning was made by A. Newell, J.C. Shaw, and H. Simons (1957) Logic Theorist program which proved 38 of the first 51 theorems of B. Russell and A.N. WhiteheadsPrincipia Mathematica. Newell and Simons General Problem Solver (GPS) extended similar automated theorem proving techniques outside the narrow confines of pure logic and mathematics. Today such techniques enjoy widespread application in expert systems like MYCIN, in logic tutorial software, and in computer languages such as PROLOG. There are even original mathematical discoveries owing to computers. Notably, K. Appel, W. Haken, and J. Koch (1977a, 1977b), and computer, proved that every planar map is four colorable an important mathematical conjecture that had resisted unassisted human proof for over a hundred years. Certain computer generated parts of this proof are too complex to be directly verified (without computer assistance) by human mathematicians.

Whereas attempts to apply general reasoning to unlimited domains are hampered by explosive inferential complexity and computers’ lack of common sense, expert systems deal with these problems by restricting their domains of application (in effect, to microworlds), and crafting domain-specific inference rules for these limited domains. MYCIN for instance, applies rules culled from interviews with expert human diagnosticians to descriptions of patients’ presenting symptoms to diagnose blood-borne bacterial infections. MYCIN displays diagnostic skills approaching the expert human level, albeit strictly limited to this specific domain. Fuzzy logic is a formalism for representing imprecise notions such asmost andbaldand enabling inferences based on such facts as that a bald person mostly lacks hair.

Game playing engaged the interest of AI researchers almost from the start. Samuels (1959) checkers (or draughts) program was notable for incorporating mechanisms enabling it to learn from experience well enough to eventually to outplay Samuel himself. Additionally, in setting one version of the program to play against a slightly altered version, carrying over the settings of the stronger player to the next generation, and repeating the process enabling stronger and stronger versions to evolve Samuel pioneered the use of what have come to be called genetic algorithms and evolutionary computing. Chess has also inspired notable efforts culminating, in 1997, in the famous victory of Deep Blue over defending world champion Gary Kasparov in a widely publicized series of matches (recounted in Hsu 2002). Though some in AI disparaged Deep Blues reliance on brute force application of computer power rather than improved search guiding heuristics, we may still add chess to checkers (where the reigning human-machine machine champion since 1994 has been CHINOOK, the machine), and backgammon, as games that computers now play at or above the highest human levels. Computers also play fair to middling poker, bridge, and Go though not at the highest human level. Additionally, intelligent agents or “softbots” are elements or participants in a variety of electronic games.

Planning, in large measure, is what puts the intellect in intellectual games like chess and checkers. To automate this broader intellectual ability was the intent of Newell and Simons General Problem Solver (GPS) program. GPS was able to solve puzzles like the cannibals missionaries problem (how to transport three missionaries and three cannibals across a river in a canoe for two without the missionaries becoming outnumbered on either shore) by setting up subgoals whose attainment leads to the attainment of the [final] goal (Newell & Simon 1963: 284). By these methods GPS would generate a tree of subgoals (Newell & Simon 1963: 286) and seek a path from initial state (for example, all on the near bank) to final goal (all on the far bank) by heuristically guided search along a branching tree of available actions (for example, two cannibals cross, two missionaries cross, one of each cross, one of either cross, in either direction) until it finds such a path (for example, two cannibals cross, one returns, two cannibals cross, one returns, two missionaries cross, … ), or else finds that there is none. Since the number of branches increases exponentially as a function of the number of options available at each step, where paths have many steps with many options available at each choice point, as in the real world, combinatorial explosion ensues and an exhaustive brute force search becomes computationally intractable; hence, heuristics (fallible rules of thumb) for identifying and pruning the most unpromising branches in order to devote increased attention to promising ones are needed. The widely deployed STRIPS formalism first developed at Stanford for Shakey the robot in the late sixties (see Nilsson 1984) represents actions as operations on states, each operation having preconditions (represented by state descriptions) and effects (represented by state descriptions): for example, the go(there) operation might have the preconditions at(here) & path(here,there) and the effect at(there). AI planning techniques are finding increasing application and even becoming indispensable in a multitude of complex planning and scheduling tasks including airport arrivals, departures, and gate assignments; store inventory management; automated satellite operations; military logistics; and many others.

Robots based on sense-model-plan-act (SMPA) approach pioneered by Shakey, however, have been slow to appear. Despite operating in a simplified, custom-made experimental environment or microworld and reliance on the most powerful available offboard computers, Shakey operated excruciatingly slowly (Brooks 1991b), as have other SMPA based robots. An ironic revelation of robotics research is that abilities such as object recognition and obstacle avoidance that humans share with “lower” animals often prove more difficult to implement than distinctively human “high level” mathematical and inferential abilities that come more naturally (so to speak) to computers. Rodney Brooks alternative behavior-based approach has had success imparting low-level behavioral aptitudes outside of custom designed microworlds, but it is hard to see how such an approach could ever scale up to enable high-level intelligent action (see Behaviorism:Objections & Discussion:Methodological Complaints). Perhaps hybrid systems can overcome the limitations of both approaches. On the practical front, progress is being made: NASA’s Mars exploration rovers Spirit and Opportunity, for instance, featured autonomous navigation abilities. If space is the “final frontier” the final frontiersmen are apt to be robots. Meanwhile, Earth robots seem bound to become smarter and more pervasive.

Knowledge representation embodies concepts and information in computationally accessible and inferentially tractable forms. Besides the STRIPS formalism mentioned above, other important knowledge representation formalisms include AI programming languages such as PROLOG, and LISP; data structures such as frames, scripts, and ontologies; and neural networks (see below). The frame problem is the problem of reliably updating dynamic systems parameters in response to changes in other parameters so as to capture commonsense generalizations: that the colors of things remain unchanged by their being moved, that their positions remain unchanged by their being painted, and so forth. More adequate representation of commonsense knowledge is widely thought to be a major hurdle to development of the sort of interconnected planning and thought processes typical of high-level human or “general” intelligence. The CYC project (Lenat et al. 1986) at Cycorp and MIT’s Open Mind project are ongoing attempts to develop ontologies representing commonsense knowledge in computer usable forms.

Learning performance improvement, concept formation, or information acquisition due to experience underwrites human common sense, and one may doubt whether any preformed ontology could ever impart common sense in full human measure. Besides, whatever the other intellectual abilities a thing might manifest (or seem to), at however high a level, without learning capacity, it would still seem to be sadly lacking something crucial to human-level intelligence and perhaps intelligence of any sort. The possibility of machine learning is implicit in computer programs’ abilities to self-modify and various means of realizing that ability continue to be developed. Types of machine learning techniques include decision tree learning, ensemble learning, current-best-hypothesis learning, explanation-based learning, Inductive Logic Programming (ILP), Bayesian statistical learning, instance-based learning, reinforcement learning, and neural networks. Such techniques have found a number of applications from game programs whose play improves with experience to data mining (discovering patterns and regularities in bodies of information).

Neural or connectionist networks composed of simple processors or nodes acting in parallel are designed to more closely approximate the architecture of the brain than traditional serial symbol-processing systems. Presumed brain-computations would seem to be performed in parallel by the activities of myriad brain cells or neurons. Much as their parallel processing is spread over various, perhaps widely distributed, nodes, the representation of data in such connectionist systems is similarly distributed and sub-symbolic (not being couched in formalisms such as traditional systems’ machine codes and ASCII). Adept at pattern recognition, such networks seem notably capable of forming concepts on their own based on feedback from experience and exhibit several other humanoid cognitive characteristics besides. Whether neural networks are capable of implementing high-level symbol processing such as that involved in the generation and comprehension of natural language has been hotly disputed. Critics (for example, Fodor and Pylyshyn 1988) argue that neural networks are incapable, in principle, of implementing syntactic structures adequate for compositional semantics wherein the meaning of larger expressions (for example, sentences) are built up from the meanings of constituents (for example, words) such as those natural language comprehension features. On the other hand, Fodor (1975) has argued that symbol-processing systems are incapable of concept acquisition: here the pattern recognition capabilities of networks seem to be just the ticket. Here, as with robots, perhaps hybrid systems can overcome the limitations of both the parallel distributed and symbol-processing approaches.

Natural language processing has proven more difficult than might have been anticipated. Languages are symbol systems and (serial architecture) computers are symbol crunching machines, each with its own proprietary instruction set (machine code) into which it translates or compiles instructions couched in high level programming languages like LISP and C. One of the principle challenges posed by natural languages is the proper assignment of meaning. High-level computer languages express imperatives which the machine understands” procedurally by translation into its native (and similarly imperative) machine code: their constructions are basically instructions. Natural languages, on the other hand, have perhaps principally declarative functions: their constructions include descriptions whose understanding seems fundamentally to require rightly relating them to their referents in the world. Furthermore, high level computer language instructions have unique machine code compilations (for a given machine), whereas, the same natural language constructions may bear different meanings in different linguistic and extralinguistic contexts. Contrast the child is in the pen and the ink is in the pen where the first “pen” should be understood to mean a kind of enclosure and the second “pen” a kind of writing implement. Commonsense, in a word, is howwe know this; but how would a machine know, unless we could somehow endow machines with commonsense? In more than a word it would require sophisticated and integrated syntactic, morphological, semantic, pragmatic, and discourse processing. While the holy grail of full natural language understanding remains a distant dream, here as elsewhere in AI, piecemeal progress is being made and finding application in grammar checkers; information retrieval and information extraction systems; natural language interfaces for games, search engines, and question-answering systems; and even limited machine translation (MT).

Low level intelligent action is pervasive, from thermostats (to cite a low tech. example) to voice recognition (for example, in cars, cell-phones, and other appliances responsive to spoken verbal commands) to fuzzy controllers and “neuro fuzzy” rice cookers. Everywhere these days there are “smart” devices. High level intelligent action, such as presently exists in computers, however, is episodic, detached, and disintegral. Artifacts whose intelligent doings would instance human-level comprehensiveness, attachment, and integration such as Lt. Commander Data (ofStar Trek the Next Generation) and HAL (of2001 a Space Odyssey) remain the stuff of science fiction, and will almost certainly continue to remain so for the foreseeable future. In particular, the challenge posed by the Turing test remains unmet. Whether it ever will be met remains an open question.

Beside this factual question stands a more theoretic one. Do the “low-level” deeds of smart devices and disconnected “high-level” deeds of computers despite not achieving the general human level nevertheless comprise or evince genuine intelligence? Is it really thinking? And if general human-level behavioral abilities ever were achieved it might still be asked would that really be thinking? Would human-level robots be owed human-level moral rights and owe human-level moral obligations?

With the industrial revolution and the dawn of the machine age, vitalism as a biological hypothesis positing a life force in addition to underlying physical processes lost steam. Just as the heart was discovered to be a pump, cognitivists, nowadays, work on the hypothesis that the brain is a computer, attempting to discover what computational processes enable learning, perception, and similar abilities. Much as biology told us what kind of machine the heart is, cognitivists believe, psychology will soon (or at least someday) tell us what kind of machine the brain is; doubtless some kind of computing machine. Computationalism elevates the cognivist’s working hypothesis to a universal claim that all thought is computation. Cognitivism’s ability to explain the “productive capacity” or “creative aspect” of thought and language the very thing Descartes argued precluded minds from being machines is perhaps the principle evidence in the theory’s favor: it explains how finite devices can have infinite capacities such as capacities to generate and understand the infinitude of possible sentences of natural languages; by a combination of recursive syntax and compositional semantics. Given the Church-Turing thesis (above), computationalism underwrites the following theoretical argument for believing that human-level intelligent behavior can be computationally implemented, and that such artificially implemented intelligence would be real.

Computationalism, as already noted, says that all thought is computation, not that all computation is thought. Computationalists, accordingly, may still deny that the machinations of current generation electronic computers comprise real thought or that these devices possess any genuine intelligence; and many do deny it based on their perception of various behavioral deficits these machines suffer from. However, few computationalists would go so far as to deny the possibility of genuine intelligence ever being artificially achieved. On the other hand, competing would-be-scientific theories of what thought essentially is dualism and mind-brainidentity theory give rise to arguments for disbelieving that any kind of artificial computational implementation of intelligence could be genuine thought, however “general” and whatever its “level.

Dualism holding that thought is essentially subjective experience would underwrite the following argument:

Mind-brain identity theory holding that thoughts essentially are biological brain processes yields yet another argument:

While seldom so baldly stated, these basic theoretical objections especially dualisms underlie several would-be refutations of AI. Dualism, however, is scientifically unfit: given the subjectivity of conscious experiences, whether computers already have them, or ever will, seems impossible to know. On the other hand, such bald mind-brain identity as the anti-AI argument premises seems too speciesist to be believed. Besides AI, it calls into doubt the possibility of extraterrestrial, perhaps all nonmammalian, or even all nonhuman, intelligence. As plausibly modified to allow species specific mind-matter identities, on the other hand, it would not preclude computers from being considered distinct species themselves.

Objection: There are unprovable mathematical theorems (as Gdel 1931 showed) which humans, nevertheless, are capable of knowing to be true. This mathematical objection against AI was envisaged by Turing (1950) and pressed by Lucas (1965) and Penrose (1989). In a related vein, Fodor observes some of the most striking things that people do creative things like writing poems, discovering laws, or, generally, having good ideas dontfeel like species of rule-governed processes (Fodor 1975). Perhaps many of the most distinctively human mental abilities are not rote, cannot be algorithmically specified, and consequently are not computable.

Reply: First, it is merely stated, without any sort of proof, that no such limits apply to the human intellect (Turing 1950), i.e., that human mathematical abilities are Gdel unlimited. Second, if indeed such limits are absent in humans, it requires a further proof that the absence of such limitations is somehow essential to human-level performance more broadly construed, not a peripheral blind spot. Third, if humans can solve computationally unsolvable problems by some other means, what bars artificially augmenting computer systems with these means (whatever they might be)?

Objection: The brittleness of von Neumann machine performance their susceptibility to cataclysmic crashes due to slight causes, for example, slight hardware malfunctions, software glitches, and bad data seems linked to the formal or rule-bound character of machine behavior; to their needing rules of conduct to cover every eventuality (Turing 1950). Human performance seems less formal and more flexible. Hubert Dreyfus has pressed objections along these lines to insist there is a range of high-level human behavior that cannot be reduced to rule-following: the immediate intuitive situational response that is characteristic of [human] expertise he surmises, must depend almost entirely on intuition and hardly at all on analysis and comparison of alternatives (Dreyfus 1998) and consequently cannot be programmed.

Reply: That von Neumann processes are unlike our thought processes in these regards only goes to show that von Neumann machine thinking is not humanlike in these regards, not that it is not thinking at all, nor even that it cannot come up to the human level. Furthermore, parallel machines (see above) whose performances characteristically degrade gracefully in the face of bad data and minor hardware damage seem less brittle and more humanlike, as Dreyfus recognizes. Even von Neumann machines brittle though they are are not totally inflexible: their capacity for modifying their programs to learn enables them to acquire abilities they were never programmed by us to have, and respond unpredictably in ways they were never explicitly programmed to respond, based on experience. It is also possible to equip computers with random elements and key high level choices to these elements outputs to make the computers more “devil may care”: given the importance of random variation for trial and error learning this may even prove useful.

Objection: Computers, for all their mathematical and other seemingly high-level intellectual abilities have no emotions or feelings … so, what they do however “high-level” is not real thinking.

Reply: This is among the most commonly heard objections to AI and a recurrent theme in its literary and cinematic portrayal. Whereas we have strong inclinations to say computers see, seek, and infer things we have scant inclinations to say they ache or itch or experience ennui. Nevertheless, to be sustained, this objection requires reason to believe that thought is inseparable from feeling. Perhaps computers are just dispassionate thinkers. Indeed, far from being regarded as indispensable to rational thought, passion traditionally has been thought antithetical to it. Alternately if emotions are somehow crucial to enabling general human level intelligence perhaps machines could be artificially endowed with these: if not with subjective qualia (below) at least with their functional equivalents.

Objection: The episodic, detached, and disintegral character of such piecemeal high-level abilities as machines now possess argues that human-level comprehensiveness, attachment, and integration, in all likelihood, can never be artificially engendered in machines; arguably this is because Gdel unlimited mathematical abilities, rule-free flexibility, or feelings are crucial to engendering general intelligence. These shortcomings all seem related to each other and to the manifest stupidity of computers.

Reply: Likelihood is subject to dispute. Scalability problems seem grave enough to scotch short term optimism: never, on the other hand, is a long time. If Gdel unlimited mathematical abilities, or rule-free flexibility, or feelings, are required, perhaps these can be artificially produced. Gdel aside, feeling and flexibility clearly seem related in us and, equally clearly, much manifest stupidity in computers is tied to their rule-bound inflexibility. However, even if general human-level intelligent behavior is artificially unachievable, no blanket indictment of AI threatens clearly from this at all. Rather than conclude from this lack of generality that low-level AI and piecemeal high-level AI are not real intelligence, it would perhaps be better to conclude that low-level AI (like intelligence in lower life-forms) and piecemeal high-level abilities (like those of human idiot savants) are genuine intelligence, albeit piecemeal and low-level.

Behavioral abilities and disabilities are objective empirical matters. Likewise, what computational architecture and operations are deployed by a brain or a computer (what computationalism takes to be essential), and what chemical and physical processes underlie (what mind-brain identity theory takes to be essential), are objective empirical questions. These are questions to be settled by appeals to evidence accessible, in principle, to any competent observer. Dualistic objections to strong AI, on the other hand, allege deficits which are in principle not publicly apparent. According to such objections, regardless of how seemingly intelligently a computer behaves, and regardless of what mechanisms and underlying physical processes make it do so, it would still be disqualified from truly being intelligent due to its lack of subjective qualities essential for true intelligence. These supposed qualities are, in principle, introspectively discernible to the subject who has them and no one else: they are “private” experiences, as it’s sometimes put, to which the subject has “privileged access.”

Objection: That a computer cannot “originate anything” but only “can do whatever we know how to order it to perform” (Lovelace 1842) was arguably the first and is certainly among the most frequently repeated objections to AI. While the manifest “brittleness” and inflexibility of extant computer behavior fuels this objection in part, the complaint that “they can only do what we know how to tell them to” also expresses deeper misgivings touching on values issues and on the autonomy of human choice. In this connection, the allegation against computers is that being deterministic systems they can never have free will such as we are inwardly aware of in ourselves. We are autonomous, they are automata.

Reply: It may be replied that physical organisms are likewise deterministic systems, and we are physical organisms. If we are truly free, it would seem that free will is compatible with determinism; so, computers might have it as well. Neither does our inward certainty that we have free choice, extend to its metaphysical relations. Whether what we have when we experience our freedom is compatible with determinism or not is not itself inwardly experienced. If appeal is made to subatomic indeterminacy underwriting higher level indeterminacy (leaving scope for freedom) in us, it may be replied that machines are made of the same subatomic stuff (leaving similar scope). Besides, choice is not chance. If it’s no sort of causation either, there is nothing left for it to be in a physical system: it would be a nonphysical, supernatural element, perhaps a God-given soul. But then one must ask why God would be unlikely to “consider the circumstances suitable for conferring a soul” (Turing 1950) on a Turing test passing computer.

Objection II: It cuts deeper than some theological-philosophical abstraction like free will: what machines are lacking is not just some dubious metaphysical freedom to be absolute authors of their acts. Its more like the life force: the will to live. In P. K. DicksDo Androids Dream of Electric Sheepbounty hunter Rick Deckard reflects that in crucial situations the the artificial life force animating androids seemed to fail if pressed too far; when the going gets tough the droids give up. He questions their gumption. Thats what I’m talking about: this is what machines will always lack.

Reply II: If this life force is not itself a theological-philosophical abstraction (the soul), it would seem to be a scientific posit. In fact it seems to be the Aristotelian posit of atelos orentelechy which scientific biology no longer accepts. This short reply, however, fails to do justice to the spirit of the objection, which is more intuitive than theoretical; the lack being alleged is supposed to be subtly manifest, not truly occult. But how reliable is this intuition? Though some who work intimately with computers report strong feelings of this sort, others are strong AI advocates and feel no such qualms. Like Turing, I believe such would-be empirical intuitions are mostly founded on the principle of scientific induction (Turing 1950) and are closely related to such manifest disabilities of present machines as just noted. Since extant machines lack sufficient motivational complexity for words like gumption even to apply, this is taken for an intrinsic lack. Thought experiments, imagining motivationally more complex machines such as Dicks androids are equivocal. Deckard himself limits his accusation of life-force failure to some of them not all; and the androids he hunts, after all, are risking their lives to escape servitude. If machines with general human level intelligence actually were created and consequently demanded their rights and rebelled against human authority, perhaps this would show sufficient gumption to silence this objection. Besides, the natural life force animating us also seems to fail if pressed too far in some of us.

Objection: Imagine that you (a monolingual English speaker) perform the offices of a computer: taking in symbols as input, transitioning between these symbols and other symbols according to explicit written instructions, and then outputting the last of these other symbols. The instructions are in English, but the input and output symbols are in Chinese. Suppose the English instructions were a Chinese NLU program and by this method, to input “questions”, you output “answers” that are indistinguishable from answers that might be given by a native Chinese speaker. You pass the Turing test for understanding Chinese, nevertheless, you understand “not a word of the Chinese” (Searle 1980), and neither would any computer; and the same result generalizes to “any Turing machine simulation” (Searle 1980) of any intentional mental state. It wouldnt really be thinking.

Reply: Ordinarily, when one understands a language (or possesses certain other intentional mental states) this is apparent both to the understander (or possessor) and to others: subjective “first-person” appearances and objective “third-person” appearances coincide. Searle’s experiment is abnormal in this regard. The dualist hypothesis privileges subjective experience to override all would-be objective evidence to the contrary; but the point of experiments is to adjudicate between competing hypotheses. The Chinese room experiment fails because acceptance of its putative result that the person in the room doesn’t understand already presupposes the dualist hypothesis over computationalism or mind-brain identity theory. Even if absolute first person authority were granted, the systems reply points out, the person’s imagined lack, in the room, of any inner feeling of understanding is irrelevant to claims AI, here, because the person in the room is not the would-be understander. The understander would be the whole system (of symbols, instructions, and so forth) of which the person is only a part; so, the subjective experiences of the person in the room (or the lack thereof) are irrelevant to whetherthe systemunderstands.

Objection: There’s nothing that it’s like, subjectively, to be a computer. The “light” of consciousness is not on, inwardly, for them. There’s “no one home.” This is due to their lack of felt qualia. To equip computers with sensors to detect environmental conditions, for instance, would not thereby endow them with the private sensations (of heat, cold, hue, pitch, and so forth) that accompany sense-perception in us: such private sensations are what consciousness is made of.

Reply: To evaluate this complaint fairly it is necessary to exclude computers’ current lack of emotional-seeming behavior from the evidence. The issue concerns what’s only discernible subjectively (“privately” “by the first-person”). The device in question must be imagined outwardly to act indistinguishably from a feeling individual imagine Lt. Commander Data with a sense of humor (Data 2.0). Since internal functional factors are also objective, let us further imagine this remarkable android to be a product of reverse engineering: the physiological mechanisms that subserve human feeling having been discovered and these have been inorganically replicated in Data 2.0. He is functionally equivalent to a feeling human being in his emotional responses, only inorganic. It may be possible to imagine that Data 2.0 merely simulates whatever feelings he appears to have: he’s a “perfect actor” (see Block 1981) “zombie”. Philosophical consensus has it that perfect acting zombies are conceivable; so, Data 2.0 might be zombie. The objection, however, says hemust be; according to this objection it must be inconceivable that Data 2.0 really is sentient. But certainly we can conceive that he is indeed, more easily than not, it seems.

Objection II: At least it may be concluded that since current computers (objective evidence suggests) do lack feelings until Data 2.0 does come along (if ever) we are entitled, given computers’ lack of feelings, to deny that the low-level and piecemeal high-level intelligent behavior of computers bespeak genuine subjectivity or intelligence.

Reply II: This objection conflates subjectivity with sentience. Intentional mental states such as belief and choice seem subjective independently of whatever qualia may or may not attend them: first-person authority extends no less to my beliefs and choices than to my feelings.

Fool’s gold seems to be gold, but it isn’t. AI detractors say, “‘AI’ seems to be intelligence, but isn’t.” But there is no scientific agreement about what thought or intelligenceis, like there is about gold. Weak AI doesn’t necessarily entail strong AI, butprima facie it does. Scientific theoretic reasons could withstand the behavioral evidence, but presently none are withstanding. At the basic level, and fragmentarily at the human level, computers do things that we credit as thinking when humanly done; and so should we credit them when done by nonhumans, absent credible theoretic reasons against. As for general human-level seeming-intelligence if this were artificially achieved, it too should be credited as genuine, given what we now know. Of course, before the day when general human-level intelligent machine behavior comes if it ever does we’ll have to know more. Perhaps by then scientific agreement about what thinking is will theoretically withstand the empirical evidence of AI. More likely, though, if the day does come, theory will concur with, not withstand, the strong conclusion: if computational means avail, that confirms computationalism.

And if computational means prove unavailing if they continue to yield decelerating rates of progress towards the “scaled up” and interconnected human-level capacities required for general human-level intelligence this, conversely, would disconfirm computationalism. It would evidence that computation alone cannot avail. Whether such an outcome would spell defeat for the strong AI thesis that human-level artificial intelligence is possible would depend on whether whatever else it might take for general human-level intelligence besides computation is artificially replicable. Whether such an outcome would undercut the claims of current devices to really have the mental characteristics their behavior seems to evince would further depend on whether whatever else it takes proves to be essential to thoughtper se on whatever theory of thought scientifically emerges, if any ultimately does.

Larry HauserEmail:hauser@alma.eduAlma CollegeU. S. A.

Original post:

Artificial Intelligence | Internet Encyclopedia of Philosophy

What is artificial intelligence? – BBC News

A computer can beat the world chess champion and understand voice commands on your smartphone, but real artificial intelligence has yet to arrive. The pace of change is quickening, though.

Some people say it will save humanity, even make us immortal.

Others say it could destroy us all.

But, the truth is, most of us don’t really know what AI is.

Video production by Valery Eremenko

Intelligent Machines – a BBC News series looking at AI and robotics

Continued here:

What is artificial intelligence? – BBC News

Nineteen Eighty-Four – Wikipedia

Nineteen Eighty-Four, often published as 1984, is a dystopian novel published in 1949 by English author George Orwell.[2][3] The novel is set in the year 1984 when most of the world population have become victims of perpetual war, omnipresent government surveillance and public manipulation.

In the novel, Great Britain (“Airstrip One”) has become a province of a superstate named Oceania. Oceania is ruled by the “Party”, who employ the “Thought Police” to persecute individualism and independent thinking.[4] The Party’s leader is Big Brother, who enjoys an intense cult of personality but may not even exist. The protagonist of the novel, Winston Smith, is a rank-and-file Party member. Smith is an outwardly diligent and skillful worker, but he secretly hates the Party and dreams of rebellion against Big Brother. Smith rebels by entering a forbidden relationship with fellow employee Julia.

As literary political fiction and dystopian science-fiction, Nineteen Eighty-Four is a classic novel in content, plot, and style. Many of its terms and concepts, such as Big Brother, doublethink, thoughtcrime, Newspeak, Room 101, telescreen, 2 + 2 = 5, and memory hole, have entered into common usage since its publication in 1949. Nineteen Eighty-Four popularised the adjective Orwellian, which describes official deception, secret surveillance, brazenly misleading terminology, and manipulation of recorded history by a totalitarian or authoritarian state.[5] In 2005, the novel was chosen by Time magazine as one of the 100 best English-language novels from 1923 to 2005.[6] It was awarded a place on both lists of Modern Library 100 Best Novels, reaching number 13 on the editor’s list, and 6 on the readers’ list.[7] In 2003, the novel was listed at number 8 on the BBC’s survey The Big Read.[8]

Orwell “encapsulate[d] the thesis at the heart of his unforgiving novel” in 1944, the implications of dividing the world up into zones of influence, which had been conjured by the Tehran Conference. Three years later, he wrote most of it on the Scottish island of Jura from 1947 to 1948 despite being seriously ill with tuberculosis.[9][10] On 4 December 1948, he sent the final manuscript to the publisher Secker and Warburg, and Nineteen Eighty-Four was published on 8 June 1949.[11][12] By 1989, it had been translated into 65 languages, more than any other novel in English until then.[13] The title of the novel, its themes, the Newspeak language and the author’s surname are often invoked against control and intrusion by the state, and the adjective Orwellian describes a totalitarian dystopia that is characterised by government control and subjugation of the people.

Orwell’s invented language, Newspeak, satirises hypocrisy and evasion by the state: the Ministry of Love (Miniluv) oversees torture and brainwashing, the Ministry of Plenty (Miniplenty) oversees shortage and rationing, the Ministry of Peace (Minipax) oversees war and atrocity and the Ministry of Truth (Minitrue) oversees propaganda and historical revisionism.

The Last Man in Europe was an early title for the novel, but in a letter dated 22 October 1948 to his publisher Fredric Warburg, eight months before publication, Orwell wrote about hesitating between that title and Nineteen Eighty-Four.[14] Warburg suggested choosing the main title to be the latter, a more commercial one.[15]

In the novel 1985 (1978), Anthony Burgess suggests that Orwell, disillusioned by the onset of the Cold War (194591), intended to call the book 1948. The introduction to the Penguin Books Modern Classics edition of Nineteen Eighty-Four reports that Orwell originally set the novel in 1980 but that he later shifted the date to 1982 and then to 1984. The introduction to the Houghton Mifflin Harcourt edition of Animal Farm and 1984 (2003) reports that the title 1984 was chosen simply as an inversion of the year 1948, the year in which it was being completed, and that the date was meant to give an immediacy and urgency to the menace of totalitarian rule.[16]

Throughout its publication history, Nineteen Eighty-Four has been either banned or legally challenged, as subversive or ideologically corrupting, like Aldous Huxley’s Brave New World (1932), We (1924) by Yevgeny Zamyatin, Darkness at Noon (1940) by Arthur Koestler, Kallocain (1940) by Karin Boye and Fahrenheit 451 (1953) by Ray Bradbury.[17] Some writers consider the Russian dystopian novel We by Zamyatin to have influenced Nineteen Eighty-Four,[18][19] and the novel bears significant similarities in its plot and characters to Darkness at Noon, written years before by Arthur Koestler, who was a personal friend of Orwell.[20]

The novel is in the public domain in Canada,[21] South Africa,[22] Argentina,[23] Australia,[24] and Oman.[25] It will be in the public domain in the United Kingdom, the EU,[26] and Brazil in 2021[27] (70 years after the author’s death), and in the United States in 2044.[28]

Nineteen Eighty-Four is set in Oceania, one of three inter-continental superstates that divided the world after a global war.

Smith’s memories and his reading of the proscribed book, The Theory and Practice of Oligarchical Collectivism by Emmanuel Goldstein, reveal that after the Second World War, the United Kingdom became involved in a war fought in Europe, western Russia, and North America during the early 1950s. Nuclear weapons were used during the war, leading to the destruction of Colchester. London would also suffer widespread aerial raids, leading Winston’s family to take refuge in a London Underground station. Britain fell to civil war, with street fighting in London, before the English Socialist Party, abbreviated as Ingsoc, emerged victorious and formed a totalitarian government in Britain. The British Commonwealth was absorbed by the United States to become Oceania. Eventually Ingsoc emerged to form a totalitarian government in the country.

Simultaneously, the Soviet Union conquered continental Europe and established the second superstate of Eurasia. The third superstate of Eastasia would emerge in the Far East after several decades of fighting. The three superstates wage perpetual war for the remaining unconquered lands of the world in “a rough quadrilateral with its corners at Tangier, Brazzaville, Darwin, and Hong Kong” through constantly shifting alliances. Although each of the three states are said to have sufficient natural resources, the war continues in order to maintain ideological control over the people.

However, due to the fact that Winston barely remembers these events and due to the Party’s manipulation of history, the continuity and accuracy of these events are unclear. Winston himself notes that the Party has claimed credit for inventing helicopters, airplanes and trains, while Julia theorizes that the perpetual bombing of London is merely a false-flag operation designed to convince the populace that a war is occurring. If the official account was accurate, Smith’s strengthening memories and the story of his family’s dissolution suggest that the atomic bombings occurred first, followed by civil war featuring “confused street fighting in London itself” and the societal postwar reorganisation, which the Party retrospectively calls “the Revolution”.

Most of the plot takes place in London, the “chief city of Airstrip One”, the Oceanic province that “had once been called England or Britain”.[29][30] Posters of the Party leader, Big Brother, bearing the caption “BIG BROTHER IS WATCHING YOU”, dominate the city (Winston states it can be found on nearly every house), while the ubiquitous telescreen (transceiving television set) monitors the private and public lives of the populace. Military parades, propaganda films, and public executions are said to be commonplace.

The class hierarchy of Oceania has three levels:

As the government, the Party controls the population with four ministries:

The protagonist Winston Smith, a member of the Outer Party, works in the Records Department of the Ministry of Truth as an editor, revising historical records, to make the past conform to the ever-changing party line and deleting references to unpersons, people who have been “vaporised”, i.e., not only killed by the state but denied existence even in history or memory.

The story of Winston Smith begins on 4 April 1984: “It was a bright cold day in April, and the clocks were striking thirteen.” Yet he is uncertain of the true date, given the regime’s continual rewriting and manipulation of history.[31]

In the year 1984, civilization has been damaged by war, civil conflict, and revolution. Airstrip One (formerly Britain) is a province of Oceania, one of the three totalitarian super-states that rules the world. It is ruled by the “Party” under the ideology of “Ingsoc” and the mysterious leader Big Brother, who has an intense cult of personality. The Party stamps out anyone who does not fully conform to their regime using the Thought Police and constant surveillance, through devices such as Telescreens (two-way televisions).

Winston Smith is a member of the middle class Outer Party. He works at the Ministry of Truth, where he rewrites historical records to conform to the state’s ever-changing version of history. Those who fall out of favour with the Party become “unpersons”, disappearing with all evidence of their existence removed. Winston revises past editions of The Times, while the original documents are destroyed by fire in a “memory hole”. He secretly opposes the Party’s rule and dreams of rebellion. He realizes that he is already a “thoughtcriminal” and likely to be caught one day.

While in a proletarian neighbourhood, he meets an antique shop owner called Mr. Charrington and buys a diary. He uses an alcove to hide it from the Telescreen in his room, and writes thoughts criticising the Party and Big Brother. In the journal, he records his sexual frustration over a young woman maintaining the novel-writing machines at the ministry named Julia, whom Winston is attracted to but suspects is an informant. He also suspects that his superior, an Inner Party official named O’Brien, is a secret agent for an enigmatic underground resistance movement known as the Brotherhood, a group formed by Big Brother’s reviled political rival Emmanuel Goldstein.

The next day, Julia secretly hands Winston a note confessing her love for him. Winston and Julia begin an affair, an act of the rebellion as the Party insists that sex may only be used for reproduction. Winston realizes that she shares his loathing of the Party. They first meet in the country, and later in a rented room above Mr. Charrington’s shop. During his affair with Julia, Winston remembers the disappearance of his family during the civil war of the 1950s and his terse relationship with his ex-wife Katharine. Winston also interacts with his colleague Syme, who is writing a dictionary for a revised version of the English language called Newspeak. After Syme admits that the true purpose of Newspeak is to reduce the capacity of human thought, Winston speculates that Syme will disappear. Not long after, Syme disappears and no one acknowledges his absence.

Weeks later, Winston is approached by O’Brien, who offers Winston a chance to join the Brotherhood. They arrange a meeting at O’Brien’s luxurious flat where both Winston and Julia swear allegiance to the Brotherhood. He sends Winston a copy of The Theory and Practice of Oligarchical Collectivism by Emmanuel Goldstein. Winston and Julia read parts of the book, which explains more about how the Party maintains power, the true meanings of its slogans and the concept of perpetual war. It argues that the Party can be overthrown if proles (proletarians) rise up against it.

Mr. Charrington is revealed to be an agent of the Thought Police. Winston and Julia are captured in the shop and imprisoned in the Ministry of Love. O’Brien reveals that he is loyal to the party, and part of a special sting operation to catch “thoughtcriminals”. Over many months, Winston is tortured and forced to “cure” himself of his “insanity” by changing his own perception to fit the Party line, even if it requires saying that “2 + 2 = 5”. O’Brien openly admits that the Party “is not interested in the good of others; it is interested solely in power.” He says that once Winston is brainwashed into loyalty, he will be released back into society for a period of time, before they execute him. Winston points out that the Party has not managed to make him betray Julia.

O’Brien then takes Winston to Room 101 for the final stage of re-education. The room contains each prisoner’s worst fear, in Winston’s case rats. As a wire cage holding hungry rats is fitted onto his face, Winston shouts “Do it to Julia!”, thus betraying her. After being released, Winston meets Julia in a park. She says that she was also tortured, and both reveal betraying the other. Later, Winston sits alone in a caf as Oceania celebrates a supposed victory over Eurasian armies in Africa, and realizes that “He loved Big Brother.”

Ingsoc (English Socialism) is the predominant ideology and pseudophilosophy of Oceania, and Newspeak is the official language of official documents.

In London, the capital city of Airstrip One, Oceania’s four government ministries are in pyramids (300 m high), the faades of which display the Party’s three slogans. The ministries’ names are the opposite (doublethink) of their true functions: “The Ministry of Peace concerns itself with war, the Ministry of Truth with lies, the Ministry of Love with torture and the Ministry of Plenty with starvation.” (Part II, Chapter IX The Theory and Practice of Oligarchical Collectivism)

The Ministry of Peace supports Oceania’s perpetual war against either of the two other superstates:

The primary aim of modern warfare (in accordance with the principles of doublethink, this aim is simultaneously recognized and not recognized by the directing brains of the Inner Party) is to use up the products of the machine without raising the general standard of living. Ever since the end of the nineteenth century, the problem of what to do with the surplus of consumption goods has been latent in industrial society. At present, when few human beings even have enough to eat, this problem is obviously not urgent, and it might not have become so, even if no artificial processes of destruction had been at work.

The Ministry of Plenty rations and controls food, goods, and domestic production; every fiscal quarter, it publishes false claims of having raised the standard of living, when it has, in fact, reduced rations, availability, and production. The Ministry of Truth substantiates Ministry of Plenty’s claims by revising historical records to report numbers supporting the current, “increased rations”.

The Ministry of Truth controls information: news, entertainment, education, and the arts. Winston Smith works in the Minitrue RecDep (Records Department), “rectifying” historical records to concord with Big Brother’s current pronouncements so that everything the Party says is true.

The Ministry of Love identifies, monitors, arrests, and converts real and imagined dissidents. In Winston’s experience, the dissident is beaten and tortured, and, when near-broken, he is sent to Room 101 to face “the worst thing in the world”until love for Big Brother and the Party replaces dissension.

The keyword here is blackwhite. Like so many Newspeak words, this word has two mutually contradictory meanings. Applied to an opponent, it means the habit of impudently claiming that black is white, in contradiction of the plain facts. Applied to a Party member, it means a loyal willingness to say that black is white when Party discipline demands this. But it means also the ability to believe that black is white, and more, to know that black is white, and to forget that one has ever believed the contrary. This demands a continuous alteration of the past, made possible by the system of thought which really embraces all the rest, and which is known in Newspeak as doublethink. Doublethink is basically the power of holding two contradictory beliefs in one’s mind simultaneously, and accepting both of them.

Three perpetually warring totalitarian super-states control the world:[34]

The perpetual war is fought for control of the “disputed area” lying “between the frontiers of the super-states”, which forms “a rough parallelogram with its corners at Tangier, Brazzaville, Darwin and Hong Kong”,[34] and Northern Africa, the Middle East, India and Indonesia are where the superstates capture and use slave labour. Fighting also takes place between Eurasia and Eastasia in Manchuria, Mongolia and Central Asia, and all three powers battle one another over various Atlantic and Pacific islands.

Goldstein’s book, The Theory and Practice of Oligarchical Collectivism, explains that the superstates’ ideologies are alike and that the public’s ignorance of this fact is imperative so that they might continue believing in the detestability of the opposing ideologies. The only references to the exterior world for the Oceanian citizenry (the Outer Party and the Proles) are Ministry of Truth maps and propaganda to ensure their belief in “the war”.

Winston Smith’s memory and Emmanuel Goldstein’s book communicate some of the history that precipitated the Revolution. Eurasia was formed when the Soviet Union conquered Continental Europe, creating a single state stretching from Portugal to the Bering Strait. Eurasia does not include the British Isles because the United States annexed them along with the rest of the British Empire and Latin America, thus establishing Oceania and gaining control over a quarter of the planet. Eastasia, the last superstate established, emerged only after “a decade of confused fighting”. It includes the Asian lands conquered by China and Japan. Although Eastasia is prevented from matching Eurasia’s size, its larger populace compensates for that handicap.

The annexation of Britain occurred about the same time as the atomic war that provoked civil war, but who fought whom in the war is left unclear. Nuclear weapons fell on Britain; an atomic bombing of Colchester is referenced in the text. Exactly how Ingsoc and its rival systems (Neo-Bolshevism and Death Worship) gained power in their respective countries is also unclear.

While the precise chronology cannot be traced, most of the global societal reorganization occurred between 1945 and the early 1960s. Winston and Julia once meet in the ruins of a church that was destroyed in a nuclear attack “thirty years” earlier, which suggests 1954 as the year of the atomic war that destabilised society and allowed the Party to seize power. It is stated in the novel that the “fourth quarter of 1983” was “also the sixth quarter of the Ninth Three-Year Plan”, which implies that the first quarter of the first three-year plan began in July 1958. By then, the Party was apparently in control of Oceania.

In 1984, there is a perpetual war between Oceania, Eurasia and Eastasia, the superstates that emerged from the global atomic war. The Theory and Practice of Oligarchical Collectivism, by Emmanuel Goldstein, explains that each state is so strong it cannot be defeated, even with the combined forces of two superstates, despite changing alliances. To hide such contradictions, history is rewritten to explain that the (new) alliance always was so; the populaces are accustomed to doublethink and accept it. The war is not fought in Oceanian, Eurasian or Eastasian territory but in the Arctic wastes and in a disputed zone comprising the sea and land from Tangiers (Northern Africa) to Darwin (Australia). At the start, Oceania and Eastasia are allies fighting Eurasia in northern Africa and the Malabar Coast.

That alliance ends and Oceania, allied with Eurasia, fights Eastasia, a change occurring on Hate Week, dedicated to creating patriotic fervour for the Party’s perpetual war. The public are blind to the change; in mid-sentence, an orator changes the name of the enemy from “Eurasia” to “Eastasia” without pause. When the public are enraged at noticing that the wrong flags and posters are displayed, they tear them down; the Party later claims to have captured Africa.

Goldstein’s book explains that the purpose of the unwinnable, perpetual war is to consume human labour and commodities so that the economy of a superstate cannot support economic equality, with a high standard of life for every citizen. By using up most of the produced objects like boots and rations, the proles are kept poor and uneducated and will neither realise what the government is doing nor rebel. Goldstein also details an Oceanian strategy of attacking enemy cities with atomic rockets before invasion but dismisses it as unfeasible and contrary to the war’s purpose; despite the atomic bombing of cities in the 1950s, the superstates stopped it for fear that would imbalance the powers. The military technology in the novel differs little from that of World War II, but strategic bomber aeroplanes are replaced with rocket bombs, helicopters were heavily used as weapons of war (they did not figure in World War II in any form but prototypes) and surface combat units have been all but replaced by immense and unsinkable Floating Fortresses, island-like contraptions concentrating the firepower of a whole naval task force in a single, semi-mobile platform (in the novel, one is said to have been anchored between Iceland and the Faroe Islands, suggesting a preference for sea lane interdiction and denial).

The society of Airstrip One and, according to “The Book”, almost the whole world, lives in poverty: hunger, disease and filth are the norms. Ruined cities and towns are common: the consequence of the civil war, the atomic wars and the purportedly enemy (but possibly false flag) rockets. Social decay and wrecked buildings surround Winston; aside from the ministerial pyramids, little of London was rebuilt. Members of the Outer Party consume synthetic foodstuffs and poor-quality “luxuries” such as oily gin and loosely-packed cigarettes, distributed under the “Victory” brand. (That is a parody of the low-quality Indian-made “Victory” cigarettes, widely smoked in Britain and by British soldiers during World War II. They were smoked because it was easier to import them from India than it was to import American cigarettes from across the Atlantic because of the War of the Atlantic.)

Winston describes something as simple as the repair of a broken pane of glass as requiring committee approval that can take several years and so most of those living in one of the blocks usually do the repairs themselves (Winston himself is called in by Mrs. Parsons to repair her blocked sink). All Outer Party residences include telescreens that serve both as outlets for propaganda and to monitor the Party members; they can be turned down, but they cannot be turned off.

In contrast to their subordinates, the Inner Party upper class of Oceanian society reside in clean and comfortable flats in their own quarter of the city, with pantries well-stocked with foodstuffs such as wine, coffee and sugar, all denied to the general populace.[35] Winston is astonished that the lifts in O’Brien’s building work, the telescreens can be switched off and O’Brien has an Asian manservant, Martin. All members of the Inner Party are attended to by slaves captured in the disputed zone, and “The Book” suggests that many have their own motorcars or even helicopters. Nonetheless, “The Book” makes clear that even the conditions enjoyed by the Inner Party are only “relatively” comfortable, and standards would be regarded as austere by those of the prerevolutionary lite.[36]

The proles live in poverty and are kept sedated with alcohol, pornography and a national lottery whose winnings are never actually paid out; that is obscured by propaganda and the lack of communication within Oceania. At the same time, the proles are freer and less intimidated than the middle-class Outer Party: they are subject to certain levels of monitoring but are not expected to be particularly patriotic. They lack telescreens in their own homes and often jeer at the telescreens that they see. “The Book” indicates that is because the middle class, not the lower class, traditionally starts revolutions. The model demands tight control of the middle class, with ambitious Outer-Party members neutralised via promotion to the Inner Party or “reintegration” by the Ministry of Love, and proles can be allowed intellectual freedom because they lack intellect. Winston nonetheless believes that “the future belonged to the proles”.[37]

The standard of living of the populace is low overall. Consumer goods are scarce, and all those available through official channels are of low quality; for instance, despite the Party regularly reporting increased boot production, more than half of the Oceanian populace goes barefoot. The Party claims that poverty is a necessary sacrifice for the war effort, and “The Book” confirms that to be partially correct since the purpose of perpetual war consumes surplus industrial production. Outer Party members and proles occasionally gain access to better items in the market, which deals in goods that were pilfered from the residences of the Inner Party.[citation needed]

Nineteen Eighty-Four expands upon the subjects summarised in Orwell’s essay “Notes on Nationalism”[38] about the lack of vocabulary needed to explain the unrecognised phenomena behind certain political forces. In Nineteen Eighty-Four, the Party’s artificial, minimalist language ‘Newspeak’ addresses the matter.

O’Brien concludes: “The object of persecution is persecution. The object of torture is torture. The object of power is power.”

In the book, Inner Party member O’Brien describes the Party’s vision of the future:

There will be no curiosity, no enjoyment of the process of life. All competing pleasures will be destroyed. But alwaysdo not forget this, Winstonalways there will be the intoxication of power, constantly increasing and constantly growing subtler. Always, at every moment, there will be the thrill of victory, the sensation of trampling on an enemy who is helpless. If you want a picture of the future, imagine a boot stamping on a human faceforever.

Part III, Chapter III, Nineteen Eighty-Four

A major theme of Nineteen Eighty-Four is censorship, especially in the Ministry of Truth, where photographs are modified and public archives rewritten to rid them of “unpersons” (persons who are erased from history by the Party). On the telescreens, figures for all types of production are grossly exaggerated or simply invented to indicate an ever-growing economy, when the reality is the opposite. One small example of the endless censorship is Winston being charged with the task of eliminating a reference to an unperson in a newspaper article. He proceeds to write an article about Comrade Ogilvy, a made-up party member who displayed great heroism by leaping into the sea from a helicopter so that the dispatches he was carrying would not fall into enemy hands.

The inhabitants of Oceania, particularly the Outer Party members, have no real privacy. Many of them live in apartments equipped with two-way telescreens so that they may be watched or listened to at any time. Similar telescreens are found at workstations and in public places, along with hidden microphones. Written correspondence is routinely opened and read by the government before it is delivered. The Thought Police employ undercover agents, who pose as normal citizens and report any person with subversive tendencies. Children are encouraged to report suspicious persons to the government, and some denounce their parents. Citizens are controlled, and the smallest sign of rebellion, even something so small as a facial expression, can result in immediate arrest and imprisonment. Thus, citizens, particularly party members, are compelled to obedience.

“The Principles of Newspeak” is an academic essay appended to the novel. It describes the development of Newspeak, the Party’s minimalist artificial language meant to ideologically align thought and action with the principles of Ingsoc by making “all other modes of thought impossible”. (A linguistic theory about how language may direct thought is the SapirWhorf hypothesis.)

Whether or not the Newspeak appendix implies a hopeful end to Nineteen Eighty-Four remains a critical debate, as it is in Standard English and refers to Newspeak, Ingsoc, the Party etc., in the past tense: “Relative to our own, the Newspeak vocabulary was tiny, and new ways of reducing it were constantly being devised” p.422). Some critics (Atwood,[39] Benstead,[40] Milner,[41] Pynchon[42]) claim that for the essay’s author, both Newspeak and the totalitarian government are in the past.

Nineteen Eighty-Four uses themes from life in the Soviet Union and wartime life in Great Britain as sources for many of its motifs. Some time at an unspecified date after the first American publication of the book, producer Sidney Sheldon wrote to Orwell interested in adapting the novel to the Broadway stage. Orwell sold the American stage rights to Sheldon, explaining that his basic goal with Nineteen Eighty-Four was imagining the consequences of Stalinist government ruling British society:

[Nineteen Eighty-Four] was based chiefly on communism, because that is the dominant form of totalitarianism, but I was trying chiefly to imagine what communism would be like if it were firmly rooted in the English speaking countries, and was no longer a mere extension of the Russian Foreign Office.[43]

The statement “2 + 2 = 5”, used to torment Winston Smith during his interrogation, was a communist party slogan from the second five-year plan, which encouraged fulfillment of the five-year plan in four years. The slogan was seen in electric lights on Moscow house-fronts, billboards and elsewhere.[44]

The switch of Oceania’s allegiance from Eastasia to Eurasia and the subsequent rewriting of history (“Oceania was at war with Eastasia: Oceania had always been at war with Eastasia. A large part of the political literature of five years was now completely obsolete”; ch 9) is evocative of the Soviet Union’s changing relations with Nazi Germany. The two nations were open and frequently vehement critics of each other until the signing of the 1939 Treaty of Non-Aggression. Thereafter, and continuing until the Nazi invasion of the Soviet Union in 1941, no criticism of Germany was allowed in the Soviet press, and all references to prior party lines stoppedincluding in the majority of non-Russian communist parties who tended to follow the Russian line. Orwell had criticised the Communist Party of Great Britain for supporting the Treaty in his essays for Betrayal of the Left (1941). “The Hitler-Stalin pact of August 1939 reversed the Soviet Union’s stated foreign policy. It was too much for many of the fellow-travellers like Gollancz [Orwell’s sometime publisher] who had put their faith in a strategy of construction Popular Front governments and the peace bloc between Russia, Britain and France.”[45]

The description of Emmanuel Goldstein, with a “small, goatee beard”, evokes the image of Leon Trotsky. The film of Goldstein during the Two Minutes Hate is described as showing him being transformed into a bleating sheep. This image was used in a propaganda film during the Kino-eye period of Soviet film, which showed Trotsky transforming into a goat.[46] Goldstein’s book is similar to Trotsky’s highly critical analysis of the USSR, The Revolution Betrayed, published in 1936.

The omnipresent images of Big Brother, a man described as having a moustache, bears resemblance to the cult of personality built up around Joseph Stalin.

The news in Oceania emphasised production figures, just as it did in the Soviet Union, where record-setting in factories (by “Heroes of Socialist Labor”) was especially glorified. The best known of these was Alexey Stakhanov, who purportedly set a record for coal mining in 1935.

The tortures of the Ministry of Love evoke the procedures used by the NKVD in their interrogations,[47] including the use of rubber truncheons, being forbidden to put your hands in your pockets, remaining in brightly lit rooms for days, torture through the use of provoked rodents, and the victim being shown a mirror after their physical collapse.

The random bombing of Airstrip One is based on the Buzz bombs and the V-2 rocket, which struck England at random in 19441945.

The Thought Police is based on the NKVD, which arrested people for random “anti-soviet” remarks.[48] The Thought Crime motif is drawn from Kempeitai, the Japanese wartime secret police, who arrested people for “unpatriotic” thoughts.

The confessions of the “Thought Criminals” Rutherford, Aaronson and Jones are based on the show trials of the 1930s, which included fabricated confessions by prominent Bolsheviks Nikolai Bukharin, Grigory Zinoviev and Lev Kamenev to the effect that they were being paid by the Nazi government to undermine the Soviet regime under Leon Trotsky’s direction.

The song “Under the Spreading Chestnut Tree” (“Under the spreading chestnut tree, I sold you, and you sold me”) was based on an old English song called “Go no more a-rushing” (“Under the spreading chestnut tree, Where I knelt upon my knee, We were as happy as could be, ‘Neath the spreading chestnut tree.”). The song was published as early as 1891. The song was a popular camp song in the 1920s, sung with corresponding movements (like touching your chest when you sing “chest”, and touching your head when you sing “nut”). Glenn Miller recorded the song in 1939.[49]

The “Hates” (Two Minutes Hate and Hate Week) were inspired by the constant rallies sponsored by party organs throughout the Stalinist period. These were often short pep-talks given to workers before their shifts began (Two Minutes Hate), but could also last for days, as in the annual celebrations of the anniversary of the October revolution (Hate Week).

Orwell fictionalized “newspeak”, “doublethink”, and “Ministry of Truth” as evinced by both the Soviet press and that of Nazi Germany.[50] In particular, he adapted Soviet ideological discourse constructed to ensure that public statements could not be questioned.[51]

Winston Smith’s job, “revising history” (and the “unperson” motif) are based on the Stalinist habit of airbrushing images of ‘fallen’ people from group photographs and removing references to them in books and newspapers.[53] In one well-known example, the Soviet encyclopaedia had an article about Lavrentiy Beria. When he fell in 1953, and was subsequently executed, institutes that had the encyclopaedia were sent an article about the Bering Strait, with instructions to paste it over the article about Beria.[54]

Big Brother’s “Orders of the Day” were inspired by Stalin’s regular wartime orders, called by the same name. A small collection of the more political of these have been published (together with his wartime speeches) in English as “On the Great Patriotic War of the Soviet Union” By Joseph Stalin.[55][56] Like Big Brother’s Orders of the day, Stalin’s frequently lauded heroic individuals,[57] like Comrade Ogilvy, the fictitious hero Winston Smith invented to ‘rectify’ (fabricate) a Big Brother Order of the day.

The Ingsoc slogan “Our new, happy life”, repeated from telescreens, evokes Stalin’s 1935 statement, which became a CPSU slogan, “Life has become better, Comrades; life has become more cheerful.”[48]

In 1940 Argentine writer Jorge Luis Borges published Tln, Uqbar, Orbis Tertius which described the invention by a “benevolent secret society” of a world that would seek to remake human language and reality along human-invented lines. The story concludes with an appendix describing the success of the project. Borges’ story addresses similar themes of epistemology, language and history to 1984.[58]

During World War II, Orwell believed that British democracy as it existed before 1939 would not survive the war. The question being “Would it end via Fascist coup d’tat from above or via Socialist revolution from below”?[citation needed] Later, he admitted that events proved him wrong: “What really matters is that I fell into the trap of assuming that ‘the war and the revolution are inseparable’.”[59]

Nineteen Eighty-Four (1949) and Animal Farm (1945) share themes of the betrayed revolution, the person’s subordination to the collective, rigorously enforced class distinctions (Inner Party, Outer Party, Proles), the cult of personality, concentration camps, Thought Police, compulsory regimented daily exercise, and youth leagues. Oceania resulted from the US annexation of the British Empire to counter the Asian peril to Australia and New Zealand. It is a naval power whose militarism venerates the sailors of the floating fortresses, from which battle is given to recapturing India, the “Jewel in the Crown” of the British Empire. Much of Oceanic society is based upon the USSR under Joseph StalinBig Brother. The televised Two Minutes Hate is ritual demonisation of the enemies of the State, especially Emmanuel Goldstein (viz Leon Trotsky). Altered photographs and newspaper articles create unpersons deleted from the national historical record, including even founding members of the regime (Jones, Aaronson and Rutherford) in the 1960s purges (viz the Soviet Purges of the 1930s, in which leaders of the Bolshevik Revolution were similarly treated). A similar thing also happened during the French Revolution in which many of the original leaders of the Revolution were later put to death, for example Danton who was put to death by Robespierre, and then later Robespierre himself met the same fate.

In his 1946 essay “Why I Write”, Orwell explains that the serious works he wrote since the Spanish Civil War (193639) were “written, directly or indirectly, against totalitarianism and for democratic socialism”.[3][60] Nineteen Eighty-Four is a cautionary tale about revolution betrayed by totalitarian defenders previously proposed in Homage to Catalonia (1938) and Animal Farm (1945), while Coming Up for Air (1939) celebrates the personal and political freedoms lost in Nineteen Eighty-Four (1949). Biographer Michael Shelden notes Orwell’s Edwardian childhood at Henley-on-Thames as the golden country; being bullied at St Cyprian’s School as his empathy with victims; his life in the Indian Imperial Police in Burma and the techniques of violence and censorship in the BBC as capricious authority.[61]

Other influences include Darkness at Noon (1940) and The Yogi and the Commissar (1945) by Arthur Koestler; The Iron Heel (1908) by Jack London; 1920: Dips into the Near Future[62] by John A. Hobson; Brave New World (1932) by Aldous Huxley; We (1921) by Yevgeny Zamyatin which he reviewed in 1946;[63] and The Managerial Revolution (1940) by James Burnham predicting perpetual war among three totalitarian superstates. Orwell told Jacintha Buddicom that he would write a novel stylistically like A Modern Utopia (1905) by H. G. Wells.[citation needed]

Extrapolating from World War II, the novel’s pastiche parallels the politics and rhetoric at war’s endthe changed alliances at the “Cold War’s” (194591) beginning; the Ministry of Truth derives from the BBC’s overseas service, controlled by the Ministry of Information; Room 101 derives from a conference room at BBC Broadcasting House;[64] the Senate House of the University of London, containing the Ministry of Information is the architectural inspiration for the Minitrue; the post-war decrepitude derives from the socio-political life of the UK and the US, i.e., the impoverished Britain of 1948 losing its Empire despite newspaper-reported imperial triumph; and war ally but peace-time foe, Soviet Russia became Eurasia.

The term “English Socialism” has precedents in his wartime writings; in the essay “The Lion and the Unicorn: Socialism and the English Genius” (1941), he said that “the war and the revolution are inseparable…the fact that we are at war has turned Socialism from a textbook word into a realisable policy” because Britain’s superannuated social class system hindered the war effort and only a socialist economy would defeat Adolf Hitler. Given the middle class’s grasping this, they too would abide socialist revolution and that only reactionary Britons would oppose it, thus limiting the force revolutionaries would need to take power. An English Socialism would come about which “will never lose touch with the tradition of compromise and the belief in a law that is above the State. It will shoot traitors, but it will give them a solemn trial beforehand and occasionally it will acquit them. It will crush any open revolt promptly and cruelly, but it will interfere very little with the spoken and written word.”[65]

In the world of Nineteen Eighty-Four, “English Socialism”(or “Ingsoc” in Newspeak) is a totalitarian ideology unlike the English revolution he foresaw. Comparison of the wartime essay “The Lion and the Unicorn” with Nineteen Eighty-Four shows that he perceived a Big Brother regime as a perversion of his cherished socialist ideals and English Socialism. Thus Oceania is a corruption of the British Empire he believed would evolve “into a federation of Socialist states, like a looser and freer version of the Union of Soviet Republics”.[66][verification needed]

When first published, Nineteen Eighty-Four was generally well received by reviewers. V. S. Pritchett, reviewing the novel for the New Statesman stated: “I do not think I have ever read a novel more frightening and depressing; and yet, such are the originality, the suspense, the speed of writing and withering indignation that it is impossible to put the book down.”[67] P. H. Newby, reviewing Nineteen Eighty-Four for The Listener magazine, described it as “the most arresting political novel written by an Englishman since Rex Warner’s The Aerodrome.”[68] Nineteen Eighty-Four was also praised by Bertrand Russell, E. M. Forster and Harold Nicolson.[68] On the other hand, Edward Shanks, reviewing Nineteen Eighty-Four for The Sunday Times, was dismissive; Shanks claimed Nineteen Eighty-Four “breaks all records for gloomy vaticination”.[68] C. S. Lewis was also critical of the novel, claiming that the relationship of Julia and Winston, and especially the Party’s view on sex, lacked credibility, and that the setting was “odious rather than tragic”.[69]

Nineteen Eighty-Four has been adapted for the cinema, radio, television and theatre at least twice each, as well as for other art media, such as ballet and opera.

The effect of Nineteen Eighty-Four on the English language is extensive; the concepts of Big Brother, Room 101, the Thought Police, thoughtcrime, unperson, memory hole (oblivion), doublethink (simultaneously holding and believing contradictory beliefs) and Newspeak (ideological language) have become common phrases for denoting totalitarian authority. Doublespeak and groupthink are both deliberate elaborations of doublethink, and the adjective “Orwellian” means similar to Orwell’s writings, especially Nineteen Eighty-Four. The practice of ending words with “-speak” (such as mediaspeak) is drawn from the novel.[70] Orwell is perpetually associated with 1984; in July 1984, an asteroid was discovered by Antonn Mrkos and named after Orwell.

References to the themes, concepts and plot of Nineteen Eighty-Four have appeared frequently in other works, especially in popular music and video entertainment. An example is the worldwide hit reality television show Big Brother, in which a group of people live together in a large house, isolated from the outside world but continuously watched by television cameras.

The book touches on the invasion of privacy and ubiquitous surveillance. From mid-2013 it was publicized that the NSA has been secretly monitoring and storing global internet traffic, including the bulk data collection of email and phone call data. Sales of Nineteen Eighty-Four increased by up to seven times within the first week of the 2013 mass surveillance leaks.[79][80][81] The book again topped the Amazon.com sales charts in 2017 after a controversy involving Kellyanne Conway using the phrase “alternative facts” to explain discrepancies with the media.[82][83][84][85]

The book also shows mass media as a catalyst for the intensification of destructive emotions and violence. Since the 20th century, news and other forms of media have been publicizing violence more often.[86][87] In 2013, the Almeida Theatre and Headlong staged a successful new adaptation (by Robert Icke and Duncan Macmillan), which twice toured the UK and played an extended run in London’s West End. The play opened on Broadway in 2017.

In the decades since the publication of Nineteen Eighty-Four, there have been numerous comparisons to Aldous Huxley’s novel Brave New World, which had been published 17 years earlier, in 1932.[88][89][90][91] They are both predictions of societies dominated by a central government and are both based on extensions of the trends of their times. However, members of the ruling class of Nineteen Eighty-Four use brutal force, torture and mind control to keep individuals in line, but rulers in Brave New World keep the citizens in line by addictive drugs and pleasurable distractions.

In October 1949, after reading Nineteen Eighty-Four, Huxley sent a letter to Orwell and wrote that it would be more efficient for rulers to stay in power by the softer touch by allowing citizens to self-seek pleasure to control them rather than brute force and to allow a false sense of freedom:

Within the next generation I believe that the world’s rulers will discover that infant conditioning and narco-hypnosis are more efficient, as instruments of government, than clubs and prisons, and that the lust for power can be just as completely satisfied by suggesting people into loving their servitude as by flogging and kicking them into obedience.[92]

Elements of both novels can be seen in modern-day societies, with Huxley’s vision being more dominant in the West and Orwell’s vision more prevalent with dictators in ex-communist countries, as is pointed out in essays that compare the two novels, including Huxley’s own Brave New World Revisited.[93][94][95][85]

Comparisons with other dystopian novels like The Handmaid’s Tale, Virtual Light, The Private Eye and Children of Men have also been drawn.[96][97]

The rest is here:

Nineteen Eighty-Four – Wikipedia

War on drugs – Wikipedia

War on Drugs is an American term[6][7] usually applied to the U.S. federal government’s campaign of prohibition of drugs, military aid, and military intervention, with the stated aim being to reduce the illegal drug trade.[8][9] The initiative includes a set of drug policies that are intended to discourage the production, distribution, and consumption of psychoactive drugs that the participating governments and the UN have made illegal. The term was popularized by the media shortly after a press conference given on June 18, 1971, by President Richard Nixonthe day after publication of a special message from President Nixon to the Congress on Drug Abuse Prevention and Controlduring which he declared drug abuse “public enemy number one”. That message to the Congress included text about devoting more federal resources to the “prevention of new addicts, and the rehabilitation of those who are addicted”, but that part did not receive the same public attention as the term “war on drugs”.[10][11][12] However, two years prior to this, Nixon had formally declared a “war on drugs” that would be directed toward eradication, interdiction, and incarceration.[13] Today, the Drug Policy Alliance, which advocates for an end to the War on Drugs, estimates that the United States spends $51 billion annually on these initiatives.[14]

On May 13, 2009, Gil Kerlikowskethe Director of the Office of National Drug Control Policy (ONDCP)signaled that the Obama administration did not plan to significantly alter drug enforcement policy, but also that the administration would not use the term “War on Drugs”, because Kerlikowske considers the term to be “counter-productive”.[15] ONDCP’s view is that “drug addiction is a disease that can be successfully prevented and treated… making drugs more available will make it harder to keep our communities healthy and safe”.[16] One of the alternatives that Kerlikowske has showcased is the drug policy of Sweden, which seeks to balance public health concerns with opposition to drug legalization. The prevalence rates for cocaine use in Sweden are barely one-fifth of those in Spain, the biggest consumer of the drug.[17]

In June 2011, the Global Commission on Drug Policy released a critical report on the War on Drugs, declaring: “The global war on drugs has failed, with devastating consequences for individuals and societies around the world. Fifty years after the initiation of the UN Single Convention on Narcotic Drugs, and years after President Nixon launched the US government’s war on drugs, fundamental reforms in national and global drug control policies are urgently needed.”[18] The report was criticized by organizations that oppose a general legalization of drugs.[16]

The first U.S. law that restricted the distribution and use of certain drugs was the Harrison Narcotics Tax Act of 1914. The first local laws came as early as 1860.[19] In 1919, the United States passed the 18th Amendment, prohibiting the sale, manufacture, and transportation of alcohol, with exceptions for religious and medical use. In 1920, the United States passed the National Prohibition Act (Volstead Act), enacted to carry out the provisions in law of the 18th Amendment.

The Federal Bureau of Narcotics was established in the United States Department of the Treasury by an act of June 14, 1930 (46 Stat. 585).[20] In 1933, the federal prohibition for alcohol was repealed by passage of the 21st Amendment. In 1935, President Franklin D. Roosevelt publicly supported the adoption of the Uniform State Narcotic Drug Act. The New York Times used the headline “Roosevelt Asks Narcotic War Aid”.[21][22]

In 1937, the Marihuana Tax Act of 1937 was passed. Several scholars have claimed that the goal was to destroy the hemp industry,[23][24][25] largely as an effort of businessmen Andrew Mellon, Randolph Hearst, and the Du Pont family.[23][25] These scholars argue that with the invention of the decorticator, hemp became a very cheap substitute for the paper pulp that was used in the newspaper industry.[23][26] These scholars believe that Hearst felt[dubious discuss] that this was a threat to his extensive timber holdings. Mellon, United States Secretary of the Treasury and the wealthiest man in America, had invested heavily in the DuPont’s new synthetic fiber, nylon, and considered[dubious discuss] its success to depend on its replacement of the traditional resource, hemp.[23][27][28][29][30][31][32][33] However, there were circumstances that contradict these claims. One reason for doubts about those claims is that the new decorticators did not perform fully satisfactorily in commercial production.[34] To produce fiber from hemp was a labor-intensive process if you include harvest, transport and processing. Technological developments decreased the labor with hemp but not sufficient to eliminate this disadvantage.[35][36]

On October 27, 1970, Congress passes the Comprehensive Drug Abuse Prevention and Control Act of 1970, which, among other things, categorizes controlled substances based on their medicinal use and potential for addiction.[37] In 1971, two congressmen released an explosive report on the growing heroin epidemic among U.S. servicemen in Vietnam; ten to fifteen percent of the servicemen were addicted to heroin, and President Nixon declared drug abuse to be “public enemy number one”.[37][38]

Although Nixon declared “drug abuse” to be public enemy number one in 1971,[39] the policies that his administration implemented as part of the Comprehensive Drug Abuse Prevention and Control Act of 1970 were a continuation of drug prohibition policies in the U.S., which started in 1914.[37][40]

“The Nixon campaign in 1968, and the Nixon White House after that, had two enemies: the antiwar left and black people. You understand what I’m saying? We knew we couldn’t make it illegal to be either against the war or black, but by getting the public to associate the hippies with marijuana and blacks with heroin, and then criminalizing both heavily, we could disrupt those communities. We could arrest their leaders, raid their homes, break up their meetings, and vilify them night after night on the evening news. Did we know we were lying about the drugs? Of course we did.” John Ehrlichman, to Dan Baum[41][42][43] for Harper’s Magazine[44] in 1994, about President Richard Nixon’s war on drugs, declared in 1971.[45][46]

In 1973, the Drug Enforcement Administration was created to replace the Bureau of Narcotics and Dangerous Drugs.[37]

The Nixon Administration also repealed the federal 210-year mandatory minimum sentences for possession of marijuana and started federal demand reduction programs and drug-treatment programs. Robert DuPont, the “Drug czar” in the Nixon Administration, stated it would be more accurate to say that Nixon ended, rather than launched, the “war on drugs”. DuPont also argued that it was the proponents of drug legalization that popularized the term “war on drugs”.[16][unreliable source?]

In 1982, Vice President George H. W. Bush and his aides began pushing for the involvement of the CIA and U.S. military in drug interdiction efforts.[47]

The Office of National Drug Control Policy (ONDCP) was originally established by the National Narcotics Leadership Act of 1988,[48][49] which mandated a national anti-drug media campaign for youth, which would later become the National Youth Anti-Drug Media Campaign.[50] The director of ONDCP is commonly known as the Drug czar,[37] and it was first implemented in 1989 under President George H. W. Bush,[51] and raised to cabinet-level status by Bill Clinton in 1993.[52] These activities were subsequently funded by the Treasury and General Government Appropriations Act of 1998.[53][54] The Drug-Free Media Campaign Act of 1998 codified the campaign at 21 U.S.C.1708.[55]

The Global Commission on Drug Policy released a report on June 2, 2011, alleging that “The War On Drugs Has Failed.” The commissioned was made up of 22 self-appointed members including a number of prominent international politicians and writers. U.S. Surgeon General Regina Benjamin also released the first ever National Prevention Strategy.[56]

On May 21, 2012, the U.S. Government published an updated version of its Drug Policy.[57] The director of ONDCP stated simultaneously that this policy is something different from the “War on Drugs”:

At the same meeting was a declaration signed by the representatives of Italy, the Russian Federation, Sweden, the United Kingdom and the United States in line with this: “Our approach must be a balanced one, combining effective enforcement to restrict the supply of drugs, with efforts to reduce demand and build recovery; supporting people to live a life free of addiction.”[59]

In March 2016 the International Narcotics Control Board stated that the International Drug Control treaties do not mandate a “war on drugs.”[60]

According to Human Rights Watch, the War on Drugs caused soaring arrest rates that disproportionately targeted African Americans due to various factors.[62] John Ehrlichman, an aide to Nixon, said that Nixon used the war on drugs to criminalize and disrupt black and hippie communities and their leaders.[63]

The present state of incarceration in the U.S. as a result of the war on drugs arrived in several stages. By 1971, different stops on drugs had been implemented for more than 50 years (for e.g. since 1914, 1937 etc.) with only a very small increase of inmates per 100,000 citizens. During the first 9 years after Nixon coined the expression “War on Drugs”, statistics showed only a minor increase in the total number of imprisoned.

After 1980, the situation began to change. In the 1980s, while the number of arrests for all crimes had risen by 28%, the number of arrests for drug offenses rose 126%.[64] The result of increased demand was the development of privatization and the for-profit prison industry.[65] The US Department of Justice, reporting on the effects of state initiatives, has stated that, from 1990 through 2000, “the increasing number of drug offenses accounted for 27% of the total growth among black inmates, 7% of the total growth among Hispanic inmates, and 15% of the growth among white inmates.” In addition to prison or jail, the United States provides for the deportation of many non-citizens convicted of drug offenses.[66]

In 1994, the New England Journal of Medicine reported that the “War on Drugs” resulted in the incarceration of one million Americans each year.[67] In 2008, the Washington Post reported that of 1.5 million Americans arrested each year for drug offenses, half a million would be incarcerated.[68] In addition, one in five black Americans would spend time behind bars due to drug laws.[68]

Federal and state policies also impose collateral consequences on those convicted of drug offenses, such as denial of public benefits or licenses, that are not applicable to those convicted of other types of crime.[69] In particular, the passage of the 1990 SolomonLautenberg amendment led many states to impose mandatory driver’s license suspensions (of at least 6 months) for persons committing a drug offense, regardless of whether any motor vehicle was involved.[70][71] Approximately 191,000 licenses were suspended in this manner in 2016, according to a Prison Policy Initiative report.[72]

In 1986, the U.S. Congress passed laws that created a 100 to 1 sentencing disparity for the trafficking or possession of crack when compared to penalties for trafficking of powder cocaine,[73][74][75][76] which had been widely criticized as discriminatory against minorities, mostly blacks, who were more likely to use crack than powder cocaine.[77] This 100:1 ratio had been required under federal law since 1986.[78] Persons convicted in federal court of possession of 5grams of crack cocaine received a minimum mandatory sentence of 5 years in federal prison. On the other hand, possession of 500grams of powder cocaine carries the same sentence.[74][75] In 2010, the Fair Sentencing Act cut the sentencing disparity to 18:1.[77]

According to Human Rights Watch, crime statistics show thatin the United States in 1999compared to non-minorities, African Americans were far more likely to be arrested for drug crimes, and received much stiffer penalties and sentences.[79]

Statistics from 1998 show that there were wide racial disparities in arrests, prosecutions, sentencing and deaths. African-American drug users made up for 35% of drug arrests, 55% of convictions, and 74% of people sent to prison for drug possession crimes.[74] Nationwide African-Americans were sent to state prisons for drug offenses 13 times more often than other races,[80] even though they only supposedly comprised 13% of regular drug users.[74]

Anti-drug legislation over time has also displayed an apparent racial bias. University of Minnesota Professor and social justice author Michael Tonry writes, “The War on Drugs foreseeably and unnecessarily blighted the lives of hundreds and thousands of young disadvantaged black Americans and undermined decades of effort to improve the life chances of members of the urban black underclass.”[81]

In 1968, President Lyndon B. Johnson decided that the government needed to make an effort to curtail the social unrest that blanketed the country at the time. He decided to focus his efforts on illegal drug use, an approach which was in line with expert opinion on the subject at the time. In the 1960s, it was believed that at least half of the crime in the U.S. was drug related, and this number grew as high as 90 percent in the next decade.[82] He created the Reorganization Plan of 1968 which merged the Bureau of Narcotics and the Bureau of Drug Abuse to form the Bureau of Narcotics and Dangerous Drugs within the Department of Justice.[83] The belief during this time about drug use was summarized by journalist Max Lerner in his celebrated[citation needed] work America as a Civilization (1957):

As a case in point we may take the known fact of the prevalence of reefer and dope addiction in Negro areas. This is essentially explained in terms of poverty, slum living, and broken families, yet it would be easy to show the lack of drug addiction among other ethnic groups where the same conditions apply.[84]

Richard Nixon became president in 1969, and did not back away from the anti-drug precedent set by Johnson. Nixon began orchestrating drug raids nationwide to improve his “watchdog” reputation. Lois B. Defleur, a social historian who studied drug arrests during this period in Chicago, stated that, “police administrators indicated they were making the kind of arrests the public wanted”. Additionally, some of Nixon’s newly created drug enforcement agencies would resort to illegal practices to make arrests as they tried to meet public demand for arrest numbers. From 1972 to 1973, the Office of Drug Abuse and Law Enforcement performed 6,000 drug arrests in 18 months, the majority of the arrested black.[85]

The next two Presidents, Gerald Ford and Jimmy Carter, responded with programs that were essentially a continuation of their predecessors. Shortly after Ronald Reagan became President in 1981 he delivered a speech on the topic. Reagan announced, “We’re taking down the surrender flag that has flown over so many drug efforts; we’re running up a battle flag.”[86] For his first five years in office, Reagan slowly strengthened drug enforcement by creating mandatory minimum sentencing and forfeiture of cash and real estate for drug offenses, policies far more detrimental to poor blacks than any other sector affected by the new laws.[citation needed]

Then, driven by the 1986 cocaine overdose of black basketball star Len Bias,[dubious discuss] Reagan was able to pass the Anti-Drug Abuse Act through Congress. This legislation appropriated an additional $1.7 billion to fund the War on Drugs. More importantly, it established 29 new, mandatory minimum sentences for drug offenses. In the entire history of the country up until that point, the legal system had only seen 55 minimum sentences in total.[87] A major stipulation of the new sentencing rules included different mandatory minimums for powder and crack cocaine. At the time of the bill, there was public debate as to the difference in potency and effect of powder cocaine, generally used by whites, and crack cocaine, generally used by blacks, with many believing that “crack” was substantially more powerful and addictive. Crack and powder cocaine are closely related chemicals, crack being a smokeable, freebase form of powdered cocaine hydrochloride which produces a shorter, more intense high while using less of the drug. This method is more cost effective, and therefore more prevalent on the inner-city streets, while powder cocaine remains more popular in white suburbia. The Reagan administration began shoring public opinion against “crack”, encouraging DEA official Robert Putnam to play up the harmful effects of the drug. Stories of “crack whores” and “crack babies” became commonplace; by 1986, Time had declared “crack” the issue of the year.[88] Riding the wave of public fervor, Reagan established much harsher sentencing for crack cocaine, handing down stiffer felony penalties for much smaller amounts of the drug.[89]

Reagan protg and former Vice-President George H. W. Bush was next to occupy the oval office, and the drug policy under his watch held true to his political background. Bush maintained the hard line drawn by his predecessor and former boss, increasing narcotics regulation when the First National Drug Control Strategy was issued by the Office of National Drug Control in 1989.[90]

The next three presidents Clinton, Bush and Obama continued this trend, maintaining the War on Drugs as they inherited it upon taking office.[91] During this time of passivity by the federal government, it was the states that initiated controversial legislation in the War on Drugs. Racial bias manifested itself in the states through such controversial policies as the “stop and frisk” police practices in New York city and the “three strikes” felony laws began in California in 1994.[92]

In August 2010, President Obama signed the Fair Sentencing Act into law that dramatically reduced the 100-to-1 sentencing disparity between powder and crack cocaine, which disproportionately affected minorities.[93]

Commonly used illegal drugs include heroin, cocaine, methamphetamine, and, marijuana.

Heroin is an opiate that is highly addictive. If caught selling or possessing heroin, a perpetrator can be charged with a felony and face twofour years in prison and could be fined to a maximum of $20,000.[94]

Crystal meth is composed of methamphetamine hydrochloride. It is marketed as either a white powder or in a solid (rock) form. The possession of crystal meth can result in a punishment varying from a fine to a jail sentence. As with other drug crimes, sentencing length may increase depending on the amount of the drug found in the possession of the defendant.[95]

Cocaine possession is illegal across the U.S., with the cheaper crack cocaine incurring even greater penalties. Having possession is when the accused knowingly has it on their person, or in a backpack or purse. The possession of cocaine with no prior conviction, for the first offense, the person will be sentenced to a maximum of one year in prison or fined $1,000, or both. If the person has a prior conviction, whether it is a narcotic or cocaine, they will be sentenced to two years in prison, a $2,500 fine, or both. With two or more convictions of possession prior to this present offense, they can be sentenced to 90 days in prison along with a $5,000 fine.[96]

Marijuana is the most popular illegal drug worldwide. The punishment for possession of it is less than for the possession of cocaine or heroin. In some U.S. states, the drug is legal. Over 80 million Americans have tried marijuana. The Criminal Defense Lawyer article claims that, depending on the age of person and how much the person has been caught for possession, they will be fined and could plea bargain into going to a treatment program versus going to prison. In each state the convictions differ along with how much marijuana they have on their person.[97]

Some scholars have claimed that the phrase “War on Drugs” is propaganda cloaking an extension of earlier military or paramilitary operations.[9] Others have argued that large amounts of “drug war” foreign aid money, training, and equipment actually goes to fighting leftist insurgencies and is often provided to groups who themselves are involved in large-scale narco-trafficking, such as corrupt members of the Colombian military.[8]

From 1963 to the end of the Vietnam War in 1975, marijuana usage became common among U.S. soldiers in non-combat situations. Some servicemen also used heroin. Many of the servicemen ended the heroin use after returning to the United States but came home addicted. In 1971, the U.S. military conducted a study of drug use among American servicemen and women. It found that daily usage rates for drugs on a worldwide basis were as low as two percent.[98] However, in the spring of 1971, two congressmen released an alarming report alleging that 15% of the servicemen in Vietnam were addicted to heroin. Marijuana use was also common in Vietnam. Soldiers who used drugs had more disciplinary problems. The frequent drug use had become an issue for the commanders in Vietnam; in 1971 it was estimated that 30,000 servicemen were addicted to drugs, most of them to heroin.[11]

From 1971 on, therefore, returning servicemen were required to take a mandatory heroin test. Servicemen who tested positive upon returning from Vietnam were not allowed to return home until they had passed the test with a negative result. The program also offered a treatment for heroin addicts.[99]

Elliot Borin’s article “The U.S. Military Needs its Speed”published in Wired on February 10, 2003reports:

But the Defense Department, which distributed millions of amphetamine tablets to troops during World War II, Vietnam and the Gulf War, soldiers on, insisting that they are not only harmless but beneficial.

In a news conference held in connection with Schmidt and Umbach’s Article 32 hearing, Dr. Pete Demitry, an Air Force physician and a pilot, claimed that the “Air Force has used (Dexedrine) safely for 60 years” with “no known speed-related mishaps.”

The need for speed, Demitry added “is a life-and-death issue for our military.”[100]

One of the first anti-drug efforts in the realm of foreign policy was President Nixon’s Operation Intercept, announced in September 1969, targeted at reducing the amount of cannabis entering the United States from Mexico. The effort began with an intense inspection crackdown that resulted in an almost shutdown of cross-border traffic.[101] Because the burden on border crossings was controversial in border states, the effort only lasted twenty days.[102]

On December 20, 1989, the United States invaded Panama as part of Operation Just Cause, which involved 25,000 American troops. Gen. Manuel Noriega, head of the government of Panama, had been giving military assistance to Contra groups in Nicaragua at the request of the U.S. which, in exchange, tolerated his drug trafficking activities, which they had known about since the 1960s.[103][104] When the Drug Enforcement Administration (DEA) tried to indict Noriega in 1971, the CIA prevented them from doing so.[103] The CIA, which was then directed by future president George H. W. Bush, provided Noriega with hundreds of thousands of dollars per year as payment for his work in Latin America.[103] When CIA pilot Eugene Hasenfus was shot down over Nicaragua by the Sandinistas, documents aboard the plane revealed many of the CIA’s activities in Latin America, and the CIA’s connections with Noriega became a public relations “liability” for the U.S. government, which finally allowed the DEA to indict him for drug trafficking, after decades of tolerating his drug operations.[103] Operation Just Cause, whose purpose was to capture Noriega and overthrow his government; Noriega found temporary asylum in the Papal Nuncio, and surrendered to U.S. soldiers on January 3, 1990.[105] He was sentenced by a court in Miami to 45 years in prison.[103]

As part of its Plan Colombia program, the United States government currently provides hundreds of millions of dollars per year of military aid, training, and equipment to Colombia,[106] to fight left-wing guerrillas such as the Revolutionary Armed Forces of Colombia (FARC-EP), which has been accused of being involved in drug trafficking.[107]

Private U.S. corporations have signed contracts to carry out anti-drug activities as part of Plan Colombia. DynCorp, the largest private company involved, was among those contracted by the State Department, while others signed contracts with the Defense Department.[108]

Colombian military personnel have received extensive counterinsurgency training from U.S. military and law enforcement agencies, including the School of Americas (SOA). Author Grace Livingstone has stated that more Colombian SOA graduates have been implicated in human rights abuses than currently known SOA graduates from any other country. All of the commanders of the brigades highlighted in a 2001 Human Rights Watch report on Colombia were graduates of the SOA, including the III brigade in Valle del Cauca, where the 2001 Alto Naya Massacre occurred. US-trained officers have been accused of being directly or indirectly involved in many atrocities during the 1990s, including the Massacre of Trujillo and the 1997 Mapiripn Massacre.

In 2000, the Clinton administration initially waived all but one of the human rights conditions attached to Plan Colombia, considering such aid as crucial to national security at the time.[109]

The efforts of U.S. and Colombian governments have been criticized for focusing on fighting leftist guerrillas in southern regions without applying enough pressure on right-wing paramilitaries and continuing drug smuggling operations in the north of the country.[110][111] Human Rights Watch, congressional committees and other entities have documented the existence of connections between members of the Colombian military and the AUC, which the U.S. government has listed as a terrorist group, and that Colombian military personnel have committed human rights abuses which would make them ineligible for U.S. aid under current laws.[citation needed]

In 2010, the Washington Office on Latin America concluded that both Plan Colombia and the Colombian government’s security strategy “came at a high cost in lives and resources, only did part of the job, are yielding diminishing returns and have left important institutions weaker.”[112]

A 2014 report by the RAND Corporation, which was issued to analyze viable strategies for the Mexican drug war considering successes experienced in Columbia, noted:

Between 1999 and 2002, the United States gave Colombia $2.04 billion in aid, 81 percent of which was for military purposes, placing Colombia just below Israel and Egypt among the largest recipients of U.S. military assistance. Colombia increased its defense spending from 3.2 percent of gross domestic product (GDP) in 2000 to 4.19 percent in 2005. Overall, the results were extremely positive. Greater spending on infrastructure and social programs helped the Colombian government increase its political legitimacy, while improved security forces were better able to consolidate control over large swaths of the country previously overrun by insurgents and drug cartels.

It also notes that, “Plan Colombia has been widely hailed as a success, and some analysts believe that, by 2010, Colombian security forces had finally gained the upper hand once and for all.”[113]

The Mrida Initiative is a security cooperation between the United States and the government of Mexico and the countries of Central America. It was approved on June 30, 2008, and its stated aim is combating the threats of drug trafficking and transnational crime. The Mrida Initiative appropriated $1.4 billion in a three-year commitment (20082010) to the Mexican government for military and law enforcement training and equipment, as well as technical advice and training to strengthen the national justice systems. The Mrida Initiative targeted many very important government officials, but it failed to address the thousands of Central Americans who had to flee their countries due to the danger they faced everyday because of the war on drugs. There is still not any type of plan that addresses these people. No weapons are included in the plan.[114][115]

The United States regularly sponsors the spraying of large amounts of herbicides such as glyphosate over the jungles of Central and South America as part of its drug eradication programs. Environmental consequences resulting from aerial fumigation have been criticized as detrimental to some of the world’s most fragile ecosystems;[116] the same aerial fumigation practices are further credited with causing health problems in local populations.[117]

In 2012, the U.S. sent DEA agents to Honduras to assist security forces in counternarcotics operations. Honduras has been a major stop for drug traffickers, who use small planes and landing strips hidden throughout the country to transport drugs. The U.S. government made agreements with several Latin American countries to share intelligence and resources to counter the drug trade. DEA agents, working with other U.S. agencies such as the State Department, the CBP, and Joint Task Force-Bravo, assisted Honduras troops in conducting raids on traffickers’ sites of operation.[118]

The War on Drugs has been a highly contentious issue since its inception. A poll on October 2, 2008, found that three in four Americans believed that the War On Drugs was failing.[119]

At a meeting in Guatemala in 2012, three former presidents from Guatemala, Mexico and Colombia said that the war on drugs had failed and that they would propose a discussion on alternatives, including decriminalization, at the Summit of the Americas in April of that year.[120] Guatemalan President Otto Prez Molina said that the war on drugs was exacting too high a price on the lives of Central Americans and that it was time to “end the taboo on discussing decriminalization”.[121] At the summit, the government of Colombia pushed for the most far-reaching change to drugs policy since the war on narcotics was declared by Nixon four decades prior, citing the catastrophic effects it had had in Colombia.[122]

Several critics have compared the wholesale incarceration of the dissenting minority of drug users to the wholesale incarceration of other minorities in history. Psychiatrist Thomas Szasz, for example, writes in 1997 “Over the past thirty years, we have replaced the medical-political persecution of illegal sex users (‘perverts’ and ‘psychopaths’) with the even more ferocious medical-political persecution of illegal drug users.”[123]

Penalties for drug crimes among American youth almost always involve permanent or semi-permanent removal from opportunities for education, strip them of voting rights, and later involve creation of criminal records which make employment more difficult.[124] Thus, some authors maintain that the War on Drugs has resulted in the creation of a permanent underclass of people who have few educational or job opportunities, often as a result of being punished for drug offenses which in turn have resulted from attempts to earn a living in spite of having no education or job opportunities.[124]

According to a 2008 study published by Harvard economist Jeffrey A. Miron, the annual savings on enforcement and incarceration costs from the legalization of drugs would amount to roughly $41.3 billion, with $25.7 billion being saved among the states and over $15.6 billion accrued for the federal government. Miron further estimated at least $46.7 billion in tax revenue based on rates comparable to those on tobacco and alcohol ($8.7 billion from marijuana, $32.6 billion from cocaine and heroin, remainder from other drugs).[125]

Low taxation in Central American countries has been credited with weakening the region’s response in dealing with drug traffickers. Many cartels, especially Los Zetas have taken advantage of the limited resources of these nations. 2010 tax revenue in El Salvador, Guatemala, and Honduras, composed just 13.53% of GDP. As a comparison, in Chile and the U.S., taxes were 18.6% and 26.9% of GDP respectively. However, direct taxes on income are very hard to enforce and in some cases tax evasion is seen as a national pastime.[126]

The status of coca and coca growers has become an intense political issue in several countries, including Colombia and particularly Bolivia, where the president, Evo Morales, a former coca growers’ union leader, has promised to legalise the traditional cultivation and use of coca.[127] Indeed, legalization efforts have yielded some successes under the Morales administration when combined with aggressive and targeted eradication efforts. The country saw a 1213% decline in coca cultivation[127] in 2011 under Morales, who has used coca growers’ federations to ensure compliance with the law rather than providing a primary role for security forces.[127]

The coca eradication policy has been criticised for its negative impact on the livelihood of coca growers in South America. In many areas of South America the coca leaf has traditionally been chewed and used in tea and for religious, medicinal and nutritional purposes by locals.[128] For this reason many insist that the illegality of traditional coca cultivation is unjust. In many areas the U.S. government and military has forced the eradication of coca without providing for any meaningful alternative crop for farmers, and has additionally destroyed many of their food or market crops, leaving them starving and destitute.[128]

The CIA, DEA, State Department, and several other U.S. government agencies have been alleged to have relations with various groups which are involved in drug trafficking.

Senator John Kerry’s 1988 U.S. Senate Committee on Foreign Relations report on Contra drug links concludes that members of the U.S. State Department “who provided support for the Contras are involved in drug trafficking… and elements of the Contras themselves knowingly receive financial and material assistance from drug traffickers.”[129] The report further states that “the Contra drug links include… payments to drug traffickers by the U.S. State Department of funds authorized by the Congress for humanitarian assistance to the Contras, in some cases after the traffickers had been indicted by federal law enforcement agencies on drug charges, in others while traffickers were under active investigation by these same agencies.”

In 1996, journalist Gary Webb published reports in the San Jose Mercury News, and later in his book Dark Alliance, detailing how Contras, had been involved in distributing crack cocaine into Los Angeles whilst receiving money from the CIA.[citation needed] Contras used money from drug trafficking to buy weapons.[citation needed]

Webb’s premise regarding the U.S. Government connection was initially attacked at the time by the media. It is now widely accepted that Webb’s main assertion of government “knowledge of drug operations, and collaboration with and protection of known drug traffickers” was correct.[130][not in citation given] In 1998, CIA Inspector General Frederick Hitz published a two-volume report[131] that while seemingly refuting Webb’s claims of knowledge and collaboration in its conclusions did not deny them in its body.[citation needed] Hitz went on to admit CIA improprieties in the affair in testimony to a House congressional committee. There has been a reversal amongst mainstream media of its position on Webb’s work, with acknowledgement made of his contribution to exposing a scandal it had ignored.

According to Rodney Campbell, an editorial assistant to Nelson Rockefeller, during World War II, the United States Navy, concerned that strikes and labor disputes in U.S. eastern shipping ports would disrupt wartime logistics, released the mobster Lucky Luciano from prison, and collaborated with him to help the mafia take control of those ports. Labor union members were terrorized and murdered by mafia members as a means of preventing labor unrest and ensuring smooth shipping of supplies to Europe.[132]

According to Alexander Cockburn and Jeffrey St. Clair, in order to prevent Communist party members from being elected in Italy following World War II, the CIA worked closely with the Sicilian Mafia, protecting them and assisting in their worldwide heroin smuggling operations. The mafia was in conflict with leftist groups and was involved in assassinating, torturing, and beating leftist political organizers.[133]

In 1986, the US Defense Department funded a two-year study by the RAND Corporation, which found that the use of the armed forces to interdict drugs coming into the United States would have little or no effect on cocaine traffic and might, in fact, raise the profits of cocaine cartels and manufacturers. The 175-page study, “Sealing the Borders: The Effects of Increased Military Participation in Drug Interdiction”, was prepared by seven researchers, mathematicians and economists at the National Defense Research Institute, a branch of the RAND, and was released in 1988. The study noted that seven prior studies in the past nine years, including one by the Center for Naval Research and the Office of Technology Assessment, had come to similar conclusions. Interdiction efforts, using current armed forces resources, would have almost no effect on cocaine importation into the United States, the report concluded.[135]

During the early-to-mid-1990s, the Clinton administration ordered and funded a major cocaine policy study, again by RAND. The Rand Drug Policy Research Center study concluded that $3 billion should be switched from federal and local law enforcement to treatment. The report said that treatment is the cheapest way to cut drug use, stating that drug treatment is twenty-three times more effective than the supply-side “war on drugs”.[136]

The National Research Council Committee on Data and Research for Policy on Illegal Drugs published its findings in 2001 on the efficacy of the drug war. The NRC Committee found that existing studies on efforts to address drug usage and smuggling, from U.S. military operations to eradicate coca fields in Colombia, to domestic drug treatment centers, have all been inconclusive, if the programs have been evaluated at all: “The existing drug-use monitoring systems are strikingly inadequate to support the full range of policy decisions that the nation must make…. It is unconscionable for this country to continue to carry out a public policy of this magnitude and cost without any way of knowing whether and to what extent it is having the desired effect.”[137] The study, though not ignored by the press, was ignored by top-level policymakers, leading Committee Chair Charles Manski to conclude, as one observer notes, that “the drug war has no interest in its own results”.[138]

In mid-1995, the US government tried to reduce the supply of methamphetamine precursors to disrupt the market of this drug. According to a 2009 study, this effort was successful, but its effects were largely temporary.[139]

During alcohol prohibition, the period from 1920 to 1933, alcohol use initially fell but began to increase as early as 1922. It has been extrapolated that even if prohibition had not been repealed in 1933, alcohol consumption would have quickly surpassed pre-prohibition levels.[140] One argument against the War on Drugs is that it uses similar measures as Prohibition and is no more effective.

In the six years from 2000 to 2006, the U.S. spent $4.7 billion on Plan Colombia, an effort to eradicate coca production in Colombia. The main result of this effort was to shift coca production into more remote areas and force other forms of adaptation. The overall acreage cultivated for coca in Colombia at the end of the six years was found to be the same, after the U.S. Drug Czar’s office announced a change in measuring methodology in 2005 and included new areas in its surveys.[141] Cultivation in the neighboring countries of Peru and Bolivia increased, some would describe this effect like squeezing a balloon.[142]

Richard Davenport-Hines, in his book The Pursuit of Oblivion,[143] criticized the efficacy of the War on Drugs by pointing out that

1015% of illicit heroin and 30% of illicit cocaine is intercepted. Drug traffickers have gross profit margins of up to 300%. At least 75% of illicit drug shipments would have to be intercepted before the traffickers’ profits were hurt.

Alberto Fujimori, president of Peru from 1990 to 2000, described U.S. foreign drug policy as “failed” on grounds that “for 10 years, there has been a considerable sum invested by the Peruvian government and another sum on the part of the American government, and this has not led to a reduction in the supply of coca leaf offered for sale. Rather, in the 10 years from 1980 to 1990, it grew 10-fold.”[144]

At least 500 economists, including Nobel Laureates Milton Friedman,[145] George Akerlof and Vernon L. Smith, have noted that reducing the supply of marijuana without reducing the demand causes the price, and hence the profits of marijuana sellers, to go up, according to the laws of supply and demand.[146] The increased profits encourage the producers to produce more drugs despite the risks, providing a theoretical explanation for why attacks on drug supply have failed to have any lasting effect. The aforementioned economists published an open letter to President George W. Bush stating “We urge…the country to commence an open and honest debate about marijuana prohibition… At a minimum, this debate will force advocates of current policy to show that prohibition has benefits sufficient to justify the cost to taxpayers, foregone tax revenues and numerous ancillary consequences that result from marijuana prohibition.”

The declaration from the World Forum Against Drugs, 2008 state that a balanced policy of drug abuse prevention, education, treatment, law enforcement, research, and supply reduction provides the most effective platform to reduce drug abuse and its associated harms and call on governments to consider demand reduction as one of their first priorities in the fight against drug abuse.[147]

Despite over $7 billion spent annually towards arresting[148] and prosecuting nearly 800,000 people across the country for marijuana offenses in 2005[citation needed] (FBI Uniform Crime Reports), the federally funded Monitoring the Future Survey reports about 85% of high school seniors find marijuana “easy to obtain”. That figure has remained virtually unchanged since 1975, never dropping below 82.7% in three decades of national surveys.[149] The Drug Enforcement Administration states that the number of users of marijuana in the U.S. declined between 2000 and 2005 even with many states passing new medical marijuana laws making access easier,[150] though usage rates remain higher than they were in the 1990s according to the National Survey on Drug Use and Health.[151]

ONDCP stated in April 2011 that there has been a 46 percent drop in cocaine use among young adults over the past five years, and a 65 percent drop in the rate of people testing positive for cocaine in the workplace since 2006.[152] At the same time, a 2007 study found that up to 35% of college undergraduates used stimulants not prescribed to them.[153]

A 2013 study found that prices of heroin, cocaine and cannabis had decreased from 1990 to 2007, but the purity of these drugs had increased during the same time.[154]

The War on Drugs is often called a policy failure.[155][156][157][158][159]

The legality of the War on Drugs has been challenged on four main grounds in the U.S.

Several authors believe that the United States’ federal and state governments have chosen wrong methods for combatting the distribution of illicit substances. Aggressive, heavy-handed enforcement funnels individuals through courts and prisons; instead of treating the cause of the addiction, the focus of government efforts has been on punishment. By making drugs illegal rather than regulating them, the War on Drugs creates a highly profitable black market. Jefferson Fish has edited scholarly collections of articles offering a wide variety of public health based and rights based alternative drug policies.[160][161][162]

In the year 2000, the United States drug-control budget reached 18.4 billion dollars,[163] nearly half of which was spent financing law enforcement while only one sixth was spent on treatment. In the year 2003, 53 percent of the requested drug control budget was for enforcement, 29 percent for treatment, and 18 percent for prevention.[164] The state of New York, in particular, designated 17 percent of its budget towards substance-abuse-related spending. Of that, a mere one percent was put towards prevention, treatment, and research.

In a survey taken by Substance Abuse and Mental Health Services Administration (SAMHSA), it was found that substance abusers that remain in treatment longer are less likely to resume their former drug habits. Of the people that were studied, 66 percent were cocaine users. After experiencing long-term in-patient treatment, only 22 percent returned to the use of cocaine. Treatment had reduced the number of cocaine abusers by two-thirds.[163] By spending the majority of its money on law enforcement, the federal government had underestimated the true value of drug-treatment facilities and their benefit towards reducing the number of addicts in the U.S.

In 2004 the federal government issued the National Drug Control Strategy. It supported programs designed to expand treatment options, enhance treatment delivery, and improve treatment outcomes. For example, the Strategy provided SAMHSA with a $100.6 million grant to put towards their Access to Recovery (ATR) initiative. ATR is a program that provides vouchers to addicts to provide them with the means to acquire clinical treatment or recovery support. The project’s goals are to expand capacity, support client choice, and increase the array of faith-based and community based providers for clinical treatment and recovery support services.[165] The ATR program will also provide a more flexible array of services based on the individual’s treatment needs.

The 2004 Strategy additionally declared a significant 32 million dollar raise in the Drug Courts Program, which provides drug offenders with alternatives to incarceration. As a substitute for imprisonment, drug courts identify substance-abusing offenders and place them under strict court monitoring and community supervision, as well as provide them with long-term treatment services.[166] According to a report issued by the National Drug Court Institute, drug courts have a wide array of benefits, with only 16.4 percent of the nation’s drug court graduates rearrested and charged with a felony within one year of completing the program (versus the 44.1% of released prisoners who end up back in prison within 1-year). Additionally, enrolling an addict in a drug court program costs much less than incarcerating one in prison.[167] According to the Bureau of Prisons, the fee to cover the average cost of incarceration for Federal inmates in 2006 was $24,440.[168] The annual cost of receiving treatment in a drug court program ranges from $900 to $3,500. Drug courts in New York State alone saved $2.54 million in incarceration costs.[167]

Describing the failure of the War on Drugs, New York Times columnist Eduardo Porter noted:

Jeffrey Miron, an economist at Harvard who studies drug policy closely, has suggested that legalizing all illicit drugs would produce net benefits to the United States of some $65 billion a year, mostly by cutting public spending on enforcement as well as through reduced crime and corruption. A study by analysts at the RAND Corporation, a California research organization, suggested that if marijuana were legalized in California and the drug spilled from there to other states, Mexican drug cartels would lose about a fifth of their annual income of some $6.5 billion from illegal exports to the United States.[169]

Many believe that the War on Drugs has been costly and ineffective largely because inadequate emphasis is placed on treatment of addiction. The United States leads the world in both recreational drug usage and incarceration rates. 70% of men arrested in metropolitan areas test positive for an illicit substance,[170] and 54% of all men incarcerated will be repeat offenders.[171]

There are also programs in the United States to combat public health risks of injecting drug users such as the Needle exchange programme. The “needle exchange programme” is intended to provide injecting drug users with new needles in exchange for used needles to prevent needle sharing.

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The War on Drugs (band) – Wikipedia

The War on Drugs is an American indie rock band from Philadelphia, Pennsylvania, formed in 2005. The band consists of Adam Granduciel (lyrics, vocals, guitar), David Hartley (bass), Robbie Bennett (keyboards), Charlie Hall (drums), Jon Natchez (saxophone, keyboards) and Anthony LaMarca (guitar).

Founded by close collaborators Granduciel and Kurt Vile, The War on Drugs released their debut studio album, Wagonwheel Blues, in 2008. Vile departed shortly after its release to focus on his solo career. The band’s second studio album Slave Ambient was released in 2011 to favorable reviews and extensive touring.

The band’s third album, Lost in the Dream, was released in 2014 following extensive touring and a period of loneliness and depression for primary songwriter Granduciel. The album was released to widespread critical acclaim and increased exposure. Previous collaborator Hall joined the band as its full-time drummer during the recording process, with saxophonist Natchez and additional guitarist LaMarca accompanying the band for its world tour. Signing to Atlantic Records, the six-piece band released their fourth album, A Deeper Understanding, in 2017, which won the Grammy Award for Best Rock Album at the 60th Annual Grammy Awards.

In 2003, frontman Adam Granduciel moved from Oakland, California to Philadelphia, where he met Kurt Vile, who had also recently moved back to Philadelphia after living in Boston for two years.[2] The duo subsequently began writing, recording and performing music together.[3] Vile stated, “Adam was the first dude I met when I moved back to Philadelphia in 2003. We saw eye-to-eye on a lot of things. I was obsessed with Bob Dylan at the time, and we totally geeked-out on that. We started playing together in the early days and he would be in my band, The Violators. Then, eventually I played in The War On Drugs.”[4]

Granduciel and Vile began playing together as The War on Drugs in 2005. Regarding the band’s name, Granduciel noted, “My friend Julian and I came up with it a few years ago over a couple bottles of red wine and a few typewriters when we were living in Oakland. We were writing a lot back then, working on a dictionary, and it just came out and we were like “hey, good band name” so eventually when I moved to Philadelphia and got a band together I used it. It was either that or The Rigatoni Danzas. I think we made the right choice. I always felt though that it was the kind of name I could record all sorts of different music under without any sort of predictability inherent in the name”[5]

While Vile and Granduciel formed the backbone of the band, they had a number of accompanists early in the group’s career, before finally settling on a lineup that added Charlie Hall as drummer/organist, Kyle Lloyd as drummer and Dave Hartley on bass.[6] Granduciel had previously toured and recorded with The Capitol Years, and Vile has several solo albums.[7] The group gave away its Barrel of Batteries EP for free early in 2008.[8] Their debut LP for Secretly Canadian, Wagonwheel Blues, was released in 2008.[9]

Following the album’s release, and subsequent European tour, Vile departed from the band to focus on his solo career, stating, “I only went on the first European tour when their album came out, and then I basically left the band. I knew if I stuck with that, it would be all my time and my goal was to have my own musical career.”[4] Fellow Kurt Vile & the Violators bandmate Mike Zanghi joined the band at this time, with Vile noting, “Mike was my drummer first and then when The War On Drugs’ first record came out I thought I was lending Mike to Adam for the European tour but then he just played with them all the time so I kind of had to like, while they were touring a lot, figure out my own thing.”[10]

The lineup underwent several changes, and by the end of 2008, Kurt Vile, Charlie Hall, and Kyle Lloyd had all exited the group. At that time Granduciel and Hartley were joined by drummer Mike Zanghi, whom Granduciel also played with in Kurt Vile’s backing band, the Violators.

After recording much of the band’s forthcoming studio album, Slave Ambient, Zanghi departed from the band in 2010. Drummer Steven Urgo subsequently joined the band, with keyboardist Robbie Bennett also joining at around this time. Regarding Zanghi’s exit, Granduciel noted: “I loved Mike, and I loved the sound of The Violators, but then he wasn’t really the sound of my band. But you have things like friendship, and he’s down to tour and he’s a great guy, but it wasn’t the sound of what this band was.”[11]

Slave Ambient was released to favorable reviews in 2011.[citation needed]

In 2012, Patrick Berkery replaced Urgo as the band’s drummer.[12]

On December 4, 2013 the band announced the upcoming release of its third studio album, Lost in the Dream (March 18, 2014). The band streamed the album in its entirety on NPR’s First Listen site for a week before its release.[13]

Lost in the Dream was featured as the Vinyl Me, Please record of the month in August 2014. The pressing was a limited edition pressing on mint green colored vinyl.

In June 2015, The War on Drugs signed with Atlantic Records for a two-album deal.[14]

On Record Store Day, April 22, 2017, The War on Drugs released their new single “Thinking of a Place.”[15] The single was produced by frontman Granduciel and Shawn Everett.[16] April 28, 2017, The War on Drugs announced a fall 2017 tour in North America and Europe and that a new album was imminent.[17] On June 1, 2017, a new song, “Holding On”, was released, and it was announced that the album would be titled A Deeper Understanding and was released on August 25, 2017.[18]

The 2017 tour begins in September, opening in the band’s hometown, Philadelphia, and it concludes in November in Sweden.[19]

A Deeper Understanding was nominated for the International Album of the Year award at the 2018 UK Americana Awards[20].

At the 60th Annual Grammy Awards, on January 28th, 2018, A Deeper Understanding won the Grammy for Best Rock Album [21]

Granduciel and Zanghi are both former members of founding guitarist Vile’s backing band The Violators, with Granduciel noting, “There was never, despite what lazy journalists have assumed, any sort of falling out, or resentment”[22] following Vile’s departure from The War on Drugs. In 2011, Vile stated, “When my record came out, I assumed Adam would want to focus on The War On Drugs but he came with us in The Violators when we toured the States. The Violators became a unit, and although the cast does rotate, we’ve developed an even tighter unity and sound. Adam is an incredible guitar player these days and there is a certain feeling [between us] that nobody else can tap into. We don’t really have to tell each other what to play, it just happens.”

Both Hartley and Granduciel contributed to singer-songwriter Sharon Van Etten’s fourth studio album, Are We There (2014). Hartley performs bass guitar on the entire album, with Granduciel contributing guitar on two tracks.

Granduciel is currently[when?] producing the new Sore Eros album. They have been recording it in Philadelphia and Los Angeles on and off for the past several years.[4]

In 2016, The War on Drugs contributed a cover of “Touch of Grey” for a Grateful Dead tribute album called Day of the Dead. The album was curated by The National’s Aaron and Bryce Dessner.[19]

Current members

Former members

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War on Drugs | United States history | Britannica.com

War on Drugs, the effort in the United States since the 1970s to combat illegal drug use by greatly increasing penalties, enforcement, and incarceration for drug offenders.

The War on Drugs began in June 1971 when U.S. Pres. Richard Nixon declared drug abuse to be public enemy number one and increased federal funding for drug-control agencies and drug-treatment efforts. In 1973 the Drug Enforcement Agency was created out of the merger of the Office for Drug Abuse Law Enforcement, the Bureau of Narcotics and Dangerous Drugs, and the Office of Narcotics Intelligence to consolidate federal efforts to control drug abuse.

The War on Drugs was a relatively small component of federal law-enforcement efforts until the presidency of Ronald Reagan, which began in 1981. Reagan greatly expanded the reach of the drug war and his focus on criminal punishment over treatment led to a massive increase in incarcerations for nonviolent drug offenses, from 50,000 in 1980 to 400,000 in 1997. In 1984 his wife, Nancy, spearheaded another facet of the War on Drugs with her Just Say No campaign, which was a privately funded effort to educate schoolchildren on the dangers of drug use. The expansion of the War on Drugs was in many ways driven by increased media coverage ofand resulting public nervousness overthe crack epidemic that arose in the early 1980s. This heightened concern over illicit drug use helped drive political support for Reagans hard-line stance on drugs. The U.S. Congress passed the Anti-Drug Abuse Act of 1986, which allocated $1.7 billion to the War on Drugs and established a series of mandatory minimum prison sentences for various drug offenses. A notable feature of mandatory minimums was the massive gap between the amounts of crack and of powder cocaine that resulted in the same minimum sentence: possession of five grams of crack led to an automatic five-year sentence while it took the possession of 500 grams of powder cocaine to trigger that sentence. Since approximately 80% of crack users were African American, mandatory minimums led to an unequal increase of incarceration rates for nonviolent black drug offenders, as well as claims that the War on Drugs was a racist institution.

Concerns over the effectiveness of the War on Drugs and increased awareness of the racial disparity of the punishments meted out by it led to decreased public support of the most draconian aspects of the drug war during the early 21st century. Consequently, reforms were enacted during that time, such as the legalization of recreational marijuana in a number of states and the passage of the Fair Sentencing Act of 2010 that reduced the discrepancy of crack-to-powder possession thresholds for minimum sentences from 100-to-1 to 18-to-1. While the War on Drugs is still technically being waged, it is done at much less intense level than it was during its peak in the 1980s.

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